6-backtest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9aee241",
   "metadata": {},
   "source": [
    "At this stage, we will do a detailed backtest for individual pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "a9ed8f63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:10:31.550492Z",
     "start_time": "2022-11-01T17:10:31.512477Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18f5bb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-24T23:11:05.837372Z",
     "start_time": "2022-10-24T23:11:05.826330Z"
    }
   },
   "source": [
    "### GET SELECTED TICKERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "5642c551",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:10:50.892441Z",
     "start_time": "2022-11-01T17:10:50.870437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('AEP', 'VST'): 0.25,\n",
       " ('OGE', 'XEL'): 1.79,\n",
       " ('CMS', 'DUK'): 1.6,\n",
       " ('ED', 'VST'): 0.25,\n",
       " ('CNP', 'VST'): 0.78}"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('pairs_final.pkl', 'rb') as f:\n",
    "    pairs_dic = pickle.load(f)\n",
    "pairs_dic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c820b9e2",
   "metadata": {},
   "source": [
    "Print pair equations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "74c98de8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:10:55.233540Z",
     "start_time": "2022-11-01T17:10:55.220538Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EQUATION: VST=0.25*AEP\n",
      "EQUATION: XEL=1.79*OGE\n",
      "EQUATION: DUK=1.6*CMS\n",
      "EQUATION: VST=0.25*ED\n",
      "EQUATION: VST=0.78*CNP\n"
     ]
    }
   ],
   "source": [
    "for key in pairs_dic:\n",
    "    ticker1 = key[0]\n",
    "    ticker2 = key[1]\n",
    "    hedge = pairs_dic[key]\n",
    "    print(f\"EQUATION: {ticker2}={hedge}*{ticker1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9dbd159",
   "metadata": {},
   "source": [
    "Select tickers and resolution to backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "e5e878fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:23.567855Z",
     "start_time": "2022-11-01T17:24:23.560855Z"
    }
   },
   "outputs": [],
   "source": [
    "resolution = '1D'\n",
    "ticker1 =\"CNP\"\n",
    "ticker2 =\"VST\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "728ce81b",
   "metadata": {},
   "source": [
    "#### OPTION 1: GET ALL TICKERS AND SLICE FOR THE SELECTED TICKER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "e0bfc5fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:25.755007Z",
     "start_time": "2022-11-01T17:24:25.681004Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-01 00:00:00-04:00</th>\n",
       "      <td>-0.7736</td>\n",
       "      <td>-0.4430</td>\n",
       "      <td>-0.7712</td>\n",
       "      <td>-0.4962</td>\n",
       "      <td>-0.60840</td>\n",
       "      <td>-152978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02 00:00:00-04:00</th>\n",
       "      <td>-0.5586</td>\n",
       "      <td>-0.5486</td>\n",
       "      <td>-0.5224</td>\n",
       "      <td>-0.5582</td>\n",
       "      <td>-0.66808</td>\n",
       "      <td>-377990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03 00:00:00-04:00</th>\n",
       "      <td>-0.6490</td>\n",
       "      <td>-0.7026</td>\n",
       "      <td>-0.7278</td>\n",
       "      <td>-0.8262</td>\n",
       "      <td>-0.92865</td>\n",
       "      <td>-153602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04 00:00:00-04:00</th>\n",
       "      <td>-1.0932</td>\n",
       "      <td>-1.2772</td>\n",
       "      <td>-1.5918</td>\n",
       "      <td>-1.4600</td>\n",
       "      <td>-1.54473</td>\n",
       "      <td>3468956.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05 00:00:00-04:00</th>\n",
       "      <td>-0.8338</td>\n",
       "      <td>-0.1842</td>\n",
       "      <td>-0.6654</td>\n",
       "      <td>-0.3026</td>\n",
       "      <td>-0.42235</td>\n",
       "      <td>6656480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-08 00:00:00-05:00</th>\n",
       "      <td>-0.1724</td>\n",
       "      <td>-0.2416</td>\n",
       "      <td>-0.8184</td>\n",
       "      <td>-0.9456</td>\n",
       "      <td>-1.04563</td>\n",
       "      <td>3550338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-09 00:00:00-05:00</th>\n",
       "      <td>-0.7556</td>\n",
       "      <td>-0.8258</td>\n",
       "      <td>-1.2968</td>\n",
       "      <td>-1.3456</td>\n",
       "      <td>-1.43381</td>\n",
       "      <td>1349456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-10 00:00:00-05:00</th>\n",
       "      <td>-1.4678</td>\n",
       "      <td>-1.1896</td>\n",
       "      <td>-1.4310</td>\n",
       "      <td>-1.2924</td>\n",
       "      <td>-1.38247</td>\n",
       "      <td>-399064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-11 00:00:00-05:00</th>\n",
       "      <td>-1.0388</td>\n",
       "      <td>-1.1840</td>\n",
       "      <td>-1.2860</td>\n",
       "      <td>-1.2806</td>\n",
       "      <td>-1.37194</td>\n",
       "      <td>-1522266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-12 00:00:00-05:00</th>\n",
       "      <td>-1.1838</td>\n",
       "      <td>-1.1062</td>\n",
       "      <td>-0.8744</td>\n",
       "      <td>-1.0980</td>\n",
       "      <td>-1.19391</td>\n",
       "      <td>-292394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15 00:00:00-05:00</th>\n",
       "      <td>-1.1384</td>\n",
       "      <td>-0.5488</td>\n",
       "      <td>-1.1682</td>\n",
       "      <td>-0.5610</td>\n",
       "      <td>-0.67447</td>\n",
       "      <td>542532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16 00:00:00-05:00</th>\n",
       "      <td>-0.4944</td>\n",
       "      <td>-0.4594</td>\n",
       "      <td>-0.4480</td>\n",
       "      <td>-0.4484</td>\n",
       "      <td>-0.56438</td>\n",
       "      <td>3815360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17 00:00:00-05:00</th>\n",
       "      <td>-0.3736</td>\n",
       "      <td>-0.3888</td>\n",
       "      <td>-0.5632</td>\n",
       "      <td>-0.6260</td>\n",
       "      <td>-0.86628</td>\n",
       "      <td>41126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-18 00:00:00-05:00</th>\n",
       "      <td>-0.6982</td>\n",
       "      <td>-0.6796</td>\n",
       "      <td>-0.5908</td>\n",
       "      <td>-0.4576</td>\n",
       "      <td>-0.70014</td>\n",
       "      <td>604974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-19 00:00:00-05:00</th>\n",
       "      <td>-0.4418</td>\n",
       "      <td>-0.5524</td>\n",
       "      <td>-0.5318</td>\n",
       "      <td>-0.6590</td>\n",
       "      <td>-0.89685</td>\n",
       "      <td>1320792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 00:00:00-05:00</th>\n",
       "      <td>-0.6512</td>\n",
       "      <td>-0.1260</td>\n",
       "      <td>-0.5450</td>\n",
       "      <td>-0.4836</td>\n",
       "      <td>-0.72731</td>\n",
       "      <td>1182750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-23 00:00:00-05:00</th>\n",
       "      <td>-0.6184</td>\n",
       "      <td>0.0192</td>\n",
       "      <td>-0.4990</td>\n",
       "      <td>-0.1358</td>\n",
       "      <td>-0.38969</td>\n",
       "      <td>5029038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-24 00:00:00-05:00</th>\n",
       "      <td>-0.2536</td>\n",
       "      <td>-0.1124</td>\n",
       "      <td>-0.1834</td>\n",
       "      <td>-0.2606</td>\n",
       "      <td>-0.51236</td>\n",
       "      <td>966444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-26 00:00:00-05:00</th>\n",
       "      <td>-0.5098</td>\n",
       "      <td>-0.2810</td>\n",
       "      <td>-0.4146</td>\n",
       "      <td>-0.2994</td>\n",
       "      <td>-0.54478</td>\n",
       "      <td>1393766.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-29 00:00:00-05:00</th>\n",
       "      <td>-0.2388</td>\n",
       "      <td>-0.3948</td>\n",
       "      <td>-0.8016</td>\n",
       "      <td>-0.6502</td>\n",
       "      <td>-0.88870</td>\n",
       "      <td>1668554.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Open    High     Low   Close  Adj Close  \\\n",
       "time                                                                   \n",
       "2021-11-01 00:00:00-04:00 -0.7736 -0.4430 -0.7712 -0.4962   -0.60840   \n",
       "2021-11-02 00:00:00-04:00 -0.5586 -0.5486 -0.5224 -0.5582   -0.66808   \n",
       "2021-11-03 00:00:00-04:00 -0.6490 -0.7026 -0.7278 -0.8262   -0.92865   \n",
       "2021-11-04 00:00:00-04:00 -1.0932 -1.2772 -1.5918 -1.4600   -1.54473   \n",
       "2021-11-05 00:00:00-04:00 -0.8338 -0.1842 -0.6654 -0.3026   -0.42235   \n",
       "2021-11-08 00:00:00-05:00 -0.1724 -0.2416 -0.8184 -0.9456   -1.04563   \n",
       "2021-11-09 00:00:00-05:00 -0.7556 -0.8258 -1.2968 -1.3456   -1.43381   \n",
       "2021-11-10 00:00:00-05:00 -1.4678 -1.1896 -1.4310 -1.2924   -1.38247   \n",
       "2021-11-11 00:00:00-05:00 -1.0388 -1.1840 -1.2860 -1.2806   -1.37194   \n",
       "2021-11-12 00:00:00-05:00 -1.1838 -1.1062 -0.8744 -1.0980   -1.19391   \n",
       "2021-11-15 00:00:00-05:00 -1.1384 -0.5488 -1.1682 -0.5610   -0.67447   \n",
       "2021-11-16 00:00:00-05:00 -0.4944 -0.4594 -0.4480 -0.4484   -0.56438   \n",
       "2021-11-17 00:00:00-05:00 -0.3736 -0.3888 -0.5632 -0.6260   -0.86628   \n",
       "2021-11-18 00:00:00-05:00 -0.6982 -0.6796 -0.5908 -0.4576   -0.70014   \n",
       "2021-11-19 00:00:00-05:00 -0.4418 -0.5524 -0.5318 -0.6590   -0.89685   \n",
       "2021-11-22 00:00:00-05:00 -0.6512 -0.1260 -0.5450 -0.4836   -0.72731   \n",
       "2021-11-23 00:00:00-05:00 -0.6184  0.0192 -0.4990 -0.1358   -0.38969   \n",
       "2021-11-24 00:00:00-05:00 -0.2536 -0.1124 -0.1834 -0.2606   -0.51236   \n",
       "2021-11-26 00:00:00-05:00 -0.5098 -0.2810 -0.4146 -0.2994   -0.54478   \n",
       "2021-11-29 00:00:00-05:00 -0.2388 -0.3948 -0.8016 -0.6502   -0.88870   \n",
       "\n",
       "                              Volume  \n",
       "time                                  \n",
       "2021-11-01 00:00:00-04:00  -152978.0  \n",
       "2021-11-02 00:00:00-04:00  -377990.0  \n",
       "2021-11-03 00:00:00-04:00  -153602.0  \n",
       "2021-11-04 00:00:00-04:00  3468956.0  \n",
       "2021-11-05 00:00:00-04:00  6656480.0  \n",
       "2021-11-08 00:00:00-05:00  3550338.0  \n",
       "2021-11-09 00:00:00-05:00  1349456.0  \n",
       "2021-11-10 00:00:00-05:00  -399064.0  \n",
       "2021-11-11 00:00:00-05:00 -1522266.0  \n",
       "2021-11-12 00:00:00-05:00  -292394.0  \n",
       "2021-11-15 00:00:00-05:00   542532.0  \n",
       "2021-11-16 00:00:00-05:00  3815360.0  \n",
       "2021-11-17 00:00:00-05:00    41126.0  \n",
       "2021-11-18 00:00:00-05:00   604974.0  \n",
       "2021-11-19 00:00:00-05:00  1320792.0  \n",
       "2021-11-22 00:00:00-05:00  1182750.0  \n",
       "2021-11-23 00:00:00-05:00  5029038.0  \n",
       "2021-11-24 00:00:00-05:00   966444.0  \n",
       "2021-11-26 00:00:00-05:00  1393766.0  \n",
       "2021-11-29 00:00:00-05:00  1668554.0  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltickersfile = \"data/tickers_\"+ resolution +\".csv\"\n",
    "df = pd.read_csv(alltickersfile)\n",
    "df_sorted = df.set_index([\"ticker\", \"time\"]).sort_index() # set indexes\n",
    "df1_sorted = df_sorted.xs(ticker1)  # the first ticker\n",
    "df2_sorted = df_sorted.xs(ticker2)  # the second ticker\n",
    "\n",
    "df1_h = hedge * df1_sorted\n",
    "df_spread = df2_sorted.subtract(df1_h).round(5)\n",
    "df_spread.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e1215d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T22:27:31.491165Z",
     "start_time": "2022-10-26T22:27:31.478165Z"
    }
   },
   "source": [
    "#### OPTION 2: GET INDIVIDUAL TICKER FILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "b2efa25f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:27.848199Z",
     "start_time": "2022-11-01T17:24:27.821197Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EQUATION: VST=0.78*CNP\n"
     ]
    }
   ],
   "source": [
    "# PICK FILENAME FORMAT\n",
    "# \"Y\": \"MA_1D.csv\" for downloads with yfinance\n",
    "# \"T\": \"NYSE_MA, 1D.csv\" for downloads from TradingView\n",
    "\n",
    "file_format = \"Y\"\n",
    "\n",
    "hedge = pairs_dic[(ticker1,ticker2)]\n",
    "print(f\"EQUATION: {ticker2}={hedge}*{ticker1}\")\n",
    "\n",
    "# need exchange info if fileformat is T\n",
    "ticker1_exchange = 'NYSE'\n",
    "ticker2_exchange = 'NYSE'\n",
    "\n",
    "if file_format == \"T\":\n",
    "    filename1 = \"data/\"+ ticker1_exchange.upper() +\"_\" + ticker1 + \", \" + resolution.upper() + \".csv\"\n",
    "    filename2 = \"data/\"+ ticker2_exchange.upper() +\"_\" + ticker2 + \", \" + resolution.upper() + \".csv\"\n",
    "elif file_format == \"Y\":\n",
    "    filename1 = \"data/\"+ ticker1 +\"_\" + resolution.upper() + \".csv\"\n",
    "    filename2 = \"data/\"+ ticker2 +\"_\" + resolution.upper() + \".csv\"\n",
    "else:\n",
    "    filename1 = \"data/NYSE_MA, 1D.csv\"\n",
    "    filename2 = \"data/NYSE_V, 1D.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "d40b75dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:29.898489Z",
     "start_time": "2022-11-01T17:24:29.745974Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-01 00:00:00-04:00</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>20.110001</td>\n",
       "      <td>19.540001</td>\n",
       "      <td>20.010000</td>\n",
       "      <td>19.418678</td>\n",
       "      <td>2756500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-02 00:00:00-04:00</td>\n",
       "      <td>20.010000</td>\n",
       "      <td>20.020000</td>\n",
       "      <td>19.625000</td>\n",
       "      <td>19.870001</td>\n",
       "      <td>19.282814</td>\n",
       "      <td>4006000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-03 00:00:00-04:00</td>\n",
       "      <td>19.670000</td>\n",
       "      <td>19.905001</td>\n",
       "      <td>19.559999</td>\n",
       "      <td>19.680000</td>\n",
       "      <td>19.098429</td>\n",
       "      <td>2460100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-04 00:00:00-04:00</td>\n",
       "      <td>19.530001</td>\n",
       "      <td>19.580000</td>\n",
       "      <td>18.930000</td>\n",
       "      <td>19.209999</td>\n",
       "      <td>18.642319</td>\n",
       "      <td>6093500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-05 00:00:00-04:00</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.790001</td>\n",
       "      <td>19.950001</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>19.894196</td>\n",
       "      <td>9136100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2022-10-25 00:00:00-04:00</td>\n",
       "      <td>21.520000</td>\n",
       "      <td>22.219999</td>\n",
       "      <td>21.480000</td>\n",
       "      <td>21.990000</td>\n",
       "      <td>21.990000</td>\n",
       "      <td>4838400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2022-10-26 00:00:00-04:00</td>\n",
       "      <td>22.080000</td>\n",
       "      <td>22.209999</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>3419400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2022-10-27 00:00:00-04:00</td>\n",
       "      <td>22.120001</td>\n",
       "      <td>22.309999</td>\n",
       "      <td>21.889999</td>\n",
       "      <td>21.980000</td>\n",
       "      <td>21.980000</td>\n",
       "      <td>3738400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2022-10-28 00:00:00-04:00</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>22.028999</td>\n",
       "      <td>22.980000</td>\n",
       "      <td>22.980000</td>\n",
       "      <td>4167400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2022-10-31 00:00:00-04:00</td>\n",
       "      <td>22.930000</td>\n",
       "      <td>23.230000</td>\n",
       "      <td>22.770000</td>\n",
       "      <td>22.969999</td>\n",
       "      <td>22.969999</td>\n",
       "      <td>4428700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          time       Open       High        Low      Close  \\\n",
       "0    2021-11-01 00:00:00-04:00  19.600000  20.110001  19.540001  20.010000   \n",
       "1    2021-11-02 00:00:00-04:00  20.010000  20.020000  19.625000  19.870001   \n",
       "2    2021-11-03 00:00:00-04:00  19.670000  19.905001  19.559999  19.680000   \n",
       "3    2021-11-04 00:00:00-04:00  19.530001  19.580000  18.930000  19.209999   \n",
       "4    2021-11-05 00:00:00-04:00  20.000000  20.790001  19.950001  20.500000   \n",
       "..                         ...        ...        ...        ...        ...   \n",
       "247  2022-10-25 00:00:00-04:00  21.520000  22.219999  21.480000  21.990000   \n",
       "248  2022-10-26 00:00:00-04:00  22.080000  22.209999  21.820000  21.900000   \n",
       "249  2022-10-27 00:00:00-04:00  22.120001  22.309999  21.889999  21.980000   \n",
       "250  2022-10-28 00:00:00-04:00  22.100000  23.000000  22.028999  22.980000   \n",
       "251  2022-10-31 00:00:00-04:00  22.930000  23.230000  22.770000  22.969999   \n",
       "\n",
       "     Adj Close   Volume  \n",
       "0    19.418678  2756500  \n",
       "1    19.282814  4006000  \n",
       "2    19.098429  2460100  \n",
       "3    18.642319  6093500  \n",
       "4    19.894196  9136100  \n",
       "..         ...      ...  \n",
       "247  21.990000  4838400  \n",
       "248  21.900000  3419400  \n",
       "249  21.980000  3738400  \n",
       "250  22.980000  4167400  \n",
       "251  22.969999  4428700  \n",
       "\n",
       "[252 rows x 7 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(filename1, parse_dates=[\"time\"])\n",
    "df2=pd.read_csv(filename2, parse_dates=[\"time\"])\n",
    "\n",
    "df1=df1.drop('ticker', axis=1)\n",
    "df2=df2.drop('ticker', axis=1)\n",
    "\n",
    "df1 = df1.dropna()\n",
    "df2 = df2.dropna()\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "49906813",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:31.920593Z",
     "start_time": "2022-11-01T17:24:31.870594Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-01 00:00:00-04:00</th>\n",
       "      <td>-0.7736</td>\n",
       "      <td>-0.4430</td>\n",
       "      <td>-0.7712</td>\n",
       "      <td>-0.4962</td>\n",
       "      <td>-0.60840</td>\n",
       "      <td>-152978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02 00:00:00-04:00</th>\n",
       "      <td>-0.5586</td>\n",
       "      <td>-0.5486</td>\n",
       "      <td>-0.5224</td>\n",
       "      <td>-0.5582</td>\n",
       "      <td>-0.66808</td>\n",
       "      <td>-377990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03 00:00:00-04:00</th>\n",
       "      <td>-0.6490</td>\n",
       "      <td>-0.7026</td>\n",
       "      <td>-0.7278</td>\n",
       "      <td>-0.8262</td>\n",
       "      <td>-0.92864</td>\n",
       "      <td>-153602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04 00:00:00-04:00</th>\n",
       "      <td>-1.0932</td>\n",
       "      <td>-1.2772</td>\n",
       "      <td>-1.5918</td>\n",
       "      <td>-1.4600</td>\n",
       "      <td>-1.54473</td>\n",
       "      <td>3468956.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05 00:00:00-04:00</th>\n",
       "      <td>-0.8338</td>\n",
       "      <td>-0.1842</td>\n",
       "      <td>-0.6654</td>\n",
       "      <td>-0.3026</td>\n",
       "      <td>-0.42236</td>\n",
       "      <td>6656480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-10-25 00:00:00-04:00</th>\n",
       "      <td>0.3040</td>\n",
       "      <td>0.5204</td>\n",
       "      <td>0.2640</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.42300</td>\n",
       "      <td>2064720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-26 00:00:00-04:00</th>\n",
       "      <td>0.3726</td>\n",
       "      <td>0.3934</td>\n",
       "      <td>0.3310</td>\n",
       "      <td>0.3720</td>\n",
       "      <td>0.37200</td>\n",
       "      <td>976284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27 00:00:00-04:00</th>\n",
       "      <td>0.3658</td>\n",
       "      <td>0.2672</td>\n",
       "      <td>0.1748</td>\n",
       "      <td>0.1478</td>\n",
       "      <td>0.14780</td>\n",
       "      <td>1528270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28 00:00:00-04:00</th>\n",
       "      <td>0.2054</td>\n",
       "      <td>0.5204</td>\n",
       "      <td>0.1344</td>\n",
       "      <td>0.5082</td>\n",
       "      <td>0.50820</td>\n",
       "      <td>1980748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31 00:00:00-04:00</th>\n",
       "      <td>0.4660</td>\n",
       "      <td>0.7036</td>\n",
       "      <td>0.5946</td>\n",
       "      <td>0.6542</td>\n",
       "      <td>0.65420</td>\n",
       "      <td>941944.0</td>\n",
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       "<p>252 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Open    High     Low   Close  Adj Close  \\\n",
       "time                                                                   \n",
       "2021-11-01 00:00:00-04:00 -0.7736 -0.4430 -0.7712 -0.4962   -0.60840   \n",
       "2021-11-02 00:00:00-04:00 -0.5586 -0.5486 -0.5224 -0.5582   -0.66808   \n",
       "2021-11-03 00:00:00-04:00 -0.6490 -0.7026 -0.7278 -0.8262   -0.92864   \n",
       "2021-11-04 00:00:00-04:00 -1.0932 -1.2772 -1.5918 -1.4600   -1.54473   \n",
       "2021-11-05 00:00:00-04:00 -0.8338 -0.1842 -0.6654 -0.3026   -0.42236   \n",
       "...                           ...     ...     ...     ...        ...   \n",
       "2022-10-25 00:00:00-04:00  0.3040  0.5204  0.2640  0.4230    0.42300   \n",
       "2022-10-26 00:00:00-04:00  0.3726  0.3934  0.3310  0.3720    0.37200   \n",
       "2022-10-27 00:00:00-04:00  0.3658  0.2672  0.1748  0.1478    0.14780   \n",
       "2022-10-28 00:00:00-04:00  0.2054  0.5204  0.1344  0.5082    0.50820   \n",
       "2022-10-31 00:00:00-04:00  0.4660  0.7036  0.5946  0.6542    0.65420   \n",
       "\n",
       "                              Volume  \n",
       "time                                  \n",
       "2021-11-01 00:00:00-04:00  -152978.0  \n",
       "2021-11-02 00:00:00-04:00  -377990.0  \n",
       "2021-11-03 00:00:00-04:00  -153602.0  \n",
       "2021-11-04 00:00:00-04:00  3468956.0  \n",
       "2021-11-05 00:00:00-04:00  6656480.0  \n",
       "...                              ...  \n",
       "2022-10-25 00:00:00-04:00  2064720.0  \n",
       "2022-10-26 00:00:00-04:00   976284.0  \n",
       "2022-10-27 00:00:00-04:00  1528270.0  \n",
       "2022-10-28 00:00:00-04:00  1980748.0  \n",
       "2022-10-31 00:00:00-04:00   941944.0  \n",
       "\n",
       "[252 rows x 6 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_1=df1.set_index('time')\n",
    "df1_1 = hedge * df1_1\n",
    "\n",
    "df2_1=df2.set_index('time')\n",
    "df_spread = df2_1.subtract(df1_1).round(5)\n",
    "\n",
    "df_spread"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31155310",
   "metadata": {},
   "source": [
    "### DEFINE SMA & BOLLINGER BANDS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "989d4e7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:34.259561Z",
     "start_time": "2022-11-01T17:24:34.208225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sma_20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-19 00:00:00-04:00</th>\n",
       "      <td>1.4878</td>\n",
       "      <td>2.0904</td>\n",
       "      <td>1.5138</td>\n",
       "      <td>2.1652</td>\n",
       "      <td>1.90460</td>\n",
       "      <td>5221120.0</td>\n",
       "      <td>0.91307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-20 00:00:00-04:00</th>\n",
       "      <td>1.9984</td>\n",
       "      <td>2.4672</td>\n",
       "      <td>1.2506</td>\n",
       "      <td>1.3776</td>\n",
       "      <td>1.12943</td>\n",
       "      <td>2987676.0</td>\n",
       "      <td>0.95582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-23 00:00:00-04:00</th>\n",
       "      <td>1.2612</td>\n",
       "      <td>1.5820</td>\n",
       "      <td>1.2018</td>\n",
       "      <td>1.6748</td>\n",
       "      <td>1.42023</td>\n",
       "      <td>897536.0</td>\n",
       "      <td>1.02731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Open    High     Low   Close  Adj Close  \\\n",
       "time                                                                   \n",
       "2022-05-19 00:00:00-04:00  1.4878  2.0904  1.5138  2.1652    1.90460   \n",
       "2022-05-20 00:00:00-04:00  1.9984  2.4672  1.2506  1.3776    1.12943   \n",
       "2022-05-23 00:00:00-04:00  1.2612  1.5820  1.2018  1.6748    1.42023   \n",
       "\n",
       "                              Volume   sma_20  \n",
       "time                                           \n",
       "2022-05-19 00:00:00-04:00  5221120.0  0.91307  \n",
       "2022-05-20 00:00:00-04:00  2987676.0  0.95582  \n",
       "2022-05-23 00:00:00-04:00   897536.0  1.02731  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def SMA(values, n):\n",
    "    return pd.Series(values).rolling(n).mean()\n",
    "\n",
    "df_spread['sma_20']=SMA(df_spread.Close, 20)\n",
    "\n",
    "if resolution.upper() == '1H':\n",
    "    df_spread['sma_20d']=SMA(df_spread.Close, 20*7) # add 20d sma for 1H only\n",
    "\n",
    "df_spread[138:141]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "0934c5cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:36.492267Z",
     "start_time": "2022-11-01T17:24:36.408268Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>sma_20</th>\n",
       "      <th>bb_up_1</th>\n",
       "      <th>bb_low_1</th>\n",
       "      <th>bb_up_2</th>\n",
       "      <th>bb_low_2</th>\n",
       "      <th>bb_up_3</th>\n",
       "      <th>bb_low_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-29 00:00:00-05:00</th>\n",
       "      <td>-0.2388</td>\n",
       "      <td>-0.3948</td>\n",
       "      <td>-0.8016</td>\n",
       "      <td>-0.6502</td>\n",
       "      <td>-0.88870</td>\n",
       "      <td>1668554.0</td>\n",
       "      <td>-0.70935</td>\n",
       "      <td>-0.311684</td>\n",
       "      <td>-1.107016</td>\n",
       "      <td>0.085982</td>\n",
       "      <td>-1.504682</td>\n",
       "      <td>0.483648</td>\n",
       "      <td>-1.902348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30 00:00:00-05:00</th>\n",
       "      <td>-0.5584</td>\n",
       "      <td>-0.1776</td>\n",
       "      <td>-0.4120</td>\n",
       "      <td>-0.3298</td>\n",
       "      <td>-0.57118</td>\n",
       "      <td>2367786.0</td>\n",
       "      <td>-0.70103</td>\n",
       "      <td>-0.296980</td>\n",
       "      <td>-1.105080</td>\n",
       "      <td>0.107069</td>\n",
       "      <td>-1.509129</td>\n",
       "      <td>0.511119</td>\n",
       "      <td>-1.913179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01 00:00:00-05:00</th>\n",
       "      <td>-0.3162</td>\n",
       "      <td>0.2180</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.4572</td>\n",
       "      <td>0.19402</td>\n",
       "      <td>3288116.0</td>\n",
       "      <td>-0.65026</td>\n",
       "      <td>-0.170599</td>\n",
       "      <td>-1.129921</td>\n",
       "      <td>0.309061</td>\n",
       "      <td>-1.609581</td>\n",
       "      <td>0.788722</td>\n",
       "      <td>-2.089242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-02 00:00:00-05:00</th>\n",
       "      <td>0.5436</td>\n",
       "      <td>0.6056</td>\n",
       "      <td>0.5126</td>\n",
       "      <td>0.5084</td>\n",
       "      <td>0.23925</td>\n",
       "      <td>3559852.0</td>\n",
       "      <td>-0.58353</td>\n",
       "      <td>-0.040930</td>\n",
       "      <td>-1.126130</td>\n",
       "      <td>0.501671</td>\n",
       "      <td>-1.668731</td>\n",
       "      <td>1.044271</td>\n",
       "      <td>-2.211331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-03 00:00:00-05:00</th>\n",
       "      <td>0.4892</td>\n",
       "      <td>0.0634</td>\n",
       "      <td>0.0560</td>\n",
       "      <td>-0.3110</td>\n",
       "      <td>-0.56301</td>\n",
       "      <td>-324216.0</td>\n",
       "      <td>-0.52608</td>\n",
       "      <td>-0.021681</td>\n",
       "      <td>-1.030479</td>\n",
       "      <td>0.482718</td>\n",
       "      <td>-1.534878</td>\n",
       "      <td>0.987118</td>\n",
       "      <td>-2.039278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Open    High     Low   Close  Adj Close  \\\n",
       "time                                                                   \n",
       "2021-11-29 00:00:00-05:00 -0.2388 -0.3948 -0.8016 -0.6502   -0.88870   \n",
       "2021-11-30 00:00:00-05:00 -0.5584 -0.1776 -0.4120 -0.3298   -0.57118   \n",
       "2021-12-01 00:00:00-05:00 -0.3162  0.2180  0.0700  0.4572    0.19402   \n",
       "2021-12-02 00:00:00-05:00  0.5436  0.6056  0.5126  0.5084    0.23925   \n",
       "2021-12-03 00:00:00-05:00  0.4892  0.0634  0.0560 -0.3110   -0.56301   \n",
       "\n",
       "                              Volume   sma_20   bb_up_1  bb_low_1   bb_up_2  \\\n",
       "time                                                                          \n",
       "2021-11-29 00:00:00-05:00  1668554.0 -0.70935 -0.311684 -1.107016  0.085982   \n",
       "2021-11-30 00:00:00-05:00  2367786.0 -0.70103 -0.296980 -1.105080  0.107069   \n",
       "2021-12-01 00:00:00-05:00  3288116.0 -0.65026 -0.170599 -1.129921  0.309061   \n",
       "2021-12-02 00:00:00-05:00  3559852.0 -0.58353 -0.040930 -1.126130  0.501671   \n",
       "2021-12-03 00:00:00-05:00  -324216.0 -0.52608 -0.021681 -1.030479  0.482718   \n",
       "\n",
       "                           bb_low_2   bb_up_3  bb_low_3  \n",
       "time                                                     \n",
       "2021-11-29 00:00:00-05:00 -1.504682  0.483648 -1.902348  \n",
       "2021-11-30 00:00:00-05:00 -1.509129  0.511119 -1.913179  \n",
       "2021-12-01 00:00:00-05:00 -1.609581  0.788722 -2.089242  \n",
       "2021-12-02 00:00:00-05:00 -1.668731  1.044271 -2.211331  \n",
       "2021-12-03 00:00:00-05:00 -1.534878  0.987118 -2.039278  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def bb(data, sma, sd=2.0, n=20):\n",
    "    std = data.rolling(n).std(ddof=1) # default ddof=1, sample standard deviation, divide by (n-1)\n",
    "    bb_up = sma + std * sd\n",
    "    bb_low = sma - std * sd\n",
    "    return bb_up, bb_low\n",
    "\n",
    "for i in range(1,4):\n",
    "      df_spread['bb_up_'+str(i)], df_spread['bb_low_'+str(i)] = bb(df_spread['Close'], df_spread['sma_20'], i , 20)\n",
    "\n",
    "\n",
    "df_spread_bb = df_spread.copy()\n",
    "df_spread_bb['bb_up_2'], df_spread_bb['bb_low_2'] = bb(df_spread['Close'], df_spread['sma_20'], 2.0 , 20)\n",
    "\n",
    "df_spread_bb = df_spread_bb.dropna()\n",
    "\n",
    "df_spread_bb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f41f18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T22:37:18.690851Z",
     "start_time": "2022-10-26T22:37:18.686848Z"
    }
   },
   "source": [
    "#### EXERCISE: DEFINE BOLLINGER BANDS WITH TEHNICAL ANALYSIS LIBRARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "ba6c4589",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:38.959994Z",
     "start_time": "2022-11-01T17:24:38.933988Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas_ta as ta\n",
    "#dfSPY.ta.indicators()\n",
    "#help(ta.bbands)\n",
    "my_bbands = ta.bbands(df_spread['Close'], length=20, std=2.0, ddof=1)  # default ddof=0, population standard deviation, divide by n\n",
    "\n",
    "df_spread_bb = df_spread.copy()\n",
    "df_spread_bb['bb_up_2'], df_spread_bb['bb_low_2'] = my_bbands['BBU_20_2.0'], my_bbands['BBL_20_2.0']\n",
    "\n",
    "df_spread_bb = df_spread_bb.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f04ae4",
   "metadata": {},
   "source": [
    "### GRAPH"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0e5be6",
   "metadata": {},
   "source": [
    "#### OPTION 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "602bc79f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:41.581663Z",
     "start_time": "2022-11-01T17:24:41.197252Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize'] = (20, 10)\n",
    "\n",
    "def plot1(df, buysell=False):\n",
    "    df['Close'].plot(label = 'Spread', color = 'darkcyan', linewidth = 2)\n",
    "    df['bb_up_1'].plot(label = 'BB 1 std upper', linestyle = '--', linewidth = 1, color = 'darkgray')\n",
    "    df['bb_low_1'].plot(label = 'BB 1 std lower', linestyle = '--', linewidth = 1, color = 'darkgray')\n",
    "    df['bb_up_2'].plot(label = 'BB 2 std upper', linestyle = '--', linewidth = 1, color = 'dimgray')\n",
    "    df['bb_low_2'].plot(label = 'BB 2 std lower', linestyle = '--', linewidth = 1.4, color = 'dimgray')\n",
    "    df['sma_20'].plot(label = 'SMA 20',  linewidth = 1.2, color = 'darkgray')\n",
    "    if resolution.upper() == '1H':\n",
    "        df['sma_20d'].plot(label = 'SMA 20D',  linewidth = 1.4, color = 'darkgray')\n",
    "    plt.legend(loc = 'lower left')\n",
    "    plt.title('Boll. Bands.')\n",
    "    \n",
    "    if buysell:\n",
    "        plt.scatter(df.index, buy_price, marker = '^', color = 'green', label = 'BUY', s = 200)\n",
    "        plt.scatter(df.index, sell_price, marker = 'v', color = 'red', label = 'SELL', s = 200)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "plot1(df_spread_bb,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a44979",
   "metadata": {},
   "source": [
    "#### OPTION 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "32cc2078",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:44.474668Z",
     "start_time": "2022-11-01T17:24:44.433666Z"
    }
   },
   "outputs": [],
   "source": [
    "# add short date & time for plotly\n",
    "df_spread_bb['shortdate'] = df_spread_bb.index\n",
    "df_spread_bb['shortdate']  = pd.to_datetime(df_spread_bb.shortdate,utc=True)\n",
    "df_spread_bb['shortdate']  = df_spread_bb['shortdate'].dt.tz_convert(\"Canada/Pacific\")\n",
    "df_spread_bb['shortdate'] = df_spread_bb['shortdate'].dt.strftime('%m/%d %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "eda5feee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:24:45.771812Z",
     "start_time": "2022-11-01T17:24:45.700285Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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       "                            \n",
       "var gd = document.getElementById('76ebf8d1-e587-475e-a381-5ee6fef2b742');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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     "metadata": {},
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "def plot2(df, buysell=False):\n",
    "    fig = go.Figure(data=[go.Candlestick(x=df.shortdate,\n",
    "            open=df['Open'],\n",
    "            high=df['High'],\n",
    "            low=df['Low'],\n",
    "            close=df['Close'],\n",
    "            increasing_line_color= 'PaleGreen', decreasing_line_color= 'salmon'),\n",
    "            go.Scatter(x=df.shortdate, y=df.sma_20, line=dict(color='darkgray', width=1), name=\"SMA 20\"),\n",
    "#             go.Scatter(x=df.shortdate, y=df.sma_20d, line=dict(color='darkgray', width=2), name=\"SMA 20D\"),\n",
    "            go.Scatter(x=df.shortdate, y=df['bb_low_2'], line=dict(color='dimgray', width=1, dash='dot'), name=\"BB 2 std lower\"),\n",
    "            go.Scatter(x=df.shortdate, y=df['bb_up_2'], line=dict(color='dimgray', width=1, dash='dot'), name=\"BB 2 std upper\"),\n",
    "            go.Scatter(x=df.shortdate, y=df['bb_low_1'], line=dict(color='darkgray', width=1, dash='dot'), name=\"BB 1 std lower\"),\n",
    "            go.Scatter(x=df.shortdate, y=df['bb_up_1'], line=dict(color='darkgray', width=1, dash='dot'), name=\"BB 1 std upper\"),])\n",
    "\n",
    "    if resolution.upper() == '1H':\n",
    "        fig.add_scatter(x=df.shortdate, y=df.sma_20d, line=dict(color='darkgray', width=2), name=\"SMA 20D\")\n",
    "\n",
    "    fig.update_layout(xaxis_rangeslider_visible=False)\n",
    "    #fig.update_layout(legend=dict(yanchor=\"bottom\", y=1.0, xanchor=\"left\", x=0.01))\n",
    "    fig.update_xaxes(nticks=12) #default is 5\n",
    "    fig.update_layout(xaxis=dict(type = \"category\")) #to eliminate after-hours\n",
    "    \n",
    "    if buysell:\n",
    "        fig.add_scatter(x=df.shortdate, y=buy_price, mode=\"markers\", marker_symbol=\"triangle-up\", marker=dict(size=10, color=\"Green\"), name=\"buy\")\n",
    "        fig.add_scatter(x=df.shortdate, y=sell_price, mode=\"markers\", marker_symbol=\"triangle-down\",marker=dict(size=10, color=\"Red\"), name=\"sell\")\n",
    "        #fig.update_layout(xaxis_rangeslider_visible=True)\n",
    "    \n",
    "    fig.show()\n",
    " \n",
    "plot2(df_spread_bb,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1318619f",
   "metadata": {},
   "source": [
    "### STRATEGY & POSITIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b1d3a1b",
   "metadata": {},
   "source": [
    "#### STRATEGY 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "39b8bdd4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:24.157418Z",
     "start_time": "2022-11-01T17:25:24.108385Z"
    }
   },
   "outputs": [],
   "source": [
    "# buy and sell at upper and lower bollinger band, exit at sma\n",
    "def strategy_exit_sma(data, lower_bb, upper_bb, sma_20):\n",
    "    buy_price = []\n",
    "    sell_price = []\n",
    "    bb_signal = []\n",
    "    position = []\n",
    "    signal = 0\n",
    "      \n",
    "    position = [0] * len(data)\n",
    "      \n",
    "    for i in range(len(data)):\n",
    "        # define band crossings\n",
    "        if data[i - 1] > lower_bb[i - 1] and data[i] < lower_bb[i]:\n",
    "            if position[i-1] == 0:\n",
    "                buy_price.append(data[i])\n",
    "                sell_price.append(np.nan)\n",
    "                signal = 1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        elif data[i - 1] < upper_bb[i - 1] and data[i] > upper_bb[i]:\n",
    "            if position[i-1] == 0:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(data[i])\n",
    "                signal = -1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = -1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "\n",
    "        # define sma crossings\n",
    "        elif data[i - 1] > sma_20[i - 1] and data[i] < sma_20[i]:\n",
    "            if position[i-1] != 0:\n",
    "                buy_price.append(data[i])\n",
    "                sell_price.append(np.nan)\n",
    "                signal = 1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 0\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        elif data[i - 1] < sma_20[i - 1] and data[i] > sma_20[i]:\n",
    "            if position[i-1] != 0:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(data[i])\n",
    "                signal = -1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 0\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "\n",
    "        else:\n",
    "            buy_price.append(np.nan)\n",
    "            sell_price.append(np.nan)\n",
    "            bb_signal.append(0)\n",
    "            position[i] = position[i-1]\n",
    "\n",
    "    return buy_price, sell_price, bb_signal, position\n",
    "\n",
    "\n",
    "buy_price, sell_price, bb_signal, position = strategy_exit_sma(\n",
    "    df_spread_bb[\"Close\"],\n",
    "    df_spread_bb[\"bb_low_2\"],\n",
    "    df_spread_bb[\"bb_up_2\"],\n",
    "    df_spread_bb[\"sma_20\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76e8c90",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-26T22:40:58.790598Z",
     "start_time": "2022-10-26T22:40:58.783600Z"
    }
   },
   "source": [
    "#### STRATEGY 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "37745b72",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:06.389494Z",
     "start_time": "2022-11-01T17:25:06.346955Z"
    }
   },
   "outputs": [],
   "source": [
    "# buy and sell at upper and lower bollinger band, keep position, no exit\n",
    "def strategy_no_exit(data, lower_bb, upper_bb):\n",
    "\n",
    "    buy_price = []\n",
    "    sell_price = []\n",
    "    bb_signal = []\n",
    "    position = []\n",
    "    signal = 0\n",
    "    \n",
    "    # create a zero position list until the first signal\n",
    "    for i in range(len(data)):\n",
    "        position.append(0)\n",
    "    \n",
    "    for i in range(len(data)):\n",
    "        if data[i - 1] > lower_bb[i - 1] and data[i] < lower_bb[i]:\n",
    "            if signal != 1:\n",
    "                buy_price.append(data[i])\n",
    "                sell_price.append(np.nan)\n",
    "                signal = 1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = 1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        elif data[i - 1] < upper_bb[i - 1] and data[i] > upper_bb[i]:\n",
    "            if signal != -1:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(data[i])\n",
    "                signal = -1\n",
    "                bb_signal.append(signal)\n",
    "                position[i] = -1\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "        else:\n",
    "            buy_price.append(np.nan)\n",
    "            sell_price.append(np.nan)\n",
    "            bb_signal.append(0)\n",
    "            position[i] = position[i-1]\n",
    "\n",
    "    return buy_price, sell_price, bb_signal, position\n",
    "\n",
    "\n",
    "buy_price, sell_price, bb_signal, position = strategy_no_exit(\n",
    "    df_spread_bb[\"Close\"], df_spread_bb[\"bb_low_2\"], df_spread_bb[\"bb_up_2\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f83f48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-29T22:35:03.420788Z",
     "start_time": "2022-10-29T22:35:03.406755Z"
    }
   },
   "source": [
    "#### STRATEGY 3 ('1H' RESOLUTION ONLY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "8eb014e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:03.123733Z",
     "start_time": "2022-11-01T17:25:03.086741Z"
    }
   },
   "outputs": [],
   "source": [
    "if resolution.upper() == '1H':  # enable only for '1H resolution'\n",
    "\n",
    "    # buy and sell at upper and lower bollinger band, exit at sma, no exit if moving towards 20d-sma\n",
    "    def strategy_skp_sma20d(data, lower_bb, upper_bb, sma_20, sma_20d):\n",
    "        buy_price = []\n",
    "        sell_price = []\n",
    "        bb_signal = []\n",
    "        position = []\n",
    "        signal = 0\n",
    "\n",
    "        position = [0] * len(data)\n",
    "\n",
    "        for i in range(len(data)):\n",
    "            # define band crossings\n",
    "            if data[i - 1] > lower_bb[i - 1] and data[i] < lower_bb[i]:\n",
    "                if position[i-1] != 1:\n",
    "                    buy_price.append(data[i])\n",
    "                    sell_price.append(np.nan)\n",
    "                    signal = 1\n",
    "                    bb_signal.append(signal)\n",
    "                    position[i] = 1\n",
    "                else:\n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(np.nan)\n",
    "                    bb_signal.append(0)\n",
    "                    position[i] = position[i-1]\n",
    "            elif data[i - 1] < upper_bb[i - 1] and data[i] > upper_bb[i]:\n",
    "                if position[i-1] != -1:\n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(data[i])\n",
    "                    signal = -1\n",
    "                    bb_signal.append(signal)\n",
    "                    position[i] = -1\n",
    "                else:\n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(np.nan)\n",
    "                    bb_signal.append(0)\n",
    "                    position[i] = position[i-1]\n",
    "\n",
    "            # define sma crossings\n",
    "            elif data[i - 1] > sma_20[i - 1] and data[i] < sma_20[i]:\n",
    "                if data[i] < sma_20d[i]: # check 20d-sma\n",
    "                    if position[i-1] == -1:\n",
    "                        buy_price.append(data[i])\n",
    "                        sell_price.append(np.nan)\n",
    "                        signal = 1\n",
    "                        bb_signal.append(signal)\n",
    "                        position[i] = 0\n",
    "                    else:\n",
    "                        buy_price.append(np.nan)\n",
    "                        sell_price.append(np.nan)\n",
    "                        bb_signal.append(0)\n",
    "                        position[i] = position[i-1]\n",
    "\n",
    "                else: # moving towards 20d-sma               \n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(np.nan)\n",
    "                    bb_signal.append(0)\n",
    "                    position[i] = position[i-1]\n",
    "\n",
    "\n",
    "            elif data[i - 1] < sma_20[i - 1] and data[i] > sma_20[i]:\n",
    "                if data[i] > sma_20d[i]: # check 20d-sma\n",
    "                    if position[i-1] == 1:\n",
    "                        buy_price.append(np.nan)\n",
    "                        sell_price.append(data[i])\n",
    "                        signal = -1\n",
    "                        bb_signal.append(signal)\n",
    "                        position[i] = 0\n",
    "                    else:\n",
    "                        buy_price.append(np.nan)\n",
    "                        sell_price.append(np.nan)\n",
    "                        bb_signal.append(0)\n",
    "                        position[i] = position[i-1]\n",
    "\n",
    "                else: # moving towards 20d-sma               \n",
    "                    buy_price.append(np.nan)\n",
    "                    sell_price.append(np.nan)\n",
    "                    bb_signal.append(0)\n",
    "                    position[i] = position[i-1]\n",
    "\n",
    "            else:\n",
    "                buy_price.append(np.nan)\n",
    "                sell_price.append(np.nan)\n",
    "                bb_signal.append(0)\n",
    "                position[i] = position[i-1]\n",
    "\n",
    "        return buy_price, sell_price, bb_signal, position\n",
    "\n",
    "\n",
    "    buy_price, sell_price, bb_signal, position = strategy_skp_sma20d(\n",
    "        df_spread_bb[\"Close\"],\n",
    "        df_spread_bb[\"bb_low_2\"],\n",
    "        df_spread_bb[\"bb_up_2\"],\n",
    "        df_spread_bb[\"sma_20\"],\n",
    "        df_spread_bb[\"sma_20d\"],\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4440e7d7",
   "metadata": {},
   "source": [
    "#### PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "62522c62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:28.023634Z",
     "start_time": "2022-11-01T17:25:27.934770Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
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        "plotlyServerURL": "https://plot.ly"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2(df_spread_bb,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3997bc",
   "metadata": {},
   "source": [
    "### BACKTEST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c1926f2",
   "metadata": {},
   "source": [
    "Prepare a new df for the strategy signal & positions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "fd927eff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:31.219851Z",
     "start_time": "2022-11-01T17:25:31.195847Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Select bb to backtest\n",
    "upper_bb = df_spread_bb['bb_up_2']\n",
    "lower_bb = df_spread_bb['bb_low_2']\n",
    "close_price = df_spread_bb['Close']\n",
    "bb_signal_df = pd.DataFrame(bb_signal).rename(columns = {0:'bb_signal'}).set_index(df_spread_bb.index)\n",
    "position_df = pd.DataFrame(position).rename(columns = {0:'bb_position'}).set_index(df_spread_bb.index)\n",
    "\n",
    "frames = [close_price, upper_bb, lower_bb, bb_signal_df, position_df]\n",
    "strategy = pd.concat(frames, join = 'inner', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bfc75d8",
   "metadata": {},
   "source": [
    "Define investment value & cost factors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "a92f7009",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:32.871154Z",
     "start_time": "2022-11-01T17:25:32.860157Z"
    }
   },
   "outputs": [],
   "source": [
    "contract_price = round(df2_1.iloc[-1,df2_1.columns.get_loc('Close')],0) # get the latest price\n",
    "avg_fin_cost = 0.0002\n",
    "pos_value = 7500 # x2 for pair buy & sell\n",
    "mar_req = 0.3 # margin requirement\n",
    "cash_req = mar_req * pos_value * 2 # required cash\n",
    "avg_slip = 0.001 # per order per position value\n",
    "avg_commission = 0.007 # per contract\n",
    "\n",
    "if resolution.upper() == '1H':\n",
    "    avg_fin_cost = 0.0002/7\n",
    "\n",
    "contracts = pos_value/contract_price\n",
    "number_of_stocks = math.floor(contracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "14af28a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:34.717638Z",
     "start_time": "2022-11-01T17:25:34.683626Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "slip = bb_signal_df.bb_signal.abs()*avg_slip*pos_value\n",
    "commission = bb_signal_df.bb_signal.abs()*avg_commission*contracts*2\n",
    "fincost = position_df.bb_position.abs()*avg_fin_cost*(2*pos_value-cash_req)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9113d262",
   "metadata": {},
   "source": [
    "#### BACKTEST & PRINT INITIAL RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "b45e55ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:37.198867Z",
     "start_time": "2022-11-01T17:25:37.158865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mProfit/Loss (costs excluded): 3345.87\u001b[0m\n",
      "\u001b[1mProfit/Loss (costs excluded): 74%\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from termcolor import colored as cl\n",
    "\n",
    "df_spread_bb_ret = pd.DataFrame(np.diff(df_spread_bb['Close'])).rename(columns = {0:'returns'})\n",
    "bb_strategy_ret = []\n",
    "\n",
    "# calculate return per bar\n",
    "for i in range(len(df_spread_bb_ret)):\n",
    "    try:\n",
    "        returns = df_spread_bb_ret['returns'][i]*strategy['bb_position'][i]\n",
    "        bb_strategy_ret.append(returns)    \n",
    "        \n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "bb_strategy_ret_df = pd.DataFrame(bb_strategy_ret).rename(columns = {0:'bb_returns'})\n",
    "\n",
    "bb_investment_ret = []\n",
    "\n",
    "for i in range(len(bb_strategy_ret_df['bb_returns'])):\n",
    "    returns = number_of_stocks*bb_strategy_ret_df['bb_returns'][i]\n",
    "    bb_investment_ret.append(returns)\n",
    "\n",
    "bb_investment_ret_df = pd.DataFrame(bb_investment_ret).rename(columns = {0:'investment_returns'})\n",
    "total_investment_ret = round(sum(bb_investment_ret_df['investment_returns']), 2)\n",
    "profit_percentage = math.floor((total_investment_ret/cash_req)*100)\n",
    "print(cl('Profit/Loss (costs excluded): {}'.format(total_investment_ret), attrs = ['bold']))\n",
    "print(cl('Profit/Loss (costs excluded): {}%'.format(profit_percentage), attrs = ['bold']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e659965",
   "metadata": {},
   "source": [
    "Calculate cumulative returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "e92385cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:39.355943Z",
     "start_time": "2022-11-01T17:25:39.333481Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>investment_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>3345.8684</td>\n",
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       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>3345.8684</td>\n",
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       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>3345.8684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>3345.8684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>3345.8684</td>\n",
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      "text/plain": [
       "     investment_returns\n",
       "227           3345.8684\n",
       "228           3345.8684\n",
       "229           3345.8684\n",
       "230           3345.8684\n",
       "231           3345.8684"
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     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cum_inv_ret = bb_investment_ret_df.cumsum()\n",
    "cum_inv_ret.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad22a1eb",
   "metadata": {},
   "source": [
    "List orders:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "58f3adbf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:41.885156Z",
     "start_time": "2022-11-01T17:25:41.847180Z"
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    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>bb_signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-09-12 00:00:00-04:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022-09-23 00:00:00-04:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-10-04 00:00:00-04:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-10-06 00:00:00-04:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-10-24 00:00:00-04:00</td>\n",
       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                         time  bb_signal\n",
       "17  2022-09-12 00:00:00-04:00         -1\n",
       "18  2022-09-23 00:00:00-04:00          1\n",
       "19  2022-10-04 00:00:00-04:00         -1\n",
       "20  2022-10-06 00:00:00-04:00         -1\n",
       "21  2022-10-24 00:00:00-04:00          1"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = strategy.bb_signal!=0\n",
    "orders = strategy[mask][['bb_signal']].reset_index()\n",
    "orders.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d07d44e",
   "metadata": {},
   "source": [
    "Calculate order returns & open position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "22b54363",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:43.685088Z",
     "start_time": "2022-11-01T17:25:43.647049Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2021-11-29 00:00:00-05:00</td>\n",
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       "      <th>1</th>\n",
       "      <td>2021-11-30 00:00:00-05:00</td>\n",
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       "      <th>228</th>\n",
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       "      <th>230</th>\n",
       "      <td>2022-10-27 00:00:00-04:00</td>\n",
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       "    <tr>\n",
       "      <th>231</th>\n",
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       "      <th>232</th>\n",
       "      <td>2022-10-31 00:00:00-04:00</td>\n",
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       "</table>\n",
       "<p>233 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                          time  bb_signal  cum_return\n",
       "0    2021-11-29 00:00:00-05:00          0      0.0000\n",
       "1    2021-11-30 00:00:00-05:00          0      0.0000\n",
       "2    2021-12-01 00:00:00-05:00         -1    -16.6912\n",
       "3    2021-12-02 00:00:00-05:00          0    250.4332\n",
       "4    2021-12-03 00:00:00-05:00          0    377.7688\n",
       "..                         ...        ...         ...\n",
       "228  2022-10-25 00:00:00-04:00          0   3345.8684\n",
       "229  2022-10-26 00:00:00-04:00          0   3345.8684\n",
       "230  2022-10-27 00:00:00-04:00          0   3345.8684\n",
       "231  2022-10-28 00:00:00-04:00          0   3345.8684\n",
       "232  2022-10-31 00:00:00-04:00          0         NaN\n",
       "\n",
       "[233 rows x 3 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategy2 = strategy[['bb_signal']].reset_index()\n",
    "strategy2['cum_return'] = cum_inv_ret\n",
    "strategy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "c6817d9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:25:45.938451Z",
     "start_time": "2022-11-01T17:25:45.874453Z"
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    {
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       "      <th>4</th>\n",
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       "      <td>497.4760</td>\n",
       "      <td>-7.2372</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
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       "      <th>6</th>\n",
       "      <td>62</td>\n",
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       "      <th>7</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1165.7760</td>\n",
       "      <td>329.5208</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>1276.6160</td>\n",
       "      <td>110.8400</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>1375.1984</td>\n",
       "      <td>98.5824</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>131</td>\n",
       "      <td>-1</td>\n",
       "      <td>1361.2456</td>\n",
       "      <td>-13.9528</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>135</td>\n",
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       "      <td>1771.1580</td>\n",
       "      <td>409.9124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.2204</td>\n",
       "      <td>-70.9376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>159</td>\n",
       "      <td>-1</td>\n",
       "      <td>1982.2104</td>\n",
       "      <td>281.9900</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>167</td>\n",
       "      <td>-1</td>\n",
       "      <td>2126.6936</td>\n",
       "      <td>144.4832</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>172</td>\n",
       "      <td>1</td>\n",
       "      <td>2333.7688</td>\n",
       "      <td>207.0752</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>194</td>\n",
       "      <td>1</td>\n",
       "      <td>2410.9004</td>\n",
       "      <td>77.1316</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>197</td>\n",
       "      <td>-1</td>\n",
       "      <td>2599.3284</td>\n",
       "      <td>188.4280</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>206</td>\n",
       "      <td>1</td>\n",
       "      <td>2572.9224</td>\n",
       "      <td>-26.4060</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>213</td>\n",
       "      <td>-1</td>\n",
       "      <td>3000.7648</td>\n",
       "      <td>427.8424</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>215</td>\n",
       "      <td>-1</td>\n",
       "      <td>3087.6764</td>\n",
       "      <td>86.9116</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>3345.8684</td>\n",
       "      <td>258.1920</td>\n",
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       "    index  bb_signal  cum_return  prof_loss\n",
       "0       2         -1    -16.6912        NaN\n",
       "1       5          1    377.7688   394.4600\n",
       "2      17         -1    485.7400   107.9712\n",
       "3      36          1    504.7132    18.9732\n",
       "4      41          1    497.4760    -7.2372\n",
       "5      50         -1    665.6268   168.1508\n",
       "6      62         -1    836.2552   170.6284\n",
       "7      65          1   1165.7760   329.5208\n",
       "8      77          1   1276.6160   110.8400\n",
       "9     114          1   1375.1984    98.5824\n",
       "10    131         -1   1361.2456   -13.9528\n",
       "11    135          1   1771.1580   409.9124\n",
       "12    147          1   1700.2204   -70.9376\n",
       "13    159         -1   1982.2104   281.9900\n",
       "14    167         -1   2126.6936   144.4832\n",
       "15    172          1   2333.7688   207.0752\n",
       "16    194          1   2410.9004    77.1316\n",
       "17    197         -1   2599.3284   188.4280\n",
       "18    206          1   2572.9224   -26.4060\n",
       "19    213         -1   3000.7648   427.8424\n",
       "20    215         -1   3087.6764    86.9116\n",
       "21    227          1   3345.8684   258.1920"
      ]
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       "      <td>17.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
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       "      <td>18.9732</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>497.4760</td>\n",
       "      <td>-7.2372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>665.6268</td>\n",
       "      <td>168.1508</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>836.2552</td>\n",
       "      <td>170.6284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1165.7760</td>\n",
       "      <td>329.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>77.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1276.6160</td>\n",
       "      <td>110.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>114.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1375.1984</td>\n",
       "      <td>98.5824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>131.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1361.2456</td>\n",
       "      <td>-13.9528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>135.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1771.1580</td>\n",
       "      <td>409.9124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1700.2204</td>\n",
       "      <td>-70.9376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>159.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1982.2104</td>\n",
       "      <td>281.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>167.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2126.6936</td>\n",
       "      <td>144.4832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>172.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2333.7688</td>\n",
       "      <td>207.0752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>194.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2410.9004</td>\n",
       "      <td>77.1316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>197.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2599.3284</td>\n",
       "      <td>188.4280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2572.9224</td>\n",
       "      <td>-26.4060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>213.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3000.7648</td>\n",
       "      <td>427.8424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>215.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3087.6764</td>\n",
       "      <td>86.9116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>227.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3345.8684</td>\n",
       "      <td>258.1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3345.8700</td>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  bb_signal  cum_return  prof_loss\n",
       "0     2.0       -1.0    -16.6912   -16.6912\n",
       "1     5.0        1.0    377.7688   394.4600\n",
       "2    17.0       -1.0    485.7400   107.9712\n",
       "3    36.0        1.0    504.7132    18.9732\n",
       "4    41.0        1.0    497.4760    -7.2372\n",
       "5    50.0       -1.0    665.6268   168.1508\n",
       "6    62.0       -1.0    836.2552   170.6284\n",
       "7    65.0        1.0   1165.7760   329.5208\n",
       "8    77.0        1.0   1276.6160   110.8400\n",
       "9   114.0        1.0   1375.1984    98.5824\n",
       "10  131.0       -1.0   1361.2456   -13.9528\n",
       "11  135.0        1.0   1771.1580   409.9124\n",
       "12  147.0        1.0   1700.2204   -70.9376\n",
       "13  159.0       -1.0   1982.2104   281.9900\n",
       "14  167.0       -1.0   2126.6936   144.4832\n",
       "15  172.0        1.0   2333.7688   207.0752\n",
       "16  194.0        1.0   2410.9004    77.1316\n",
       "17  197.0       -1.0   2599.3284   188.4280\n",
       "18  206.0        1.0   2572.9224   -26.4060\n",
       "19  213.0       -1.0   3000.7648   427.8424\n",
       "20  215.0       -1.0   3087.6764    86.9116\n",
       "21  227.0        1.0   3345.8684   258.1920\n",
       "22    NaN        NaN   3345.8700     0.0016"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = strategy2.bb_signal!=0\n",
    "orders = strategy2[mask][['bb_signal','cum_return']]\n",
    "orders = orders.reset_index().dropna()\n",
    "\n",
    "# values = df.Prices * df.Amount\n",
    "# df['Values'] = values.where(df.Action == 'Sell', other=-values)\n",
    "\n",
    "orders['prof_loss'] = orders.cum_return.diff()\n",
    "# set the fist order profit/loss\n",
    "display(orders)\n",
    "orders.at[0, 'prof_loss'] = orders['cum_return'][0]\n",
    "\n",
    "# set open position profit/loss\n",
    "orders.at[len(orders), 'prof_loss'] = total_investment_ret - orders.loc[orders.index[-1],'cum_return']\n",
    "# orders.at[len(orders)-1, 'index'] = len(strategy2)-1\n",
    "# orders.at[len(orders)-1, 'bb_signal'] = -orders.loc[orders.index[-2],'bb_signal']\n",
    "orders.at[len(orders)-1, 'cum_return'] = total_investment_ret\n",
    "\n",
    "orders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840016cc",
   "metadata": {},
   "source": [
    "#### RESULT SUMMARY"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4d721c",
   "metadata": {},
   "source": [
    "Observe the profit loss distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2583c1b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:10:33.523323Z",
     "start_time": "2022-11-01T17:10:33.523323Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sb\n",
    "_ = plt.figure(figsize=(5,3))\n",
    "sb.violinplot(data=orders[[\"prof_loss\"]], inner=None);\n",
    "sb.swarmplot(data=orders[[\"prof_loss\"]], size=10, color=\"k\", alpha=0.5);\n",
    "# sb.boxplot(data=orders[[\"prof_loss\"]]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3927fe",
   "metadata": {},
   "source": [
    "Print final results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aea793e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:10:33.526326Z",
     "start_time": "2022-11-01T17:10:33.526326Z"
    }
   },
   "outputs": [],
   "source": [
    "print(cl('Gross return: {}'.format(round(sum(orders['prof_loss']), 2)), attrs = ['bold']))\n",
    "print(cl('Financial cost: {}'.format(-fincost.sum().round(1)), attrs = ['bold']))\n",
    "print(cl('Total slip: {}'.format(-slip.sum().round(1)), attrs = ['bold']))\n",
    "print(cl('Total commisson: {}'.format(-commission.sum().round(1)), attrs = ['bold']))\n",
    "total_cost = slip.sum().round(1) + commission.sum().round(1)+fincost.sum().round(1)\n",
    "print(cl('Net return: {}'.format(round(sum(orders['prof_loss'])-total_cost, 2)), attrs = ['bold']))"
   ]
  }
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