{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: xts\n",
      "Loading required package: zoo\n",
      "\n",
      "Attaching package: 'zoo'\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    as.Date, as.Date.numeric\n",
      "\n",
      "Loading required package: TTR\n",
      "Version 0.4-0 included new data defaults. See ?getSymbols.\n",
      "\n",
      "Attaching package: 'data.table'\n",
      "\n",
      "The following objects are masked from 'package:xts':\n",
      "\n",
      "    first, last\n",
      "\n",
      "Loading required package: timeDate\n",
      "Loading required package: timeSeries\n",
      "\n",
      "Attaching package: 'timeSeries'\n",
      "\n",
      "The following object is masked from 'package:zoo':\n",
      "\n",
      "    time<-\n",
      "\n",
      "Loading required package: fBasics\n",
      "\n",
      "\n",
      "Rmetrics Package fBasics\n",
      "Analysing Markets and calculating Basic Statistics\n",
      "Copyright (C) 2005-2014 Rmetrics Association Zurich\n",
      "Educational Software for Financial Engineering and Computational Science\n",
      "Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY.\n",
      "https://www.rmetrics.org --- Mail to: info@rmetrics.org\n",
      "\n",
      "Attaching package: 'fBasics'\n",
      "\n",
      "The following object is masked from 'package:TTR':\n",
      "\n",
      "    volatility\n",
      "\n",
      "\n",
      "Attaching package: 'fTrading'\n",
      "\n",
      "The following object is masked from 'package:TTR':\n",
      "\n",
      "    SMA\n",
      "\n",
      "\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "The following objects are masked from 'package:timeSeries':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following object is masked from 'package:gridExtra':\n",
      "\n",
      "    combine\n",
      "\n",
      "The following objects are masked from 'package:data.table':\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "The following objects are masked from 'package:xts':\n",
      "\n",
      "    first, last\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Leandro Guerra\n",
    "#www.outspokenmarket.com\n",
    "#21/07/2018\n",
    "\n",
    "#Loading Libraries\n",
    "library(quantmod)\n",
    "library(ggplot2)\n",
    "library(data.table)\n",
    "library(gridExtra)\n",
    "library(fTrading)\n",
    "library(TTR)\n",
    "library(dplyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Time (CAT)</th><th scope=col>Open</th><th scope=col>High</th><th scope=col>Low</th><th scope=col>Close</th><th scope=col>Pips</th><th scope=col>Pips_Shift</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>01/01/2015 00:00</td><td>1.20969         </td><td>1.20976         </td><td>1.20969         </td><td>1.20976         </td><td>0               </td><td>0               </td></tr>\n",
       "\t<tr><td>01/01/2015 01:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td></tr>\n",
       "\t<tr><td>01/01/2015 02:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td></tr>\n",
       "\t<tr><td>01/01/2015 03:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td></tr>\n",
       "\t<tr><td>01/01/2015 04:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td></tr>\n",
       "\t<tr><td>01/01/2015 05:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Time (CAT) & Open & High & Low & Close & Pips & Pips\\_Shift\\\\\n",
       "\\hline\n",
       "\t 01/01/2015 00:00 & 1.20969          & 1.20976          & 1.20969          & 1.20976          & 0                & 0               \\\\\n",
       "\t 01/01/2015 01:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0               \\\\\n",
       "\t 01/01/2015 02:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0               \\\\\n",
       "\t 01/01/2015 03:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0               \\\\\n",
       "\t 01/01/2015 04:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0               \\\\\n",
       "\t 01/01/2015 05:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Time (CAT) | Open | High | Low | Close | Pips | Pips_Shift | \n",
       "|---|---|---|---|---|---|\n",
       "| 01/01/2015 00:00 | 1.20969          | 1.20976          | 1.20969          | 1.20976          | 0                | 0                | \n",
       "| 01/01/2015 01:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | \n",
       "| 01/01/2015 02:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | \n",
       "| 01/01/2015 03:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | \n",
       "| 01/01/2015 04:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | \n",
       "| 01/01/2015 05:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Time (CAT)       Open    High    Low     Close   Pips Pips_Shift\n",
       "1 01/01/2015 00:00 1.20969 1.20976 1.20969 1.20976 0    0         \n",
       "2 01/01/2015 01:00 1.20976 1.20976 1.20976 1.20976 0    0         \n",
       "3 01/01/2015 02:00 1.20976 1.20976 1.20976 1.20976 0    0         \n",
       "4 01/01/2015 03:00 1.20976 1.20976 1.20976 1.20976 0    0         \n",
       "5 01/01/2015 04:00 1.20976 1.20976 1.20976 1.20976 0    0         \n",
       "6 01/01/2015 05:00 1.20976 1.20976 1.20976 1.20976 0    0         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Loading Data\n",
    "EURUSD <- fread(\"D:/Projetos/LBI/Outspoken Market/EURUSD_H1_Hour.csv\", sep = \";\",header=T)\n",
    "EURUSD <- as.data.frame(EURUSD)\n",
    "\n",
    "head(EURUSD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Date</th><th scope=col>Open</th><th scope=col>High</th><th scope=col>Low</th><th scope=col>Close</th><th scope=col>Pips</th><th scope=col>Pips_Shift</th><th scope=col>Hours</th><th scope=col>Year</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>01/01/2015 00:00</td><td>1.20969         </td><td>1.20976         </td><td>1.20969         </td><td>1.20976         </td><td>0               </td><td>0               </td><td>0               </td><td>2015            </td></tr>\n",
       "\t<tr><td>01/01/2015 01:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td><td>1               </td><td>2015            </td></tr>\n",
       "\t<tr><td>01/01/2015 02:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td><td>2               </td><td>2015            </td></tr>\n",
       "\t<tr><td>01/01/2015 03:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td><td>3               </td><td>2015            </td></tr>\n",
       "\t<tr><td>01/01/2015 04:00</td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>1.20976         </td><td>0               </td><td>0               </td><td>4               </td><td>2015            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       " Date & Open & High & Low & Close & Pips & Pips\\_Shift & Hours & Year\\\\\n",
       "\\hline\n",
       "\t 01/01/2015 00:00 & 1.20969          & 1.20976          & 1.20969          & 1.20976          & 0                & 0                & 0                & 2015            \\\\\n",
       "\t 01/01/2015 01:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0                & 1                & 2015            \\\\\n",
       "\t 01/01/2015 02:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0                & 2                & 2015            \\\\\n",
       "\t 01/01/2015 03:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0                & 3                & 2015            \\\\\n",
       "\t 01/01/2015 04:00 & 1.20976          & 1.20976          & 1.20976          & 1.20976          & 0                & 0                & 4                & 2015            \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Date | Open | High | Low | Close | Pips | Pips_Shift | Hours | Year | \n",
       "|---|---|---|---|---|\n",
       "| 01/01/2015 00:00 | 1.20969          | 1.20976          | 1.20969          | 1.20976          | 0                | 0                | 0                | 2015             | \n",
       "| 01/01/2015 01:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | 1                | 2015             | \n",
       "| 01/01/2015 02:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | 2                | 2015             | \n",
       "| 01/01/2015 03:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | 3                | 2015             | \n",
       "| 01/01/2015 04:00 | 1.20976          | 1.20976          | 1.20976          | 1.20976          | 0                | 0                | 4                | 2015             | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Date             Open    High    Low     Close   Pips Pips_Shift Hours Year\n",
       "1 01/01/2015 00:00 1.20969 1.20976 1.20969 1.20976 0    0          0     2015\n",
       "2 01/01/2015 01:00 1.20976 1.20976 1.20976 1.20976 0    0          1     2015\n",
       "3 01/01/2015 02:00 1.20976 1.20976 1.20976 1.20976 0    0          2     2015\n",
       "4 01/01/2015 03:00 1.20976 1.20976 1.20976 1.20976 0    0          3     2015\n",
       "5 01/01/2015 04:00 1.20976 1.20976 1.20976 1.20976 0    0          4     2015"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Manipulating date to get hours and years\n",
    "names(EURUSD)[1] <- \"Date\"\n",
    "EURUSD$Hours <- substr(as.character(EURUSD$Date), 12, 13)\n",
    "EURUSD$Hours <- as.numeric(EURUSD$Hours)\n",
    "EURUSD$Year <- substr(as.character(EURUSD$Date), 7, 10)\n",
    "head(EURUSD,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Date</th><th scope=col>Open</th><th scope=col>High</th><th scope=col>Low</th><th scope=col>Close</th><th scope=col>Pips</th><th scope=col>Pips_Shift</th><th scope=col>Hours</th><th scope=col>Year</th><th scope=col>STD</th><th scope=col>RSI</th><th scope=col>CCI</th><th scope=col>CLV</th><th scope=col>VHF</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>29</th><td>02/01/2015 04:00</td><td>1.20670         </td><td>1.20687         </td><td>1.20476         </td><td>1.20588         </td><td>-8.2            </td><td>-7.1            </td><td>4               </td><td>2015            </td><td>0.001395559     </td><td> 7.094876       </td><td>-268.9198       </td><td> 0.06161137     </td><td>0.9181034       </td></tr>\n",
       "\t<tr><th scope=row>30</th><td>02/01/2015 05:00</td><td>1.20586         </td><td>1.20627         </td><td>1.20552         </td><td>1.20574         </td><td>-1.4            </td><td>-2.8            </td><td>5               </td><td>2015            </td><td>0.001617777     </td><td> 6.836045       </td><td>-196.5544       </td><td>-0.41333333     </td><td>0.9205021       </td></tr>\n",
       "\t<tr><th scope=row>31</th><td>02/01/2015 06:00</td><td>1.20574         </td><td>1.20597         </td><td>1.20492         </td><td>1.20529         </td><td>-4.5            </td><td> 3.2            </td><td>6               </td><td>2015            </td><td>0.001823387     </td><td> 6.069567       </td><td>-172.4746       </td><td>-0.29523810     </td><td>0.9273423       </td></tr>\n",
       "\t<tr><th scope=row>32</th><td>02/01/2015 07:00</td><td>1.20529         </td><td>1.20555         </td><td>1.20461         </td><td>1.20517         </td><td>-1.2            </td><td>-4.5            </td><td>7               </td><td>2015            </td><td>0.001972964     </td><td> 5.880227       </td><td>-148.8883       </td><td> 0.19148936     </td><td>0.9289720       </td></tr>\n",
       "\t<tr><th scope=row>33</th><td>02/01/2015 08:00</td><td>1.20517         </td><td>1.20577         </td><td>1.20485         </td><td>1.20546         </td><td> 2.9            </td><td> 3.6            </td><td>8               </td><td>2015            </td><td>0.002038003     </td><td>12.947731       </td><td>-116.2236       </td><td> 0.32608696     </td><td>0.8812057       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & Date & Open & High & Low & Close & Pips & Pips\\_Shift & Hours & Year & STD & RSI & CCI & CLV & VHF\\\\\n",
       "\\hline\n",
       "\t29 & 02/01/2015 04:00 & 1.20670          & 1.20687          & 1.20476          & 1.20588          & -8.2             & -7.1             & 4                & 2015             & 0.001395559      &  7.094876        & -268.9198        &  0.06161137      & 0.9181034       \\\\\n",
       "\t30 & 02/01/2015 05:00 & 1.20586          & 1.20627          & 1.20552          & 1.20574          & -1.4             & -2.8             & 5                & 2015             & 0.001617777      &  6.836045        & -196.5544        & -0.41333333      & 0.9205021       \\\\\n",
       "\t31 & 02/01/2015 06:00 & 1.20574          & 1.20597          & 1.20492          & 1.20529          & -4.5             &  3.2             & 6                & 2015             & 0.001823387      &  6.069567        & -172.4746        & -0.29523810      & 0.9273423       \\\\\n",
       "\t32 & 02/01/2015 07:00 & 1.20529          & 1.20555          & 1.20461          & 1.20517          & -1.2             & -4.5             & 7                & 2015             & 0.001972964      &  5.880227        & -148.8883        &  0.19148936      & 0.9289720       \\\\\n",
       "\t33 & 02/01/2015 08:00 & 1.20517          & 1.20577          & 1.20485          & 1.20546          &  2.9             &  3.6             & 8                & 2015             & 0.002038003      & 12.947731        & -116.2236        &  0.32608696      & 0.8812057       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Date | Open | High | Low | Close | Pips | Pips_Shift | Hours | Year | STD | RSI | CCI | CLV | VHF | \n",
       "|---|---|---|---|---|\n",
       "| 29 | 02/01/2015 04:00 | 1.20670          | 1.20687          | 1.20476          | 1.20588          | -8.2             | -7.1             | 4                | 2015             | 0.001395559      |  7.094876        | -268.9198        |  0.06161137      | 0.9181034        | \n",
       "| 30 | 02/01/2015 05:00 | 1.20586          | 1.20627          | 1.20552          | 1.20574          | -1.4             | -2.8             | 5                | 2015             | 0.001617777      |  6.836045        | -196.5544        | -0.41333333      | 0.9205021        | \n",
       "| 31 | 02/01/2015 06:00 | 1.20574          | 1.20597          | 1.20492          | 1.20529          | -4.5             |  3.2             | 6                | 2015             | 0.001823387      |  6.069567        | -172.4746        | -0.29523810      | 0.9273423        | \n",
       "| 32 | 02/01/2015 07:00 | 1.20529          | 1.20555          | 1.20461          | 1.20517          | -1.2             | -4.5             | 7                | 2015             | 0.001972964      |  5.880227        | -148.8883        |  0.19148936      | 0.9289720        | \n",
       "| 33 | 02/01/2015 08:00 | 1.20517          | 1.20577          | 1.20485          | 1.20546          |  2.9             |  3.6             | 8                | 2015             | 0.002038003      | 12.947731        | -116.2236        |  0.32608696      | 0.8812057        | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   Date             Open    High    Low     Close   Pips Pips_Shift Hours Year\n",
       "29 02/01/2015 04:00 1.20670 1.20687 1.20476 1.20588 -8.2 -7.1       4     2015\n",
       "30 02/01/2015 05:00 1.20586 1.20627 1.20552 1.20574 -1.4 -2.8       5     2015\n",
       "31 02/01/2015 06:00 1.20574 1.20597 1.20492 1.20529 -4.5  3.2       6     2015\n",
       "32 02/01/2015 07:00 1.20529 1.20555 1.20461 1.20517 -1.2 -4.5       7     2015\n",
       "33 02/01/2015 08:00 1.20517 1.20577 1.20485 1.20546  2.9  3.6       8     2015\n",
       "   STD         RSI       CCI       CLV         VHF      \n",
       "29 0.001395559  7.094876 -268.9198  0.06161137 0.9181034\n",
       "30 0.001617777  6.836045 -196.5544 -0.41333333 0.9205021\n",
       "31 0.001823387  6.069567 -172.4746 -0.29523810 0.9273423\n",
       "32 0.001972964  5.880227 -148.8883  0.19148936 0.9289720\n",
       "33 0.002038003 12.947731 -116.2236  0.32608696 0.8812057"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating more info\n",
    "EURUSD$STD <- runSD(EURUSD$Close,15)                                          # I will explain it later on\n",
    "EURUSD$RSI <- RSI(EURUSD$Close)                                              # I will explain it later on\n",
    "EURUSD$CCI <- CCI(EURUSD[,c(\"High\",\"Low\",\"Close\")])\n",
    "EURUSD$CLV <- CLV(EURUSD[,c(\"High\",\"Low\",\"Close\")])\n",
    "EURUSD$VHF <- VHF(EURUSD[,\"Close\"])\n",
    "\n",
    "EURUSD <- na.omit(EURUSD)\n",
    "head(EURUSD,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluating the variation in the number os pips per hour, from 2015 to 20/07/2018 and creating the plot\n",
    "AcumHour <- data.frame(Pips=0, Hours=seq(0,23),stringsAsFactors=FALSE)\n",
    "for(i in 0:23){\n",
    "  AcumHour$Pips[i+1] = 100*sum(EURUSD[EURUSD$Hours==(i),]$Pips)\n",
    "}\n",
    "\n",
    "hist <- ggplot(data=AcumHour, aes(x=Hours, y=Pips)) +\n",
    "          geom_bar(stat=\"identity\") + ggtitle(\"Outspoken Market - Test of Hours Strategy\") +\n",
    "          xlab(\"\") + ylab(\"2015 to 2018\")\n",
    "\n",
    "#2015\n",
    "EUR2015 <- EURUSD[EURUSD$Year==2015,]\n",
    "\n",
    "AcumHour <- data.frame(Pips=0,Hours=seq(0,23),stringsAsFactors=FALSE)\n",
    "\n",
    "for(i in 0:23){\n",
    "  AcumHour$Pips[i+1] = 100*sum(EUR2015[EUR2015$Hours==(i),]$Pips)\n",
    "}\n",
    "\n",
    "hist2015 <- ggplot(data=AcumHour, aes(x=Hours, y=Pips)) +\n",
    "              geom_bar(stat=\"identity\") +\n",
    "              xlab(\"\") + ylab(\"2015\")\n",
    "\n",
    "#2016\n",
    "EUR2016 <- EURUSD[EURUSD$Year==2016,]\n",
    "\n",
    "AcumHour <- data.frame(Pips=0,Hours=seq(0,23),stringsAsFactors=FALSE)\n",
    "\n",
    "for(i in 0:23){\n",
    "  AcumHour$Pips[i+1] = 100*sum(EUR2016[EUR2016$Hours==(i),]$Pips)\n",
    "}\n",
    "\n",
    "hist2016 <- ggplot(data=AcumHour, aes(x=Hours, y=Pips)) +\n",
    "            geom_bar(stat=\"identity\") +\n",
    "            xlab(\"\") + ylab(\"2016\") \n",
    "\n",
    "#2017\n",
    "EUR2017 <- EURUSD[EURUSD$Year==2017,]\n",
    "\n",
    "AcumHour <- data.frame(Pips=0,Hours=seq(0,23),stringsAsFactors=FALSE)\n",
    "\n",
    "for(i in 0:23){\n",
    "  AcumHour$Pips[i+1] = 100*sum(EUR2017[EUR2017$Hours==(i),]$Pips)\n",
    "}\n",
    "\n",
    "hist2017<-ggplot(data=AcumHour, aes(x=Hours, y=Pips)) +\n",
    "          geom_bar(stat=\"identity\") +\n",
    "          xlab(\"\") + ylab(\"2017\")\n",
    "\n",
    "#2018\n",
    "EUR2018 <- EURUSD[EURUSD$Year==2018,]\n",
    "\n",
    "AcumHour <- data.frame(Pips=0,Hours=seq(0,23),stringsAsFactors=FALSE)\n",
    "\n",
    "for(i in 0:23){\n",
    "  AcumHour$Pips[i+1] = 100*sum(EUR2018[EUR2018$Hours==(i),]$Pips)\n",
    "}\n",
    "\n",
    "hist2018 <- ggplot(data=AcumHour, aes(x=Hours, y=Pips)) +\n",
    "          geom_bar(stat=\"identity\") +\n",
    "          xlab(\"Hour GMT +1\") + ylab(\"2018\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JEAFEAkAAUQCUABRAJQAJEAFEAkAAUQ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\nL8Qbi7BhuBohiORfpi/SMas+aH7N9oj0RLyxCBuGqxGCSP5l+iL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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting the results\n",
    "grid.arrange(hist,hist2015, hist2016, hist2017, hist2018, nrow = 5, ncol=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#There is a relevant negative accumulation of pips in the hours 8 and 13 and a positive one in the hours 18 and 19\n",
    "#Lets create a simple and ilustrative strategy on it where:\n",
    "    # Go short at 8am and 13 (1pm) GMT + 1\n",
    "    # Go long at 18 (6pm) and 19 (7pm) GMT + 1\n",
    "\n",
    "#Creating the Hours Strategy\n",
    "pips_hours <- subset(EURUSD,\n",
    "                   (EURUSD$Hours == 23) |      #sell\n",
    "                     (EURUSD$Hours == 13)|    #sell\n",
    "                     (EURUSD$Hours == 18) |   #buy\n",
    "                     (EURUSD$Hours == 19)    #buy\n",
    "                     )\n",
    "\n",
    "#Adjusting the pips signal when the hours are to selling\n",
    "pips_hours$Pips <- ifelse(pips_hours$Hours == \"23\",-1*pips_hours$Pips ,pips_hours$Pips )\n",
    "pips_hours$Pips <- ifelse(pips_hours$Hours == \"13\",-1*pips_hours$Pips ,pips_hours$Pips )\n",
    "cumulative_buy_pips <- cumsum(pips_hours$Pips)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Sharpe Ratio\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "0.0686282294809926"
      ],
      "text/latex": [
       "0.0686282294809926"
      ],
      "text/markdown": [
       "0.0686282294809926"
      ],
      "text/plain": [
       "[1] 0.06862823"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"W/L Ration\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "0.516346933261281"
      ],
      "text/latex": [
       "0.516346933261281"
      ],
      "text/markdown": [
       "0.516346933261281"
      ],
      "text/plain": [
       "[1] 0.5163469"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Profit Factor\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "-1.10352973939365"
      ],
      "text/latex": [
       "-1.10352973939365"
      ],
      "text/markdown": [
       "-1.10352973939365"
      ],
      "text/plain": [
       "[1] -1.10353"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Max DD in Pips\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "688.9"
      ],
      "text/latex": [
       "688.9"
      ],
      "text/markdown": [
       "688.9"
      ],
      "text/plain": [
       "[1] 688.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Max DD Duration\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "290"
      ],
      "text/latex": [
       "290"
      ],
      "text/markdown": [
       "290"
      ],
      "text/plain": [
       "[1] 290"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Evaluating strategy KPIs\n",
    "\n",
    "#KPIs\n",
    "Bull_wins_PIPS <- sum(pips_hours$Pips > 0)\n",
    "Bull_losses_PIPS <- sum(pips_hours$Pips <= 0)\n",
    "Bull_totalTrades_PIPS <- Bull_wins_PIPS + Bull_losses_PIPS\n",
    "Bull_AVG_Win_PIPS <- mean(pips_hours[10][which(pips_hours$Pips > 0),])\n",
    "Bull_AVG_Loss_PIPS <- -mean(pips_hours[10][which(pips_hours$Pips < 0),])\n",
    "Bull_WL_Ratio_PIPS <- Bull_wins_PIPS/Bull_totalTrades_PIPS\n",
    "Bull_EV_PIPS <- Bull_AVG_Win_PIPS*Bull_WL_Ratio_PIPS - Bull_AVG_Loss_PIPS*(1-Bull_WL_Ratio_PIPS)\n",
    "Bull_PF_PIPS <- -sum(pips_hours[10][which(pips_hours$Pips > 0),])/sum(pips_hours[10][which(pips_hours$Pips < 0),])\n",
    "\n",
    "SHARPE <- mean(pips_hours$Pips, na.rm = T)/sd(pips_hours$Pips, na.rm = T)\n",
    "print (\"Sharpe Ratio\")\n",
    "SHARPE\n",
    "print (\"W/L Ration\")\n",
    "Bull_WL_Ratio_PIPS\n",
    "print (\"Profit Factor\")\n",
    "Bull_PF_PIPS\n",
    "\n",
    "#DrawDown PIPS\n",
    "Bull_maxDD_PIPS <- maxDrawDown(cumulative_buy_pips)$maxdrawdown[1]\n",
    "Bull_maxDD_duration_PIPS <-maxDrawDown(cumulative_buy_pips)$to - maxDrawDown(cumulative_buy_pips)$from[1]\n",
    "print (\"Max DD in Pips\")\n",
    "Bull_maxDD_PIPS\n",
    "print (\"Max DD Duration\")\n",
    "Bull_maxDD_duration_PIPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZeIJHJ3OEQ6wyCSN3jd1dBb6cZc8bphh0h+4YjkDW6qHuIRkc5vxePpbNUyiOQT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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting Strategy Results - Only to illustrate the results. The strategy lacks of consistency verifications\n",
    "df <- data.frame(seq.int(dim(pips_hours)[1]),cumulative_buy_pips)\n",
    "g <- ggplot(df, aes(seq.int(dim(pips_hours)[1])))\n",
    "g <- g + geom_line(aes(y=cumulative_buy_pips,linetype=\"Pips\"), colour=\"blue\")\n",
    "g <- g + ggtitle(\"Outspoken Market - Test of Hours Strategy\")+labs(x=\"Test timeframe: 2015 - 2018\",\n",
    "                                                                                      y=\"Total Pips\") \n",
    "g <- g + annotate(\"text\", x = dim(pips_hours)[1]-500, y = tail(cumulative_buy_pips,1)-45, label = paste(round(tail(cumulative_buy_pips,1),2)))\n",
    "g <- g + theme(legend.position=c(.24, .95), legend.justification = c(1, 1),\n",
    "               legend.background = element_rect(color = \"black\",fill = \"grey90\", size = 1, linetype = \"solid\")) + scale_shape_discrete(name=\"\")\n",
    "g <- g + labs(linetype=\"Hours Strategy\")\n",
    "g\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Don't forget: Strategies may look good until you add operational costs\n",
    "\n",
    "#Simulating 0.5 and 1 pips of operational costs\n",
    "\n",
    "costs1 <- 0.5\n",
    "costs2 <- 1\n",
    "pips_hours$Pips2 <- pips_hours$Pips-costs2\n",
    "pips_hours$Pips <- pips_hours$Pips-costs1\n",
    "cumulative_buy_pips <- cumsum(pips_hours$Pips)\n",
    "cumulative_buy_pips2 <- cumsum(pips_hours$Pips2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V5OZTHYACT5nTdIKTmiAJL68xpB0j3JDiAdGiQLGQDJy7wW5jKutgmJABJASqgj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\n4Lc3jwIpl7mTKJhTAmk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804CXOAdBpz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\nCYIS6P8D4eV1eX1MvcIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting Strategy Results not with operational costs - All is different now\n",
    "df <- data.frame(seq.int(dim(pips_hours)[1]),cumulative_buy_pips)\n",
    "g <- ggplot(df, aes(seq.int(dim(pips_hours)[1])))\n",
    "g <- g + geom_line(aes(y=cumulative_buy_pips,linetype=\"0.5 Pips\"), colour=\"blue\")\n",
    "g <- g + geom_line(aes(y=cumulative_buy_pips2,linetype=\"1.0 Pips\"), colour=\"red\")\n",
    "g <- g + ggtitle(\"Outspoken Market - Test of Hours Strategy\")+labs(x=\"Test timeframe: 2015 - 2018\",\n",
    "                                                                                      y=\"Total Pips\") \n",
    "g <- g + annotate(\"text\", x = dim(pips_hours)[1]-500, y = tail(cumulative_buy_pips,1)-45, label = paste(round(tail(cumulative_buy_pips,1),2)))\n",
    "g <- g + annotate(\"text\", x = dim(pips_hours)[1]-500, y = tail(cumulative_buy_pips2,1)-45, label = paste(round(tail(cumulative_buy_pips2,1),2)))\n",
    "g <- g + theme(legend.position=c(.30, .95), legend.justification = c(1, 1),\n",
    "               legend.background = element_rect(color = \"black\",fill = \"grey90\", size = 1, linetype = \"solid\")) + scale_shape_discrete(name=\"\")\n",
    "g <- g + labs(linetype=\"With operational Costs\")\n",
    "g\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Pips_Shift ~ STD + VHF + CCI + VHF * Hours + STD * \n",
       "    RSI + Hours * STD + RSI * CCI * Hours, data = pips_hours_traning)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-317.75   -9.70   -0.03    9.80  296.28 \n",
       "\n",
       "Coefficients:\n",
       "                Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)   -2.261e+00  2.847e+00  -0.794   0.4272    \n",
       "STD            1.949e+03  4.555e+02   4.279 1.89e-05 ***\n",
       "VHF           -7.776e-01  3.488e+00  -0.223   0.8236    \n",
       "CCI            5.121e-03  1.666e-02   0.307   0.7585    \n",
       "Hours         -1.847e-02  2.043e-01  -0.090   0.9280    \n",
       "RSI            4.944e-02  5.316e-02   0.930   0.3524    \n",
       "VHF:Hours     -1.567e-01  2.761e-01  -0.568   0.5702    \n",
       "STD:RSI       -3.565e+01  7.260e+00  -4.910 9.20e-07 ***\n",
       "STD:Hours     -2.109e+01  2.025e+01  -1.042   0.2975    \n",
       "CCI:RSI       -1.388e-04  3.049e-04  -0.455   0.6490    \n",
       "Hours:RSI      9.975e-04  3.790e-03   0.263   0.7924    \n",
       "CCI:Hours     -1.779e-03  1.177e-03  -1.511   0.1307    \n",
       "CCI:Hours:RSI  4.082e-05  2.131e-05   1.915   0.0555 .  \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 23.86 on 18727 degrees of freedom\n",
       "Multiple R-squared:  0.002452,\tAdjusted R-squared:  0.001813 \n",
       "F-statistic: 3.837 on 12 and 18727 DF,  p-value: 6.95e-06\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How could we improve the strategy and make it profitable even with operational costs?\n",
    "#Remember the elements Pip_Shift and STD that I mentioned in the begining?\n",
    "#Let's use them as target and predictiors for a simple linear regression\n",
    "set.seed(2016)\n",
    "#Traning set - 20015 to 2017 data\n",
    "pips_hours_traning <- subset(EURUSD,EURUSD$Year < 2018) \n",
    "#Test set - 2018\n",
    "pips_hours_test <- subset(EURUSD,EURUSD$Year == 2018) \n",
    "\n",
    "linear1 = lm(Pips_Shift ~ STD + VHF + CCI + VHF*Hours + STD*RSI + Hours*STD + RSI*CCI*Hours , \n",
    "             data = pips_hours_traning)\n",
    "summary(linear1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#You can see that the linear regression is not good. However, could it still be profitable?\n",
    "#Let's apply it to the data and find out\n",
    "#In the end only the RSI variable has a decent significance level. In theory you can remove the other variables\n",
    "#I will keep all for the simplicity of the example. Please try it removing and adding more variables :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>qlinear</th><th scope=col>Pips_Shift</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>[-21.1,-1.26]     </td><td>-1488.6           </td></tr>\n",
       "\t<tr><td>(-1.26,-0.885]    </td><td>  850.1           </td></tr>\n",
       "\t<tr><td>(-0.885,-0.691]   </td><td>-1558.4           </td></tr>\n",
       "\t<tr><td>(-0.691,-0.558]   </td><td> -258.9           </td></tr>\n",
       "\t<tr><td>(-0.558,-0.448]   </td><td>-1320.7           </td></tr>\n",
       "\t<tr><td>(-0.448,-0.351]   </td><td> -161.8           </td></tr>\n",
       "\t<tr><td>(-0.351,-0.268]   </td><td>-1692.7           </td></tr>\n",
       "\t<tr><td>(-0.268,-0.194]   </td><td> -815.2           </td></tr>\n",
       "\t<tr><td>(-0.194,-0.126]   </td><td>   34.6           </td></tr>\n",
       "\t<tr><td>(-0.126,-0.0624]  </td><td> -191.2           </td></tr>\n",
       "\t<tr><td>(-0.0624,-0.00448]</td><td> -392.1           </td></tr>\n",
       "\t<tr><td>(-0.00448,0.0499] </td><td>  255.7           </td></tr>\n",
       "\t<tr><td>(0.0499,0.106]    </td><td>  630.0           </td></tr>\n",
       "\t<tr><td>(0.106,0.176]     </td><td>  -81.0           </td></tr>\n",
       "\t<tr><td>(0.176,0.272]     </td><td>  172.1           </td></tr>\n",
       "\t<tr><td>(0.272,0.413]     </td><td>  310.9           </td></tr>\n",
       "\t<tr><td>(0.413,0.627]     </td><td>  -82.8           </td></tr>\n",
       "\t<tr><td>(0.627,0.974]     </td><td> 1484.3           </td></tr>\n",
       "\t<tr><td>(0.974,1.68]      </td><td> 1908.1           </td></tr>\n",
       "\t<tr><td>(1.68,17.9]       </td><td> 2224.0           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " qlinear & Pips\\_Shift\\\\\n",
       "\\hline\n",
       "\t {[}-21.1,-1.26{]}      & -1488.6               \\\\\n",
       "\t (-1.26,-0.885{]}     &   850.1             \\\\\n",
       "\t (-0.885,-0.691{]}    & -1558.4             \\\\\n",
       "\t (-0.691,-0.558{]}    &  -258.9             \\\\\n",
       "\t (-0.558,-0.448{]}    & -1320.7             \\\\\n",
       "\t (-0.448,-0.351{]}    &  -161.8             \\\\\n",
       "\t (-0.351,-0.268{]}    & -1692.7             \\\\\n",
       "\t (-0.268,-0.194{]}    &  -815.2             \\\\\n",
       "\t (-0.194,-0.126{]}    &    34.6             \\\\\n",
       "\t (-0.126,-0.0624{]}   &  -191.2             \\\\\n",
       "\t (-0.0624,-0.00448{]} &  -392.1             \\\\\n",
       "\t (-0.00448,0.0499{]}  &   255.7             \\\\\n",
       "\t (0.0499,0.106{]}     &   630.0             \\\\\n",
       "\t (0.106,0.176{]}      &   -81.0             \\\\\n",
       "\t (0.176,0.272{]}      &   172.1             \\\\\n",
       "\t (0.272,0.413{]}      &   310.9             \\\\\n",
       "\t (0.413,0.627{]}      &   -82.8             \\\\\n",
       "\t (0.627,0.974{]}      &  1484.3             \\\\\n",
       "\t (0.974,1.68{]}       &  1908.1             \\\\\n",
       "\t (1.68,17.9{]}        &  2224.0             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "qlinear | Pips_Shift | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| [-21.1,-1.26]      | -1488.6            | \n",
       "| (-1.26,-0.885]     |   850.1            | \n",
       "| (-0.885,-0.691]    | -1558.4            | \n",
       "| (-0.691,-0.558]    |  -258.9            | \n",
       "| (-0.558,-0.448]    | -1320.7            | \n",
       "| (-0.448,-0.351]    |  -161.8            | \n",
       "| (-0.351,-0.268]    | -1692.7            | \n",
       "| (-0.268,-0.194]    |  -815.2            | \n",
       "| (-0.194,-0.126]    |    34.6            | \n",
       "| (-0.126,-0.0624]   |  -191.2            | \n",
       "| (-0.0624,-0.00448] |  -392.1            | \n",
       "| (-0.00448,0.0499]  |   255.7            | \n",
       "| (0.0499,0.106]     |   630.0            | \n",
       "| (0.106,0.176]      |   -81.0            | \n",
       "| (0.176,0.272]      |   172.1            | \n",
       "| (0.272,0.413]      |   310.9            | \n",
       "| (0.413,0.627]      |   -82.8            | \n",
       "| (0.627,0.974]      |  1484.3            | \n",
       "| (0.974,1.68]       |  1908.1            | \n",
       "| (1.68,17.9]        |  2224.0            | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   qlinear            Pips_Shift\n",
       "1  [-21.1,-1.26]      -1488.6   \n",
       "2  (-1.26,-0.885]       850.1   \n",
       "3  (-0.885,-0.691]    -1558.4   \n",
       "4  (-0.691,-0.558]     -258.9   \n",
       "5  (-0.558,-0.448]    -1320.7   \n",
       "6  (-0.448,-0.351]     -161.8   \n",
       "7  (-0.351,-0.268]    -1692.7   \n",
       "8  (-0.268,-0.194]     -815.2   \n",
       "9  (-0.194,-0.126]       34.6   \n",
       "10 (-0.126,-0.0624]    -191.2   \n",
       "11 (-0.0624,-0.00448]  -392.1   \n",
       "12 (-0.00448,0.0499]    255.7   \n",
       "13 (0.0499,0.106]       630.0   \n",
       "14 (0.106,0.176]        -81.0   \n",
       "15 (0.176,0.272]        172.1   \n",
       "16 (0.272,0.413]        310.9   \n",
       "17 (0.413,0.627]        -82.8   \n",
       "18 (0.627,0.974]       1484.3   \n",
       "19 (0.974,1.68]        1908.1   \n",
       "20 (1.68,17.9]         2224.0   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying the linear regression to the data \n",
    "\n",
    "pips_hours_traning$linear1 = predict(linear1,pips_hours_traning)\n",
    "pips_hours_test$linear1 = predict(linear1,pips_hours_test)\n",
    "\n",
    "#Now let's not only run the regression, but discover the most profitable quantiles\n",
    "q = quantile(pips_hours_traning$linear1, seq(0, 1, by = 0.05))\n",
    "pips_hours_traning$qlinear <- cut(pips_hours_traning$linear1, q, include.lowest=TRUE)\n",
    "\n",
    "\n",
    "Pips_qlinear <- aggregate(Pips_Shift ~ qlinear, data = pips_hours_traning, FUN = sum)\n",
    "Pips_qlinear\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Date'</li>\n",
       "\t<li>'Open'</li>\n",
       "\t<li>'High'</li>\n",
       "\t<li>'Low'</li>\n",
       "\t<li>'Close'</li>\n",
       "\t<li>'Pips'</li>\n",
       "\t<li>'Pips_Shift'</li>\n",
       "\t<li>'Hours'</li>\n",
       "\t<li>'Year'</li>\n",
       "\t<li>'STD'</li>\n",
       "\t<li>'RSI'</li>\n",
       "\t<li>'CCI'</li>\n",
       "\t<li>'CLV'</li>\n",
       "\t<li>'VHF'</li>\n",
       "\t<li>'linear1'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Date'\n",
       "\\item 'Open'\n",
       "\\item 'High'\n",
       "\\item 'Low'\n",
       "\\item 'Close'\n",
       "\\item 'Pips'\n",
       "\\item 'Pips\\_Shift'\n",
       "\\item 'Hours'\n",
       "\\item 'Year'\n",
       "\\item 'STD'\n",
       "\\item 'RSI'\n",
       "\\item 'CCI'\n",
       "\\item 'CLV'\n",
       "\\item 'VHF'\n",
       "\\item 'linear1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Date'\n",
       "2. 'Open'\n",
       "3. 'High'\n",
       "4. 'Low'\n",
       "5. 'Close'\n",
       "6. 'Pips'\n",
       "7. 'Pips_Shift'\n",
       "8. 'Hours'\n",
       "9. 'Year'\n",
       "10. 'STD'\n",
       "11. 'RSI'\n",
       "12. 'CCI'\n",
       "13. 'CLV'\n",
       "14. 'VHF'\n",
       "15. 'linear1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Date\"       \"Open\"       \"High\"       \"Low\"        \"Close\"     \n",
       " [6] \"Pips\"       \"Pips_Shift\" \"Hours\"      \"Year\"       \"STD\"       \n",
       "[11] \"RSI\"        \"CCI\"        \"CLV\"        \"VHF\"        \"linear1\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating the Linear Regression Strategy\n",
    "pips_linear <- subset(pips_hours_test,\n",
    "                   (pips_hours_test$linear1 <=  0) |      #sell\n",
    "                     (pips_hours_test$linear1 > 0)    #buy\n",
    "                     )\n",
    "\n",
    "#Adjusting the pips signal when the hours are to selling\n",
    "pips_linear$Pips_Shift <- ifelse(pips_linear$linear1 <=  0,-1*pips_linear$Pips_Shift ,pips_linear$Pips_Shift )\n",
    "#Setting a Stop\n",
    "pips_linear$Pips_Shift <- ifelse(pips_linear$Pips_Shift <=  -40,-41 ,pips_linear$Pips_Shift )\n",
    "cumulative_buy_pips <- cumsum(pips_linear$Pips_Shift)\n",
    "names(pips_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Sharpe Ratio\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "0.0657817229553716"
      ],
      "text/latex": [
       "0.0657817229553716"
      ],
      "text/markdown": [
       "0.0657817229553716"
      ],
      "text/plain": [
       "[1] 0.06578172"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"W/L Ration\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "0.512583164593578"
      ],
      "text/latex": [
       "0.512583164593578"
      ],
      "text/markdown": [
       "0.512583164593578"
      ],
      "text/plain": [
       "[1] 0.5125832"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Profit Factor\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "-0.310786819882015"
      ],
      "text/latex": [
       "-0.310786819882015"
      ],
      "text/markdown": [
       "-0.310786819882015"
      ],
      "text/plain": [
       "[1] -0.3107868"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Max DD in Pips\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "907.9"
      ],
      "text/latex": [
       "907.9"
      ],
      "text/markdown": [
       "907.9"
      ],
      "text/plain": [
       "[1] 907.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Max DD Duration\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>264</li>\n",
       "\t<li>265</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 264\n",
       "\\item 265\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 264\n",
       "2. 265\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 264 265"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Evaluating the Linear Regressoin Strategy KPIs\n",
    "\n",
    "#KPIs\n",
    "Bull_wins_PIPS <- sum(pips_linear$Pips_Shift > 0)\n",
    "Bull_losses_PIPS <- sum(pips_linear$Pips_Shift <= 0)\n",
    "Bull_totalTrades_PIPS <- Bull_wins_PIPS + Bull_losses_PIPS\n",
    "Bull_AVG_Win_PIPS <- mean(pips_linear[7][which(pips_linear$Pips_Shift > 0),])\n",
    "Bull_AVG_Loss_PIPS <- -mean(pips_linear[7][which(pips_linear$Pips_Shift < 0),])\n",
    "Bull_WL_Ratio_PIPS <- Bull_wins_PIPS/Bull_totalTrades_PIPS\n",
    "Bull_EV_PIPS <- Bull_AVG_Win_PIPS*Bull_WL_Ratio_PIPS - Bull_AVG_Loss_PIPS*(1-Bull_WL_Ratio_PIPS)\n",
    "Bull_PF_PIPS <- -sum(pips_linear[7][which(pips_linear$Pips_Shift > 0),])/sum(pips_linear[11][which(pips_linear$Pips_Shift < 0),])\n",
    "\n",
    "SHARPE <- mean(pips_linear$Pips_Shift, na.rm = T)/sd(pips_linear$Pips_Shift, na.rm = T)\n",
    "print (\"Sharpe Ratio\")\n",
    "SHARPE\n",
    "print (\"W/L Ration\")\n",
    "Bull_WL_Ratio_PIPS\n",
    "print (\"Profit Factor\")\n",
    "Bull_PF_PIPS\n",
    "\n",
    "#DrawDown PIPS\n",
    "Bull_maxDD_PIPS <- maxDrawDown(cumulative_buy_pips)$maxdrawdown[1]\n",
    "Bull_maxDD_duration_PIPS <-maxDrawDown(cumulative_buy_pips)$to - maxDrawDown(cumulative_buy_pips)$from[1]\n",
    "print (\"Max DD in Pips\")\n",
    "Bull_maxDD_PIPS\n",
    "print (\"Max DD Duration\")\n",
    "Bull_maxDD_duration_PIPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xa9ya6o4p8KvRITUVcXa2VViy5XjkhW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\nqwcIW0a0IjUdd3t67xApIoYMRFoiKWciDWdGpGAMGYikROoadWr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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting Linear Regression Strategy Results - Only to illustrate the results. The strategy lacks of consistency verifications\n",
    "df <- data.frame(seq.int(dim(pips_linear)[1]),cumulative_buy_pips)\n",
    "g <- ggplot(df, aes(seq.int(dim(pips_linear)[1])))\n",
    "g <- g + geom_line(aes(y=cumulative_buy_pips,linetype=\"Pips\"), colour=\"blue\")\n",
    "g <- g + ggtitle(\"Outspoken Market - Linear Regression in Hours Strategy\")+labs(x=\"Test timeframe: 2018\",\n",
    "                                                                                      y=\"Total Pips\") \n",
    "g <- g + annotate(\"text\", x = dim(pips_linear)[1]-10, y = tail(cumulative_buy_pips,1)-45, label = paste(round(tail(cumulative_buy_pips,1),2)))\n",
    "g <- g + theme(legend.position=c(.24, .95), legend.justification = c(1, 1),\n",
    "               legend.background = element_rect(color = \"black\",fill = \"grey90\", size = 1, linetype = \"solid\")) + scale_shape_discrete(name=\"\")\n",
    "g <- g + labs(linetype=\"Hours Strategy\")\n",
    "g\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Don't forget: Strategies may look good untill you add operational costs\n",
    "\n",
    "#Simulating 0.5 and 1 pips of operational costs\n",
    "\n",
    "costs1 <- 0.5\n",
    "costs2 <- 1\n",
    "pips_linear$Pips_Shift2 <- pips_linear$Pips_Shift-costs2\n",
    "pips_linear$Pips_Shift <- pips_linear$Pips_Shift-costs1\n",
    "cumulative_buy_pips <- cumsum(pips_linear$Pips_Shift)\n",
    "cumulative_buy_pips2 <- cumsum(pips_linear$Pips_Shift2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SC1vEWaG1Z5h4QCSOm0K0gkACRLyQVIBVkApHgW7F2M0j1ChDGmWXsFJNEhhuwg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\nFzfyDaeOpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting Strategy Results not with operational costs - All is different now\n",
    "df <- data.frame(seq.int(dim(pips_linear)[1]),cumulative_buy_pips)\n",
    "g <- ggplot(df, aes(seq.int(dim(pips_linear)[1])))\n",
    "g <- g + geom_line(aes(y=cumulative_buy_pips,linetype=\"0.5 Pips\"), colour=\"blue\")\n",
    "g <- g + geom_line(aes(y=cumulative_buy_pips2,linetype=\"1.0 Pips\"), colour=\"red\")\n",
    "g <- g + ggtitle(\"Outspoken Market - Linear Regression in Hours Strategy\")+labs(x=\"Test timeframe: 2018\",\n",
    "                                                                                      y=\"Total Pips\") \n",
    "g <- g + annotate(\"text\", x = dim(pips_linear)[1]-10, y = tail(cumulative_buy_pips,1)-45, label = paste(round(tail(cumulative_buy_pips,1),2)))\n",
    "g <- g + annotate(\"text\", x = dim(pips_linear)[1]-10, y = tail(cumulative_buy_pips2,1)-45, label = paste(round(tail(cumulative_buy_pips2,1),2)))\n",
    "g <- g + theme(legend.position=c(.30, .95), legend.justification = c(1, 1),\n",
    "               legend.background = element_rect(color = \"black\",fill = \"grey90\", size = 1, linetype = \"solid\")) + scale_shape_discrete(name=\"\")\n",
    "g <- g + labs(linetype=\"With operational Costs\")\n",
    "g\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Leandro Guerra - www.outspokenmarket.com"
   ]
  }
 ],
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