03_dx_valuation_single_risk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"45%\" align=\"right\" border=\"4\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single-Risk Derivatives Valuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This part introduces into the modeling and valuation of derivatives instruments (contingent claims) based on a single risk factor (e.g. a stock price, stock index level or interest rate). It also shows how to model and value portfolios composed of such instruments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from dx import *\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following **single risk factor valuation classes** are available:\n",
    "\n",
    "* `valuation_mcs_european_single` for derivatives with European exercise\n",
    "* `valuation_mcs_american_single` for derivatives with American/Bermudan exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling the Risk Factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving on to the valuation classes, we need to model an instantiate an underlying risk factor, in this case a `geometric_brownian_motion` object. Background information is provided in the respective part of the documentation about model classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r = constant_short_rate('r', 0.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me = market_environment('me', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.add_constant('initial_value', 36.)\n",
    "me.add_constant('volatility', 0.2)\n",
    "me.add_constant('final_date', dt.datetime(2015, 12, 31))\n",
    "me.add_constant('currency', 'EUR')\n",
    "me.add_constant('frequency', 'W')\n",
    "me.add_constant('paths', 25000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.add_curve('discount_curve', r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gbm = geometric_brownian_motion('gbm', me)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## valuation_mcs_european_single"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first instrument we value is a European call option written on the single relevant risk factor as embodied by the `gbm` model object. To this end, we add a **maturity date** to the market environment and a **strike price**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.add_constant('maturity', dt.datetime(2015, 12, 31))\n",
    "me.add_constant('strike', 40.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate a the `valuation_mcs_european_single` class, the following information/data is to be provided:\n",
    "\n",
    "* name as a `string` object\n",
    "* instance of a model class\n",
    "* market environment\n",
    "* payoff of the instrument a `string` object and containing \"regular\" Python/NumPy code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "call_eur = valuation_mcs_european_single(\n",
    "                name='call_eur',\n",
    "                underlying=gbm,\n",
    "                mar_env=me,\n",
    "                payoff_func='np.maximum(maturity_value - strike, 0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the payoff is that of a regular, plain vanilla European call option. If $T$ is the maturity date, $S_T$ the value of the relevant risk factor at that date and $K$ the strike price, the payoff $h_T$ at maturity of such an option is given by\n",
    "\n",
    "$$\n",
    "h_T = \\max[S_T - K, 0]\n",
    "$$\n",
    "\n",
    "`maturity_value` represents the value vector of the risk factor at maturity. Any other \"sensible\" payoff definition is possible. For instance, the following works as well: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "payoff = 'np.maximum(np.minimum(maturity_value) * 2 - 50, 0)'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other standardized payoff elemenets include `mean_value, max_value` and `min_value` representing maturity value vectors with the pathwise means, maxima and minima. Using these payoff elements allows the easy definition of options with **Asian features**.\n",
    "\n",
    "\n",
    "Having instantiated the valuation class, the `present_value` method returns the present value Monte Carlo estimator for the call option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.181851"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_eur.present_value()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, the `delta` and `vega` methods return the delta and the vega of the option, estimated numerically by a forward difference scheme and Monte Carlo simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43630000000000002"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_eur.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.1882"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_eur.vega()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This approach allows to work with such a valuation object similar to an analytical valuation formula like the one of Black-Scholes-Merton (1973). For example, you can **estimate and plot present values, deltas, gammas, vegas, thetas and rhos** for a range of different initial values of the risk factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.92 s, sys: 878 ms, total: 6.8 s\n",
      "Wall time: 6.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "k_list = np.arange(26., 46.1, 2)\n",
    "pv = []; de = []; ve = []; th = []; rh = []; ga = []\n",
    "for k in k_list:\n",
    "    call_eur.update(strike=k)\n",
    "    pv.append(call_eur.present_value())\n",
    "    de.append(call_eur.delta(0.5))\n",
    "    ve.append(call_eur.vega(0.2))\n",
    "    th.append(call_eur.theta())\n",
    "    rh.append(call_eur.rho())\n",
    "    ga.append(call_eur.gamma())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a little plot helper function available to plot these statistics conveniently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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3byFTEN3mxsjR5cBynKlzVStyKLDZsqyTVbatLttPzoNhwCWXBPnd74rZsSOf558v4oor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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ab16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_option_stats_full(k_list, pv, de, ga, ve, th, rh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## valuation_mcs_american_single"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The modeling and valuation of derivatives with American/Bermudan exercise is almost completely the same as in the more simple case of European exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.add_constant('initial_value', 36.)\n",
    "  # reset initial_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "put_ame = valuation_mcs_american_single(\n",
    "                name='put_eur',\n",
    "                underlying=gbm,\n",
    "                mar_env=me,\n",
    "                payoff_func='np.maximum(strike - instrument_values, 0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference to consider here is that for American options where exercise can take place at any time before maturity, the inner value of the option (payoff of immediate exercise) is relevant over the whole set of dates. Therefore, `maturity_value` needs to be replaced by `instrument_values` in the definition of the payoff function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.452"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "put_ame.present_value()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since DX Analytics relies on Monte Carlo simulation and other *numerical* methods, the calculation of the delta and vega of such an option is identical to the European exercise case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.66549999999999998"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "put_ame.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.312200000000001"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "put_ame.vega()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.9 s, sys: 759 ms, total: 13.7 s\n",
      "Wall time: 13.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "k_list = np.arange(26., 46.1, 2.)\n",
    "pv = []; de = []; ve = []\n",
    "for k in k_list:\n",
    "    put_ame.update(strike=k)\n",
    "    pv.append(put_ame.present_value())\n",
    "    de.append(put_ame.delta(.5))\n",
    "    ve.append(put_ame.vega(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4v9o0zULDMKI1FBEROUhkZjqYPt3D9OkeNm50lruva9cAI0YUMXRoEYmJYRzM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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b17c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_option_stats(k_list, pv, de, ve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolio Valuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, market players (asset managers, investment banks, hedge funds, insurance companies, etc.) have to value not only single derivatvies instruments but rather portfolios composed of several derivatives instruments. A consistent derivatives portfolio valuation is particularly important when there are multiple derivatives written on the same risk factor and/or correlations between different risk factors.\n",
    "\n",
    "These are the classes availble for a consistent portfolio valuation:\n",
    "\n",
    "* `derivatives_position` to model a portfolio position\n",
    "* `derivatives_portfolio` to model a derivatives portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### derivatives_position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We work with the `market_environment` object from before and add information about the risk factor model we are using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.add_constant('model', 'gbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A derivatives position consists of \"data only\" and not instantiated model or valuation objects. The necessary model and valuation objects are instantiated during the portfolio valuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "put = derivatives_position(\n",
    "                name='put',  # name of position\n",
    "                quantity=1,  # number of instruments\n",
    "                underlyings=['gbm'],  # relevant risk factors\n",
    "                mar_env=me,  # market environment\n",
    "                otype='American single',  # the option type\n",
    "                payoff_func='np.maximum(40. - instrument_values, 0)')\n",
    "                    # the payoff funtion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `get_info` prints an overview of the all relevant information stored for the respective `derivatives_position` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME\n",
      "put \n",
      "\n",
      "QUANTITY\n",
      "1 \n",
      "\n",
      "UNDERLYINGS\n",
      "['gbm'] \n",
      "\n",
      "MARKET ENVIRONMENT\n",
      "\n",
      "**Constants**\n",
      "paths 25000\n",
      "strike 40.0\n",
      "volatility 0.2\n",
      "initial_value 36.0\n",
      "maturity 2015-12-31 00:00:00\n",
      "model gbm\n",
      "final_date 2015-12-31 00:00:00\n",
      "frequency W\n",
      "currency EUR\n",
      "\n",
      "**Lists**\n",
      "\n",
      "**Curves**\n",
      "discount_curve <dx.frame.constant_short_rate object at 0x106e356d8>\n",
      "\n",
      "OPTION TYPE\n",
      "American single \n",
      "\n",
      "PAYOFF FUNCTION\n",
      "np.maximum(40. - instrument_values, 0)\n"
     ]
    }
   ],
   "source": [
    "put.get_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### derivatives_portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `derivatives_portfolio` class implements the core portfolio valuation tasks. This sub-section illustrates to cases, one with uncorrelated underlyings and another one with correlated underlyings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uncorrelated Underlyings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first example is based on a portfolio with two single-risk factor instruments on **two different risk factors which are not correlated**. In addition to the `gbm` object, we define a `jump_diffusion` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me_jump = market_environment('me_jump', dt.datetime(2015, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me_jump.add_environment(me)\n",
    "me_jump.add_constant('lambda', 0.8)\n",
    "me_jump.add_constant('mu', -0.8)\n",
    "me_jump.add_constant('delta', 0.1)\n",
    "me_jump.add_constant('model', 'jd')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this new risk factor model object, a European call option is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "call_jump = derivatives_position(\n",
    "                name='call_jump',\n",
    "                quantity=3,\n",
    "                underlyings=['jd'],\n",
    "                mar_env=me_jump,\n",
    "                otype='European single',\n",
    "                payoff_func='np.maximum(maturity_value - 36., 0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our **relevant market** now takes on the following form (defined a `dictionary` objects):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "risk_factors = {'gbm': me, 'jd' : me_jump}\n",
    "positions = {'put' : put, 'call_jump' : call_jump}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate the `derivatives_portfolio` class, a **valuation environment** (instance of `market_environment` class) is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val_env = market_environment('general', dt.datetime(2015, 1, 1))\n",
    "val_env.add_constant('frequency', 'M')\n",
    "val_env.add_constant('paths', 50000)\n",
    "val_env.add_constant('starting_date', val_env.pricing_date)\n",
    "val_env.add_constant('final_date', val_env.pricing_date)\n",
    "val_env.add_curve('discount_curve', r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the **instantiation**, we pass all the elements to the portfolio class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "port = derivatives_portfolio(\n",
    "            name='portfolio',  # name \n",
    "            positions=positions,  # derivatives positions\n",
    "            val_env=val_env,  # valuation environment\n",
    "            risk_factors=risk_factors, # relevant risk factors\n",
    "            correlations=False,  # correlation between risk factors\n",
    "            fixed_seed=False,  # fixed seed for randon number generation\n",
    "            parallel=False)  # parallel valuation of portfolio positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once instantiated, the method `get_statistics` provides major portfolio statistics like position values, position deltas ans position vegas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totals\n",
      " pos_value    34.6770\n",
      "pos_delta     2.3206\n",
      "pos_vega     22.1461\n",
      "dtype: float64\n",
      "CPU times: user 728 ms, sys: 68.1 ms, total: 796 ms\n",
      "Wall time: 771 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "stats = port.get_statistics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</th>\n",
       "      <th>name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>otype</th>\n",
       "      <th>risk_facts</th>\n",
       "      <th>value</th>\n",
       "      <th>currency</th>\n",
       "      <th>pos_value</th>\n",
       "      <th>pos_delta</th>\n",
       "      <th>pos_vega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>call_jump</td>\n",
       "      <td>call_jump</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[jd]</td>\n",
       "      <td>10.076</td>\n",
       "      <td>EUR</td>\n",
       "      <td>30.228</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>11.2230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>put</td>\n",
       "      <td>put</td>\n",
       "      <td>1</td>\n",
       "      <td>American single</td>\n",
       "      <td>[gbm]</td>\n",
       "      <td>4.449</td>\n",
       "      <td>EUR</td>\n",
       "      <td>4.449</td>\n",
       "      <td>-0.6794</td>\n",
       "      <td>10.9231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    position       name  quantity            otype risk_facts   value  \\\n",
       "0  call_jump  call_jump         3  European single       [jd]  10.076   \n",
       "1        put        put         1  American single      [gbm]   4.449   \n",
       "\n",
       "  currency  pos_value  pos_delta  pos_vega  \n",
       "0      EUR     30.228     3.0000   11.2230  \n",
       "1      EUR      4.449    -0.6794   10.9231  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method returns a standard pandas `DataFrame` object with which you can work as you are used to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos_value    34.6770\n",
       "pos_delta     2.3206\n",
       "pos_vega     22.1461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[['pos_value', 'pos_delta', 'pos_vega']].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metod `get_values` only calculates the present values of the derivatives instruments and positions and is therefore a bit less compute and time intensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total\n",
      " pos_value    34.73486\n",
      "dtype: float64\n",
      "CPU times: user 161 ms, sys: 12.3 ms, total: 174 ms\n",
      "Wall time: 169 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</th>\n",
       "      <th>name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>otype</th>\n",
       "      <th>risk_facts</th>\n",
       "      <th>value</th>\n",
       "      <th>currency</th>\n",
       "      <th>pos_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>call_jump</td>\n",
       "      <td>call_jump</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[jd]</td>\n",
       "      <td>10.09762</td>\n",
       "      <td>EUR</td>\n",
       "      <td>30.29286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>put</td>\n",
       "      <td>put</td>\n",
       "      <td>1</td>\n",
       "      <td>American single</td>\n",
       "      <td>[gbm]</td>\n",
       "      <td>4.44200</td>\n",
       "      <td>EUR</td>\n",
       "      <td>4.44200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    position       name  quantity            otype risk_facts     value  \\\n",
       "0  call_jump  call_jump         3  European single       [jd]  10.09762   \n",
       "1        put        put         1  American single      [gbm]   4.44200   \n",
       "\n",
       "  currency  pos_value  \n",
       "0      EUR   30.29286  \n",
       "1      EUR    4.44200  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time port.get_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `get_positions` provides detailed information about the single derivatives positions of the `derivatives_portfolio` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------\n",
      "NAME\n",
      "call_jump \n",
      "\n",
      "QUANTITY\n",
      "3 \n",
      "\n",
      "UNDERLYINGS\n",
      "['jd'] \n",
      "\n",
      "MARKET ENVIRONMENT\n",
      "\n",
      "**Constants**\n",
      "paths 50000\n",
      "volatility 0.2\n",
      "model jd\n",
      "currency EUR\n",
      "lambda 0.8\n",
      "mu -0.8\n",
      "initial_value 36.0\n",
      "final_date 2015-12-31 00:00:00\n",
      "strike 40.0\n",
      "delta 0.1\n",
      "maturity 2015-12-31 00:00:00\n",
      "starting_date 2015-01-01 00:00:00\n",
      "frequency M\n",
      "\n",
      "**Lists**\n",
      "random_numbers [[[ 1.59911088 -1.4046112  -1.41690211 ..., -0.02367133  0.64452581\n",
      "    0.83372797]\n",
      "  [-1.71068746 -0.17718321  1.19908444 ..., -0.12887556  1.84851716\n",
      "    1.99223491]\n",
      "  [ 0.91793265  0.527942   -0.51603028 ...,  0.84100797 -1.36662164\n",
      "   -0.60581475]\n",
      "  ..., \n",
      "  [ 1.2128477  -0.02445883  0.28700922 ..., -0.68947286 -0.04293013\n",
      "   -0.15945594]\n",
      "  [-1.32537389 -0.70003276  1.90365643 ..., -0.2332303  -0.0369755\n",
      "    1.44280306]\n",
      "  [ 0.20542848 -0.7790572  -0.75625624 ..., -1.470969   -1.00645074\n",
      "   -0.12511486]]\n",
      "\n",
      " [[-2.1046119   1.11433037 -1.44243622 ...,  0.95216647 -0.4916233\n",
      "    0.14714287]\n",
      "  [ 0.88624348 -0.10835483  1.09678341 ..., -0.02139183  1.17828289\n",
      "   -0.6156402 ]\n",
      "  [-0.49969036 -1.61494765 -0.6953123  ...,  0.09100147  1.12869001\n",
      "   -0.65676166]\n",
      "  ..., \n",
      "  [ 0.04256569  0.51287869  0.39970509 ...,  2.38658577  1.64993773\n",
      "   -0.16761589]\n",
      "  [ 0.69762962  0.89591537  0.14343585 ..., -0.62170926  0.95533176\n",
      "   -0.03614675]\n",
      "  [-0.88760744 -1.24120179  2.66507407 ...,  0.40644798 -1.64541865\n",
      "    1.36340621]]]\n",
      "correlation_matrix      gbm   jd\n",
      "gbm  1.0  0.0\n",
      "jd   0.0  1.0\n",
      "time_grid [datetime.datetime(2015, 1, 1, 0, 0) datetime.datetime(2015, 1, 31, 0, 0)\n",
      " datetime.datetime(2015, 2, 28, 0, 0) datetime.datetime(2015, 3, 31, 0, 0)\n",
      " datetime.datetime(2015, 4, 30, 0, 0) datetime.datetime(2015, 5, 31, 0, 0)\n",
      " datetime.datetime(2015, 6, 30, 0, 0) datetime.datetime(2015, 7, 31, 0, 0)\n",
      " datetime.datetime(2015, 8, 31, 0, 0) datetime.datetime(2015, 9, 30, 0, 0)\n",
      " datetime.datetime(2015, 10, 31, 0, 0)\n",
      " datetime.datetime(2015, 11, 30, 0, 0)\n",
      " datetime.datetime(2015, 12, 31, 0, 0)]\n",
      "cholesky_matrix [[ 1.  0.]\n",
      " [ 0.  1.]]\n",
      "rn_set {'gbm': 0, 'jd': 1}\n",
      "\n",
      "**Curves**\n",
      "discount_curve <dx.frame.constant_short_rate object at 0x106e356d8>\n",
      "\n",
      "OPTION TYPE\n",
      "European single \n",
      "\n",
      "PAYOFF FUNCTION\n",
      "np.maximum(maturity_value - 36., 0)\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "--------------------------------------------------\n",
      "NAME\n",
      "put \n",
      "\n",
      "QUANTITY\n",
      "1 \n",
      "\n",
      "UNDERLYINGS\n",
      "['gbm'] \n",
      "\n",
      "MARKET ENVIRONMENT\n",
      "\n",
      "**Constants**\n",
      "paths 50000\n",
      "strike 40.0\n",
      "volatility 0.2\n",
      "initial_value 36.0\n",
      "maturity 2015-12-31 00:00:00\n",
      "starting_date 2015-01-01 00:00:00\n",
      "model gbm\n",
      "final_date 2015-12-31 00:00:00\n",
      "frequency M\n",
      "currency EUR\n",
      "\n",
      "**Lists**\n",
      "random_numbers [[[ 1.59911088 -1.4046112  -1.41690211 ..., -0.02367133  0.64452581\n",
      "    0.83372797]\n",
      "  [-1.71068746 -0.17718321  1.19908444 ..., -0.12887556  1.84851716\n",
      "    1.99223491]\n",
      "  [ 0.91793265  0.527942   -0.51603028 ...,  0.84100797 -1.36662164\n",
      "   -0.60581475]\n",
      "  ..., \n",
      "  [ 1.2128477  -0.02445883  0.28700922 ..., -0.68947286 -0.04293013\n",
      "   -0.15945594]\n",
      "  [-1.32537389 -0.70003276  1.90365643 ..., -0.2332303  -0.0369755\n",
      "    1.44280306]\n",
      "  [ 0.20542848 -0.7790572  -0.75625624 ..., -1.470969   -1.00645074\n",
      "   -0.12511486]]\n",
      "\n",
      " [[-2.1046119   1.11433037 -1.44243622 ...,  0.95216647 -0.4916233\n",
      "    0.14714287]\n",
      "  [ 0.88624348 -0.10835483  1.09678341 ..., -0.02139183  1.17828289\n",
      "   -0.6156402 ]\n",
      "  [-0.49969036 -1.61494765 -0.6953123  ...,  0.09100147  1.12869001\n",
      "   -0.65676166]\n",
      "  ..., \n",
      "  [ 0.04256569  0.51287869  0.39970509 ...,  2.38658577  1.64993773\n",
      "   -0.16761589]\n",
      "  [ 0.69762962  0.89591537  0.14343585 ..., -0.62170926  0.95533176\n",
      "   -0.03614675]\n",
      "  [-0.88760744 -1.24120179  2.66507407 ...,  0.40644798 -1.64541865\n",
      "    1.36340621]]]\n",
      "correlation_matrix      gbm   jd\n",
      "gbm  1.0  0.0\n",
      "jd   0.0  1.0\n",
      "time_grid [datetime.datetime(2015, 1, 1, 0, 0) datetime.datetime(2015, 1, 31, 0, 0)\n",
      " datetime.datetime(2015, 2, 28, 0, 0) datetime.datetime(2015, 3, 31, 0, 0)\n",
      " datetime.datetime(2015, 4, 30, 0, 0) datetime.datetime(2015, 5, 31, 0, 0)\n",
      " datetime.datetime(2015, 6, 30, 0, 0) datetime.datetime(2015, 7, 31, 0, 0)\n",
      " datetime.datetime(2015, 8, 31, 0, 0) datetime.datetime(2015, 9, 30, 0, 0)\n",
      " datetime.datetime(2015, 10, 31, 0, 0)\n",
      " datetime.datetime(2015, 11, 30, 0, 0)\n",
      " datetime.datetime(2015, 12, 31, 0, 0)]\n",
      "cholesky_matrix [[ 1.  0.]\n",
      " [ 0.  1.]]\n",
      "rn_set {'gbm': 0, 'jd': 1}\n",
      "\n",
      "**Curves**\n",
      "discount_curve <dx.frame.constant_short_rate object at 0x106e356d8>\n",
      "\n",
      "OPTION TYPE\n",
      "American single \n",
      "\n",
      "PAYOFF FUNCTION\n",
      "np.maximum(40. - instrument_values, 0)\n",
      "\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "port.get_positions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlated Underlyings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second example case is exactly the same but now with a **highly positive correlation** between the two relevant risk factors. Correlations are to be provided as a `list`  of `list` objects using the risk factor model names to reference them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "correlations = [['gbm', 'jd', 0.9]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Except from now passing this new object, the application and usage remains the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "port = derivatives_portfolio(\n",
    "            name='portfolio',\n",
    "            positions=positions,\n",
    "            val_env=val_env,\n",
    "            risk_factors=risk_factors,\n",
    "            correlations=correlations,\n",
    "            fixed_seed=True,\n",
    "            parallel=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totals\n",
      " pos_value    35.1350\n",
      "pos_delta     1.6345\n",
      "pos_vega     21.8606\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position</th>\n",
       "      <th>name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>otype</th>\n",
       "      <th>risk_facts</th>\n",
       "      <th>value</th>\n",
       "      <th>currency</th>\n",
       "      <th>pos_value</th>\n",
       "      <th>pos_delta</th>\n",
       "      <th>pos_vega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>call_jump</td>\n",
       "      <td>call_jump</td>\n",
       "      <td>3</td>\n",
       "      <td>European single</td>\n",
       "      <td>[jd]</td>\n",
       "      <td>10.234</td>\n",
       "      <td>EUR</td>\n",
       "      <td>30.702</td>\n",
       "      <td>2.2893</td>\n",
       "      <td>11.0271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>put</td>\n",
       "      <td>put</td>\n",
       "      <td>1</td>\n",
       "      <td>American single</td>\n",
       "      <td>[gbm]</td>\n",
       "      <td>4.433</td>\n",
       "      <td>EUR</td>\n",
       "      <td>4.433</td>\n",
       "      <td>-0.6548</td>\n",
       "      <td>10.8335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    position       name  quantity            otype risk_facts   value  \\\n",
       "0  call_jump  call_jump         3  European single       [jd]  10.234   \n",
       "1        put        put         1  American single      [gbm]   4.433   \n",
       "\n",
       "  currency  pos_value  pos_delta  pos_vega  \n",
       "0      EUR     30.702     2.2893   11.0271  \n",
       "1      EUR      4.433    -0.6548   10.8335  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port.get_statistics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Cholesky matrix** has been added to the valuation environment (which gets passed to the risk factor model objects)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.        ],\n",
       "       [ 0.9       ,  0.43588989]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port.val_env.lists['cholesky_matrix']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us pick **two specific simulated paths**, one for each risk factor, and let us visualize these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path_no = 0\n",
    "paths1 = port.underlying_objects['gbm'].get_instrument_values()[:, path_no]\n",
    "paths2 = port.underlying_objects['jd'].get_instrument_values()[:, path_no]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot illustrates that the two paths are indeed **highly positively correlated**. However, in this case a **large jump** occurs for the `jump_diffusion` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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779n56CMb997rZ+hQLzVrhrukkSE7GzZsMMLS+vUW1q9XcbtV/P6Su5IcDp1L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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cc55978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(port.time_grid, paths1, 'r', label='gbm')\n",
    "plt.plot(port.time_grid, paths2, 'b', label='jd')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.legend(loc=0); plt.grid(True)\n",
    "# highly correlated underlyings\n",
    "# -- with a large jump for one risk factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Copyright, License & Disclaimer**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "DX Analytics (the \"dx library\") is licensed under the GNU Affero General Public License\n",
    "version 3 or later (see [http://www.gnu.org/licenses/](http://www.gnu.org/licenses/)).\n",
    "\n",
    "DX Analytics comes with no representations\n",
    "or warranties, to the extent permitted by applicable law.\n",
    "\n",
    "\n",
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "[http://tpq.io](http://tpq.io) | [team@tpq.io](mailto:team@tpq.io) | [http://twitter.com/dyjh](http://twitter.com/dyjh)\n",
    "\n",
    "**Quant Platform** |\n",
    "[http://quant-platform.com](http://quant-platform.com)\n",
    "\n",
    "**Derivatives Analytics with Python (Wiley Finance)** |\n",
    "[http://derivatives-analytics-with-python.com](http://derivatives-analytics-with-python.com)\n",
    "\n",
    "**Python for Finance (O'Reilly)** |\n",
    "[http://python-for-finance.com](http://python-for-finance.com)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py3]",
   "language": "python",
   "name": "conda-env-py3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}