Estimating Financial Risk through Monte Carlo Simulation.html

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<title>[Lecture 7+8]-Estimating-Financial-Risk-through-Monte-Carlo-Simulation (2) (2)</title>

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  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: thin dotted;
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: thin dotted;
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: thin dotted;
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#login_widget {
  float: right;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: baseline;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
@-moz-document url-prefix() {
  div.inner_cell {
    overflow-x: hidden;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  padding: 0.4em;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 20ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  display: none;
}
.command-shortcut:before {
  content: "(command)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
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.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
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.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
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.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
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.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
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.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
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.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}

@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<div>
<h1>Run the cell below to generate the road map (do not modify it)</h1></div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[50]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="nx">javascript</span>
<span class="kd">var</span> <span class="nx">kernel</span> <span class="o">=</span> <span class="nx">IPython</span><span class="p">.</span><span class="nx">notebook</span><span class="p">.</span><span class="nx">kernel</span><span class="p">;</span><span class="kd">var</span> <span class="nx">thename</span> <span class="o">=</span> <span class="nb">window</span><span class="p">.</span><span class="nb">document</span><span class="p">.</span><span class="nx">getElementById</span><span class="p">(</span><span class="s2">&quot;notebook_name&quot;</span><span class="p">).</span><span class="nx">innerHTML</span><span class="p">;</span><span class="kd">var</span> <span class="nx">command</span> <span class="o">=</span> <span class="s2">&quot;THE_NOTEBOOK = &quot;</span> <span class="o">+</span> <span class="s2">&quot;&#39;&quot;</span><span class="o">+</span><span class="nx">thename</span><span class="o">+</span><span class="s2">&quot;&#39;&quot;</span><span class="p">;</span><span class="nx">kernel</span><span class="p">.</span><span class="nx">execute</span><span class="p">(</span><span class="nx">command</span><span class="p">);</span><span class="nx">command</span><span class="o">=</span><span class="s2">&quot;os.environ[&#39;THE_NOTEBOOK&#39;] = THE_NOTEBOOK&quot;</span><span class="p">;</span><span class="nx">kernel</span><span class="p">.</span><span class="nx">execute</span><span class="p">(</span><span class="nx">command</span><span class="p">);</span><span class="kd">var</span> <span class="nx">cell</span> <span class="o">=</span> <span class="nx">IPython</span><span class="p">.</span><span class="nx">notebook</span><span class="p">.</span><span class="nx">get_cell</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span><span class="nx">cell</span><span class="p">.</span><span class="nx">execute</span><span class="p">();</span><span class="nx">IPython</span><span class="p">.</span><span class="nx">notebook</span><span class="p">.</span><span class="nx">get_cell</span><span class="p">(</span><span class="mi">3</span><span class="p">).</span><span class="nx">focus_cell</span><span class="p">();</span><span class="kd">var</span> <span class="nx">x</span> <span class="o">=</span> <span class="nx">$</span><span class="p">(</span><span class="s1">&#39;.code_cell&#39;</span><span class="p">);</span><span class="nx">$</span><span class="p">(</span><span class="nx">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]).</span><span class="nx">children</span><span class="p">(</span><span class="s1">&#39;.input&#39;</span><span class="p">).</span><span class="nx">hide</span><span class="p">();</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt"></div>


<div id="b59c3e70-7a84-41d2-aafe-aa0c07ca3c2f"></div>
<div class="output_subarea output_javascript ">
<script type="text/javascript">
var element = $('#b59c3e70-7a84-41d2-aafe-aa0c07ca3c2f');
var kernel = IPython.notebook.kernel;var thename = window.document.getElementById("notebook_name").innerHTML;var command = "THE_NOTEBOOK = " + "'"+thename+"'";kernel.execute(command);command="os.environ['THE_NOTEBOOK'] = THE_NOTEBOOK";kernel.execute(command);var cell = IPython.notebook.get_cell(2);cell.execute();IPython.notebook.get_cell(3).focus_cell();var x = $('.code_cell');$(x[1]).children('.input').hide();
</script>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[51]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">outputdir</span> <span class="o">=</span> <span class="s2">&quot;/tmp/tools/&quot;</span>
<span class="o">!</span>mkdir -p <span class="nv">$outputdir</span>
<span class="o">!</span>wget <span class="s2">&quot;https://www.dropbox.com/s/4g0pigmro4vo1b4/menutemplate?dl=0&quot;</span> -O /tmp/tools/menutemplate &gt;&gt; /tmp/toollog 2&gt;<span class="p">&amp;</span><span class="m">1</span> 
<span class="o">!</span>wget <span class="s2">&quot;https://www.dropbox.com/s/3flttpzhsja8td7/construct_menu.py?dl=0&quot;</span> -O /tmp/tools/construct_menu.py &gt;&gt; /tmp/toollog 2&gt;<span class="p">&amp;</span><span class="m">1</span> 
<span class="o">!</span>python /tmp/tools/construct_menu.py <span class="s2">&quot;{THE_NOTEBOOK}.ipynb&quot;</span> <span class="o">{</span>outputdir<span class="o">}</span>
<span class="kn">from</span> <span class="nn">IPython.core.display</span> <span class="k">import</span> <span class="n">HTML</span>
<span class="n">output_file_name</span> <span class="o">=</span> <span class="n">outputdir</span> <span class="o">+</span> <span class="n">THE_NOTEBOOK</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;[&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;]&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.ipynb.html&quot;</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">output_file_name</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
    <span class="n">html</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">HTML</span><span class="p">(</span><span class="n">html</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[51]:</div>

<div class="output_html rendered_html output_subarea output_execute_result">

   <script src="http://code.jquery.com/jquery-latest.min.js" type="text/javascript"></script>
   <script>
      (function($){
         $(document).ready(function(){

         $('#cssmenu li.active').addClass('open').children('ul').show();
            $('#cssmenu li.has-sub>a').on('click', function(){
               $(this).removeAttr('href');
               var margin_left = parseInt($(this).css('padding-left').replace('px', ''));
               var element = $(this).parent('li');
               if (element.hasClass('open')) {
                  element.removeClass('open');
                  element.find('li').removeClass('open');
                  element.find('ul').slideUp(200);
               }
               else {
                  element.addClass('open');
                  element.children('ul').slideDown(200);
                  element.siblings('li').children('ul').slideUp(200);
                  element.siblings('li').removeClass('open');
                  element.siblings('li').find('li').removeClass('open');
                  element.siblings('li').find('ul').slideUp(200);
                  element.children().find('a').css('padding-left', margin_left + 16 + "px");
               }
            });

            var screen_height = $(window).height(); 
            var headerHeight = $("#header").outerHeight();
            var max_height_menu = screen_height - headerHeight;
            $("#cssmenu").css('max-height', max_height_menu + 'px');
            $("#cssmenu").css('top', headerHeight + 'px');
            $("#menu-activator").css('top', headerHeight + 'px');
            $("#cssmenu").show();
            $("#menu-activator").hide();


            $("#menu-activator").mouseover(function(){
              $("#cssmenu").show();
              $("#menu-activator").hide();
            });


            $("#cssmenu").mouseleave(function(){
              $("#cssmenu").hide();
              $("#menu-activator").show();
            });

            $("div.anchor").parent('div').addClass('bcolor');

         });
      })(jQuery);
   </script>
   <style>
      @import url(http://fonts.googleapis.com/css?family=Raleway:400,200);
      #cssmenu,
      #cssmenu ul,
      #cssmenu ul li,
      #cssmenu ul li a {
        margin: 0;
        padding: 0;
        border: 0;
        list-style: none;
        line-height: 1;
        display: block;
        position: relative;
        -webkit-box-sizing: border-box;
        -moz-box-sizing: border-box;
        box-sizing: border-box;
      }
      #cssmenu {
        width: 220px;
        font-family: Raleway, sans-serif;
        color: #ffffff;
        max-height:600px;
        overflow-y:scroll;
        overflow-x:hidden;
        position:fixed;
        top:130px;
        left:0px;
      }
      #cssmenu ul ul {
        display: none;
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<li class='has-sub'><a href="#">ROADMAP</a>
<ul><li class='has-sub'><a href='#Estimating-Financial-Risk-through-Monte-Carlo-Simulation'>Estimating Financial Risk through Monte Carlo Simulation</a><ul><li><a href='#Goals'>Goals</a></li><li><a href='#Steps'>Steps</a></li><li><a href='#Reference'>Reference</a></li></ul></li><li class='has-sub'><a href='#1.-Introduction'>1. Introduction</a><ul><li><a href='#1.1.-Monte-Carlo-Simulation-(MCS)'>1.1. Monte Carlo Simulation (MCS)</a></li><li><a href='#1.2.-How-does-it-work?'>1.2. How does it work?</a></li></ul></li><li class='has-sub'><a href='#2.-Illustrative-example'>2. Illustrative example</a><ul><li class='has-sub'><a href='#2.1.-Unit-sales-and-unit-price'>2.1. Unit sales and unit price</a><ul><li><a href='#Question-1'>Question 1</a></li></ul></li><li><a href='#2.2.-Unit-Cost'>2.2. Unit Cost</a></li><li class='has-sub'><a href='#2.3.-A-Flawed-Model:-using-averages-to-represent-our-random-variables'>2.3. A Flawed Model: using averages to represent our random variables</a><ul><li class='has-sub'><a href='#Question-2'>Question 2</a><ul><li><a href='#Question-2.1'>Question 2.1</a></li><li><a href='#Question-2.2'>Question 2.2</a></li></ul></li></ul></li><li class='has-sub'><a href='#2.4.-Using-the-Monte-Carlo-Simulation-method-to-improve-our-model'>2.4. Using the Monte Carlo Simulation method to improve our model</a><ul><li class='has-sub'><a href='#Question-3'>Question 3</a><ul><li><a href='#Question-3.1'>Question 3.1</a></li><li><a href='#Question-3.2'>Question 3.2</a></li></ul></li></ul></li></ul></li><li><a href='#3.-A-brief-summary-of-the-Monte-Carlo-Simulation-(MCS)-technique'>3. A brief summary of the Monte Carlo Simulation (MCS) technique</a></li><li><a href='#4.-Common-distributions-used-in-MCS'>4. Common distributions used in MCS</a></li><li class='has-sub'><a href='#5.-A-real-use-case:-estimating-the-financial-risk-of-a-portfolio-of-stocks'>5. A real use case: estimating the financial risk of a portfolio of stocks</a><ul><li><a href='#5.1.-Terminology'>5.1. Terminology</a></li><li><a href='#5.2.-The-context-of-our-use-case'>5.2. The context of our use case</a></li><li><a href='#5.3.-The-Data'>5.3. The Data</a></li><li class='has-sub'><a href='#5.4.-Data-preprocessing'>5.4. Data preprocessing</a><ul><li><a href='#Factor-data-pre-processing'>Factor data pre-processing</a></li><li class='has-sub'><a href='#Question-4'>Question 4</a><ul><li><a href='#Question-4.1'>Question 4.1</a></li><li><a href='#Question-4.2'>Question 4.2</a></li></ul></li><li class='has-sub'><a href='#Stock-data-pre-processing'>Stock data pre-processing</a><ul><li><a href='#Question-4.3'>Question 4.3</a></li></ul></li><li class='has-sub'><a href='#Time-alignment-for-our-data'>Time alignment for our data</a><ul><li><a href='#Question-4.4'>Question 4.4</a></li><li><a href='#Question-4.5'>Question 4.5</a></li><li><a href='#Question-4.6'>Question 4.6</a></li></ul></li></ul></li><li><a href='#5.5.-Summary-guidelines-to-apply-the-MCS-method-on-the-data-we-prepared'>5.5. Summary guidelines to apply the MCS method on the data we prepared</a></li><li class='has-sub'><a href='#5.6.-Applying-MCS'>5.6. Applying MCS</a><ul><li><a href='#Step-1:-Defining-relationship-between-market-factors-and-instrument's-returns'>Step 1: Defining relationship between market factors and instrument's returns</a></li><li class='has-sub'><a href='#Question-5'>Question 5</a><ul><li><a href='#Question-5.1'>Question 5.1</a></li><li><a href='#Question-5.2'>Question 5.2</a></li><li><a href='#Question-5.3'>Question 5.3</a></li></ul></li><li><a href='#Step-2:-Defining-the-distributions-for-the-market-conditions'>Step 2: Defining the distributions for the market conditions</a></li><li class='has-sub'><a href='#Question-6'>Question 6</a><ul><li><a href='#Question-6.1'>Question 6.1</a></li><li><a href='#Question-6.2'>Question 6.2</a></li></ul></li><li><a href='#Step-3&4:-Generating-samples,-running-simulation-and-calculating-the-VaR'>Step 3&4: Generating samples, running simulation and calculating the VaR</a></li><li><a href='#Question-7'>Question 7</a></li><li><a href='#Step-5:-Evaluating-the-results-using-backtesting-method'>Step 5: Evaluating the results using backtesting method</a></li><li class='has-sub'><a href='#Question-8'>Question 8</a><ul><li><a href='#Question-8.1'>Question 8.1</a></li><li><a href='#Question-8.2'>Question 8.2</a></li><li><a href='#Question-8.3'>Question 8.3</a></li></ul></li><li><a href='#Question-9'>Question 9</a></li><li><a href='#Question-10'>Question 10</a></li><li><a href='#Extra-Work-1'>Extra Work 1</a></li><li><a href='#Extra-Work-2'>Extra Work 2</a></li></ul></li></ul></li><li><a href='#6.-Summary'>6. Summary</a></li><li><a href='#References'>References</a></li></ul>
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<li class='has-sub'><a href="#">QUESTIONS</a>
<ul><li><a href='#Question-1'>Question 1</a></li><li class='has-sub'><a href='#Question-2'>Question 2</a><ul><li><a href='#Question-2.1'>Question 2.1</a></li><li><a href='#Question-2.2'>Question 2.2</a></li></ul></li><li class='has-sub'><a href='#Question-3'>Question 3</a><ul><li><a href='#Question-3.1'>Question 3.1</a></li><li><a href='#Question-3.2'>Question 3.2</a></li></ul></li><li class='has-sub'><a href='#Question-4'>Question 4</a><ul><li><a href='#Question-4.1'>Question 4.1</a></li><li><a href='#Question-4.2'>Question 4.2</a></li><li><a href='#Question-4.3'>Question 4.3</a></li><li><a href='#Question-4.4'>Question 4.4</a></li><li><a href='#Question-4.5'>Question 4.5</a></li><li><a href='#Question-4.6'>Question 4.6</a></li></ul></li><li class='has-sub'><a href='#Question-5'>Question 5</a><ul><li><a href='#Question-5.1'>Question 5.1</a></li><li><a href='#Question-5.2'>Question 5.2</a></li><li><a href='#Question-5.3'>Question 5.3</a></li></ul></li><li class='has-sub'><a href='#Question-6'>Question 6</a><ul><li><a href='#Question-6.1'>Question 6.1</a></li><li><a href='#Question-6.2'>Question 6.2</a></li></ul></li><li><a href='#Question-7'>Question 7</a></li><li class='has-sub'><a href='#Question-8'>Question 8</a><ul><li><a href='#Question-8.1'>Question 8.1</a></li><li><a href='#Question-8.2'>Question 8.2</a></li><li><a href='#Question-8.3'>Question 8.3</a></li></ul></li><li><a href='#Question-9'>Question 9</a></li><li><a href='#Question-10'>Question 10</a></li></ul>
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<h1 id="Estimating-Financial-Risk-through-Monte-Carlo-Simulation">Estimating Financial Risk through Monte Carlo Simulation<a class="anchor-link" href="#Estimating-Financial-Risk-through-Monte-Carlo-Simulation">&#182;</a></h1><p>Risk analysis is part of every decision we make when faced with uncertainty, ambiguity, and variability. Indeed, even though we have unprecedented access to information, we can't accurately predict the future. In finance, there is a fair amount of uncertainty and risk involved with estimating the future value of financial products, due to the wide variety of potential outcomes. Monte Carlo simulation (also known as the Monte Carlo Method) allows inspecting many possible outcomes of the decision making process, and can be used to assess the impact of risk: this, in turns, allows for better decision making under uncertainty.</p>
<h2 id="Goals">Goals<a class="anchor-link" href="#Goals">&#182;</a></h2><p>The main objectives we set for this Notebook are as follows:</p>
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<li>Develop fudamental knowledge about Risk analysis</li>
<li>Understand Monte Carlo Simulation (MCS)</li>
<li>Apply Monte Carlo Simulation for predicting risk</li>
</ol>
<h2 id="Steps">Steps<a class="anchor-link" href="#Steps">&#182;</a></h2><ol>
<li>First, in section 1, we introduce the basics of MCS</li>
<li>In section 2, we work on a simple example to where we apply the MCS method</li>
<li>In section 3, we briefly summarize the main characteristics of the Monte Carlo Simulation (MCS) technique</li>
<li>In section 4, we overview the common distributions which are often used in MCS</li>
<li>In section 5, we work on a real use case, that focuses on estimating financial risk. We will use techniques such as featurizing (that is, generating additional features to improve model accuracy), linear regression, kernel density estimation, sampling distributions and so on ...</li>
</ol>
<h2 id="Reference">Reference<a class="anchor-link" href="#Reference">&#182;</a></h2><p>This Notebook is inspired by Chapter 9 of the book <a href="http://shop.oreilly.com/product/0636920035091.do">Advanced Analytics with Spark</a> by Josh Wills, Sandy Ryza, Sean Owen, and Uri Laserson. It is strongly suggested to read this Chapter to get a general idea of the topic of this Notebook.</p>
<h1 id="1.-Introduction">1. Introduction<a class="anchor-link" href="#1.-Introduction">&#182;</a></h1><h2 id="1.1.-Monte-Carlo-Simulation-(MCS)">1.1. Monte Carlo Simulation (MCS)<a class="anchor-link" href="#1.1.-Monte-Carlo-Simulation-(MCS)">&#182;</a></h2><p>Monte Carlo simulation is a computerized mathematical technique that can be applied such that it is possible to account for risk in quantitative analysis and decision making. This technique is used in many different fields, such as R&amp;D, risk management, portfolio management, pricing derivatives, strategic planning, project planning, cost modeling and many more.</p>
<p>In general, MCS is a technique that "converts" uncertainty on input variables of a model into <strong>probability distributions</strong>. By combining the distributions and randomly selecting values from them, it recalculates the simulated model many times, to determine the probability of the output.</p>
<p>Historically, this technique was first used by scientists working on the atomic bomb: it was named after Monte Carlo, the Monaco resort town renowned for its casinos.  Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems.</p>
<h2 id="1.2.-How-does-it-work?">1.2. How does it work?<a class="anchor-link" href="#1.2.-How-does-it-work?">&#182;</a></h2><p>Monte Carlo simulation performs risk analysis by building models of possible results by <em>substituting a range of possible input values, that constitute uncertainty, into a statistical distribution</em>. It then computes possible outcomes repeatedly, each time using a different set of random values from the probability functions that "model" the input. Depending upon the number of random input variables and their distribution, a Monte Carlo simulation could involve thousands or tens of thousands of "rounds" before it is complete. When complete, <em>Monte Carlo simulation produces distributions of possible outcome values</em>.</p>
<p>By using probability distributions instead of actual input samples, it is possible to model more accurately uncertainty: different choices of distributions will yield different outputs.</p>

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<h1 id="2.-Illustrative-example">2. Illustrative example<a class="anchor-link" href="#2.-Illustrative-example">&#182;</a></h1><p>Imagine you are the marketing manager for a firm that is planning to introduce a new product. You need to estimate the first year net profit from this product, which might depend on:</p>
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<li>Sales volume in units</li>
<li>Price per unit (also called "Selling price")</li>
<li>Unit cost</li>
<li>Fixed costs</li>
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<p>Net profit will be calculated as $Net Profit = Sales Volume* (Selling Price - Unit cost) - Fixed costs$.  Fixed costs (accounting for various overheads, advertising budget, etc.) are known to be \$ 120,000, which we assume to be deterministic. All other factors, instead, involve some uncertainty: <em>sales volume</em> (in units) can cover quite a large range, the <em>selling price</em> per unit will depend on competitor actions, which are hard to predict, and <em>unit costs</em> will also vary depending on vendor prices and production experience, for example.</p>
<p>Now, to build a risk analysis model, we must first identify the uncertain variables -- which are essentially random variables.  While there's some uncertainty in almost all variables in a business model, we want to focus on variables where the range of values is significant.</p>

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<h2 id="2.1.-Unit-sales-and-unit-price">2.1. Unit sales and unit price<a class="anchor-link" href="#2.1.-Unit-sales-and-unit-price">&#182;</a></h2><p>Based on an hypothetical market research you have done, you have beliefs that there are equal chances for the market to be <code>slow</code>, <code>normal</code>, or <code>hot</code>:</p>
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<li>In a "slow" market, you expect to sell 50,000 units at an average selling price of \$11.00 per unit</li>
<li>In a "normal" market, you expect to sell 75,000 units, but you'll likely realize a lower average selling price of \$10.00 per unit</li>
<li>In a "hot" market, you expect to sell 100,000 units, but this will bring in competitors, who will drive down the average selling price to \$8.00 per unit</li>
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<h3 id="Question-1">Question 1<a class="anchor-link" href="#Question-1">&#182;</a></h3><p>Calculate the average units and the unit price that you expect to sell, which depend on the market state. Use the assumptions above to compute the expected quantity of products and their expected unit price.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">average_unit</span> <span class="o">=</span> <span class="p">(</span><span class="mi">50000</span> <span class="o">+</span> <span class="mi">75000</span> <span class="o">+</span> <span class="mi">100000</span><span class="p">)</span><span class="o">/</span><span class="mi">3</span>
<span class="n">average_price</span> <span class="o">=</span> <span class="p">(</span><span class="mi">11</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">+</span> <span class="mi">8</span><span class="p">)</span><span class="o">/</span><span class="mi">3</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;average_unit:&quot;</span><span class="p">,</span> <span class="n">average_unit</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;average_price:&quot;</span><span class="p">,</span> <span class="n">average_price</span><span class="p">)</span>
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<pre>average_unit: 75000.0
average_price: 9.666666666666666
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<h2 id="2.2.-Unit-Cost">2.2. Unit Cost<a class="anchor-link" href="#2.2.-Unit-Cost">&#182;</a></h2><p>Another uncertain variable is Unit Cost. In our illustrative example, we assume that your firm's production manager advises you that unit costs may be anywhere from \$5.50 to \$7.50, with a most likely expected cost of \$6.50. In this case, the most likely cost can be considered as the average cost.</p>
<h2 id="2.3.-A-Flawed-Model:-using-averages-to-represent-our-random-variables">2.3. A Flawed Model: using averages to represent our random variables<a class="anchor-link" href="#2.3.-A-Flawed-Model:-using-averages-to-represent-our-random-variables">&#182;</a></h2><p>Our next step is to identify uncertain functions -- also called functions of a random variable.  Recall that Net Profit is calculated as $Net Profit = Sales Volume * (Selling Price - Unit cost) - Fixed costs$.  However, Sales Volume, Selling Price and Unit Cost are all uncertain variables, so Net Profit is an uncertain function.</p>
<p>The most simple model to predict the Net Profit is using average of sales volume, average of selling price and average of unit cost for calculating. So, if only consider averages, we can say that the $Net Profit = 75,000*(9.66666666 - 6.5) - 120,000 \sim 117,500$.</p>
<p>However, as <a href="http://web.stanford.edu/~savage/faculty/savage/">Dr. Sam Savage</a> warns, "Plans based on average assumptions will be wrong on average." The calculated result is far from the actual value: indeed, the <strong>true average Net Profit</strong> is roughly  \$93,000, as we will see later in the example.</p>

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<h3 id="Question-2">Question 2<a class="anchor-link" href="#Question-2">&#182;</a></h3><h4 id="Question-2.1">Question 2.1<a class="anchor-link" href="#Question-2.1">&#182;</a></h4><p>Write a function named <code>calNetProfit</code> to calculate the Net Profit using the average of sales volume, the average of selling price and the average of unit cost.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">calNetProfit</span><span class="p">(</span><span class="n">average_unit</span><span class="p">,</span> <span class="n">average_price</span><span class="p">,</span> <span class="n">average_unitcost</span><span class="p">,</span> <span class="n">fixed_cost</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">average_unit</span><span class="o">*</span><span class="p">(</span><span class="n">average_price</span><span class="o">-</span><span class="n">average_unitcost</span><span class="p">)</span><span class="o">-</span><span class="n">fixed_cost</span>

<span class="n">average_unitcost</span> <span class="o">=</span> <span class="mf">6.5</span>
<span class="n">fixed_cost</span> <span class="o">=</span> <span class="mi">120000</span>
<span class="n">NetProfit</span> <span class="o">=</span> <span class="n">calNetProfit</span><span class="p">(</span><span class="n">average_unit</span><span class="p">,</span><span class="n">average_price</span><span class="p">,</span><span class="n">average_unitcost</span><span class="p">,</span><span class="n">fixed_cost</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Net profit:&quot;</span><span class="p">,</span> <span class="n">NetProfit</span><span class="p">)</span>
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<pre>Net profit: 117499.99999999994
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<h4 id="Question-2.2">Question 2.2<a class="anchor-link" href="#Question-2.2">&#182;</a></h4><p>Verify the warning message of Dr. Sam Savage by calculating the error of our estimated Net Profit using averages only. Recall that the true value is roughly \$93,000, so we are interested in:</p>
$$ error = \frac{your\_value - true\_value}{true\_value}$$<p>Note also we are interested in displaying the error as a percentage.</p>
<p>Looking at the error we make, do you think that we can use the current model that only relies on averages?</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">trueNetProfit</span> <span class="o">=</span> <span class="mi">92500</span>
<span class="n">error</span> <span class="o">=</span> <span class="p">(</span><span class="n">NetProfit</span> <span class="o">-</span> <span class="n">trueNetProfit</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">trueNetProfit</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error in percentage:&quot;</span><span class="p">,</span> <span class="n">error</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
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<pre>Error in percentage: 27.02702702702696
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With the error rate as 27%, we can not use averages approach for estimation. In case the relations of random numbers and final results are more complicated, the error could be worse. 

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<h2 id="2.4.-Using-the-Monte-Carlo-Simulation-method-to-improve-our-model">2.4. Using the Monte Carlo Simulation method to improve our model<a class="anchor-link" href="#2.4.-Using-the-Monte-Carlo-Simulation-method-to-improve-our-model">&#182;</a></h2><p>As discussed before, the selling price and selling volume both depend on the state of the market scenario (slow/normal/hot). So, the net profit is the result of two random variables: <code>market scenario</code> (which in turn determines <code>sales volumes</code> and <code>selling price</code>) and <code>unit cost</code>.</p>
<p>Now, let's assume (this is an <em>a-priori</em> assumption we make) that <code>market scenario</code> follows a discrete, uniform distribution and that <code>unit cost</code> also follows a uniform distribution. Then, we can compute directly the values for selling price and selling volumes based on the outcome of the random variable <code>market scenario</code>, as shown in Section 2.1.</p>
<p>From these a-priori distributions, in each run (or trial) of our Monte Carlo simulation, we can generate the sample value for each random variable and use it to calculate the Net Profit. The more simulation runs, the more accurate our results will be. For example, if we run the silmulation 100,000 times, the average net profit will amount to roughly \$92,600. Every time we run the simulation, a different prediction will be output: the average of such predictions will consistently be less than \$117,500, which we predicted using averages only.</p>
<p>Note also that in this simple example, we generate values for the <code>market scenario</code> and <code>unit cost</code> independently: we consider them to be <strong>independent random variables</strong>. This means that the eventual (and realistic!) correlation between the <code>market scenario</code> and <code>unit cost</code> variables is ignored. Later, we will learn how to be more precise and account for dependency between random variables.</p>

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<p><img src="https://farm2.staticflickr.com/1604/24934700445_833f0a5649_t.jpg" alt=""></p>

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<h3 id="Question-3">Question 3<a class="anchor-link" href="#Question-3">&#182;</a></h3><h4 id="Question-3.1">Question 3.1<a class="anchor-link" href="#Question-3.1">&#182;</a></h4><p>Write a function named <code>get_sales_volume_price</code> that returns the sales volume and price based on the market scenario. In particular, the scenario can get one of three values:</p>
<ul>
<li>0: Slow market</li>
<li>1: Normal market</li>
<li>2: Hot market</li>
</ul>
<p>The return value is a tuple in the form: <code>(sales_volume, price)</code></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Get sales volume and  price based on market scenario</span>
<span class="c1"># the function returns a tuple of (sales_volume, price)</span>
<span class="k">def</span> <span class="nf">get_sales_volume_price</span><span class="p">(</span><span class="n">scenario</span><span class="p">):</span>
    <span class="c1"># Slow market</span>
    <span class="k">if</span> <span class="n">scenario</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
        <span class="k">return</span> <span class="p">(</span><span class="mi">50000</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
    <span class="c1"># Normal market</span>
    <span class="k">if</span> <span class="n">scenario</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
        <span class="k">return</span> <span class="p">(</span><span class="mi">75000</span><span class="p">,</span><span class="mi">10</span><span class="p">)</span>
    <span class="c1"># Hot market</span>
    <span class="k">if</span> <span class="n">scenario</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
        <span class="k">return</span> <span class="p">(</span><span class="mi">100000</span><span class="p">,</span><span class="mi">8</span><span class="p">)</span>
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<h4 id="Question-3.2">Question 3.2<a class="anchor-link" href="#Question-3.2">&#182;</a></h4><p>Run 100,000 Monte Carlo simulations and calculate the average net profit they produce. Then, compare the result to the "average model" we used in the previous questions (the one we called "flawed" model). Put your comments about the discrepancies between a simplistic model, and the more accurate MCS approach.</p>
<p>Note that in each iteration, the <code>unit_cost</code> and <code>market_scenario</code> are generated according to their distributions. Also, recall what we have seen in Section 2.2: your firm account manager helped you with some research, to determine the variability of your random variables.</p>
<p>HINT: 
Funtion <code>uniform(a,b)</code> in module <code>random</code> generates a number $a<=c<=b$, which is drawn from a uniform distribution.
Function <code>randint(a,b)</code> helps you generating an integer number $a<=c<=b$</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>

<span class="n">total</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">num_simulation</span> <span class="o">=</span> <span class="mi">10000000</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">num_simulation</span><span class="p">):</span>
    <span class="n">unit_cost</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">5.5</span><span class="p">,</span><span class="mf">7.5</span><span class="p">)</span>
    <span class="n">market_scenario</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">sales_volume</span><span class="p">,</span> <span class="n">price</span> <span class="o">=</span> <span class="n">get_sales_volume_price</span><span class="p">(</span><span class="n">market_scenario</span><span class="p">)</span>
    <span class="n">netProfit</span> <span class="o">=</span> <span class="n">calNetProfit</span><span class="p">(</span><span class="n">sales_volume</span><span class="p">,</span><span class="n">price</span><span class="p">,</span><span class="n">unit_cost</span><span class="p">,</span><span class="n">fixed_cost</span><span class="p">)</span>
    <span class="n">total</span> <span class="o">=</span> <span class="n">total</span> <span class="o">+</span> <span class="n">netProfit</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;average net profit:&quot;</span><span class="p">,</span> <span class="n">total</span><span class="o">/</span><span class="n">num_simulation</span><span class="p">)</span>
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<pre>average net profit: 92476.86660010798
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">error</span> <span class="o">=</span> <span class="p">(</span><span class="n">total</span><span class="o">/</span><span class="n">num_simulation</span> <span class="o">-</span> <span class="n">trueNetProfit</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">trueNetProfit</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error in percentage:&quot;</span><span class="p">,</span> <span class="n">error</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
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<pre>Error in percentage: -0.02500908096434196
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The true net profit seems to be 92500. We can calculate for each 3 scenerios with unit cost is normaly distibuted from 5.5 to 7.5,:
<br/>
- slow market: net profit on average to be 105000
<br/>
- normal market: similarly, net profit on average is 142500
<br/>
- hot market: finally, net profit on average is 30000
<br/>
With the assumption that the market can equalily be either slow, normal or hot, we can have the over all net profit is 92500. In this case, since we have kind of simplest random variables as uniform distributions, simple relations between them and no brach, we can easily calculated. However, in reality, we can have extremely complex problems with many more variables, complex distributions and their relations, Monte Carlo simulation would be much simpler and can be applied in most general cases.

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<h1 id="3.-A-brief-summary-of-the-Monte-Carlo-Simulation-(MCS)-technique">3. A brief summary of the Monte Carlo Simulation (MCS) technique<a class="anchor-link" href="#3.-A-brief-summary-of-the-Monte-Carlo-Simulation-(MCS)-technique">&#182;</a></h1><ul>
<li>A MCS allows several inputs to be used at the same time to compute the probability distribution of one or more outputs</li>
<li>Different types of probability distributions can be assigned to the inputs of the model, depending on any <em>a-priori</em> information that is available. When the distribution is completely unknown, a common technique is to use a distribution computed by finding the best fit to the data you have</li>
<li>The MCS method is also called a <strong>stochastic method</strong> because it uses random variables. Note also that the general assumption is for input random variables to be independent from eachother. When this is not the case, there are techniques to account for correlation between random variables.</li>
<li>A MCS generates the output as a range instead of a fixed value and shows how likely the output value is to occur in that range. In other words, the model outputs a probability distribution.</li>
</ul>

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<h1 id="4.-Common-distributions-used-in-MCS">4. Common distributions used in MCS<a class="anchor-link" href="#4.-Common-distributions-used-in-MCS">&#182;</a></h1><p>In what follows, we summarize the most common probability distributions that are used as <em>a-priori</em> distributions for input random variables:</p>
<ul>
<li><p><em>Normal/Gaussian Distribution</em>: this is a continuous distribution applied in situations where the mean and the standard deviation of a given input variable are given, and the mean represents the most probable value of the variable. In other words, values "near" the mean are most likely to occur.  This is symmetric distribution, and it is not bounded in its co-domain. It is very often used to  describe natural phenomena, such as people’s heights, inflation rates, energy prices, and so on and so forth. An illustration of a normal distribution is given below:
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/7/74/Normal_Distribution_PDF.svg/320px-Normal_Distribution_PDF.svg.png" alt="normal_distribution"></p>
</li>
<li><p><em>Lognormal Distribution</em> : this is a distribution which is appropriate for variables taking values in the range $[0, \infty]$. Values are positively skewed, not symmetric like a normal distribution.  Examples of variables described by a lognormal distributions include, for example, real estate property values, stock prices, and oil reserves. An illustration of a lognormal distribution is given below:
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/a/ae/PDF-log_normal_distributions.svg/320px-PDF-log_normal_distributions.svg.png" alt="log_normal_distribution"></p>
</li>
<li><p><em>Triangular Distribution</em> : this is a continuous distribution with fixed minimum and maximum values. It is bounded by the minimum and maximum values and can be either symmetrical (the most probable value = mean = median) or asymmetrical. Values around the most likely value (e.g. the mean) are more likely to occur.  Variables that could be described by a triangular distribution include, for example, past sales history per unit of time and inventory levels. An illustration of a triangular distribution is given below:
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/45/Triangular_distribution_PMF.png/320px-Triangular_distribution_PMF.png" alt=""></p>
</li>
<li><p><em>Uniform Distribution</em> : this is a continuous distribution bounded by known minimum and maximum values. In contrast to the triangular distribution, the likelihood of occurrence of the values between the minimum and maximum is the same. In other words, all values have an equal chance of occurring, and the distribution is simply characterized by the minimum and maximum values. Examples of variables that can be described by a uniform distribution include manufacturing costs or future sales revenues for a new product. An illustration of the uniform distribution is given below:
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/96/Uniform_Distribution_PDF_SVG.svg/320px-Uniform_Distribution_PDF_SVG.svg.png" alt=""></p>
</li>
<li><p><em>Exponential Distribution</em> : this is a continuous distribution used to model the time that pass between independent occurrences, provided that the rate of occurrences is known. An example of the exponential distribution is given below:
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Exponential_pdf.svg/320px-Exponential_pdf.svg.png" alt=""></p>
</li>
<li><p><em>Discrete Distribution</em> : for this kind of distribution, the "user" defines specific values that may occur and the likelihood of each of them.  An example might be the results of a lawsuit: 20% chance of positive verdict, 30% change of negative verdict, 40% chance of settlement, and 10% chance of mistrial.</p>
</li>
</ul>

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<h1 id="5.-A-real-use-case:-estimating-the-financial-risk-of-a-portfolio-of-stocks">5. A real use case: estimating the financial risk of a portfolio of stocks<a class="anchor-link" href="#5.-A-real-use-case:-estimating-the-financial-risk-of-a-portfolio-of-stocks">&#182;</a></h1><p>We hope that by now you have a good understanding about Monte Carlo simulation. Next, we apply this method to a real use case: <em>financial risk estimation</em>.</p>
<p>Imagine that you are an investor on the stock market. You plan to buy some stocks and you want to estimate the maximum loss you could incur after two weeks of investing. This is the quantity that the financial statistic "Value at Risk" (VaR) seeks to measure. <a href="https://en.wikipedia.org/wiki/Value_at_risk">VaR</a> is defined as a measure of investment risk that can be used as a reasonable estimate of the maximum probable loss for a value of an investment portfolio, over a particular time period. A VaR statistic depends on three parameters: a portfolio, a time period, and a confidence level. A VaR of 1 million dollars with a 95% confidence level over two weeks, indicates the belief that the portfolio stands only a 5% chance of losing more than 1 million dollars over two weeks. VaR has seen widespread use across financial services organizations. This statistic plays a vital role in determining how much cash investors must hold to meet the credit ratings that they seek. In addition, it is also used to understand the risk characteristics of large portfolios: it is a good idea to compute the VaR before executing trades, such that it can help take informed decisions about investments.</p>
<p>Our goal is calculating VaR of two weeks interval with 95% confidence level and the associated <a href="http://www.investopedia.com/ask/answers/041615/whats-difference-between-confidence-level-and-confidence-interval-value-risk-var.asp">VaR confidence interval</a>.</p>
<h2 id="5.1.-Terminology">5.1. Terminology<a class="anchor-link" href="#5.1.-Terminology">&#182;</a></h2><p>In this use case, we will use some terms that might require a proper definition, given the domain. This is what we call the <em>Domain Knowledge</em>.</p>
<ul>
<li><strong>Instrument</strong>: A tradable asset, such as a bond, loan, option, or stock investment. At any particular time, an instrument is considered to have a value, which is the price for which it can be sold. In the use case of this notebook, instruments are stock investments.</li>
<li><strong>Portfolio</strong>: A collection of instruments owned by a financial institution. </li>
<li><strong>Return</strong>: The change in an instrument or portfolio’s value over a time period. </li>
<li><strong>Loss</strong>: A negative return. </li>
<li><strong>Index</strong>: An imaginary portfolio of instruments. For example, the NASDAQ Composite index includes about 3,000 stocks and similar instruments for major US and international companies. </li>
<li><strong>Market factor</strong>: A value that can be used as an indicator of macro aspects of the financial climate at a particular time. For example, the value of an index, the Gross Domestic Product of the United States, or the exchange rate between the dollar and the euro. We will often refer to market factors as just factors.</li>
</ul>
<h2 id="5.2.-The-context-of-our-use-case">5.2. The context of our use case<a class="anchor-link" href="#5.2.-The-context-of-our-use-case">&#182;</a></h2><p>We have a list of instruments that we plan to invest in. The historial data of each instrument has been collected for you. For simplicity, assume that the returns of instruments at a given time, depend on 4 market factors only:</p>
<ul>
<li>GSPC value</li>
<li>IXIC value </li>
<li>The return of crude oil</li>
<li>The return of treasury bonds</li>
</ul>
<p>Our goal is building a model to predict the loss after two weeks time interval with confidence level set to 95%.</p>
<p>As a side note, it is important to realize that the approach presented in this Notebook is a simplified version of what would happen in a real Financial firm. For example, the returns of instruments at a given time often depend on more than 4 market factors only! Moreovoer, the choice of what constitute an appropriate market factor is an art!</p>

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<h2 id="5.3.-The-Data">5.3. The Data<a class="anchor-link" href="#5.3.-The-Data">&#182;</a></h2><p>The stock data can be downloaded (or scraped) from Yahoo! by making a series of REST calls. The data includes multiple files. Each file contains the historical information of each instrument that we want to invest in. The data is in the following format (with some samples):</p>

<pre><code>Date, Open, High, Low, Close, Volume, Adj Close
2016-01-22,66.239998,68.07,65.449997,67.860001,137400,67.860001
2016-01-21,65.410004,66.18,64.459999,65.050003,148000,65.050003
2016-01-20,64.279999,66.32,62.77,65.389999,141300,65.389999
2016-01-19,67.720001,67.989998,64.720001,65.379997,178400,65.379997</code></pre>
<p>The data of GSPC and IXIC values (our two first market factors) are also available on Yahoo! and use the very same format.</p>
<p>The crude oil and treasure bonds data is collected from investing.com, and has a different format, as shown below (with some samples):</p>

<pre><code>Date    Price   Open    High    Low     Vol.    Change %
Jan 25, 2016    32.17   32.36   32.44   32.10   -       -0.59%
Jan 24, 2016    32.37   32.10   32.62   31.99   -       0.54%
Jan 22, 2016    32.19   29.84   32.35   29.53   -       9.01%
Jan 21, 2016    29.53   28.35   30.25   27.87   694.04K 11.22%
Jan 20, 2016    26.55   28.33   28.58   26.19   32.11K  -6.71%
Jan 19, 2016    28.46   29.20   30.21   28.21   188.03K -5.21%</code></pre>
<p>In our use case, the factors' data will be used jointly to build a statiscal model: as a consequence, we first need to preprocess the data to proceed.</p>
<h2 id="5.4.-Data-preprocessing">5.4. Data preprocessing<a class="anchor-link" href="#5.4.-Data-preprocessing">&#182;</a></h2><p>In this Notebook, all data files have been downloaded for you, such that you can focus on pre-processing. Next, we will:</p>
<ul>
<li>Read the factor data files which are in two different formats, process and merge them together</li>
<li>Read the stock data and pre-process it</li>
<li>Trim all data into a specific time region</li>
<li>Fill in the missing values</li>
<li>Generate the data of returns in each two weeks time interval window</li>
</ul>
<h3 id="Factor-data-pre-processing">Factor data pre-processing<a class="anchor-link" href="#Factor-data-pre-processing">&#182;</a></h3><p>We need two functions to read and parse data from Yahoo! and Investing.com respectively. We are interested only in information about the time and the corresponding returns of a factor or an instrument: as a consequence, we will project away many columns of our RAW data, and keep only the information we are interested in.</p>
<p>The 3000-instrument and the 4-factor history are small enough to be read and processed locally: we do not need to use the power of parallel computing to proceed. Note that this is true also for larger cases with hundreds of thousands of instruments and thousands of factors. The need for a distributed system like Spark comes in when actually <strong>running</strong> the Monte Carlo simulations, which can require massive amounts of computation on each instrument.</p>

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<h3 id="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#182;</a></h3><h4 id="Question-4.1">Question 4.1<a class="anchor-link" href="#Question-4.1">&#182;</a></h4><p>Write a function named <code>readInvestingDotComHistory</code> to parse data from investing.com based on the format specified above (see Section 5.3). Recall that we use two factors here: one that is related to the price of crude oil, one that is related to some specific US bonds.</p>
<p>Print the first 5 entries of the first factor (crude oil price) in the parsed data.</p>
<p>Note that we are only interested in the date and price of stocks.</p>
<p>HINT: You can parse a string to <code>datetime</code> object by using the function <code>strptime(&lt;string&gt;, &lt;dtime_format&gt;)</code>. In this case, the datetime format is <code>"%b %d, %Y"</code>. For more information, please follow this <a href="https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior">link</a>.</p>

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<p>In the next cell, we simply copy data from our HDFS cluster (that contains everything we need for this Notebook) to the instance (a Docker container) running your Notebook. This means that you will have "local" data that you can process without using Spark. Note the folder location: find and verify that you have correctly downloaded the files!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span> <span class="o">[</span> -d monte-carlo-risk <span class="o">]</span> <span class="o">||</span> <span class="o">(</span><span class="nb">echo</span> <span class="s2">&quot;Downloading prepared data from HDFS. Please wait...&quot;</span> <span class="p">;</span> hdfs dfs -copyToLocal /datasets/monte-carlo-risk . <span class="p">;</span> <span class="nb">echo</span> <span class="s2">&quot;Done!&quot;</span><span class="p">;</span><span class="o">)</span>
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<pre>Downloading prepared data from HDFS. Please wait...
Done!
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">timedelta</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">islice</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>

<span class="n">base_folder</span> <span class="o">=</span> <span class="s2">&quot;monte-carlo-risk/&quot;</span>

<span class="n">factors_folder</span><span class="o">=</span> <span class="n">base_folder</span> <span class="o">+</span> <span class="s2">&quot;factors/&quot;</span>

<span class="c1"># read data from local disk</span>
<span class="k">def</span> <span class="nf">readInvestingDotComHistory</span><span class="p">(</span><span class="n">fname</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">process_line</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
        <span class="n">cols</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
        <span class="n">date</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;%b </span><span class="si">%d</span><span class="s1">, %Y&#39;</span><span class="p">)</span>
        <span class="n">value</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
        <span class="k">return</span> <span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
    
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">content_w_header</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
        <span class="c1"># remove the first line </span>
        <span class="c1"># and reverse lines to sort the data by date, in ascending order</span>
        <span class="n">content</span> <span class="o">=</span> <span class="n">content_w_header</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">process_line</span> <span class="p">,</span> <span class="n">content</span><span class="p">))</span>

<span class="n">factor1_files</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;crudeoil.tsv&#39;</span><span class="p">,</span> <span class="s1">&#39;us30yeartreasurybonds.tsv&#39;</span><span class="p">]</span>
<span class="n">factor1_files</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">fn</span><span class="p">:</span> <span class="n">factors_folder</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">factor1_files</span><span class="p">)</span>
<span class="n">factors1</span> <span class="o">=</span> <span class="p">[</span><span class="n">readInvestingDotComHistory</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">factor1_files</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factors1</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">5</span><span class="p">])</span>
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<pre>[(datetime.datetime(2006, 1, 26, 0, 0), 66.26), (datetime.datetime(2006, 1, 27, 0, 0), 67.76), (datetime.datetime(2006, 1, 30, 0, 0), 68.35), (datetime.datetime(2006, 1, 31, 0, 0), 67.92), (datetime.datetime(2006, 2, 1, 0, 0), 66.56)]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">factors1</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factors1</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factors1</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factors1</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
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<pre>(datetime.datetime(2006, 1, 26, 0, 0), 66.26)
(datetime.datetime(2016, 1, 25, 0, 0), 32.17)
(datetime.datetime(2008, 2, 12, 0, 0), 4.457)
(datetime.datetime(2016, 1, 25, 0, 0), 2.813)
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<b>Crude oil</b> are collected from 26/1/2006 to 25/1/2016, in exactly <b>10 years</b>.
<br/>
<b>US treasury bonds</b> are collected form 12/2/2008 to 25/1/2016, in nearly <b>8 years</b>.
<br/>
The difference in time required one kind of <b>standardizing</b> them.
<br/>
These 2 factors have great effects on the stock prices since crude oil are the fuel for most of industries while US treasury bonds are considered as an <b>alternative investment</b> for investors in stock market are unstable or down. We will also consider other alternative investments like gold, real estates.

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<p>Now, the data structure <code>factors1</code> is a list, containing data that pertains to two (out of a total of four) factors that influence the market, as obtained by investing.com. Each element in the list is a tuple, containing some sort of timestamp, and the value of one of the two factors discussed above. From now on, we call these elements "<strong>records</strong>" or "<strong>entries</strong>". Visually, <code>factors1</code> looks like this:</p>
<table>
<thead><tr>
<th>0 (crude oil)</th>
<th>1 (US bonds)</th>
</tr>
</thead>
<tbody>
<tr>
<td>time_stamp, value</td>
<td>time_stamp, value</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
</tr>
<tr>
<td>time_stamp, value</td>
<td>time_stamp, value</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
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<h4 id="Question-4.2">Question 4.2<a class="anchor-link" href="#Question-4.2">&#182;</a></h4><p>Write a function named <code>readYahooHistory</code> to parse data from yahoo.com based on its format, as described in Section 5.3.</p>
<p>Print the first 5 entries of the first factor (namely GSPC). Comment the time range of the second batch of data we use in our Notebook.</p>
<p>Note that we are only interested in the date and price of stocks.</p>
<p>NOTE: The datetime format now is in a different format than the previous one.</p>
<p>HINT: Use a terminal (or put the bash commands inline in your Notebook) to list filenames in your local working directory to find and have a look at your local files.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># read data from local disk</span>
<span class="k">def</span> <span class="nf">readYahooHistory</span><span class="p">(</span><span class="n">fname</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">process_line</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
        <span class="n">cols</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
        <span class="n">date</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
        <span class="n">value</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span> <span class="c1">#?cols[6]</span>
        <span class="k">return</span> <span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
    
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">content_w_header</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
        <span class="c1"># remove the first line </span>
        <span class="c1"># and reverse lines to sort the data by date, in ascending order</span>
        <span class="n">content</span> <span class="o">=</span> <span class="n">content_w_header</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">process_line</span> <span class="p">,</span> <span class="n">content</span><span class="p">))</span>

<span class="n">factor2_files</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;GSPC.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;IXIC.csv&#39;</span><span class="p">]</span>
<span class="n">factor2_files</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">fn</span><span class="p">:</span> <span class="n">factors_folder</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">factor2_files</span><span class="p">)</span>

<span class="n">factors2</span> <span class="o">=</span> <span class="p">[</span><span class="n">readYahooHistory</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">factor2_files</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">factors2</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factors2</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
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<pre>(datetime.datetime(1950, 1, 3, 0, 0), 16.66)
(datetime.datetime(2016, 1, 22, 0, 0), 1906.900024)
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If we consider these 4 factors together, there maybe some problems:


    **1/** Data of crude oil is only available from 26/1/2006 and treasury bond is even only avalable from 12/2/2008, which means if we build the model of relation between 4 factors and the stock prices, we can only use some portions of the data not all of them.


    **2/** Since the time is not synchronized, we have to trim both for factors and the stocks price to be in a same time inverval, which again make preprocessing the most important step.


    **3/** The effects of the returns of the factors on the stock prices may be delayed a certains amount of time, which we can not know for sure. It may require **domain knowledge**.


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<p>Now, the data structure <code>factors2</code> is again list, containing data that pertains to the next two (out of a total of four) factors that influence the market, as obtained by Yahoo!. Each element in the list is a tuple, containing some sort of timestamp, and the value of one of the two factors discussed above. Visually, <code>factors2</code> looks like this:</p>
<table>
<thead><tr>
<th>0 (GSPC)</th>
<th>1 (IXIC)</th>
</tr>
</thead>
<tbody>
<tr>
<td>time_stamp, value</td>
<td>time_stamp, value</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
</tr>
<tr>
<td>time_stamp, value</td>
<td>time_stamp, value</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
</tr>
</tbody>
</table>

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<h3 id="Stock-data-pre-processing">Stock data pre-processing<a class="anchor-link" href="#Stock-data-pre-processing">&#182;</a></h3><p>Next, we prepare the data for the instruments we consider in this Notebook (i.e., the stocks we want to invest in).</p>

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<h4 id="Question-4.3">Question 4.3<a class="anchor-link" href="#Question-4.3">&#182;</a></h4><p>In this Notebook, we assume that we want to invest on the first 35 stocks out of the total 3000 stocks present in our datasets.</p>
<p>Load and prepare all the data for the considered instruments (the first 35 stocks) which have historical information for more than 5 years. This means that all instruments with less than 5 years of history should be removed.</p>
<p><strong>[HINT]</strong>: we suggest to open a terminal window (not on your local machine, but the Notebook terminal that you can find on the Jupyter dashboard) and visually check the contents of the directories holding our dataset, if you didn't do this before! Have a look at how stock data is organized!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">os</span> <span class="k">import</span> <span class="n">listdir</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">join</span>

<span class="n">stock_folder</span> <span class="o">=</span> <span class="n">base_folder</span> <span class="o">+</span> <span class="s1">&#39;stocks&#39;</span>

<span class="k">def</span> <span class="nf">process_stock_file</span><span class="p">(</span><span class="n">fname</span><span class="p">):</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">readYahooHistory</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
        <span class="k">raise</span> <span class="n">e</span>
        <span class="k">return</span> <span class="kc">None</span>


<span class="c1"># select path of all stock data files in &quot;stock_folder&quot;</span>
<span class="n">files</span> <span class="o">=</span> <span class="p">[</span><span class="n">join</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">listdir</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">)</span> <span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">,</span> <span class="n">f</span><span class="p">))]</span>

<span class="c1"># assume that we invest only the first 35 stocks (for faster computation)</span>
<span class="n">files</span> <span class="o">=</span> <span class="n">files</span><span class="p">[:</span><span class="mi">35</span><span class="p">]</span>

<span class="c1"># read each line in each file, convert it into the format: (date, value)</span>
<span class="n">rawStocks</span> <span class="o">=</span> <span class="p">[</span><span class="n">process_stock_file</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">]</span>

<span class="c1"># select only instruments which have more than 5 years of history</span>
<span class="c1"># Note: the number of business days in a year is 260</span>
<span class="n">number_of_years</span> <span class="o">=</span> <span class="mi">5</span>

<span class="n">rawStocks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">instrument</span><span class="p">:</span> <span class="n">datetime</span><span class="p">(</span><span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">year</span><span class="o">+</span><span class="n">number_of_years</span><span class="p">,</span> \
                                                    <span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">month</span><span class="p">,</span> <span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">day</span><span class="p">)</span><span class="o">&lt;</span><span class="n">instrument</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="p">,</span> <span class="n">rawStocks</span><span class="p">))</span>

<span class="c1"># For testing, print the first 5 entry of the first stock</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rawStocks</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">5</span><span class="p">])</span>
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<pre>[(datetime.datetime(1999, 11, 10, 0, 0), 20.99999), (datetime.datetime(1999, 11, 11, 0, 0), 20.99999), (datetime.datetime(1999, 11, 12, 0, 0), 20.99999), (datetime.datetime(1999, 11, 15, 0, 0), 20.99999), (datetime.datetime(1999, 11, 16, 0, 0), 20.99999)]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rawStocks</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rawStocks</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
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<pre>27
(datetime.datetime(2016, 1, 22, 0, 0), 54.98)
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There are some problems with the stock files. Each time we start downloading and filter them with the same conditions, it return different sets of files. However, the main purpose of this step is to filter stocks that reach a certain amount of time. Stocks which are not collected enough can not give a certain results.
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<h3 id="Time-alignment-for-our-data">Time alignment for our data<a class="anchor-link" href="#Time-alignment-for-our-data">&#182;</a></h3><p>Different types of instruments may trade on different days, or the data may have missing values for other reasons, so it is important to make sure that our different histories align. First, we need to trim all of our time series to the same region in time. Then, we need to fill in missing values. To deal with time series that have missing values at the start and end dates in the time region, we simply fill in those dates with nearby values in the time region.</p>

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<p><div class='anchor' ></div></p>
<h4 id="Question-4.4">Question 4.4<a class="anchor-link" href="#Question-4.4">&#182;</a></h4><p>Assume that we only focus on the data from 23/01/2009 to 23/01/2014. Write a function named <code>trimToRegion</code> to select only the records in that time interval.</p>
<p>Requirements: after processing, each instrument $i$ has a list of records: $[r_0, r_2,...,r_{m_i}]$ such that $r_0$ and $r_{m_i}$ are assigned, respectively, the first and the last values corresponding to the extremes of the given time interval. For example: $r_0$ should contain the value at date 23/01/2009.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># note that the data of crude oild and treasury is only available starting from 26/01/2006 </span>
<span class="n">start</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="o">=</span><span class="mi">2009</span><span class="p">,</span> <span class="n">month</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">day</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="o">=</span><span class="mi">2014</span><span class="p">,</span> <span class="n">month</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">day</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">trimToRegion</span><span class="p">(</span><span class="n">history</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">isInTimeRegion</span><span class="p">(</span><span class="n">entry</span><span class="p">):</span>
        <span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="o">=</span> <span class="n">entry</span>
        <span class="k">return</span> <span class="n">date</span> <span class="o">&gt;=</span> <span class="n">start</span> <span class="ow">and</span> <span class="n">date</span> <span class="o">&lt;=</span> <span class="n">end</span>

    <span class="c1"># only select entries which are in the time region</span>
    <span class="n">trimmed</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">isInTimeRegion</span><span class="p">,</span><span class="n">history</span><span class="p">))</span>
    
    <span class="c1"># if the data has incorrect time boundaries, add time boundaries</span>
    <span class="k">if</span> <span class="n">trimmed</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">start</span><span class="p">:</span>
        <span class="n">trimmed</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">trimmed</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
    <span class="k">if</span> <span class="n">trimmed</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">end</span><span class="p">:</span>
        <span class="n">trimmed</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">end</span><span class="p">,</span> <span class="n">trimmed</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
    <span class="k">return</span> <span class="n">trimmed</span>
    
<span class="c1"># test our function</span>
<span class="n">trimmedStock0</span>  <span class="o">=</span> <span class="n">trimToRegion</span><span class="p">(</span><span class="n">rawStocks</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span>
<span class="c1"># the first 5 records of stock 0</span>
<span class="nb">print</span><span class="p">(</span><span class="n">trimmedStock0</span><span class="p">[:</span><span class="mi">5</span><span class="p">])</span>
<span class="c1"># the last 5 records of stock 0</span>
<span class="nb">print</span><span class="p">(</span><span class="n">trimmedStock0</span><span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">:])</span>

<span class="k">assert</span> <span class="p">(</span><span class="n">trimmedStock0</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">start</span><span class="p">),</span><span class="s2">&quot;the first record must contain the price in the first day of time interval&quot;</span>
<span class="k">assert</span> <span class="p">(</span><span class="n">trimmedStock0</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">end</span><span class="p">),</span><span class="s2">&quot;the last record must contain the price in the last day of time interval&quot;</span>
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<pre>[(datetime.datetime(2009, 1, 23, 0, 0), 9.57), (datetime.datetime(2009, 1, 26, 0, 0), 8.65), (datetime.datetime(2009, 1, 27, 0, 0), 8.5), (datetime.datetime(2009, 1, 28, 0, 0), 8.5), (datetime.datetime(2009, 1, 29, 0, 0), 8.41)]
[(datetime.datetime(2014, 1, 16, 0, 0), 11.92), (datetime.datetime(2014, 1, 17, 0, 0), 11.92), (datetime.datetime(2014, 1, 21, 0, 0), 11.98), (datetime.datetime(2014, 1, 22, 0, 0), 11.98), (datetime.datetime(2014, 1, 23, 0, 0), 11.98)]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fillInHistory</span><span class="p">(</span><span class="n">history</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">):</span>
    <span class="n">curr</span> <span class="o">=</span> <span class="n">history</span>
    <span class="n">filled</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">curDate</span> <span class="o">=</span> <span class="n">start</span>
    <span class="n">numEntries</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">history</span><span class="p">)</span>
    <span class="k">while</span> <span class="n">curDate</span> <span class="o">&lt;</span> <span class="n">end</span><span class="p">:</span>
        
        <span class="c1"># if the next entry is in the same day</span>
        <span class="c1"># move to the next entry</span>
        <span class="k">while</span> <span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">&lt;</span> <span class="n">numEntries</span> <span class="ow">and</span> <span class="n">curr</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">curDate</span><span class="p">:</span>
            <span class="n">idx</span> <span class="o">+=</span><span class="mi">1</span>

        <span class="c1"># only add the last value of instrument in a single day</span>
        <span class="c1"># check curDate is weekday or not</span>
        <span class="c1"># 0: Monday -&gt; 5: Saturday, 6: Sunday</span>
        <span class="k">if</span> <span class="n">curDate</span><span class="o">.</span><span class="n">weekday</span><span class="p">()</span> <span class="o">&lt;</span> <span class="mi">5</span><span class="p">:</span>
            
            <span class="n">filled</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">curDate</span><span class="p">,</span> <span class="n">curr</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
            <span class="c1"># move to the next business day</span>
            <span class="n">curDate</span> <span class="o">+=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        
        <span class="c1"># skip the weekends</span>
        <span class="k">if</span> <span class="n">curDate</span><span class="o">.</span><span class="n">weekday</span><span class="p">()</span> <span class="o">&gt;=</span> <span class="mi">5</span><span class="p">:</span>
            <span class="c1"># if curDate is Sat, skip 2 days, otherwise, skip 1 day</span>
            <span class="n">curDate</span> <span class="o">+=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="o">-</span><span class="n">curDate</span><span class="o">.</span><span class="n">weekday</span><span class="p">()))</span>

    <span class="k">return</span> <span class="n">filled</span>
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<h4 id="Question-4.5">Question 4.5<a class="anchor-link" href="#Question-4.5">&#182;</a></h4><p>Trim data of stocks and factors into the given time interval.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#print rawStocks[0]</span>

<span class="c1"># trim into a specific time region</span>
<span class="c1"># and fill up the missing values</span>
<span class="n">stocks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">stock</span><span class="p">:</span> \
            <span class="n">fillInHistory</span><span class="p">(</span>
                <span class="n">trimToRegion</span><span class="p">(</span><span class="n">stock</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
            <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
        <span class="n">rawStocks</span><span class="p">))</span>


<span class="c1"># merge two factors, trim each factor into a time region</span>
<span class="c1"># and fill up the missing values</span>
<span class="n">allfactors</span> <span class="o">=</span> <span class="n">factors1</span> <span class="o">+</span> <span class="n">factors2</span>
<span class="n">factors</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">factor</span><span class="p">:</span> \
            <span class="n">fillInHistory</span><span class="p">(</span>
                <span class="n">trimToRegion</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
            <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span>
            <span class="n">allfactors</span>
            <span class="p">))</span>
            
<span class="c1"># test our code</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the first 5 records of stock 0:&quot;</span><span class="p">,</span> <span class="n">stocks</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">5</span><span class="p">],</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the last 5 records of stock 0:&quot;</span><span class="p">,</span> <span class="n">stocks</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">5</span><span class="p">:],</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the first 5 records of factor 0:&quot;</span><span class="p">,</span> <span class="n">factors</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">5</span><span class="p">],</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the first 5 records of factor 0:&quot;</span><span class="p">,</span> <span class="n">factors</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">5</span><span class="p">:],</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
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<pre>the first 5 records of stock 0: [(datetime.datetime(2009, 1, 23, 0, 0), 9.57), (datetime.datetime(2009, 1, 26, 0, 0), 8.65), (datetime.datetime(2009, 1, 27, 0, 0), 8.5), (datetime.datetime(2009, 1, 28, 0, 0), 8.5), (datetime.datetime(2009, 1, 29, 0, 0), 8.41)] 

the last 5 records of stock 0: [(datetime.datetime(2014, 1, 16, 0, 0), 11.92), (datetime.datetime(2014, 1, 17, 0, 0), 11.92), (datetime.datetime(2014, 1, 20, 0, 0), 11.92), (datetime.datetime(2014, 1, 21, 0, 0), 11.98), (datetime.datetime(2014, 1, 22, 0, 0), 11.98)] 

the first 5 records of factor 0: [(datetime.datetime(2009, 1, 23, 0, 0), 46.47), (datetime.datetime(2009, 1, 26, 0, 0), 45.73), (datetime.datetime(2009, 1, 27, 0, 0), 41.58), (datetime.datetime(2009, 1, 28, 0, 0), 42.16), (datetime.datetime(2009, 1, 29, 0, 0), 41.44)] 

the first 5 records of factor 0: [(datetime.datetime(2014, 1, 16, 0, 0), 93.96), (datetime.datetime(2014, 1, 17, 0, 0), 94.37), (datetime.datetime(2014, 1, 20, 0, 0), 93.93), (datetime.datetime(2014, 1, 21, 0, 0), 94.99), (datetime.datetime(2014, 1, 22, 0, 0), 96.73)] 

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<p>Recall that Value at Risk (VaR) deals with <strong>losses over a particular time horizon</strong>. We are not concerned with the absolute prices of instruments, but how those prices <strong>change over</strong> a given period of time. In our project, we will set that length to two weeks: we use the sliding window method to transform time series of prices into an overlapping sequence of price change over two-week intervals.</p>
<p>The figure below illustrate this process. The returns of market factors after each two-week intervals is calculated in the very same way.</p>
<p><img src="https://dl.dropboxusercontent.com/s/r9ucsicichb6gjh/two-week-time-interval.svg" alt=""></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">buildWindow</span><span class="p">(</span><span class="n">seq</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
    <span class="s2">&quot;Returns a sliding window (of width k) over data from iterable data structures&quot;</span>
    <span class="s2">&quot;   s -&gt; (s0,s1,...s[k-1]), (s1,s2,...,sk), ...                   &quot;</span>
    <span class="n">it</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">seq</span><span class="p">)</span>
    <span class="n">result</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">islice</span><span class="p">(</span><span class="n">it</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> <span class="o">==</span> <span class="n">k</span><span class="p">:</span>
        <span class="k">yield</span> <span class="n">result</span>  
    <span class="k">for</span> <span class="n">elem</span> <span class="ow">in</span> <span class="n">it</span><span class="p">:</span>
        <span class="n">result</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="p">(</span><span class="n">elem</span><span class="p">,)</span>
        <span class="k">yield</span> <span class="n">result</span>
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<h4 id="Question-4.6">Question 4.6<a class="anchor-link" href="#Question-4.6">&#182;</a></h4><p>Compute the returns of the stocks after each two week time window.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">calculateReturn</span><span class="p">(</span><span class="n">window</span><span class="p">):</span>
    <span class="c1"># return the change of value after two weeks</span>
    <span class="k">return</span> <span class="n">window</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">window</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>

<span class="k">def</span> <span class="nf">twoWeekReturns</span><span class="p">(</span><span class="n">history</span><span class="p">):</span>
    <span class="c1"># we use 10 instead of 14 to define the window</span>
    <span class="c1"># because financial data does not include weekends</span>
    <span class="k">return</span> <span class="p">[</span><span class="n">calculateReturn</span><span class="p">(</span><span class="n">entry</span><span class="p">)</span> <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">buildWindow</span><span class="p">(</span><span class="n">history</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>

<span class="n">stocksReturns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">twoWeekReturns</span><span class="p">,</span> <span class="n">stocks</span><span class="p">))</span>
<span class="n">factorsReturns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">twoWeekReturns</span><span class="p">,</span> <span class="n">factors</span><span class="p">))</span>

<span class="c1"># test our functions</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the first 5 returns of stock 0:&quot;</span><span class="p">,</span> <span class="n">stocksReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="mi">5</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;the last 5 returns of stock 0:&quot;</span><span class="p">,</span> <span class="n">stocksReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">5</span><span class="p">:])</span>
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<pre>the first 5 returns of stock 0: [-0.07000000000000028, 1.3100000000000005, 0.09999999999999964, 0.09999999999999964, 0.1899999999999995]
the last 5 returns of stock 0: [-0.27999999999999936, 0.02999999999999936, 0.02999999999999936, 0.08999999999999986, -0.15000000000000036]
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<p>Alright! Now we have data that is properly aligned to start the training process: stocks' returns and factors' returns, per time windows of two weeks. Next, we will apply the MCS method.</p>

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<h2 id="5.5.-Summary-guidelines-to-apply-the-MCS-method-on-the-data-we-prepared">5.5. Summary guidelines to apply the MCS method on the data we prepared<a class="anchor-link" href="#5.5.-Summary-guidelines-to-apply-the-MCS-method-on-the-data-we-prepared">&#182;</a></h2><p>Next, we overview the steps that you have to follow to build a model of your data, and then use Monte Carlo simulations to produce output distributions:</p>
<ul>
<li><strong>Step 1</strong>: Defining the relationship between the market factors and the instrument's returns. This relationship takes the form of a model fitted to historical data.</li>
<li><strong>Step 2</strong>: Defining the distributions for the market conditions (particularly, the returns of factors) that are straightforward to sample from. These distributions are fitted to historical data. </li>
<li><strong>Step 3</strong>: Generate the data for each trial of a Monte Carlo run: this amount to generating the random values for market conditions along with these distributions.</li>
<li><strong>Step 4</strong>: For each trial, from the above values of market conditions, and using the relationship built in step 1, we calculate the return for each instrument and the total return. We use the returns to define an empirical distribution over losses. This means that, if we run 100 trials and want to estimate the 5% VaR, we would choose it as the loss from the trial with the fifth greatest loss.</li>
<li><strong>Step 5</strong>: Evaluating the result</li>
</ul>

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<h2 id="5.6.-Applying-MCS">5.6. Applying MCS<a class="anchor-link" href="#5.6.-Applying-MCS">&#182;</a></h2><h3 id="Step-1:-Defining-relationship-between-market-factors-and-instrument's-returns">Step 1: Defining relationship between market factors and instrument's returns<a class="anchor-link" href="#Step-1:-Defining-relationship-between-market-factors-and-instrument's-returns">&#182;</a></h3><p>In our simulation, we will use a simple linear model. By our definition of return , a factor return is a <strong>change</strong> in the value of a market factor <strong>over a particular time period</strong>, e.g. if the value of the S&amp;P 500 moves from 2000 to 2100 over a time interval, its return would be 100.</p>
<p>A vector that contains the return of 4 market factors is called a <em>market factor vector</em>. Generally, instead of using this vector as features, we derive a set of features from simple transformation of it. In particular, a vector of 4 values is tranformed into a vector of length $m$ by function $F$. In the simplest case $F(v) = v$.</p>
<p>Denote $v_t$ the market factor vector, and $f_t$ the transformed features of $v_t$ at time $t$.</p>
<p>$f_{tj}$ is the value of feature $j$ in $f_t$.</p>
<p>Denote $r_{it}$ the return of instrument $i$ at time $t$ and $c_i$ the <a href="http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-to-interpret-the-constant-y-intercept">intercept term</a> of instrument $i$.</p>
<p>We will use a simple linear function to calculate $r_{it}$ from $f_t$:</p>
$$
r_{it} = c_i + \sum_{j=1}^{m}{w_{ij}*f_{tj}}
$$<p>where $w_{ij}$ is the weight of feature $j$ for instrument $i$.</p>
<p>All that above means that given a market factor vector, we have to "featurize it" and then use the result as a surrogate for calculating the return of the instruments, using the above linear function.</p>
<p>There are two questions that we should consider: <strong>how to featurize a factor vector ?</strong> and <strong>how to pick values for $w_{ij}$?</strong></p>
<p><strong>How to featurize a factor vector ?</strong>
In fact, the instruments' returns may be non-linear functions of the factor returns. So, we should not use factor returns as features in the the above linear function. Instead, we tranform them into a set of features with different size. In this Notebook, we can include some additional features in our model that we derive from non-linear transformations of the factor returns. We will try adding two more features for each factor return: its square and its square root values. So, we can still assume that our model is a linear model in the sense that the response variable is a linear function of the new features. <em>Note that the particular feature transformation described here is meant to be an illustrative example of some of the options that are available: it shouldn't be considered as the state of the art in predictive financial modeling!!</em>.</p>
<p><strong>How to pick values for $w_{ij}$ ?</strong></p>
<p>For all the market factor vectors in our historical data, we tranform them to feature vectors. Now, we have feature vectors in many two-week intervals and the corresponding instrument's returns in these interval. We can use Odinary Least Square (OLS) regression model to estimate the weights for each instrument such that our linear function can fit to the data. The parameters for OLS function are:</p>
<ul>
<li><code>x</code>: The collection of columns where <strong>each column</strong> is the value of <strong>a feature</strong> in many two-week interval</li>
<li><code>y</code>: The return of an instrument in the corresponding time interval of x.</li>
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<p>The figure below shows the basic idea of the process to build a statistial model for predicting the returns of stock X.</p>
<p><img src="https://dl.dropboxusercontent.com/s/kbjtwmnr4w31uev/process-build-model3.svg" alt=""></p>

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<p><img src="https://farm2.staticflickr.com/1604/24934700445_833f0a5649_t.jpg" alt=""></p>

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<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#182;</a></h3><h4 id="Question-5.1">Question 5.1<a class="anchor-link" href="#Question-5.1">&#182;</a></h4><p>Currently, our data is in form of:</p>
$$
factorsReturns=
\begin{bmatrix}
 r_{00} & r_{01} & r_{02} & ... & r_{0k} \\
 r_{10} & r_{11} & r_{12} & ... & r_{1k} \\
 ... & ... & ... & ... & ... \\
 r_{n0} & r_{n1} & r_{n2} & ... & r_{nk}\\
\end{bmatrix}
$$$$
stocksReturns=
\begin{bmatrix}
 s_{00} & s_{01} & s_{02} & ... & s_{0k} \\
 s_{10} & s_{11} & s_{12} & ... & s_{1k} \\
 ... & ... & ... & ... & ... \\
 s_{n0} & s_{n1} & s_{n2} & ... & s_{nk}\\
\end{bmatrix}
$$<p>Where, $r_{ij}$ is the return of factor $i^{th}$ in time window $j^{th}$, $k$ is the number of time windows, and $n$ is the number of factors. A similar definition goes for $s_{ij}$.</p>
<p>In order to use OLS, the parameter must be in form of:</p>
$$
x=factorsReturns^T =
\begin{bmatrix}
 r_{00} & r_{10} & ... & r_{n0} \\
 r_{01} & r_{11} & ... & r_{n1} \\
 r_{02} & r_{12} & ... & r_{n2}\\
 ... & ... & ... & ... \\
 r_{0k} & r_{1k} & ... & r_{nk}\\
\end{bmatrix}
$$<p>Whereas, $y$ can be any row in <code>stocksReturns</code>.</p>
<p>So, we need a function to transpose a matrix. Write a function named <code>transpose</code> to do just that.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">transpose</span><span class="p">(</span><span class="n">matrix</span><span class="p">):</span>
    <span class="k">return</span> <span class="p">[</span> <span class="p">[</span><span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">))]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">]))]</span>
    
<span class="c1"># test function</span>
<span class="k">assert</span> <span class="p">(</span><span class="n">transpose</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">]])</span> <span class="o">==</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">]]),</span> <span class="s2">&quot;Function transpose runs incorrectly&quot;</span>
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<h4 id="Question-5.2">Question 5.2<a class="anchor-link" href="#Question-5.2">&#182;</a></h4><p>Write a function named <code>featurize</code> that takes a list factor's returns $[x_1, x_2,...,x_k]$ and transform it into a new list of features $[u_1,u_2,..,u_k, v_1, v_2,..,v_k, x_1,x_2,...,x_k]$.</p>
<p>Where</p>
<p>$u_i$ = $\left\{
    \begin{array}{ll}
        x_i^2 & \mbox{if } x_i \geq 0 \\
        -x_i^2 & \mbox{if } x_i < 0
    \end{array}
\right.
$</p>
<p>and</p>
<p>$v_i$ = $\left\{
    \begin{array}{ll}
        \sqrt{x_i} & \mbox{if } x_i \geq 0 \\
        -\sqrt{x_i} & \mbox{if } x_i < 0
    \end{array}
\right.
$</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="k">def</span> <span class="nf">featurize</span><span class="p">(</span><span class="n">factorReturns</span><span class="p">):</span>
    <span class="n">squaredReturns</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">*</span><span class="n">i</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="n">i</span><span class="o">*</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">factorReturns</span><span class="p">]</span>
    <span class="n">squareRootedReturns</span> <span class="o">=</span> <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="o">-</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">factorReturns</span><span class="p">]</span>
    <span class="c1"># concat new features</span>
    <span class="k">return</span>  <span class="n">squaredReturns</span> <span class="o">+</span> <span class="n">squareRootedReturns</span> <span class="o">+</span> <span class="n">factorReturns</span>

<span class="c1"># test our function</span>
<span class="k">assert</span> <span class="p">(</span><span class="n">featurize</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">==</span> <span class="p">[</span><span class="mi">16</span><span class="p">,</span> <span class="o">-</span><span class="mi">81</span><span class="p">,</span> <span class="mi">625</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="s2">&quot;Function runs incorrectly&quot;</span>
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<h4 id="Question-5.3">Question 5.3<a class="anchor-link" href="#Question-5.3">&#182;</a></h4><p>Using OLS, estimate the weights for each feature on each stock. What is the shape of <code>weights</code> (size of each dimension) ? Explain it.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">estimateParams</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span>

<span class="c1"># transpose factorsReturns</span>
<span class="n">factorMat</span> <span class="o">=</span> <span class="n">transpose</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">)</span>

<span class="c1"># featurize each row of factorMat</span>
<span class="n">factorFeatures</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">featurize</span><span class="p">,</span><span class="n">factorMat</span><span class="p">))</span>

<span class="c1"># OLS require parameter is a numpy array</span>
<span class="n">factor_columns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">factorFeatures</span><span class="p">)</span>

<span class="c1">#add a constant - the intercept term for each instrument i.</span>
<span class="n">factor_columns</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">factor_columns</span><span class="p">,</span> <span class="n">prepend</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># estimate weights</span>
<span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">estimateParams</span><span class="p">(</span><span class="n">stockReturn</span><span class="p">,</span><span class="n">factor_columns</span><span class="p">)</span> <span class="k">for</span> <span class="n">stockReturn</span> <span class="ow">in</span> <span class="n">stocksReturns</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;weights:&quot;</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<pre>weights: [array([  1.75007902e-02,  -2.28995321e-03,  -2.36638730e+00,
         4.66000193e-05,  -1.97784695e-06,  -6.36222350e-02,
        -8.09755937e-01,   2.36894895e-02,   8.39444063e-03,
         4.18816527e-02,   2.52331406e+00,  -4.98025956e-03,
        -8.27909788e-04]), array([  2.54332947e-02,   1.98253424e-03,  -2.98806448e+00,
         3.21793286e-05,   1.47464331e-06,   8.73083043e-03,
         1.04137555e-01,   6.22372017e-02,  -6.41346393e-03,
        -3.57803498e-02,   3.13437669e-02,   2.00903034e-02,
        -2.25958696e-03]), array([ -2.14703540e-03,  -1.35018828e-03,  -1.41078371e-01,
         3.24901886e-05,  -3.99308879e-06,  -6.66704400e-02,
         2.26853604e-01,   5.25231475e-02,  -2.82642456e-02,
         3.98919693e-02,  -4.83178965e-01,  -3.70829058e-03,
         9.90350208e-04]), array([ -2.79423061e-02,  -6.62996524e-04,   4.88418483e+00,
         6.48456462e-05,  -2.15183410e-05,  -1.03503389e-01,
         6.84035096e-01,   1.73944177e-02,  -2.56092827e-02,
         5.09720027e-02,  -2.84828864e+00,  -9.98181042e-03,
         9.19594190e-03]), array([  4.34285872e-02,  -9.08063674e-04,   1.60735000e+00,
        -1.07579372e-04,   1.99445704e-05,  -2.12680891e-02,
         4.82079565e-01,  -3.68023193e-02,   6.10624314e-03,
         1.60815280e-02,  -1.44384256e+00,   1.76467122e-02,
        -5.99030223e-03]), array([ -4.69996387e-02,  -7.37314702e-04,   5.37423329e-01,
         8.78026277e-05,  -1.33694541e-05,   1.23163426e-02,
         8.44438729e-02,   1.68866597e-02,  -5.23910933e-03,
         2.30883925e-03,  -3.22845977e-02,  -1.13088976e-02,
         6.81321674e-03]), array([  5.83066586e-02,   2.18680054e-03,  -3.02865820e+00,
         8.22007110e-05,  -8.03170564e-06,   1.09567076e-01,
        -3.52242730e-01,  -1.08327569e-03,   1.25083980e-02,
        -6.18430589e-02,   1.99777140e+00,   3.05996016e-03,
        -3.21194464e-03]), array([ -7.24854108e-03,   9.29724577e-04,  -1.38256996e-01,
        -1.91079741e-05,  -9.76088067e-07,   3.66319444e-02,
        -4.01335275e-02,   3.56501591e-03,  -8.60017568e-03,
        -2.08492788e-02,   2.37738358e-01,   8.98102379e-04,
         1.43458400e-03]), array([ -6.28236461e-03,  -4.51681890e-05,   1.01690447e-01,
         1.83658268e-07,  -1.09207933e-07,   8.89955571e-03,
        -1.01347299e-02,   4.80338509e-03,  -9.26264895e-04,
         5.27801525e-04,   8.99405213e-02,  -1.32192144e-03,
         1.26344037e-03]), array([ -4.52081080e-02,   2.05893846e-02,   9.17338163e+00,
        -2.82303705e-03,   4.94965893e-04,   1.10241515e+00,
        -2.93028578e+00,  -1.15869379e+00,   6.62713446e-01,
        -6.14649587e-01,   5.11256872e+00,   4.57709271e-01,
        -1.61933346e-01]), array([  1.73430233e-02,  -1.21963713e-03,   5.37854430e-01,
         7.74369750e-05,  -1.02945225e-05,   8.09449460e-03,
         2.49514798e-01,   3.31069711e-02,  -8.88016985e-03,
         1.23091762e-02,  -5.74634614e-01,  -1.13367910e-02,
         3.14819837e-03]), array([  2.41067043e-01,   1.10437818e-02,   8.04089611e+00,
         2.60630060e-04,  -3.84695280e-05,   1.96561081e-01,
         1.29377271e+00,  -2.05632311e-03,   3.08518472e-02,
        -2.02403928e-01,  -3.13176562e+00,   4.05264432e-03,
         1.38515876e-02]), array([  2.43948796e-01,  -7.10087133e-04,  -3.33549038e+00,
        -3.07382397e-05,  -8.29584953e-06,   1.03893632e-01,
        -1.71778815e+00,  -6.29185696e-02,   3.37540812e-03,
        -6.79367178e-02,   4.52529365e+00,   2.63141443e-02,
         1.09961038e-02]), array([  3.87642913e-03,   7.75205376e-04,   2.29692481e-01,
         9.98151997e-06,   2.60413510e-06,   1.41845690e-02,
         3.41631805e-02,  -4.04492389e-03,   1.20419637e-02,
        -8.08280174e-03,   3.60989341e-02,  -4.08114174e-04,
        -1.24273319e-03]), array([  3.22121394e-03,  -6.61837705e-03,   3.52204660e+00,
         1.53534143e-05,  -1.41617211e-05,  -6.15559213e-02,
         7.66347190e-01,   5.59613289e-03,  -2.38917399e-02,
         8.20886348e-02,  -2.55719652e+00,   8.12148242e-03,
         4.49587157e-03]), array([  4.66188294e-02,   1.48397681e-03,   2.10507368e-01,
        -1.06389372e-05,  -2.91519095e-07,  -3.68564181e-02,
         4.14724696e-02,   2.06423426e-02,  -1.01019121e-03,
         3.97210942e-03,   3.28883590e-02,  -9.72013245e-04,
         4.67490557e-04]), array([ -6.45031651e-02,   1.82016229e-02,   1.56639431e+00,
        -2.14391086e-04,   3.25424110e-05,   1.30583559e-01,
         6.79412869e-01,  -3.86410523e-02,   1.05708582e-02,
        -1.48565128e-01,  -2.19786763e+00,   1.38664305e-02,
        -4.74548704e-04]), array([ -1.45222970e-01,  -7.86095668e-03,   3.51690054e+00,
         3.44214721e-04,  -1.06951333e-04,  -2.41045815e-01,
        -7.15104979e-01,  -2.24846854e-02,  -3.55979374e-02,
         1.40968505e-01,   6.03541126e-01,  -3.14019128e-04,
         2.50463586e-02]), array([ -7.34284829e-03,   1.72631629e-04,  -6.79055202e+00,
         1.45649756e-04,  -2.14237834e-05,  -1.22895495e-01,
        -7.27915981e-01,   1.18333663e-01,  -2.90547824e-02,
         3.82137712e-02,   4.75085434e+00,  -2.26317446e-02,
         1.37558563e-02]), array([ -2.42730560e-02,   1.14097026e-03,  -4.32878876e-02,
         4.50179980e-06,  -5.06893329e-07,   4.13244367e-02,
         1.59197073e-02,  -1.44995004e-03,   3.40353594e-03,
        -1.76313276e-02,   9.77151482e-02,   3.26491878e-03,
         8.42602205e-04]), array([ -1.06815965e-02,   1.12204612e-02,   2.15135727e+00,
         6.54100943e-05,   2.10194831e-05,   3.10718004e-01,
         2.40115936e-01,   1.66199985e-02,   5.38761806e-02,
        -1.33430060e-01,   3.31735645e-01,   1.55393470e-02,
        -1.04153494e-02]), array([  1.73282586e-02,   7.49599569e-05,  -6.26338966e-02,
        -1.90730609e-04,   3.29570828e-05,  -3.08666161e-02,
        -2.77962139e-01,  -3.18411573e-02,   1.17387757e-02,
        -4.16333219e-03,   1.11447640e+00,   2.12719130e-02,
        -4.07865859e-03]), array([ -5.42466534e-02,  -4.08185246e-03,   2.04066459e+00,
         4.70108588e-06,  -1.52365933e-06,  -8.05866653e-03,
        -2.46170930e-01,   2.33456481e-02,   9.39328974e-04,
         2.84501774e-02,   1.24454245e+00,   4.31701820e-02,
        -9.22078809e-03]), array([ -1.06922357e-01,  -1.09244618e-02,  -1.78107797e+01,
        -4.86613305e-04,   1.37838148e-04,   1.22995200e-01,
        -2.12175282e+00,  -6.36886701e-02,   3.53486082e-02,
         3.40883690e-02,   1.12809006e+01,   4.48982852e-03,
         1.92970651e-03]), array([  1.67087217e-02,   1.36884351e-03,   8.07586171e-02,
        -5.49059463e-05,   5.10503746e-06,  -1.91878653e-04,
        -7.77066119e-02,  -3.48314571e-02,   1.28872222e-02,
        -1.30824948e-02,   6.71235146e-02,   1.01464419e-02,
        -1.75447289e-03]), array([  8.37851748e-03,   8.42144463e-04,   1.99540401e+00,
        -4.89789026e-05,   9.98207532e-06,  -2.89554387e-02,
         2.73676323e-01,   1.57382022e-02,   6.77406557e-03,
        -3.62695983e-03,  -1.27350064e+00,   1.53472963e-03,
        -6.90972042e-04]), array([ -4.19413380e-02,  -3.42684684e-03,  -2.26379104e+00,
         2.97283451e-05,  -8.27157148e-06,  -5.55913217e-02,
        -3.14608754e-01,  -3.68333622e-02,   1.99508684e-02,
         4.55914912e-02,   9.53807586e-01,   3.00578658e-02,
        -5.18274157e-03])]
27
13
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From the weights of factor to the stock prices, we can briefly find out the dependences of a stock on 4 factors. If weight index of a factor of a stock is minus, which mean that factor has negative effect on the considered stock, that stock price will increase if that factor decrease and vice-versa. In the other hand, it the weight index of a factor of a stock is positive, it has the positive effect on the stock price.
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The magnitude of the indexs of the factors in dicates how much the corresponding factor effects on the corresponding stock price.
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Based on the results of the weights, we can see that square value of the US 30 years treasury bond have the biggest impact on the stock price. The second best impact factor is the value of the US 30 years treasury bonds. The interesting is that they are always in different sign. 
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<h3 id="Step-2:-Defining-the-distributions-for-the-market-conditions">Step 2: Defining the distributions for the market conditions<a class="anchor-link" href="#Step-2:-Defining-the-distributions-for-the-market-conditions">&#182;</a></h3><p>Since we can not define the ditributions for the market factors directly, we can only approximate their distribution.
The best way to do that, is plotting their value. Howevery, these values may fluctuate quite a lot.</p>
<p>Next, we show how to use the Kernel density estimation (KDE) technique to approximate such distributions. In brief, kernel density estimation is a way of smoothing out a histogram: this is achieved by assigning (or centering) a probability distribution (usually a normal distribution) to each data point, and then summing. So a set of two-week-return samples would result in a large number of "super-imposed" normal distributions, each with a different mean.</p>
<p>To estimate the probability density at a given point, KDE evaluates the PDFs of all the normal distributions at that point and takes their average. The smoothness of a kernel density plot depends on its <em>bandwidth</em>, and the standard deviation of each of the normal distributions. For a brief introduction on KDE, please refer to this <a href="https://en.wikipedia.org/wiki/Kernel_density_estimation">link</a>.</p>

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<div class="prompt input_prompt">In&nbsp;[37]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">statsmodels.nonparametric.kernel_density</span> <span class="k">import</span> <span class="n">KDEMultivariate</span>
<span class="kn">from</span> <span class="nn">statsmodels.nonparametric.kde</span> <span class="k">import</span> <span class="n">KDEUnivariate</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">scipy</span>

<span class="k">def</span> <span class="nf">plotDistribution</span><span class="p">(</span><span class="n">samples</span><span class="p">):</span>
    <span class="n">vmin</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
    <span class="n">vmax</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
    <span class="n">stddev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
    
    <span class="n">domain</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="p">,</span> <span class="p">(</span><span class="n">vmax</span><span class="o">-</span><span class="n">vmin</span><span class="p">)</span><span class="o">/</span><span class="mi">100</span><span class="p">)</span>
    
    <span class="n">k</span> <span class="o">=</span> <span class="mi">1</span>
    

    <span class="c1"># a simple heuristic to select bandwidth</span>
    <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">k</span><span class="o">*</span><span class="mf">1.06</span> <span class="o">*</span> <span class="n">stddev</span> <span class="o">*</span> <span class="nb">pow</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="o">-.</span><span class="mi">2</span><span class="p">)</span>
    
    <span class="c1"># estimate density</span>
    <span class="n">kde</span> <span class="o">=</span> <span class="n">KDEUnivariate</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
    <span class="n">kde</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">bw</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">)</span>
    <span class="n">density</span> <span class="o">=</span> <span class="n">kde</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span>
    
    <span class="c1"># plot</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">domain</span><span class="p">,</span> <span class="n">density</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plotDistribution</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
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<p>For the sake of simplicity, we can say that our smoothed versions of the returns of each factor can be represented quite well by a a normal distribution. Of course, more exotic distributions, perhaps with fatter tails, could fit more closely the data, but it is outside the scope of this Notebook to proceed in this way.</p>
<p>Now, the simplest way to sample factors returns is to use a normal distribution for each of the factors, and sample from these distributions independently. However, this approach ignores the fact that market factors are often correlated. For example, when the price of crude oil is down, the price of treasury bonds is down too. We can check our data to verify about the correlation.</p>

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<p><img src="https://farm2.staticflickr.com/1604/24934700445_833f0a5649_t.jpg" alt=""></p>

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<h3 id="Question-6">Question 6<a class="anchor-link" href="#Question-6">&#182;</a></h3><h4 id="Question-6.1">Question 6.1<a class="anchor-link" href="#Question-6.1">&#182;</a></h4><p>Calculate the correlation between market factors and explain the result.</p>
<p>HINT: function <code>np.corrcoef</code> might be useful.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">)</span>
<span class="n">correlation</span>
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<pre>array([[ 1.        ,  0.38507954,  0.47411609,  0.45841194],
       [ 0.38507954,  1.        ,  0.55213086,  0.54918324],
       [ 0.47411609,  0.55213086,  1.        ,  0.95227596],
       [ 0.45841194,  0.54918324,  0.95227596,  1.        ]])</pre>
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All 4 factor are somehow correlated, theire correlation coefficient are possitive. However, the last 2 factors are mostly the alway correlated, their correlation coefficient are 9.5 over maximum of 1. This is actually normal, since they are 2 indices that discribe the trend of the market in that time. We will also test some other indices like DowJone Industrial Average and S&P 500 to test.
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<p>The multivariate normal distribution can help here by taking the correlation information between the factors into account. Each sample from a multivariate normal distribution can be thought of as a vector. Given values for all of the dimensions but one, the distribution of values along that dimension is normal. But, in their joint distribution, the variables are not independent.</p>
<p>For this use case, we can write:</p>
$$
\left(\begin{array}{c}f_{1}\\f_{2}\\f_{3}\\f_{4} \end{array}\right)
\sim N 
\left[
  \left(
    \begin{array}{c}
      \mu_1\\ \mu_2 \\ \mu_3 \\ \mu_4 
    \end{array}
  \right), 
  \left(
    \begin{array}{cccc}
      \sigma^2_1 & \rho_{12} \sigma_1\sigma_2 & \rho_{13} \sigma_1\sigma_3 & \rho_{14} \sigma_1\sigma_4 \\ 
      \rho_{12}\sigma_2\sigma_1 & \sigma^2_2 & \rho_{23} \sigma_2\sigma_3 & \rho_{24} \sigma_2\sigma_4\\
      \rho_{13} \sigma_3\sigma_1 & \rho_{23} \sigma_3\sigma_2 & \sigma^2_3 & \rho_{34} \sigma_3\sigma_4 \\ 
      \rho_{14} \sigma_4\sigma_1 & \rho_{24} \sigma_4\sigma_2 & \rho_{34} \sigma_3\sigma_4 & \sigma_4^2 \\ 
    \end{array}
  \right)
\right]
$$<p>Or,</p>
$$
f_t \sim N(\mu, \sum)
$$<p>Where $f_1$, $f_2$, $f_3$ and $f_4$ are the market factors, $\sigma_i$ is the standard deviation of factor $i$, $\mu$ is a vector of the empirical means of the returns of the factors and $\sum$ is the empirical covarance matrix of the returns of the factors.</p>
<p>The multivariate normal is parameterized with a mean along each dimension and a matrix describing the covariance between each pair of dimensions. When the covariance matrix is diagonal, the multivariate normal reduces to sampling along each dimension independently, but placing non-zero values in the off-diagonals helps capture the relationships between variables. Whenever having the mean of this multivariate normal distribution and it's covariance matrix, we can generate the sample values for market factors.</p>
<p>Next, we will calculate the mean and the covariance matrix of this multivariate normal distribution from the historical data.</p>

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<h4 id="Question-6.2">Question 6.2<a class="anchor-link" href="#Question-6.2">&#182;</a></h4><p>Calculate the covariance matrix $\sum$ and the means $\mu$ of factors' returns then generate a random vector of factors return that follows a multivariate normal distribution $\sim N(\mu, \sum)$</p>
<p>HINT:
Function <code>np.cov</code> can help calculating covariance matrix. Function <code>np.random.multivariate_normal(&lt;mean&gt;, &lt;cov&gt;)</code> is often used for generating samples.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">factorCov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cov</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">)</span>
<span class="n">factorMeans</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">factorsReturns</span><span class="p">]</span>
<span class="n">sample</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factorCov</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factorMeans</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
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<pre>[[  2.03712313e+01   2.70383097e-01   7.77281497e+01   1.72733934e+02]
 [  2.70383097e-01   2.42014227e-02   3.11994882e+00   7.13264857e+00]
 [  7.77281497e+01   3.11994882e+00   1.31938031e+03   2.88775882e+03]
 [  1.72733934e+02   7.13264857e+00   2.88775882e+03   6.96989443e+03]]
[0.35908880308880364, 0.0019722007722007863, 6.953868592277998, 18.7092272903475]
[  -0.24193524   -0.49750116  -77.49908189 -201.45934361]
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<h3 id="Step-3&amp;4:-Generating-samples,-running-simulation-and-calculating-the-VaR">Step 3&amp;4: Generating samples, running simulation and calculating the VaR<a class="anchor-link" href="#Step-3&amp;4:-Generating-samples,-running-simulation-and-calculating-the-VaR">&#182;</a></h3>
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<p>We define some functions that helps us calculating VaR 5%. You will see that the functions below are pretty complicated! This is why we provide a solution for you: however, study them well!!</p>
<p>The basic idea of calculating VaR 5% is that we need to find a value such that only 5% of the losses are bigger than it. That means the 5th percentile of the losses should be VaR 5%.</p>
<p>VaR can sometimes be problematic though, since it does give any information on the extent of the losses which can exceed the VaR estimate. CVar is an extension of VaR that is introduced to deal with this problem. Indeed, CVaR measures the expected value of the loss in those cases where VaR estimate has been exceeded.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">):</span>
    <span class="n">numTrials</span> <span class="o">=</span> <span class="n">trials</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
    <span class="n">topLosses</span> <span class="o">=</span> <span class="n">trials</span><span class="o">.</span><span class="n">takeOrdered</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="mf">20.0</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">topLosses</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>

<span class="c1"># an extension of VaR</span>
<span class="k">def</span> <span class="nf">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">):</span>
    <span class="n">numTrials</span> <span class="o">=</span> <span class="n">trials</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
    <span class="n">topLosses</span> <span class="o">=</span> <span class="n">trials</span><span class="o">.</span><span class="n">takeOrdered</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="mf">20.0</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">topLosses</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">topLosses</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">bootstrappedConfidenceInterval</span><span class="p">(</span>
      <span class="n">trials</span><span class="p">,</span> <span class="n">computeStatisticFunction</span><span class="p">,</span>
      <span class="n">numResamples</span><span class="p">,</span> <span class="n">pValue</span><span class="p">):</span>
    <span class="n">stats</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">numResamples</span><span class="p">):</span>
        <span class="n">resample</span> <span class="o">=</span> <span class="n">trials</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
        <span class="n">stats</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">computeStatisticFunction</span><span class="p">(</span><span class="n">resample</span><span class="p">))</span>
    <span class="nb">sorted</span><span class="p">(</span><span class="n">stats</span><span class="p">)</span>
    <span class="n">lowerIndex</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numResamples</span> <span class="o">*</span> <span class="n">pValue</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">upperIndex</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">numResamples</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">pValue</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)))</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="n">lowerIndex</span><span class="p">],</span> <span class="n">stats</span><span class="p">[</span><span class="n">upperIndex</span><span class="p">])</span>
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<p>Next, we will run the Monte Carlo simulation 10,000 times, in parallel using Spark. Since your cluster has 12 cores (two Spark worker nodes, each with 6 cores), we can set <code>parallelism = 12</code> to dispatch simulation on these cores, across the two machines (remember, those are not really "physical machines", they are Docker containers running in our infrastructure).</p>

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<p><img src="https://farm2.staticflickr.com/1604/24934700445_833f0a5649_t.jpg" alt=""></p>

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<p><div class='anchor' ></div></p>
<h3 id="Question-7">Question 7<a class="anchor-link" href="#Question-7">&#182;</a></h3><p>Complete the code below to define the simulation process and calculate VaR 5%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># RUN SILMULATION</span>
<span class="k">def</span> <span class="nf">simulateTrialReturns</span><span class="p">(</span><span class="n">numTrials</span><span class="p">,</span> <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span> <span class="n">weights</span><span class="p">):</span>
    <span class="n">trialReturns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">numTrials</span><span class="p">):</span>
        <span class="c1"># generate sample of factors&#39; returns</span>
        <span class="n">trialFactorReturns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">)</span>
        <span class="c1"># featurize the factors&#39; returns</span>
        <span class="n">trialFeatures</span> <span class="o">=</span> <span class="n">featurize</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">trialFactorReturns</span><span class="p">))</span>
        <span class="c1"># insert weight for intercept term</span>
        <span class="n">trialFeatures</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
        <span class="c1"># calculate the return of each instrument</span>
        <span class="n">trialTotalReturn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span><span class="n">trialFeatures</span><span class="p">)</span>
        <span class="c1"># then calulate the total of return for this trial features</span>
        <span class="n">trialTotalReturn</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">trialTotalReturn</span><span class="p">)</span>
        
        <span class="n">trialReturns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">trialTotalReturn</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">trialReturns</span>

<span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeights</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>

<span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                <span class="n">simulateTrialReturns</span><span class="p">(</span>
                    <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> 
                    <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span>
                    <span class="n">bFactorWeights</span><span class="o">.</span><span class="n">value</span>
                <span class="p">))</span>
<span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

<span class="n">valueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
<span class="n">conditionalValueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Value at Risk(VaR) 5%:&quot;</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Conditional Value at Risk(CVaR) 5%:&quot;</span><span class="p">,</span> <span class="n">conditionalValueAtRisk</span><span class="p">)</span>
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<pre>Value at Risk(VaR) 5%: -20.4242314773
Conditional Value at Risk(CVaR) 5%: -25.7493513618
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<p>The value of VaR depends on how many invested stocks and the chosen distribution of random variables. Assume that we get VaR 5% = -2.66,  that means that there is a 0.05 probability that the portfolio will fall in value by more than \$2.66 over a two weeks period if there is no trading. In other words, the loses are less than \$2.66 over two weeks period with 95% confidence level. When a loss over two weeks is more than \$2.66, we call it **failure** (or **exception**). Informally, because of 5% probability, we expect that there are only $0.05*W$ failures out of total $W$ windows.</p>

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<h3 id="Step-5:-Evaluating-the-results-using-backtesting-method">Step 5: Evaluating the results using backtesting method<a class="anchor-link" href="#Step-5:-Evaluating-the-results-using-backtesting-method">&#182;</a></h3><p>In general, the error in a Monte Carlo simulation should be proportional to 1/sqrt(n), where n is the number of trials. This means, for example, that quadrupling the number of trials should approximately cut the error in half. A good way to check the quality of a result is backtesting on historical data. Backtesting is a statistical procedure where actual losses are compared to the estimated VaR. For instance, if the confidence level used to calculate VaR is 95% (or VaR 5%), we expect only 5 failures over 100 two-week time windows.</p>
<p>The most common test of a VaR model is counting the number of VaR failures, i.e, in how many windows, the losses exceed VaR estimate. If the number of exceptions is less than selected confidence level would indicate, the VaR model overestimates the risk. On the contrary, if there are too many exceptions, the risk is underestimated. However, it's very hard to observe the amount of failures suggested by the confidence level exactly. Therefore, people try to study whether the number of failures is reasonable or not, or will the model be accepted or rejected.</p>
<p>One common test is Kupiec's proportion-of-failures (POF) test. This test considers how the portfolio performed at many historical time intervals and counts the number of times that the losses exceeded the VaR. The null hypothesis is that the VaR is reasonable, and a sufficiently extreme test statistic means that the VaR estimate does not accurately describe the data. The test statistic is computed as:</p>
$$
-2ln\Bigg(\frac{(1-p)^{T-x}p^x}{(1-\frac{x}{T})^{T-x}(\frac{x}{T})^x}\Bigg)
$$<p>where:</p>
<p>$p$ is the quantile-of-loss of the VaR calculation (e.g, in VaR 5%, p=0.05),</p>
<p>$x$ (the number of failures) is the number of historical intervals over which the losses exceeded the VaR</p>
<p>$T$ is  the total number of historical intervals considered</p>
<p>Or we can expand out the log for better numerical stability:</p>
$$
\begin{equation}
-2\Big((T-x)ln(1-p)+x*ln(p)-(T-x)ln(1-\frac{x}{T})-x*ln(\frac{x}{T})\Big)
\end{equation}
$$<p>If we assume the null hypothesis that the VaR is reasonable, then this test statistic is drawn from a chi-squared distribution with a single degree of freedom. By using Chi-squared distribution, we can find the <code>p-value</code> accompanying our test statistic value.  If <code>p-value</code> exceeds the critical value of the Chi-squared distribution, we do have sufficient evidence to reject the null hypothesis that the model is reasonable. Or we can say, in that case, the model is considered as inaccurate.</p>
<p>For example, assume that we calculate VaR 5% (the confidence level of the VaR model is 95%) and get value VaR = 2.26. We also observed 50 exceptions over 500 time windows. Using the formular above, the test statistic <code>p-value</code> is calculated and equal to <code>8.08</code>. Compared to <code>3.84</code>, the critical value of Chi-squared distribution with one degree of freedom at probability 5%, the test statistic is larger. So, the model is rejected. The critical values of Chi-squared can be found by following <a href="https://people.richland.edu/james/lecture/m170/tbl-chi.html">this link</a>.
However, in this Notebook, it's not a good idea to find the corresponding critical value by looking in a "messy" table, especially when we need to change the confidence level. Insteads, from <code>p-value</code>, we will calculate the probability of the test statistic in Chi-square thanks to some functions in package <code>scipy</code>. If the calculated probability is smaller than the quantile of loss (e.g, 0.05), the model is rejected and vice versa.</p>

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<h3 id="Question-8">Question 8<a class="anchor-link" href="#Question-8">&#182;</a></h3><h4 id="Question-8.1">Question 8.1<a class="anchor-link" href="#Question-8.1">&#182;</a></h4><p>Write a function to calculate the number of failures, that is when the losses (in the original data) exceed the VaR.</p>
<p>HINT:</p>
<ul>
<li>First, we need to calculate the total loss in each 2-week time interval</li>
<li>If the total loss of a time interval exceeds VaR, then we say that our VaR fails to estimate the risk in that time interval.</li>
<li>Return the number of failures</li>
</ul>
<p>NOTE: The loss is often has negative value, so, be careful when compare it to VaR.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">stats</span>
<span class="kn">import</span> <span class="nn">math</span>

<span class="k">def</span> <span class="nf">countFailures1</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">):</span>
    <span class="n">failures</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="c1"># iterate over time intervals</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
        <span class="c1"># calculate the losses in each time interval</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">returns</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">returns</span> <span class="ow">in</span> <span class="n">stocksReturns</span><span class="p">)</span>
        
        <span class="c1"># if the loss exceeds VaR</span>
        <span class="k">if</span> <span class="n">loss</span> <span class="o">&lt;</span> <span class="n">valueAtRisk</span><span class="p">:</span>
            <span class="n">failures</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">return</span> <span class="n">failures</span>
<span class="k">def</span> <span class="nf">countFailures</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">):</span>
    <span class="n">one</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">))</span>
    <span class="n">totalReturns</span>  <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">one</span><span class="p">,</span><span class="n">stocksReturns</span><span class="p">)</span>
    <span class="n">failures</span> <span class="o">=</span> <span class="n">totalReturns</span><span class="p">[</span><span class="n">totalReturns</span> <span class="o">&lt;</span> <span class="n">valueAtRisk</span><span class="p">]</span>
    <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">failures</span><span class="p">)</span>
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<h4 id="Question-8.2">Question 8.2<a class="anchor-link" href="#Question-8.2">&#182;</a></h4><p>Write a function named <code>kupiecTestStatistic</code> to calculate the test statistic which was described in the above equation.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">kupiecTestStatistic</span><span class="p">(</span><span class="n">total</span><span class="p">,</span> <span class="n">failures</span><span class="p">,</span> <span class="n">confidenceLevel</span><span class="p">):</span>
    <span class="n">failureRatio</span> <span class="o">=</span> <span class="n">failures</span><span class="o">/</span><span class="n">total</span>
    <span class="n">logNumer</span> <span class="o">=</span> <span class="p">(</span><span class="n">total</span><span class="o">-</span><span class="n">failures</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">confidenceLevel</span><span class="p">)</span> <span class="o">+</span> <span class="n">failures</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">confidenceLevel</span><span class="p">)</span>
    <span class="n">logDenom</span> <span class="o">=</span> <span class="p">(</span><span class="n">total</span><span class="o">-</span><span class="n">failures</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">failureRatio</span><span class="p">)</span> <span class="o">+</span> <span class="n">failures</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">failureRatio</span><span class="p">)</span>
    <span class="k">return</span> <span class="o">-</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">logNumer</span> <span class="o">-</span> <span class="n">logDenom</span><span class="p">)</span>
    
<span class="c1"># test the function</span>
<span class="k">assert</span> <span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">kupiecTestStatistic</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span> <span class="o">==</span> <span class="mf">4.80</span><span class="p">),</span> <span class="s2">&quot;function kupiecTestStatistic runs incorrectly&quot;</span>
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<p>Now we can find the p-value accompanying our test statistic value.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="n">confidenceLevel</span><span class="p">):</span>
    <span class="n">failures</span> <span class="o">=</span> <span class="n">countFailures1</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;num failures:&quot;</span><span class="p">,</span> <span class="n">failures</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">failures</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
        <span class="c1"># the model is very good</span>
        <span class="k">return</span> <span class="mi">1</span>
    <span class="n">total</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">testStatistic</span> <span class="o">=</span> <span class="n">kupiecTestStatistic</span><span class="p">(</span><span class="n">total</span><span class="p">,</span> <span class="n">failures</span><span class="p">,</span> <span class="n">confidenceLevel</span><span class="p">)</span>
    <span class="c1">#return 1 - stats.chi2.cdf(testStatistic, 1.0)</span>
    <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">chisqprob</span><span class="p">(</span><span class="n">testStatistic</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>

<span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
<span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;VaR confidence interval: &quot;</span> <span class="p">,</span> <span class="n">varConfidenceInterval</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CVaR confidence interval: &quot;</span> <span class="p">,</span> <span class="n">cvarConfidenceInterval</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span> <span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>VaR confidence interval:  (-20.459583328531522, -20.442120742169035)
CVaR confidence interval:  (-25.581866065337557, -25.507608477418483)
num failures: 94
Kupiec test p-value:  0.000457814024471
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<h4 id="Question-8.3">Question 8.3<a class="anchor-link" href="#Question-8.3">&#182;</a></h4><p>Discuss the results you have obtained</p>

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With 1295 2 week return data, we expect to have the value ar risk of 5% is the number that make about 65 failures. The results of more than 120 failures is a bad result. We can see the Kupiec test p-value for this result is extremely small, which it should be as close as 1 as possible. 
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The reasons for this failure could be:
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    1/ the the model of relations of factors and stocks, which could be too simple to discribe the relations. We will use more factors as mentioned above.
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    2/ parameters for modeling factor is not good, since we only approximate them with normal distributions.
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    3/ 1295 samples is not enough but we can hardly expect more.
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<p><img src="https://farm2.staticflickr.com/1604/24934700445_833f0a5649_t.jpg" alt=""></p>

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<h3 id="Question-9">Question 9<a class="anchor-link" href="#Question-9">&#182;</a></h3><p>Assume that we invest in more than 100 stocks. Use the same market factors as for the previous questions to estimate VaR by running MCS, then validate your result.</p>
<p>What is the main observation you have, once you answer this question? When you plan to invest in more instruments, how is your ability to predict the risk going to be affected?</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># assume that we invest 200</span>
<span class="n">files</span> <span class="o">=</span> <span class="p">[</span><span class="n">join</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">listdir</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">)</span> <span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">,</span> <span class="n">f</span><span class="p">))]</span>
<span class="n">files100</span> <span class="o">=</span> <span class="n">files</span><span class="p">[:</span><span class="mi">200</span><span class="p">]</span>
<span class="c1"># read each line in each file, convert it into the format: (date, value)</span>
<span class="n">rawStocks100</span> <span class="o">=</span> <span class="p">[</span><span class="n">process_stock_file</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files100</span><span class="p">]</span>
<span class="c1"># select only instruments which have more than 5 years of history</span>
<span class="c1"># Note: the number of business days in a year is 260</span>
<span class="n">number_of_years</span> <span class="o">=</span> <span class="mi">5</span>

<span class="n">rawStocks100</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">instrument</span><span class="p">:</span> <span class="n">datetime</span><span class="p">(</span><span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">year</span><span class="o">+</span><span class="n">number_of_years</span><span class="p">,</span> \
                                                    <span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">month</span><span class="p">,</span> <span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">day</span><span class="p">)</span><span class="o">&lt;</span><span class="n">instrument</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="p">,</span> <span class="n">rawStocks100</span><span class="p">))</span>

<span class="n">stocks100</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">stock</span><span class="p">:</span> \
            <span class="n">fillInHistory</span><span class="p">(</span>
                <span class="n">trimToRegion</span><span class="p">(</span><span class="n">stock</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
            <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
        <span class="n">rawStocks100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stocks100</span><span class="p">))</span>
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<pre>155
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># merge two factors, trim each factor into a time region</span>
<span class="c1"># and fill up the missing values</span>
<span class="n">stocksReturns100</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">twoWeekReturns</span><span class="p">,</span> <span class="n">stocks100</span><span class="p">))</span>

<span class="c1"># estimate weights</span>
<span class="n">weights100</span> <span class="o">=</span> <span class="p">[</span><span class="n">estimateParams</span><span class="p">(</span><span class="n">stockReturn</span><span class="p">,</span><span class="n">factor_columns</span><span class="p">)</span> <span class="k">for</span> <span class="n">stockReturn</span> <span class="ow">in</span> <span class="n">stocksReturns100</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">weights100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">weights100</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>


<span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeights100</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">weights100</span><span class="p">)</span>

<span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                <span class="n">simulateTrialReturns</span><span class="p">(</span>
                    <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> 
                    <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span>
                    <span class="n">bFactorWeights100</span><span class="o">.</span><span class="n">value</span>
                <span class="p">))</span>
<span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

<span class="n">valueAtRisk100</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
<span class="n">conditionalValueAtRisk100</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Value at Risk(VaR) 5%:&quot;</span><span class="p">,</span> <span class="n">valueAtRisk100</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Conditional Value at Risk(CVaR) 5%:&quot;</span><span class="p">,</span> <span class="n">conditionalValueAtRisk100</span><span class="p">)</span>
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<pre>155
13
Value at Risk(VaR) 5%: -124.519227854
Conditional Value at Risk(CVaR) 5%: -155.076045074
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
<span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;VaR confidence interval: &quot;</span> <span class="p">,</span> <span class="n">varConfidenceInterval</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CVaR confidence interval: &quot;</span> <span class="p">,</span> <span class="n">cvarConfidenceInterval</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span> <span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturns100</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>VaR confidence interval:  (-124.86648108667072, -124.51922785361863)
CVaR confidence interval:  (-153.21112523045275, -156.0278130300598)
num failures: 395
Kupiec test p-value:  2.46789627592e-190
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Increasing the number of stocks invested does not make the results better. It means the selected distribution does not describe the features well enough, and the model provide worse results when implementing on population. 
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The result also shows that the more stock we invest, the more value at risk increases, which seems reasonable.
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<p><img src="https://farm2.staticflickr.com/1604/24934700445_833f0a5649_t.jpg" alt=""></p>

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<p><div class='anchor' ></div></p>
<h3 id="Question-10">Question 10<a class="anchor-link" href="#Question-10">&#182;</a></h3><p>In the previous questions, we used the normal distributions to sample the factors returns.</p>
<p>Try to study how results vary when selecting other probability distributions: our goal is to improve the result of our MCS.</p>

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We use the normal distributions to sample the factor returns in the previous questions with an asumption that they are normally distributed. The strenght of normal distribution is that they can discribe the dependence of factors, which is very important. Using the other distribution such as triangular one, we may suffer from the sample that is not likely to be in the the real. Therefore, we will test the MCS again with 4 factor are **indepdently generated by normal distributions** to see how covariations of factors are important. 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">independent_factor_cov</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
    <span class="n">independent_factor_cov</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">factorCov</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">independent_factor_cov</span><span class="p">)</span>
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<pre>[[20.371231317455674, 0, 0, 0], [0, 0.024201422719650548, 0, 0], [0, 0, 1319.3803084481503, 0], [0, 0, 0, 6969.8944345802438]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># RUN SILMULATION</span>
<span class="k">def</span> <span class="nf">simulateTrialReturns_independentFactors</span><span class="p">(</span><span class="n">numTrials</span><span class="p">,</span> <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span> <span class="n">weights</span><span class="p">):</span>
    <span class="n">trialReturns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">numTrials</span><span class="p">):</span>
        <span class="c1"># generate sample of factors&#39; returns</span>
        <span class="n">trialFactorReturns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">factorMeans</span><span class="p">,</span> <span class="n">independent_factor_cov</span><span class="p">)</span>
        <span class="c1"># featurize the factors&#39; returns</span>
        <span class="n">trialFeatures</span> <span class="o">=</span> <span class="n">featurize</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">trialFactorReturns</span><span class="p">))</span>
        <span class="c1"># insert weight for intercept term</span>
        <span class="n">trialFeatures</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
        <span class="c1"># calculate the return of each instrument</span>
        <span class="n">trialTotalReturn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span><span class="n">trialFeatures</span><span class="p">)</span>
        <span class="c1"># then calulate the total of return for this trial features</span>
        <span class="n">trialTotalReturn</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">trialTotalReturn</span><span class="p">)</span>
        
        <span class="n">trialReturns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">trialTotalReturn</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">trialReturns</span>

<span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">1000000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeights</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>

<span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                <span class="n">simulateTrialReturns_independentFactors</span><span class="p">(</span>
                    <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> 
                    <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span>
                    <span class="n">bFactorWeights</span><span class="o">.</span><span class="n">value</span>
                <span class="p">))</span>
<span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

<span class="n">valueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
<span class="n">conditionalValueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Value at Risk(VaR) 5%:&quot;</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Conditional Value at Risk(CVaR) 5%:&quot;</span><span class="p">,</span> <span class="n">conditionalValueAtRisk</span><span class="p">)</span>
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<pre>Value at Risk(VaR) 5%: -17.6811008942
Conditional Value at Risk(CVaR) 5%: -22.3729297761
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
<span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;VaR confidence interval: &quot;</span> <span class="p">,</span> <span class="n">varConfidenceInterval</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CVaR confidence interval: &quot;</span> <span class="p">,</span> <span class="n">cvarConfidenceInterval</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span> <span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>VaR confidence interval:  (-17.691403307719263, -17.645356335399342)
CVaR confidence interval:  (-22.385429969585648, -22.405274744292324)
num failures: 169
Kupiec test p-value:  5.46130769818e-29
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The results are worse, this proves that the covariations of factors are really important, we will skip the test for other distribution and **keep focus on multivariate-like distribution** with other factors such as precious metal, real estate, etc.
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At first, we can start again by looking at the real histogram of factors.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">bins</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Gaussian Histogram&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Value&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">bins</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Gaussian Histogram&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Value&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="n">bins</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Gaussian Histogram&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Value&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">factorsReturns</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span><span class="n">bins</span> <span class="o">=</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Gaussian Histogram&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Value&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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Based on the histogram, factors are distributed very similarly to normal distributions, so variants of Normal distribution could be good alternative choices. Those having <b>bell-shape</b> like normal distribution are Cauchy distribution, T-distribution, Logistic Distribution, F Distribution, Chi-Square Distribution.
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As the results of the histograms we see, there are <b>a few outliners at the 2 tails</b> of the distributions, which are the most important part we care about. These value can not be well described by a simple normal distributions, we need other distributions with the same variance but more <b>likely to happen to be in 2 tails</b> and have the covariance between factors.  Such a distribution could be <b>Cauchy distribution</b> and <b>T-distribution</b>. Because <b>Cauchy distribution</b> is a special kind of <b>T-distribution</b>, we choose <b>T-distribution</b> instead.
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The following code is the t-distribution is written by Enzo Michelangeli. We modified it to suit our validation work.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#written by Enzo Michelangeli, style changes by josef-pktd</span>
<span class="c1"># Student&#39;s T random variable</span>
<span class="k">def</span> <span class="nf">multivariate_t_rvs</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;generate random variables of multivariate t distribution</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    m : array_like</span>
<span class="sd">        mean of random variable, length determines dimension of random variable</span>
<span class="sd">    S : array_like</span>
<span class="sd">        square array of covariance  matrix</span>
<span class="sd">    df : int or float</span>
<span class="sd">        degrees of freedom</span>
<span class="sd">    n : int</span>
<span class="sd">        number of observations, return random array will be (n, len(m))</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    rvs : ndarray, (n, len(m))</span>
<span class="sd">        each row is an independent draw of a multivariate t distributed</span>
<span class="sd">        random variable</span>


<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">m</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
    <span class="n">d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">df</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">:</span>
        <span class="n">x</span> <span class="o">=</span> <span class="mf">1.</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">chisquare</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span><span class="o">/</span><span class="n">df</span>
    <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">d</span><span class="p">),</span><span class="n">S</span><span class="p">,(</span><span class="n">n</span><span class="p">,))</span>
    <span class="k">return</span> <span class="n">m</span> <span class="o">+</span> <span class="n">z</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span><span class="p">)[:,</span><span class="kc">None</span><span class="p">]</span>   <span class="c1"># same output format as random.multivariate_normal</span>

<span class="c1"># RUN SILMULATION</span>
<span class="k">def</span> <span class="nf">simulateTDistTrialReturns</span><span class="p">(</span><span class="n">numTrials</span><span class="p">,</span> <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
    <span class="n">trialReturns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">numTrials</span><span class="p">):</span>
        <span class="c1"># generate sample of factors&#39; returns</span>
        <span class="p">[</span><span class="n">trialFactorReturns</span><span class="p">]</span> <span class="o">=</span> <span class="n">multivariate_t_rvs</span><span class="p">(</span><span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span><span class="n">n</span><span class="p">)</span>
        <span class="c1"># featurize the factors&#39; returns</span>
        <span class="n">trialFeatures</span> <span class="o">=</span> <span class="n">featurize</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">trialFactorReturns</span><span class="p">))</span>
        <span class="c1"># insert weight for intercept term</span>
        <span class="n">trialFeatures</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
        <span class="c1"># calculate the return of each instrument</span>
        <span class="n">trialTotalReturn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span><span class="n">trialFeatures</span><span class="p">)</span>
        <span class="c1"># then calulate the total of return for this trial features</span>
        <span class="n">trialTotalReturn</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">trialTotalReturn</span><span class="p">)</span>
        
        <span class="n">trialReturns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">trialTotalReturn</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">trialReturns</span>
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Due to the lack resourses, we use a mini version of cross validation. Using the same dataset of 35 stock loaded, we find the best the degree of freedom give best results and test it with another set of stocks.
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The following code for sample the t-distribution is written by Enzo Michelangeli, style changes by josef-pktd. We use it as the distributions for factors.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeights</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                    <span class="n">simulateTDistTrialReturns</span><span class="p">(</span>
                        <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> 
                        <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span>
                        <span class="n">bFactorWeights</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">i</span>
                    <span class="p">))</span>
    <span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

    <span class="n">valueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
    <span class="n">conditionalValueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

    <span class="c1">#print(&quot;Value at Risk(VaR) 5%:&quot;, valueAtRisk)</span>
    <span class="c1">#print(&quot;Conditional Value at Risk(CVaR) 5%:&quot;, conditionalValueAtRisk)</span>
    
    <span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
    <span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
    <span class="c1">#print(&quot;VaR confidence interval: &quot; , varConfidenceInterval)</span>
    <span class="c1">#print(&quot;CVaR confidence interval: &quot; , cvarConfidenceInterval)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Degree of freedom: &quot;</span> <span class="p">,</span> <span class="n">i</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span><span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>Degree of freedom:  1
num failures: 0
Kupiec test p-value:  1
Degree of freedom:  2
num failures: 22
Kupiec test p-value:  3.32968954766e-10
Degree of freedom:  3
num failures: 34
Kupiec test p-value:  1.7364523653e-05
Degree of freedom:  4
num failures: 51
Kupiec test p-value:  0.0690741448169
Degree of freedom:  5
num failures: 58
Kupiec test p-value:  0.381318275536
Degree of freedom:  6
num failures: 72
Kupiec test p-value:  0.363467311936
Degree of freedom:  7
num failures: 69
Kupiec test p-value:  0.591668666759
Degree of freedom:  8
num failures: 86
Kupiec test p-value:  0.00972209990518
Degree of freedom:  9
num failures: 78
Kupiec test p-value:  0.101199378449
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To test the degree of freedom, we load a completely new dataset, stock price of the next 65 companies, train them with the same setting and see the results
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">files</span> <span class="o">=</span> <span class="p">[</span><span class="n">join</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">listdir</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">)</span> <span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">stock_folder</span><span class="p">,</span> <span class="n">f</span><span class="p">))]</span>
<span class="n">filesTest</span> <span class="o">=</span> <span class="n">files</span><span class="p">[</span><span class="mi">35</span><span class="p">:</span><span class="mi">100</span><span class="p">]</span>
<span class="c1"># read each line in each file, convert it into the format: (date, value)</span>
<span class="n">rawStocksTest</span> <span class="o">=</span> <span class="p">[</span><span class="n">process_stock_file</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">filesTest</span><span class="p">]</span>
<span class="c1"># select only instruments which have more than 5 years of history</span>
<span class="c1"># Note: the number of business days in a year is 260</span>
<span class="n">number_of_years</span> <span class="o">=</span> <span class="mi">5</span>

<span class="n">rawStocksTest</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">instrument</span><span class="p">:</span> <span class="n">datetime</span><span class="p">(</span><span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">year</span><span class="o">+</span><span class="n">number_of_years</span><span class="p">,</span> \
                                                    <span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">month</span><span class="p">,</span> <span class="n">instrument</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">day</span><span class="p">)</span><span class="o">&lt;</span><span class="n">instrument</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="p">,</span> <span class="n">rawStocksTest</span><span class="p">))</span>

<span class="n">stocksTest</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">stock</span><span class="p">:</span> \
            <span class="n">fillInHistory</span><span class="p">(</span>
                <span class="n">trimToRegion</span><span class="p">(</span><span class="n">stock</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
            <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
        <span class="n">rawStocksTest</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stocksTest</span><span class="p">))</span>
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<pre>51
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># merge two factors, trim each factor into a time region</span>
<span class="c1"># and fill up the missing values</span>
<span class="n">stocksReturnsTest</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">twoWeekReturns</span><span class="p">,</span> <span class="n">stocksTest</span><span class="p">))</span>

<span class="c1"># estimate weights</span>
<span class="n">weightsTest</span> <span class="o">=</span> <span class="p">[</span><span class="n">estimateParams</span><span class="p">(</span><span class="n">stockReturn</span><span class="p">,</span><span class="n">factor_columns</span><span class="p">)</span> <span class="k">for</span> <span class="n">stockReturn</span> <span class="ow">in</span> <span class="n">stocksReturnsTest</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">weightsTest</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">weightsTest</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>


<span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeightsTest</span><span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">weightsTest</span><span class="p">)</span>

<span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                <span class="n">simulateTrialReturns</span><span class="p">(</span>
                    <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">7</span><span class="p">),</span> 
                    <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span>
                    <span class="n">bFactorWeightsTest</span><span class="o">.</span><span class="n">value</span>
                <span class="p">))</span>
<span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

<span class="n">valueAtRiskTest</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
<span class="n">conditionalValueAtRiskTest</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Value at Risk(VaR) 5%:&quot;</span><span class="p">,</span> <span class="n">valueAtRiskTest</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Conditional Value at Risk(CVaR) 5%:&quot;</span><span class="p">,</span> <span class="n">conditionalValueAtRiskTest</span><span class="p">)</span>
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<pre>51
13
Value at Risk(VaR) 5%: -48.2102015545
Conditional Value at Risk(CVaR) 5%: -63.1784825733
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
<span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span><span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturnsTest</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>num failures: 72
Kupiec test p-value:  0.363467311936
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The result is much better than using normal distribution.
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However, to validate the degree of freedom, finding the best value based on the new dataset is needed.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeightsTest</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">weightsTest</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                    <span class="n">simulateTDistTrialReturns</span><span class="p">(</span>
                        <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> 
                        <span class="n">factorMeans</span><span class="p">,</span> <span class="n">factorCov</span><span class="p">,</span>
                        <span class="n">bFactorWeightsTest</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">i</span>
                    <span class="p">))</span>
    <span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

    <span class="n">valueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
    <span class="n">conditionalValueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

    <span class="c1">#print(&quot;Value at Risk(VaR) 5%:&quot;, valueAtRisk)</span>
    <span class="c1">#print(&quot;Conditional Value at Risk(CVaR) 5%:&quot;, conditionalValueAtRisk)</span>
    
    <span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
    <span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
    <span class="c1">#print(&quot;VaR confidence interval: &quot; , varConfidenceInterval)</span>
    <span class="c1">#print(&quot;CVaR confidence interval: &quot; , cvarConfidenceInterval)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Degree of freedom: &quot;</span> <span class="p">,</span> <span class="n">i</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span><span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturnsTest</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>Degree of freedom:  1
num failures: 0
Kupiec test p-value:  1
Degree of freedom:  2
num failures: 23
Kupiec test p-value:  1.01843988292e-09
Degree of freedom:  3
num failures: 42
Kupiec test p-value:  0.00199129283816
Degree of freedom:  4
num failures: 49
Kupiec test p-value:  0.0362167981322
Degree of freedom:  5
num failures: 58
Kupiec test p-value:  0.381318275536
Degree of freedom:  6
num failures: 71
Kupiec test p-value:  0.432327505873
Degree of freedom:  7
num failures: 72
Kupiec test p-value:  0.363467311936
Degree of freedom:  8
num failures: 79
Kupiec test p-value:  0.0785749053546
Degree of freedom:  9
num failures: 72
Kupiec test p-value:  0.363467311936
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As we expected, the best degree of freedom changes when using new dataset. However, it's still near the best one of 35 stocks, and quite good approximation for a new set of stocks.
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<h3 id="Extra-Work-1">Extra Work 1<a class="anchor-link" href="#Extra-Work-1">&#182;</a></h3><p>It seemd that we have found a better distribution for describing factors. In this section, we use <strong>addition market proxies</strong> as factors for stock pricing model. They are <strong>S&amp;P 500</strong> and <strong>Dow Jones Industrial Average</strong> from <a href="https://research.stlouisfed.org/">https://research.stlouisfed.org/</a></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># read data from local disk</span>
<span class="k">def</span> <span class="nf">readEconomyResearchHistory</span><span class="p">(</span><span class="n">fname</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">process_line</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
        <span class="n">cols</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
        <span class="n">date</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">cols</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">!=</span><span class="s2">&quot;.</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">:</span>
            <span class="n">value</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="c1">#?cols[6]</span>
            <span class="k">return</span> <span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="kc">None</span>
        
    
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">content_w_header</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
        <span class="c1"># remove the first line </span>
        <span class="c1"># and reverse lines to sort the data by date, in ascending order</span>
        <span class="n">content</span> <span class="o">=</span> <span class="n">content_w_header</span><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">a</span> <span class="o">=</span>  <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">process_line</span> <span class="p">,</span> <span class="n">content</span><span class="p">))</span>
        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">!=</span><span class="kc">None</span><span class="p">,</span> <span class="n">a</span><span class="p">))</span>


<span class="n">factor3_files</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;DJIA.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;SP500.csv&#39;</span><span class="p">]</span>
<span class="n">factor3_files</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">fn</span><span class="p">:</span> <span class="n">factors_folder</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">factor3_files</span><span class="p">)</span>

<span class="n">factors3</span> <span class="o">=</span> <span class="p">[</span><span class="n">readEconomyResearchHistory</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">factor3_files</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">factors3</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">factors3</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
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<pre>(datetime.datetime(2006, 4, 18, 0, 0), 11268.77)
(datetime.datetime(2016, 4, 19, 0, 0), 18053.6)
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">allthreefactors</span> <span class="o">=</span> <span class="n">factors1</span> <span class="o">+</span> <span class="n">factors2</span> <span class="o">+</span> <span class="n">factors3</span>
<span class="n">threefactors</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">factor</span><span class="p">:</span> \
            <span class="n">fillInHistory</span><span class="p">(</span>
                <span class="n">trimToRegion</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
            <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span>
            <span class="n">allthreefactors</span>
            <span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">threeFactorsReturns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">twoWeekReturns</span><span class="p">,</span> <span class="n">threefactors</span><span class="p">))</span>

<span class="c1"># transpose factorsReturns</span>
<span class="n">threeFactorMat</span> <span class="o">=</span> <span class="n">transpose</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">)</span>

<span class="c1"># featurize each row of factorMat</span>
<span class="n">threeFactorFeatures</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">featurize</span><span class="p">,</span><span class="n">threeFactorMat</span><span class="p">))</span>

<span class="c1"># OLS require parameter is a numpy array</span>
<span class="n">three_factor_columns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">threeFactorFeatures</span><span class="p">)</span>

<span class="c1">#add a constant - the intercept term for each instrument i.</span>
<span class="n">three_factor_columns</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">three_factor_columns</span><span class="p">,</span> <span class="n">prepend</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># estimate weights</span>
<span class="n">threeWeights</span> <span class="o">=</span> <span class="p">[</span><span class="n">estimateParams</span><span class="p">(</span><span class="n">stockReturn</span><span class="p">,</span><span class="n">three_factor_columns</span><span class="p">)</span> <span class="k">for</span> <span class="n">stockReturn</span> <span class="ow">in</span> <span class="n">stocksReturns</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plotDistribution</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
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YII=
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">threeCorrelation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">)</span>
<span class="n">threeCorrelation</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[23]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([[ 1.        ,  0.38507954,  0.47411609,  0.45841194,  0.44320515,
         0.47412258],
       [ 0.38507954,  1.        ,  0.55213086,  0.54918324,  0.52582462,
         0.55213487],
       [ 0.47411609,  0.55213086,  1.        ,  0.95227596,  0.97515489,
         0.99999998],
       [ 0.45841194,  0.54918324,  0.95227596,  1.        ,  0.89897115,
         0.95227945],
       [ 0.44320515,  0.52582462,  0.97515489,  0.89897115,  1.        ,
         0.975154  ],
       [ 0.47412258,  0.55213487,  0.99999998,  0.95227945,  0.975154  ,
         1.        ]])</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">threeFactorCov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cov</span><span class="p">(</span><span class="n">threeFactorsReturns</span><span class="p">)</span>
<span class="n">threeFactorMeans</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">threeFactorsReturns</span><span class="p">]</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[49]:</div>
<div class="inner_cell">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeights</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">threeWeights</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                    <span class="n">simulateTDistTrialReturns</span><span class="p">(</span>
                        <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> 
                        <span class="n">threeFactorMeans</span><span class="p">,</span> <span class="n">threeFactorCov</span><span class="p">,</span>
                        <span class="n">bFactorWeights</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">i</span>
                    <span class="p">))</span>
    <span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

    <span class="n">valueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
    <span class="n">conditionalValueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

    <span class="c1">#print(&quot;Value at Risk(VaR) 5%:&quot;, valueAtRisk)</span>
    <span class="c1">#print(&quot;Conditional Value at Risk(CVaR) 5%:&quot;, conditionalValueAtRisk)</span>
    
    <span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
    <span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
    <span class="c1">#print(&quot;VaR confidence interval: &quot; , varConfidenceInterval)</span>
    <span class="c1">#print(&quot;CVaR confidence interval: &quot; , cvarConfidenceInterval)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Degree of freedom: &quot;</span> <span class="p">,</span> <span class="n">i</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span><span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>Degree of freedom:  1
num failures: 0
Kupiec test p-value:  1
Degree of freedom:  2
num failures: 33
Kupiec test p-value:  8.47894463823e-06
Degree of freedom:  3
num failures: 41
Kupiec test p-value:  0.00120365990348
Degree of freedom:  4
num failures: 48
Kupiec test p-value:  0.0254781986377
Degree of freedom:  5
num failures: 58
Kupiec test p-value:  0.381318275536
Degree of freedom:  6
num failures: 64
Kupiec test p-value:  0.923677398232
Degree of freedom:  7
num failures: 71
Kupiec test p-value:  0.432327505873
Degree of freedom:  8
num failures: 75
Kupiec test p-value:  0.201869186396
Degree of freedom:  9
num failures: 76
Kupiec test p-value:  0.162206355425
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Adding factors did improve the results. The best degree of freedom remains around 6-7. Another observation is that the three market indices ar highly correlated, we may need only one of them and using other factors such as precious metals, typically gold.
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<h3 id="Extra-Work-2">Extra Work 2<a class="anchor-link" href="#Extra-Work-2">&#182;</a></h3><p>In this section, we reduce the factors because most of market indices are highly correlated, then we will validate the results.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">factor4_files</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;DJIA.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;gold.csv&#39;</span><span class="p">]</span>
<span class="n">factor4_files</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">fn</span><span class="p">:</span> <span class="n">factors_folder</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">factor4_files</span><span class="p">)</span>

<span class="n">factors4</span> <span class="o">=</span> <span class="p">[</span><span class="n">readEconomyResearchHistory</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">factor4_files</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">essentialFactors</span> <span class="o">=</span> <span class="n">factors1</span> <span class="o">+</span> <span class="n">factors4</span>
<span class="n">essentialFactors</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">factor</span><span class="p">:</span> \
            <span class="n">fillInHistory</span><span class="p">(</span>
                <span class="n">trimToRegion</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span> 
            <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">),</span>
            <span class="n">essentialFactors</span>
            <span class="p">))</span>

<span class="n">essentialFactorsReturns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">twoWeekReturns</span><span class="p">,</span> <span class="n">essentialFactors</span><span class="p">))</span>

<span class="c1"># transpose factorsReturns</span>
<span class="n">essentialFactorMat</span> <span class="o">=</span> <span class="n">transpose</span><span class="p">(</span><span class="n">essentialFactorsReturns</span><span class="p">)</span>

<span class="c1"># featurize each row of factorMat</span>
<span class="n">essentialFactorFeatures</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">featurize</span><span class="p">,</span><span class="n">essentialFactorMat</span><span class="p">))</span>

<span class="c1"># OLS require parameter is a numpy array</span>
<span class="n">essential_factor_columns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">essentialFactorFeatures</span><span class="p">)</span>

<span class="c1">#add a constant - the intercept term for each instrument i.</span>
<span class="n">essential_factor_columns</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">essential_factor_columns</span><span class="p">,</span> <span class="n">prepend</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># estimate weights</span>
<span class="n">essentialWeights</span> <span class="o">=</span> <span class="p">[</span><span class="n">estimateParams</span><span class="p">(</span><span class="n">stockReturn</span><span class="p">,</span><span class="n">essential_factor_columns</span><span class="p">)</span> <span class="k">for</span> <span class="n">stockReturn</span> <span class="ow">in</span> <span class="n">stocksReturns</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plotDistribution</span><span class="p">(</span><span class="n">essentialFactorsReturns</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">essentialFactorsReturns</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">essentialFactorsReturns</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">plotDistribution</span><span class="p">(</span><span class="n">essentialFactorsReturns</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">essentialCorrelation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">essentialFactorsReturns</span><span class="p">)</span>
<span class="n">essentialCorrelation</span>
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<pre>array([[ 1.        ,  0.38507954,  0.44320515,  0.22366584],
       [ 0.38507954,  1.        ,  0.52582462, -0.0377245 ],
       [ 0.44320515,  0.52582462,  1.        ,  0.08622978],
       [ 0.22366584, -0.0377245 ,  0.08622978,  1.        ]])</pre>
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All of these factors seems to be independent, which means they will provide more information for the weights of the linear regression.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">essentialFactorCov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cov</span><span class="p">(</span><span class="n">essentialFactorsReturns</span><span class="p">)</span>
<span class="n">essentialFactorMeans</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">essentialFactorsReturns</span><span class="p">]</span>
<span class="n">essentialWeights</span>
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<pre>[array([  1.23885625e-02,  -2.10498481e-03,  -1.76868914e+00,
          2.35955046e-07,  -2.14814724e-05,  -6.17316074e-02,
         -7.25784339e-01,   7.09275871e-03,  -1.24624121e-02,
          3.94146411e-02,   2.20646431e+00,  -4.05584403e-04,
          3.50132489e-03]),
 array([  4.53397207e-02,   1.91404479e-03,  -3.02875877e+00,
          7.82199236e-07,   5.90632085e-06,   2.39808401e-02,
         -7.47152311e-02,   2.22464178e-02,  -3.25096424e-02,
         -2.67745146e-02,   5.60095307e-01,   1.20449589e-03,
          1.77105700e-03]),
 array([ -3.34441619e-03,  -1.34244807e-03,  -5.18910998e-01,
          5.56302055e-07,   2.59111765e-05,  -6.91049063e-02,
          1.58883771e-01,   1.26989194e-02,   1.37599087e-02,
          4.09402164e-02,  -2.76431725e-01,  -8.02490624e-04,
         -3.90299405e-03]),
 array([ -8.70612285e-04,  -1.45887988e-03,   4.52893976e+00,
         -8.84436779e-07,  -1.69121538e-06,  -1.03621133e-01,
          6.05964015e-01,  -1.90426040e-02,   5.29319365e-03,
          6.42046740e-02,  -2.46342923e+00,   1.80365388e-03,
         -1.88258779e-03]),
 array([  3.77556969e-02,  -4.66167466e-04,   1.66007171e+00,
         -2.30272203e-07,   1.34567243e-05,  -1.27896475e-02,
          4.41660903e-01,  -1.07230366e-02,   5.99123574e-03,
          1.02338475e-02,  -1.40223817e+00,   6.42089263e-04,
         -1.92834033e-03]),
 array([ -2.33099921e-02,  -1.22654594e-03,   5.82816306e-01,
          3.93389941e-07,   3.80079578e-07,   9.99831542e-03,
          8.51557460e-02,   3.02917617e-03,   4.35828317e-03,
          1.08332717e-02,   1.37556044e-01,   2.24017497e-04,
         -8.22040343e-04]),
 array([  4.63627606e-02,   2.57847586e-03,  -2.79041223e+00,
          3.18729483e-07,  -1.04846905e-05,   1.19036473e-01,
         -3.87478018e-01,  -5.91852567e-05,  -1.82178587e-02,
         -6.66048102e-02,   1.89144867e+00,   1.19000588e-05,
          2.15810771e-03]),
 array([ -4.51916965e-03,   6.39796575e-04,  -3.87015747e-01,
         -2.59829185e-07,   2.64806120e-07,   3.44614476e-02,
         -8.66418365e-02,  -4.78683686e-04,   2.51911665e-03,
         -1.54954510e-02,   4.17659218e-01,   2.82984977e-04,
         -8.35469865e-04]),
 array([ -1.08659552e-03,  -3.01500684e-04,  -6.38109837e-02,
          1.71610025e-07,  -7.84296397e-06,   5.01590268e-03,
         -1.99181067e-02,   4.49912344e-03,  -7.02208750e-03,
          4.75532478e-03,   2.18872289e-01,  -1.32155663e-04,
          1.69584746e-03]),
 array([ -3.80763601e-01,   2.06978019e-02,   2.93338521e+00,
          3.33096228e-06,  -7.07698890e-05,   1.04508312e+00,
         -3.70026844e+00,   2.15982843e-01,   1.21746515e-01,
         -6.09452764e-01,   8.17591845e+00,  -5.90278495e-03,
         -2.08483226e-04]),
 array([  3.34647023e-02,  -1.14050221e-03,   8.80217957e-01,
         -9.70602613e-08,   1.34204303e-05,   1.34398358e-02,
          2.67011470e-01,  -7.62288427e-03,  -9.50425572e-04,
          1.22895187e-02,  -7.11259008e-01,   4.09977115e-04,
         -1.77900502e-03]),
 array([  3.21935714e-01,   1.09677259e-02,   9.04445445e+00,
          1.94016446e-06,   4.61922027e-05,   2.06859827e-01,
          1.55987325e+00,   1.20034375e-02,   8.42939866e-02,
         -2.09584118e-01,  -2.89681619e+00,   2.86945414e-03,
         -7.33693070e-03]),
 array([  3.38275209e-01,  -8.89221152e-04,  -3.00129613e+00,
         -1.17155044e-06,   3.25188860e-05,   1.30550121e-01,
         -1.66788713e+00,  -4.86225020e-02,   1.84771623e-02,
         -6.94138240e-02,   5.10358438e+00,   6.78906826e-03,
         -2.05671080e-03]),
 array([  5.28273874e-03,   8.43103555e-04,   3.85225602e-01,
          2.30955453e-07,   1.12492365e-05,   1.54458608e-02,
          3.92523346e-02,   3.26994008e-03,   9.23248338e-03,
         -8.15935061e-03,  -3.93852811e-02,  -2.52046786e-04,
         -2.47777009e-03]),
 array([  5.14943147e-03,  -6.74326121e-03,   3.00213312e+00,
         -6.17603261e-07,  -5.55305091e-05,  -5.57104619e-02,
          7.54707985e-01,  -1.42083024e-02,  -4.66389767e-02,
          7.88670211e-02,  -2.28049974e+00,   2.21371752e-03,
          1.18265089e-02]),
 array([  5.07441930e-02,   1.26653445e-03,   1.94133893e-01,
         -1.31254703e-07,  -3.32414375e-06,  -3.96031553e-02,
          2.92194064e-02,   8.33657944e-03,  -1.29272162e-02,
          9.06248798e-03,   5.89971877e-02,  -1.08398210e-04,
          1.40096954e-03]),
 array([ -4.61410076e-02,   1.81818938e-02,   1.79123088e+00,
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         -1.49576406e-01,  -2.29542898e+00,   1.07141535e-03,
         -3.69025027e-03]),
 array([ -1.15346636e-01,  -8.95224454e-03,   3.50458761e+00,
         -2.51796641e-06,  -7.36984030e-06,  -2.28778370e-01,
         -6.99379395e-01,  -3.64359500e-02,   8.58964972e-02,
          1.47675158e-01,   1.15978921e+00,   6.42399721e-03,
         -8.34204593e-03]),
 array([  4.53873743e-02,  -4.03959034e-05,  -6.04738040e+00,
          7.97711587e-07,  -3.92826242e-05,  -1.22427448e-01,
         -4.97820703e-01,   1.95241806e-02,  -5.88782071e-02,
          3.81533130e-02,   4.52117416e+00,   8.29238529e-04,
          1.36998749e-02]),
 array([ -1.60409393e-02,   1.19313673e-03,   1.52643025e-01,
          2.10216765e-07,   4.08272618e-06,   4.21368359e-02,
          6.59239855e-02,   3.06100894e-03,   1.01190381e-02,
         -1.95987257e-02,   9.87942394e-02,   3.02148221e-04,
         -3.18965526e-04]),
 array([  1.53484577e-02,   1.21876017e-02,   3.01866721e+00,
          2.16592087e-06,   6.63518204e-05,   3.28851203e-01,
          2.87172292e-01,   1.58083780e-02,   4.11307823e-02,
         -1.44993908e-01,   2.00103600e-01,  -1.94575698e-04,
         -9.33555719e-03]),
 array([  3.20761167e-02,   3.70061083e-05,   1.36742388e-01,
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         -3.06097165e-01,  -2.97377862e-03,  -1.24385028e-02,
          1.50399205e-04,   1.24762920e+00,   1.26113839e-03,
          9.96357494e-04]),
 array([ -7.84001823e-02,  -4.41248828e-03,   1.61592052e+00,
          4.71832451e-07,   5.14713067e-05,   6.37964578e-03,
         -6.33770947e-01,   3.39528508e-02,   7.23463581e-02,
          4.62734075e-02,   2.21271023e+00,   8.62738190e-04,
         -1.73672376e-02]),
 array([ -3.64966128e-02,  -1.36473956e-02,  -1.68556059e+01,
          7.75706816e-07,  -2.33343131e-04,   1.32620803e-01,
         -2.30230357e+00,  -1.50152176e-02,  -1.78019604e-01,
          1.02796122e-01,   1.19744157e+01,   2.05324486e-03,
          3.03571374e-02]),
 array([  1.27240096e-02,   1.05299277e-03,  -1.85974226e-01,
         -3.19388255e-07,  -2.50139648e-06,  -2.55950933e-03,
         -1.28225049e-01,  -1.77325352e-03,   1.47345132e-02,
         -7.61295910e-03,   2.85685563e-01,   4.52786402e-04,
         -1.93359315e-03]),
 array([  1.59524955e-02,   6.53239975e-04,   2.22706350e+00,
         -4.91313649e-09,  -6.79998012e-06,  -3.16297596e-02,
          3.07278543e-01,   9.83535701e-03,  -7.84123242e-03,
          1.22570445e-03,  -1.32244738e+00,  -1.10673687e-04,
          1.54672852e-03]),
 array([ -5.02766418e-02,  -2.74010730e-03,  -1.87177788e+00,
         -5.33533747e-07,   1.60361887e-05,  -2.69201121e-02,
         -4.38696867e-01,  -8.57440206e-03,   2.57849010e-03,
          3.77202701e-02,   1.01205018e+00,   2.65582265e-03,
         -3.77344920e-03])]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">parallelism</span> <span class="o">=</span> <span class="mi">12</span>
<span class="n">numTrials</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">trial_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">))</span>
<span class="n">seedRDD</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">trial_indexes</span><span class="p">,</span> <span class="n">parallelism</span><span class="p">)</span>
<span class="n">bFactorWeights</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">essentialWeights</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">trials</span> <span class="o">=</span> <span class="n">seedRDD</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">idx</span><span class="p">:</span> \
                    <span class="n">simulateTDistTrialReturns</span><span class="p">(</span>
                        <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">numTrials</span><span class="o">/</span><span class="n">parallelism</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> 
                        <span class="n">essentialFactorMeans</span><span class="p">,</span> <span class="n">essentialFactorCov</span><span class="p">,</span>
                        <span class="n">bFactorWeights</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">i</span>
                    <span class="p">))</span>
    <span class="n">trials</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

    <span class="n">valueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>
    <span class="n">conditionalValueAtRisk</span> <span class="o">=</span> <span class="n">fivePercentCVaR</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span>

    <span class="c1">#print(&quot;Value at Risk(VaR) 5%:&quot;, valueAtRisk)</span>
    <span class="c1">#print(&quot;Conditional Value at Risk(CVaR) 5%:&quot;, conditionalValueAtRisk)</span>
    
    <span class="n">varConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
    <span class="n">cvarConfidenceInterval</span> <span class="o">=</span> <span class="n">bootstrappedConfidenceInterval</span><span class="p">(</span><span class="n">trials</span><span class="p">,</span> <span class="n">fivePercentCVaR</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)</span>
    <span class="c1">#print(&quot;VaR confidence interval: &quot; , varConfidenceInterval)</span>
    <span class="c1">#print(&quot;CVaR confidence interval: &quot; , cvarConfidenceInterval)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Degree of freedom: &quot;</span> <span class="p">,</span> <span class="n">i</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kupiec test p-value: &quot;</span><span class="p">,</span> <span class="n">kupiecTestPValue</span><span class="p">(</span><span class="n">stocksReturns</span><span class="p">,</span> <span class="n">valueAtRisk</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">))</span>
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<pre>Degree of freedom:  1
num failures: 1
Kupiec test p-value:  1.88048939885e-28
Degree of freedom:  2
num failures: 24
Kupiec test p-value:  2.9812277878e-09
Degree of freedom:  3
num failures: 43
Kupiec test p-value:  0.00321793199436
Degree of freedom:  4
num failures: 63
Kupiec test p-value:  0.822685405172
Degree of freedom:  5
num failures: 70
Kupiec test p-value:  0.508548905045
Degree of freedom:  6
num failures: 73
Kupiec test p-value:  0.302166934715
Degree of freedom:  7
num failures: 59
Kupiec test p-value:  0.456982789103
Degree of freedom:  8
num failures: 97
Kupiec test p-value:  0.000121885713318
Degree of freedom:  9
num failures: 88
Kupiec test p-value:  0.00483662649563
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Adding the new factors can not be validated directly by running MCS, the process of trying many degrees of freedom is to find the fittest distributions for the factors.
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We can see new factors effects only through the errors of the linear regression.
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<h1 id="6.-Summary">6. Summary<a class="anchor-link" href="#6.-Summary">&#182;</a></h1><p>In this lecture, we studied the Monte Carlo Simulation method and its application to estimate financial risk. To apply it, first, we needed to define the relationship between market factors and the instruments' returns. In such step, you must define the model which maps the market factors' values to the instruments' values: in our use case, we used a linear regression function for building our model. Next, we also had to find the parameters of our model, which are the weights of the factors we considered. Then, we had to study the distribution of each market factor. A good way to do that is using Kernel density estimation to smooth the distribution and plot it. Depending on the shape of each figure, we had to guess the best fit distribution for each factor: in our use case, we used a very simple approach, and decided that our smoothed distributions all looked normal distributions.</p>
<p>Then, the idea of Monte Carlo simulation was to generate many possible values for each factors and calculate the corresponding outcomes by a well defined model in each trial. After many trials, we were able to calculate VaR from the sequences of outcome's values. When the number of trials is large enough, the VaR converges to reasonable values, that we could validate using well-known statistica</p>

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<h1 id="References">References<a class="anchor-link" href="#References">&#182;</a></h1><ul>
<li>The example in section 2 is inspired from <a href="http://www.solver.com/monte-carlo-simulation-example">this article</a>.</li>
<li><a href="https://aaltodoc.aalto.fi/bitstream/handle/123456789/181/hse_ethesis_12049.pdf?sequence=1">Backtesting Value-at-Risk models</a> (Kansantaloustiede, 2009) - (A good reference to study Backtesting).</li>
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