Naive_BTCUSD_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "############# Initialisation ################\n",
    "\n",
    "rm(list=ls())\n",
    "shhh <- suppressPackageStartupMessages\n",
    "\n",
    "shhh(library(tidyquant))\n",
    "shhh(library(timetk))\n",
    "shhh(library(tibbletime))\n",
    "shhh(library(PerformanceAnalytics))\n",
    "shhh(library(blotter))\n",
    "shhh(library(quantstrat))\n",
    "\n",
    "set.seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Dates</th><th scope=col>Open</th><th scope=col>Close</th><th scope=col>High</th><th scope=col>Low</th><th scope=col>Value</th><th scope=col>Volume</th><th scope=col>Number.Ticks</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>06/06/2019 16:43</td><td>7692.69         </td><td>7687.31         </td><td>7692.69         </td><td>7687.26         </td><td>0               </td><td>0               </td><td>373             </td></tr>\n",
       "\t<tr><td>06/06/2019 16:42</td><td>7687.55         </td><td>7692.68         </td><td>7692.94         </td><td>7686.02         </td><td>0               </td><td>0               </td><td>407             </td></tr>\n",
       "\t<tr><td>06/06/2019 16:41</td><td>7692.35         </td><td>7687.55         </td><td>7692.55         </td><td>7687.55         </td><td>0               </td><td>0               </td><td>310             </td></tr>\n",
       "\t<tr><td>06/06/2019 16:40</td><td>7695.89         </td><td>7692.33         </td><td>7696.04         </td><td>7691.27         </td><td>0               </td><td>0               </td><td>434             </td></tr>\n",
       "\t<tr><td>06/06/2019 16:39</td><td>7698.32         </td><td>7695.89         </td><td>7702.20         </td><td>7695.88         </td><td>0               </td><td>0               </td><td>370             </td></tr>\n",
       "\t<tr><td>06/06/2019 16:38</td><td>7703.03         </td><td>7698.32         </td><td>7703.33         </td><td>7698.32         </td><td>0               </td><td>0               </td><td>279             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " Dates & Open & Close & High & Low & Value & Volume & Number.Ticks\\\\\n",
       "\\hline\n",
       "\t 06/06/2019 16:43 & 7692.69          & 7687.31          & 7692.69          & 7687.26          & 0                & 0                & 373             \\\\\n",
       "\t 06/06/2019 16:42 & 7687.55          & 7692.68          & 7692.94          & 7686.02          & 0                & 0                & 407             \\\\\n",
       "\t 06/06/2019 16:41 & 7692.35          & 7687.55          & 7692.55          & 7687.55          & 0                & 0                & 310             \\\\\n",
       "\t 06/06/2019 16:40 & 7695.89          & 7692.33          & 7696.04          & 7691.27          & 0                & 0                & 434             \\\\\n",
       "\t 06/06/2019 16:39 & 7698.32          & 7695.89          & 7702.20          & 7695.88          & 0                & 0                & 370             \\\\\n",
       "\t 06/06/2019 16:38 & 7703.03          & 7698.32          & 7703.33          & 7698.32          & 0                & 0                & 279             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Dates | Open | Close | High | Low | Value | Volume | Number.Ticks |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 06/06/2019 16:43 | 7692.69          | 7687.31          | 7692.69          | 7687.26          | 0                | 0                | 373              |\n",
       "| 06/06/2019 16:42 | 7687.55          | 7692.68          | 7692.94          | 7686.02          | 0                | 0                | 407              |\n",
       "| 06/06/2019 16:41 | 7692.35          | 7687.55          | 7692.55          | 7687.55          | 0                | 0                | 310              |\n",
       "| 06/06/2019 16:40 | 7695.89          | 7692.33          | 7696.04          | 7691.27          | 0                | 0                | 434              |\n",
       "| 06/06/2019 16:39 | 7698.32          | 7695.89          | 7702.20          | 7695.88          | 0                | 0                | 370              |\n",
       "| 06/06/2019 16:38 | 7703.03          | 7698.32          | 7703.33          | 7698.32          | 0                | 0                | 279              |\n",
       "\n"
      ],
      "text/plain": [
       "  Dates            Open    Close   High    Low     Value Volume Number.Ticks\n",
       "1 06/06/2019 16:43 7692.69 7687.31 7692.69 7687.26 0     0      373         \n",
       "2 06/06/2019 16:42 7687.55 7692.68 7692.94 7686.02 0     0      407         \n",
       "3 06/06/2019 16:41 7692.35 7687.55 7692.55 7687.55 0     0      310         \n",
       "4 06/06/2019 16:40 7695.89 7692.33 7696.04 7691.27 0     0      434         \n",
       "5 06/06/2019 16:39 7698.32 7695.89 7702.20 7695.88 0     0      370         \n",
       "6 06/06/2019 16:38 7703.03 7698.32 7703.33 7698.32 0     0      279         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################# Setting up the data ##################################################\n",
    "# This data was taken from Bloomberg terminal - May 2019\n",
    "\n",
    "data <- read.delim(\"E:/trading_model/data/XBTUSD.csv\", sep = \";\", stringsAsFactors = FALSE)\n",
    "colnames(data)[1] <- \"Dates\"\n",
    "\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Dates</th><th scope=col>Open</th><th scope=col>Close</th><th scope=col>High</th><th scope=col>Low</th><th scope=col>Value</th><th scope=col>Volume</th><th scope=col>Number.Ticks</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2019-06-06 16:43:00</td><td>7692.69            </td><td>7687.31            </td><td>7692.69            </td><td>7687.26            </td><td>0                  </td><td>0                  </td><td>373                </td></tr>\n",
       "\t<tr><td>2019-06-06 16:42:00</td><td>7687.55            </td><td>7692.68            </td><td>7692.94            </td><td>7686.02            </td><td>0                  </td><td>0                  </td><td>407                </td></tr>\n",
       "\t<tr><td>2019-06-06 16:41:00</td><td>7692.35            </td><td>7687.55            </td><td>7692.55            </td><td>7687.55            </td><td>0                  </td><td>0                  </td><td>310                </td></tr>\n",
       "\t<tr><td>2019-06-06 16:40:00</td><td>7695.89            </td><td>7692.33            </td><td>7696.04            </td><td>7691.27            </td><td>0                  </td><td>0                  </td><td>434                </td></tr>\n",
       "\t<tr><td>2019-06-06 16:39:00</td><td>7698.32            </td><td>7695.89            </td><td>7702.20            </td><td>7695.88            </td><td>0                  </td><td>0                  </td><td>370                </td></tr>\n",
       "\t<tr><td>2019-06-06 16:38:00</td><td>7703.03            </td><td>7698.32            </td><td>7703.33            </td><td>7698.32            </td><td>0                  </td><td>0                  </td><td>279                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " Dates & Open & Close & High & Low & Value & Volume & Number.Ticks\\\\\n",
       "\\hline\n",
       "\t 2019-06-06 16:43:00 & 7692.69             & 7687.31             & 7692.69             & 7687.26             & 0                   & 0                   & 373                \\\\\n",
       "\t 2019-06-06 16:42:00 & 7687.55             & 7692.68             & 7692.94             & 7686.02             & 0                   & 0                   & 407                \\\\\n",
       "\t 2019-06-06 16:41:00 & 7692.35             & 7687.55             & 7692.55             & 7687.55             & 0                   & 0                   & 310                \\\\\n",
       "\t 2019-06-06 16:40:00 & 7695.89             & 7692.33             & 7696.04             & 7691.27             & 0                   & 0                   & 434                \\\\\n",
       "\t 2019-06-06 16:39:00 & 7698.32             & 7695.89             & 7702.20             & 7695.88             & 0                   & 0                   & 370                \\\\\n",
       "\t 2019-06-06 16:38:00 & 7703.03             & 7698.32             & 7703.33             & 7698.32             & 0                   & 0                   & 279                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Dates | Open | Close | High | Low | Value | Volume | Number.Ticks |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 2019-06-06 16:43:00 | 7692.69             | 7687.31             | 7692.69             | 7687.26             | 0                   | 0                   | 373                 |\n",
       "| 2019-06-06 16:42:00 | 7687.55             | 7692.68             | 7692.94             | 7686.02             | 0                   | 0                   | 407                 |\n",
       "| 2019-06-06 16:41:00 | 7692.35             | 7687.55             | 7692.55             | 7687.55             | 0                   | 0                   | 310                 |\n",
       "| 2019-06-06 16:40:00 | 7695.89             | 7692.33             | 7696.04             | 7691.27             | 0                   | 0                   | 434                 |\n",
       "| 2019-06-06 16:39:00 | 7698.32             | 7695.89             | 7702.20             | 7695.88             | 0                   | 0                   | 370                 |\n",
       "| 2019-06-06 16:38:00 | 7703.03             | 7698.32             | 7703.33             | 7698.32             | 0                   | 0                   | 279                 |\n",
       "\n"
      ],
      "text/plain": [
       "  Dates               Open    Close   High    Low     Value Volume Number.Ticks\n",
       "1 2019-06-06 16:43:00 7692.69 7687.31 7692.69 7687.26 0     0      373         \n",
       "2 2019-06-06 16:42:00 7687.55 7692.68 7692.94 7686.02 0     0      407         \n",
       "3 2019-06-06 16:41:00 7692.35 7687.55 7692.55 7687.55 0     0      310         \n",
       "4 2019-06-06 16:40:00 7695.89 7692.33 7696.04 7691.27 0     0      434         \n",
       "5 2019-06-06 16:39:00 7698.32 7695.89 7702.20 7695.88 0     0      370         \n",
       "6 2019-06-06 16:38:00 7703.03 7698.32 7703.33 7698.32 0     0      279         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- data %>%\n",
    "  mutate(Dates = ifelse(nchar(Dates) == 10, paste(Dates, \"0:00\", sep = \" \"), Dates),\n",
    "         Dates = as.POSIXct(Dates, format=\"%d/%m/%Y %H:%M\")) %>%\n",
    "  as_tibble() %>%\n",
    "  as_tbl_time(Dates)\n",
    "\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in tk_xts_.data.frame(data = data, select = select, date_var = date_var, :\n",
      "\"Non-numeric columns being dropped: Dates\""
     ]
    },
    {
     "data": {
      "text/plain": [
       "                       Open   Close    High     Low Value Volume Number.Ticks\n",
       "2019-03-01 09:00:00 3809.62 3808.76 3809.98 3808.31     0      0          189\n",
       "2019-03-01 09:01:00 3808.76 3809.10 3809.19 3807.85     0      0          190\n",
       "2019-03-01 09:02:00 3809.16 3808.69 3809.19 3808.67     0      0          253\n",
       "2019-03-01 09:03:00 3808.69 3807.92 3809.21 3807.92     0      0          121\n",
       "2019-03-01 09:04:00 3807.92 3804.90 3807.92 3804.90     0      0          177\n",
       "2019-03-01 09:05:00 3804.90 3804.67 3805.15 3804.05     0      0          202"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df <- tk_xts(data, date_var = Dates)\n",
    "\n",
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'USD'"
      ],
      "text/latex": [
       "'USD'"
      ],
      "text/markdown": [
       "'USD'"
      ],
      "text/plain": [
       "[1] \"USD\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#setting up some initial parameters for the quantstrat trading model\n",
    "initDate=\"2019-03-01\"\n",
    "init_equity <- 100\n",
    "adjustment <- TRUE\n",
    "\n",
    ".orderqty <- 1\n",
    ".txnfees <- -0.3\n",
    "\n",
    "currency('USD')\n",
    "Sys.setenv(TZ=\"UTC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                       Open   Close    High     Low Value Volume Number.Ticks\n",
       "2019-05-17 19:09:00 7052.00 7073.89 7077.30 7051.97     0      0          580\n",
       "2019-05-17 19:10:00 7073.89 7059.98 7073.89 7059.63     0      0          635\n",
       "2019-05-17 19:11:00 7059.98 7065.32 7066.78 7058.21     0      0          439\n",
       "2019-05-17 19:12:00 7065.32 7050.65 7065.32 7049.87     0      0          401\n",
       "2019-05-17 19:13:00 7050.65 7062.01 7063.41 7048.23     0      0          605\n",
       "2019-05-17 19:14:00 7062.01 7067.95 7072.69 7060.17     0      0          633\n",
       "                    direction      rsi momentum direction_fwd\n",
       "2019-05-17 19:09:00         1 47.53122   -28.07             0\n",
       "2019-05-17 19:10:00         0 37.71151   -50.70             1\n",
       "2019-05-17 19:11:00         1 39.28530   -28.17             0\n",
       "2019-05-17 19:12:00         0 33.03566   -41.87             1\n",
       "2019-05-17 19:13:00         1 40.80070   -55.15             1\n",
       "2019-05-17 19:14:00         1 43.02266   -34.70             1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "                       Open   Close    High     Low Value Volume Number.Ticks\n",
       "2019-05-17 19:15:00 7066.41 7091.00 7091.00 7066.25     0      0          703\n",
       "2019-05-17 19:16:00 7091.00 7092.66 7094.33 7083.20     0      0          615\n",
       "2019-05-17 19:17:00 7092.66 7098.50 7102.95 7089.52     0      0          494\n",
       "2019-05-17 19:18:00 7098.45 7096.46 7099.54 7089.49     0      0          653\n",
       "2019-05-17 19:19:00 7096.46 7094.95 7096.46 7091.70     0      0          509\n",
       "2019-05-17 19:20:00 7094.95 7081.90 7096.34 7080.03     0      0          515\n",
       "                    direction      rsi momentum direction_fwd\n",
       "2019-05-17 19:15:00         1 42.50344   -10.99             1\n",
       "2019-05-17 19:16:00         1 46.90961     7.21             1\n",
       "2019-05-17 19:17:00         1 48.95390     5.97             0\n",
       "2019-05-17 19:18:00         0 53.61434    18.22             0\n",
       "2019-05-17 19:19:00         0 50.82459    26.41             0\n",
       "2019-05-17 19:20:00         0 51.25773    29.40             0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################### Simple Model ##################################\n",
    "\n",
    "df$direction <- with(df, ifelse(Close >= Open, 1, 0))\n",
    "\n",
    "#create two basic input variables - lagged\n",
    "df$rsi <- RSI(df$Close, nFast=14, nSlow = 26, nSig = 9, maType = SMA)\n",
    "df$momentum <- momentum(df$Close, n = 12)\n",
    "\n",
    "df <- df[complete.cases(df), ] \n",
    "df$direction_fwd <- lag.xts(df$direction, k = -1)\n",
    "\n",
    "# create a training and test set\n",
    "train_date <- nrow(df) *0.8\n",
    "train <- df[1:train_date,]\n",
    "test <- df[-c(1:train_date),]\n",
    "\n",
    "tail(train)\n",
    "head(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = direction_fwd ~ rsi + momentum, family = binomial, \n",
       "    data = train)\n",
       "\n",
       "Deviance Residuals: \n",
       "   Min      1Q  Median      3Q     Max  \n",
       "-1.735  -1.215   1.113   1.139   1.622  \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -0.0257646  0.0230161  -1.119    0.263    \n",
       "rsi          0.0022950  0.0004273   5.371 7.81e-08 ***\n",
       "momentum    -0.0014730  0.0003641  -4.045 5.23e-05 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 112762  on 81463  degrees of freedom\n",
       "Residual deviance: 112730  on 81461  degrees of freedom\n",
       "AIC: 112736\n",
       "\n",
       "Number of Fisher Scoring iterations: 3\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run a simple logistic regression and obtain predicted probabilities\n",
    "lm.fit <- glm(direction_fwd ~ rsi + momentum, data = train, family = binomial)\n",
    "\n",
    "summary(lm.fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the predictions on the test data\n",
    "pr.lm <- predict(lm.fit, test, type = \"response\")\n",
    "test$pred_prob <- pr.lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Accuracy: 0.514559292904493'"
      ],
      "text/latex": [
       "'Accuracy: 0.514559292904493'"
      ],
      "text/markdown": [
       "'Accuracy: 0.514559292904493'"
      ],
      "text/plain": [
       "[1] \"Accuracy: 0.514559292904493\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If the predicted probability is > 0.50 then predict the market went \"Up\", if <= 0.50 then predict the market went \"Down\"\n",
    "test$prediction <- ifelse(pr.lm > 0.50, 1, 0)\n",
    "\n",
    "paste0(\"Accuracy: \", mean(test$direction_fwd == test$prediction, na.rm = T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'test'"
      ],
      "text/latex": [
       "'test'"
      ],
      "text/markdown": [
       "'test'"
      ],
      "text/plain": [
       "[1] \"test\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'LogisticRegressionStrategy'"
      ],
      "text/latex": [
       "'LogisticRegressionStrategy'"
      ],
      "text/markdown": [
       "'LogisticRegressionStrategy'"
      ],
      "text/plain": [
       "[1] \"LogisticRegressionStrategy\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'LogisticRegressionStrategy'"
      ],
      "text/latex": [
       "'LogisticRegressionStrategy'"
      ],
      "text/markdown": [
       "'LogisticRegressionStrategy'"
      ],
      "text/plain": [
       "[1] \"LogisticRegressionStrategy\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtest the strategy\n",
    "stock(\"test\", currency=\"USD\", multiplier=1)\n",
    "strategy.st <- portfolio.st <- account.st <- \"LogisticRegressionStrategy\"\n",
    "rm.strat(strategy.st)\n",
    "rm.strat(portfolio.st)\n",
    "rm.strat(account.st)\n",
    "\n",
    "initPortf(name = portfolio.st,\n",
    "          symbols = \"test\", \n",
    "          initDate = initDate, \n",
    "          currency = 'USD')\n",
    "\n",
    "initAcct(name = account.st, \n",
    "         portfolios = portfolio.st, \n",
    "         initDate = initDate, \n",
    "         currency = 'USD',\n",
    "         initEq = init_equity)\n",
    "\n",
    "\n",
    "initOrders(portfolio.st,\n",
    "           symbols = \"test\",\n",
    "           initDate = initDate)\n",
    "\n",
    "strategy(strategy.st, store = TRUE)\n",
    "\n",
    "nMult_orderqty <- 2\n",
    "addPosLimit(portfolio.st, symbol = \"test\", timestamp = initDate, maxpos = nMult_orderqty * .orderqty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'LogisticRegressionStrategy'"
      ],
      "text/latex": [
       "'LogisticRegressionStrategy'"
      ],
      "text/markdown": [
       "'LogisticRegressionStrategy'"
      ],
      "text/plain": [
       "[1] \"LogisticRegressionStrategy\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "########## Add the buy and sell signals generated from the Logistic Model ############\n",
    "\n",
    "# Buy when prob exceeds 0.50 for the first time, using cross= TRUE\n",
    "add.signal(strategy = strategy.st,\n",
    "           name = \"sigThreshold\",\n",
    "           arguments = list(threshold = 0.50, column = \"pred_prob\", relationship = \"gt\", cross = TRUE),\n",
    "           label = \"longSig\")\n",
    "\n",
    "# Exit when prob drops below 0.5 for the first time\n",
    "add.signal(strategy = strategy.st,\n",
    "           name = \"sigThreshold\",\n",
    "           arguments = list(threshold=0.50, column = \"pred_prob\", relationship = \"lte\", cross= TRUE),\n",
    "           label = \"exitLongSig\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "[1] \"LogisticRegressionStrategy\""
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       "[1] \"LogisticRegressionStrategy\""
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   "source": [
    "# Adding the rules, enter at the low price when \"prediction\" = 1, taking transaction fees into account\n",
    "add.rule(strategy = strategy.st,\n",
    "         name = \"ruleSignal\",\n",
    "         arguments = list(sigcol = \"longSig\",\n",
    "                          sigval = 1,\n",
    "                          orderqty = .orderqty,\n",
    "                          ordertype = \"market\",\n",
    "                          orderside = \"long\",\n",
    "                          osFUN = osMaxPos,\n",
    "                          prefer = \"Open\",  #Never kknow the low in advance. Use the open, as it is for the next day (be aware that the open price for bar data has its own problems too)\n",
    "                          TxnFees = .txnfees, \n",
    "                          replace = FALSE),\n",
    "         type = \"enter\",\n",
    "         label = \"EnterLONG\")\n",
    "\n",
    "# As soon as the Logistic regression predicts a \"0\" we dump all our shares \n",
    "\n",
    "add.rule(strategy.st, \n",
    "         name = \"ruleSignal\", \n",
    "         arguments = list(sigcol = \"exitLongSig\", \n",
    "                          sigval = 1, \n",
    "                          ordertype = \"market\", \n",
    "                          orderside = \"long\",\n",
    "                          orderqty = \"all\", \n",
    "                          TxnFees = .txnfees, \n",
    "                          replace = TRUE), \n",
    "         type = \"exit\", \n",
    "         label = \"Exit2SHORT\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"2019-05-17 20:20:00 test 1 @ 7103.96\"\n",
      "[1] \"2019-05-19 21:00:00 test -1 @ 7990.1\"\n",
      "[1] \"2019-05-19 21:12:00 test 1 @ 7982.39\"\n",
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      "[1] \"2019-05-20 02:57:00 test -1 @ 7934.93\"\n",
      "[1] \"2019-05-20 02:59:00 test 1 @ 7933.11\"\n",
      "[1] \"2019-05-20 09:22:00 test -1 @ 7992.57\"\n",
      "[1] \"2019-05-20 09:26:00 test 1 @ 7984.62\"\n",
      "[1] \"2019-05-20 11:02:00 test -1 @ 7941.87\"\n",
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      "[1] \"2019-05-20 15:22:00 test -1 @ 7718\"\n",
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      "[1] \"2019-05-20 22:47:00 test -1 @ 7966.08\"\n",
      "[1] \"2019-05-20 22:49:00 test 1 @ 7958.3\"\n",
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      "[1] \"2019-05-22 17:25:00 test -1 @ 8039.46\"\n",
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      "[1] \"2019-05-23 00:18:00 test -1 @ 7655.11\"\n",
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      "[1] \"2019-05-23 02:43:00 test -1 @ 7638.72\"\n",
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      "[1] \"2019-05-26 21:13:00 test -1 @ 8083.75\"\n",
      "[1] \"2019-05-26 21:15:00 test 1 @ 8088.33\"\n",
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      "[1] \"2019-05-26 22:11:00 test 1 @ 8626.13\"\n",
      "[1] \"2019-05-26 22:30:00 test -1 @ 8679.6\"\n",
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      "[1] \"2019-05-27 00:17:00 test -1 @ 8761.5\"\n",
      "[1] \"2019-05-27 00:18:00 test 1 @ 8761.55\"\n",
      "[1] \"2019-05-27 02:23:00 test -1 @ 8909.89\"\n",
      "[1] \"2019-05-27 02:26:00 test 1 @ 8901.69\"\n",
      "[1] \"2019-05-27 02:27:00 test -1 @ 8867.94\"\n",
      "[1] \"2019-05-27 02:28:00 test 1 @ 8868.14\"\n",
      "[1] \"2019-05-27 18:56:00 test -1 @ 8722.51\"\n",
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      "[1] \"2019-05-28 15:43:00 test -1 @ 8650.87\"\n",
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      "[1] \"2019-06-02 21:00:00 test -1 @ 8686.37\"\n",
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      "[1] \"2019-06-03 09:24:00 test -1 @ 8450.45\"\n",
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      "[1] \"2019-06-05 00:12:00 test -1 @ 7528.35\"\n",
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   "source": [
    "# Apply the strategy and update the portfolios\n",
    "\n",
    "applyStrategy(strategy.st, portfolios = portfolio.st)\n",
    "\n",
    "updatePortf(portfolio.st)\n",
    "updateAcct(account.st)\n",
    "updateEndEq(account.st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CaQ9NgYru4ObT2wle09kNQjmKfMDTkyQj8tkHdN8oPBg+khbdZoq4mtX0Per3C9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\nEECCoA4CSBDUQf8fNMSRKEV0yJoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the performance of the strategy\n",
    "\n",
    "chart.Posn(portfolio.st, Symbol = \"test\", \n",
    "           TA=\"add_SMA(n = 10, col = 2); add_SMA(n = 30, col = 4)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>test</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Num.Txns</th><td>  101.00</td></tr>\n",
       "\t<tr><th scope=row>Num.Trades</th><td>   50.00</td></tr>\n",
       "\t<tr><th scope=row>Net.Trading.PL</th><td>  436.04</td></tr>\n",
       "\t<tr><th scope=row>Avg.Trade.PL</th><td>    9.66</td></tr>\n",
       "\t<tr><th scope=row>Med.Trade.PL</th><td>   -7.65</td></tr>\n",
       "\t<tr><th scope=row>Largest.Winner</th><td>  885.84</td></tr>\n",
       "\t<tr><th scope=row>Largest.Loser</th><td> -451.64</td></tr>\n",
       "\t<tr><th scope=row>Gross.Profits</th><td> 3701.87</td></tr>\n",
       "\t<tr><th scope=row>Gross.Losses</th><td>-3219.01</td></tr>\n",
       "\t<tr><th scope=row>Std.Dev.Trade.PL</th><td>  205.51</td></tr>\n",
       "\t<tr><th scope=row>Std.Err.Trade.PL</th><td>   29.06</td></tr>\n",
       "\t<tr><th scope=row>Percent.Positive</th><td>   48.00</td></tr>\n",
       "\t<tr><th scope=row>Percent.Negative</th><td>   52.00</td></tr>\n",
       "\t<tr><th scope=row>Profit.Factor</th><td>    1.15</td></tr>\n",
       "\t<tr><th scope=row>Avg.Win.Trade</th><td>  154.24</td></tr>\n",
       "\t<tr><th scope=row>Med.Win.Trade</th><td>  101.60</td></tr>\n",
       "\t<tr><th scope=row>Avg.Losing.Trade</th><td> -123.81</td></tr>\n",
       "\t<tr><th scope=row>Med.Losing.Trade</th><td>  -81.63</td></tr>\n",
       "\t<tr><th scope=row>Avg.Daily.PL</th><td>   37.14</td></tr>\n",
       "\t<tr><th scope=row>Med.Daily.PL</th><td>  -71.94</td></tr>\n",
       "\t<tr><th scope=row>Std.Dev.Daily.PL</th><td>  406.14</td></tr>\n",
       "\t<tr><th scope=row>Std.Err.Daily.PL</th><td>  112.64</td></tr>\n",
       "\t<tr><th scope=row>Ann.Sharpe</th><td>    1.45</td></tr>\n",
       "\t<tr><th scope=row>Max.Drawdown</th><td>-1551.79</td></tr>\n",
       "\t<tr><th scope=row>Profit.To.Max.Draw</th><td>    0.28</td></tr>\n",
       "\t<tr><th scope=row>Avg.WinLoss.Ratio</th><td>    1.25</td></tr>\n",
       "\t<tr><th scope=row>Med.WinLoss.Ratio</th><td>    1.24</td></tr>\n",
       "\t<tr><th scope=row>Max.Equity</th><td> 1748.05</td></tr>\n",
       "\t<tr><th scope=row>Min.Equity</th><td>  -58.96</td></tr>\n",
       "\t<tr><th scope=row>End.Equity</th><td>  436.04</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "  & test\\\\\n",
       "\\hline\n",
       "\tNum.Txns &   101.00\\\\\n",
       "\tNum.Trades &    50.00\\\\\n",
       "\tNet.Trading.PL &   436.04\\\\\n",
       "\tAvg.Trade.PL &     9.66\\\\\n",
       "\tMed.Trade.PL &    -7.65\\\\\n",
       "\tLargest.Winner &   885.84\\\\\n",
       "\tLargest.Loser &  -451.64\\\\\n",
       "\tGross.Profits &  3701.87\\\\\n",
       "\tGross.Losses & -3219.01\\\\\n",
       "\tStd.Dev.Trade.PL &   205.51\\\\\n",
       "\tStd.Err.Trade.PL &    29.06\\\\\n",
       "\tPercent.Positive &    48.00\\\\\n",
       "\tPercent.Negative &    52.00\\\\\n",
       "\tProfit.Factor &     1.15\\\\\n",
       "\tAvg.Win.Trade &   154.24\\\\\n",
       "\tMed.Win.Trade &   101.60\\\\\n",
       "\tAvg.Losing.Trade &  -123.81\\\\\n",
       "\tMed.Losing.Trade &   -81.63\\\\\n",
       "\tAvg.Daily.PL &    37.14\\\\\n",
       "\tMed.Daily.PL &   -71.94\\\\\n",
       "\tStd.Dev.Daily.PL &   406.14\\\\\n",
       "\tStd.Err.Daily.PL &   112.64\\\\\n",
       "\tAnn.Sharpe &     1.45\\\\\n",
       "\tMax.Drawdown & -1551.79\\\\\n",
       "\tProfit.To.Max.Draw &     0.28\\\\\n",
       "\tAvg.WinLoss.Ratio &     1.25\\\\\n",
       "\tMed.WinLoss.Ratio &     1.24\\\\\n",
       "\tMax.Equity &  1748.05\\\\\n",
       "\tMin.Equity &   -58.96\\\\\n",
       "\tEnd.Equity &   436.04\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | test |\n",
       "|---|---|\n",
       "| Num.Txns |   101.00 |\n",
       "| Num.Trades |    50.00 |\n",
       "| Net.Trading.PL |   436.04 |\n",
       "| Avg.Trade.PL |     9.66 |\n",
       "| Med.Trade.PL |    -7.65 |\n",
       "| Largest.Winner |   885.84 |\n",
       "| Largest.Loser |  -451.64 |\n",
       "| Gross.Profits |  3701.87 |\n",
       "| Gross.Losses | -3219.01 |\n",
       "| Std.Dev.Trade.PL |   205.51 |\n",
       "| Std.Err.Trade.PL |    29.06 |\n",
       "| Percent.Positive |    48.00 |\n",
       "| Percent.Negative |    52.00 |\n",
       "| Profit.Factor |     1.15 |\n",
       "| Avg.Win.Trade |   154.24 |\n",
       "| Med.Win.Trade |   101.60 |\n",
       "| Avg.Losing.Trade |  -123.81 |\n",
       "| Med.Losing.Trade |   -81.63 |\n",
       "| Avg.Daily.PL |    37.14 |\n",
       "| Med.Daily.PL |   -71.94 |\n",
       "| Std.Dev.Daily.PL |   406.14 |\n",
       "| Std.Err.Daily.PL |   112.64 |\n",
       "| Ann.Sharpe |     1.45 |\n",
       "| Max.Drawdown | -1551.79 |\n",
       "| Profit.To.Max.Draw |     0.28 |\n",
       "| Avg.WinLoss.Ratio |     1.25 |\n",
       "| Med.WinLoss.Ratio |     1.24 |\n",
       "| Max.Equity |  1748.05 |\n",
       "| Min.Equity |   -58.96 |\n",
       "| End.Equity |   436.04 |\n",
       "\n"
      ],
      "text/plain": [
       "                   test    \n",
       "Num.Txns             101.00\n",
       "Num.Trades            50.00\n",
       "Net.Trading.PL       436.04\n",
       "Avg.Trade.PL           9.66\n",
       "Med.Trade.PL          -7.65\n",
       "Largest.Winner       885.84\n",
       "Largest.Loser       -451.64\n",
       "Gross.Profits       3701.87\n",
       "Gross.Losses       -3219.01\n",
       "Std.Dev.Trade.PL     205.51\n",
       "Std.Err.Trade.PL      29.06\n",
       "Percent.Positive      48.00\n",
       "Percent.Negative      52.00\n",
       "Profit.Factor          1.15\n",
       "Avg.Win.Trade        154.24\n",
       "Med.Win.Trade        101.60\n",
       "Avg.Losing.Trade    -123.81\n",
       "Med.Losing.Trade     -81.63\n",
       "Avg.Daily.PL          37.14\n",
       "Med.Daily.PL         -71.94\n",
       "Std.Dev.Daily.PL     406.14\n",
       "Std.Err.Daily.PL     112.64\n",
       "Ann.Sharpe             1.45\n",
       "Max.Drawdown       -1551.79\n",
       "Profit.To.Max.Draw     0.28\n",
       "Avg.WinLoss.Ratio      1.25\n",
       "Med.WinLoss.Ratio      1.24\n",
       "Max.Equity          1748.05\n",
       "Min.Equity           -58.96\n",
       "End.Equity           436.04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print out the performance of the strategy over the testing period.\n",
    "\n",
    "data.frame(round(t(tradeStats(portfolio.st)[-c(1,2)]),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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