Finance_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CHOSEN TRADING STRATEGY\n",
    "#CCI CORRECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Commodity Channel Index Python Code\n",
    "# Load the necessary packages and modules\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas_datareader import data as pdr\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan 01, 2015</td>\n",
       "      <td>154.40</td>\n",
       "      <td>155.6</td>\n",
       "      <td>153.30</td>\n",
       "      <td>633.48K</td>\n",
       "      <td>-0.03%</td>\n",
       "      <td>154.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jan 02, 2015</td>\n",
       "      <td>154.98</td>\n",
       "      <td>159.6</td>\n",
       "      <td>154.60</td>\n",
       "      <td>1.26M</td>\n",
       "      <td>2.60%</td>\n",
       "      <td>158.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan 05, 2015</td>\n",
       "      <td>159.80</td>\n",
       "      <td>160.9</td>\n",
       "      <td>157.96</td>\n",
       "      <td>1.26M</td>\n",
       "      <td>0.33%</td>\n",
       "      <td>159.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jan 06, 2015</td>\n",
       "      <td>158.00</td>\n",
       "      <td>158.0</td>\n",
       "      <td>153.67</td>\n",
       "      <td>1.58M</td>\n",
       "      <td>-2.98%</td>\n",
       "      <td>154.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan 07, 2015</td>\n",
       "      <td>154.40</td>\n",
       "      <td>155.3</td>\n",
       "      <td>150.97</td>\n",
       "      <td>1.54M</td>\n",
       "      <td>-1.82%</td>\n",
       "      <td>151.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date    Open   High     Low     Vol. Change %   Close\n",
       "0  Jan 01, 2015  154.40  155.6  153.30  633.48K   -0.03%  154.53\n",
       "1  Jan 02, 2015  154.98  159.6  154.60    1.26M    2.60%  158.55\n",
       "2  Jan 05, 2015  159.80  160.9  157.96    1.26M    0.33%  159.08\n",
       "3  Jan 06, 2015  158.00  158.0  153.67    1.58M   -2.98%  154.34\n",
       "4  Jan 07, 2015  154.40  155.3  150.97    1.54M   -1.82%  151.53"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing data of YESB\n",
    "data=pd.read_csv(r\"F:\\Important stuff\\YESB Historical Data (2).csv\")\n",
    "data.head()\n",
    "data['Close']=data['Price']\n",
    "data.drop('Price',axis=1,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CCI(data, ndays): \n",
    " TP = (data['High'] + data['Low'] + data['Close']) / 3 \n",
    " CCI = pd.Series((TP - TP.rolling(ndays).mean()) / (0.015 * TP.rolling(ndays).std()),\n",
    " name = 'CCI') \n",
    " data = data.join(CCI) \n",
    " return data\n",
    "\n",
    "# Compute the Commodity Channel Index(CCI) for YESBANK based on the 50-day Moving average and 20-day Moving average\n",
    "n = 50\n",
    "YESB_data = CCI(data, n)\n",
    "CCI1 = YESB_data['CCI']\n",
    "YESB_data2=CCI(data,20)\n",
    "CCI2 = YESB_data2['CCI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To ensure data starts from 1st April"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "YESB_data1=YESB_data[62:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "YESB_data2=YESB_data2[62:]\n",
    "CCI2=CCI2[62:]\n",
    "CCI1=CCI1[62:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Two data sets for different CCI values(50 day and 20 day CCI)\n",
    "#YESB_data1 contains 50 day CCI\n",
    "#YESB_data2 contains 20 day CCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Close</th>\n",
       "      <th>CCI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Apr 01, 2015</td>\n",
       "      <td>162.8</td>\n",
       "      <td>170.56</td>\n",
       "      <td>161.80</td>\n",
       "      <td>1.69M</td>\n",
       "      <td>3.73%</td>\n",
       "      <td>169.40</td>\n",
       "      <td>11.806847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Apr 06, 2015</td>\n",
       "      <td>169.5</td>\n",
       "      <td>171.40</td>\n",
       "      <td>168.30</td>\n",
       "      <td>1.30M</td>\n",
       "      <td>-0.14%</td>\n",
       "      <td>169.16</td>\n",
       "      <td>43.678545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Apr 07, 2015</td>\n",
       "      <td>169.6</td>\n",
       "      <td>170.40</td>\n",
       "      <td>167.25</td>\n",
       "      <td>1.20M</td>\n",
       "      <td>-0.54%</td>\n",
       "      <td>168.24</td>\n",
       "      <td>31.765385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Apr 08, 2015</td>\n",
       "      <td>167.6</td>\n",
       "      <td>168.62</td>\n",
       "      <td>163.79</td>\n",
       "      <td>2.41M</td>\n",
       "      <td>-2.46%</td>\n",
       "      <td>164.10</td>\n",
       "      <td>-9.446423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Apr 09, 2015</td>\n",
       "      <td>164.9</td>\n",
       "      <td>168.98</td>\n",
       "      <td>164.19</td>\n",
       "      <td>1.70M</td>\n",
       "      <td>2.67%</td>\n",
       "      <td>168.48</td>\n",
       "      <td>16.461188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date   Open    High     Low   Vol. Change %   Close        CCI\n",
       "62  Apr 01, 2015  162.8  170.56  161.80  1.69M    3.73%  169.40  11.806847\n",
       "63  Apr 06, 2015  169.5  171.40  168.30  1.30M   -0.14%  169.16  43.678545\n",
       "64  Apr 07, 2015  169.6  170.40  167.25  1.20M   -0.54%  168.24  31.765385\n",
       "65  Apr 08, 2015  167.6  168.62  163.79  2.41M   -2.46%  164.10  -9.446423\n",
       "66  Apr 09, 2015  164.9  168.98  164.19  1.70M    2.67%  168.48  16.461188"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YESB_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Close</th>\n",
       "      <th>CCI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Apr 01, 2015</td>\n",
       "      <td>162.8</td>\n",
       "      <td>170.56</td>\n",
       "      <td>161.80</td>\n",
       "      <td>1.69M</td>\n",
       "      <td>3.73%</td>\n",
       "      <td>169.40</td>\n",
       "      <td>31.126471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Apr 06, 2015</td>\n",
       "      <td>169.5</td>\n",
       "      <td>171.40</td>\n",
       "      <td>168.30</td>\n",
       "      <td>1.30M</td>\n",
       "      <td>-0.14%</td>\n",
       "      <td>169.16</td>\n",
       "      <td>100.282928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Apr 07, 2015</td>\n",
       "      <td>169.6</td>\n",
       "      <td>170.40</td>\n",
       "      <td>167.25</td>\n",
       "      <td>1.20M</td>\n",
       "      <td>-0.54%</td>\n",
       "      <td>168.24</td>\n",
       "      <td>85.744567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Apr 08, 2015</td>\n",
       "      <td>167.6</td>\n",
       "      <td>168.62</td>\n",
       "      <td>163.79</td>\n",
       "      <td>2.41M</td>\n",
       "      <td>-2.46%</td>\n",
       "      <td>164.10</td>\n",
       "      <td>6.393543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Apr 09, 2015</td>\n",
       "      <td>164.9</td>\n",
       "      <td>168.98</td>\n",
       "      <td>164.19</td>\n",
       "      <td>1.70M</td>\n",
       "      <td>2.67%</td>\n",
       "      <td>168.48</td>\n",
       "      <td>48.672082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date   Open    High     Low   Vol. Change %   Close         CCI\n",
       "62  Apr 01, 2015  162.8  170.56  161.80  1.69M    3.73%  169.40   31.126471\n",
       "63  Apr 06, 2015  169.5  171.40  168.30  1.30M   -0.14%  169.16  100.282928\n",
       "64  Apr 07, 2015  169.6  170.40  167.25  1.20M   -0.54%  168.24   85.744567\n",
       "65  Apr 08, 2015  167.6  168.62  163.79  2.41M   -2.46%  164.10    6.393543\n",
       "66  Apr 09, 2015  164.9  168.98  164.19  1.70M    2.67%  168.48   48.672082"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YESB_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aditya\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "YESB_data1['Date']=pd.to_datetime(YESB_data1['Date'], format='%b %d, %Y')\n",
    "YESB_data2['Date']=pd.to_datetime(YESB_data2['Date'], format='%b %d, %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot of closing price of stock vs date  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cb5e962c50>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YESB_data1['Date'],YESB_data1['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot of CCI1(50 day CCI vs date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2040e163400>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YESB_data1['Date'],CCI1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot of CCI2(20 day CCI vs date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2040e4912b0>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YESB_data2['Date'],CCI2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#short term trading time frame and long term trading time frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CCI TRADING BIAS CREATED BY 50 day CCI\n",
    "#CCI TRADING SIGNALS CREATED BY 10 day CCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAIN ALGORITHM\n",
    "# CONSIDER 50 day periods \n",
    "# BEARISH AND BULLISH TRADING BIAS IS GAUGED BY CCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IF THE BIAS IS BULLISH\n",
    "#LOOK FOR BUY SIGNALS\n",
    "#IF BIAS IS BEARISH \n",
    "#LOOK FOR SELL SIGNALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#SELL SIGNAL(BEARISH BIAS)\n",
    "#A surge above +100 and then a move below the zero line for the 20 day cci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialize two arrays say a1 and b1\n",
    "#a1 stores value of index(starting from 62) which stores points where CCI(50 day) is greater than 100\n",
    "#b1 stores value of index(starting from 62) which stores points where CCI(50 day) is less than -100  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1=np.array([])\n",
    "b1=np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(62,556):\n",
    "    if CCI1[i]>100:\n",
    "        a1=np.append(a1,i)\n",
    "    if CCI1[i]<-100:\n",
    "        b1=np.append(b1,i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 91.,  92.,  93.,  94.,  95., 100., 101., 209., 268., 269., 270.,\n",
       "       271., 272., 273., 294., 295., 296., 297., 298., 299., 300., 301.,\n",
       "       302., 303., 304., 305., 306., 307., 308., 309., 310., 317., 318.,\n",
       "       319., 320., 323., 324., 325., 326., 327., 328., 335., 337., 338.,\n",
       "       339., 340., 341., 342., 343., 344., 345., 346., 347., 348., 349.,\n",
       "       350., 351., 352., 353., 354., 355., 356., 357., 358., 359., 365.,\n",
       "       371., 372., 373., 374., 376., 377., 378., 379., 380., 381., 382.,\n",
       "       383., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
       "       396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
       "       407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 500.,\n",
       "       501., 502., 503., 504., 505., 506., 507., 508., 509., 510., 511.,\n",
       "       512., 513., 514., 515., 516., 517., 518., 519., 524., 525., 543.,\n",
       "       544., 545., 551., 552., 553., 554., 555.])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 72.,  73.,  74., 110., 129., 141., 142., 153., 154., 155., 158.,\n",
       "       159., 160., 161., 162., 163., 164., 165., 166., 167., 168., 169.,\n",
       "       170., 171., 232., 233., 234., 235., 236., 237., 252., 253., 254.,\n",
       "       255., 256., 257., 258., 259., 260., 261., 262., 461., 462., 465.,\n",
       "       466., 467., 468., 469., 490.])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Our initial phase is bearish\n",
    "#Below contains intervals of inflexion i.e where bearish become bullish alternatively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval=np.array([62,91,110,209,232,268,461,500,555])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selling and Buying points are decide by 20 day CCI i.e CCI2\n",
    "#A bearish bias or bullish bias is derived using 50 day CCI i.e CCI1\n",
    "#Bearish bias gives sell signals and Bullish bias gives buy signals\n",
    "#Sell and Buy array points are decieded by 20 day CCI i.e. CCI2 after trading bias is known\n",
    "#i.e Bullish->buy\n",
    "#i.e Bearish->sell "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sell=np.array([])\n",
    "buy=np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(62,91):\n",
    "    if CCI2[i]<=0:\n",
    "        for j in range(62,i):\n",
    "            if CCI2[j]>=100:\n",
    "                sell=np.append(sell,i)\n",
    "        break       \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(91,110):\n",
    "    if CCI2[i]>=0:\n",
    "        for j in range(91,i):\n",
    "            if CCI2[j]<=-100:\n",
    "                buy=np.append(buy,i)\n",
    "        break       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(110,209):\n",
    "    if CCI2[i]<=0:\n",
    "        for j in range(110,i):\n",
    "            if CCI2[j]>=100:\n",
    "                sell=np.append(sell,i)\n",
    "                \n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(209,232):\n",
    "    if CCI2[i]>=0:\n",
    "        for j in range(209,i):\n",
    "            if CCI2[j]<=-100:\n",
    "                buy=np.append(buy,i)\n",
    "                break\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(232,268):\n",
    "    if CCI2[i]<=0:\n",
    "        for j in range(232,i):\n",
    "                if CCI2[j]>=100:\n",
    "                    sell=np.append(sell,i)\n",
    "                break\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(268,461):\n",
    "    if CCI2[i]>=0:\n",
    "        for j in range(268,461):\n",
    "            if CCI2[j]<=-100:\n",
    "                buy=np.append(buy,i)\n",
    "                break\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(461,500):\n",
    "    if CCI2[i]<=0:\n",
    "        for j in range(461,500):\n",
    "            if CCI2[j]>=100:\n",
    "                sell=np.append(sell,i)\n",
    "                break\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(500,556):\n",
    "    if CCI2[i]>=0:\n",
    "        for j in range(500,556):\n",
    "            if CCI2[j]<=-100:\n",
    "                buy=np.append(buy,i)\n",
    "                break\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Final buy signals are recorded on the following indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([268.])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Final sell signals are recorded on the following indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 71., 461.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Net gain or profit considering we have stock on 1st April"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Intial portfolio value is zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Profit1=YESB_data1.Close[71]-YESB_data1.Close[62]+YESB_data1.Close[461]-YESB_data1.Close[268]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1=YESB_data1.Close[71]-YESB_data1.Close[62]\n",
    "y2=YESB_data1.Close[461]-YESB_data1.Close[268]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "YESB_data_plot=np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,9):\n",
    "    YESB_data_plot=np.append(YESB_data_plot,0)\n",
    "for i in range(9,206):\n",
    "    YESB_data_plot=np.append(YESB_data_plot,y1)\n",
    "for i in range(206,399):\n",
    "    YESB_data_plot=np.append(YESB_data_plot,(YESB_data1.Close[i+62]+y1-YESB_data1.Close[268]))\n",
    "for i in range(399,495):\n",
    "    YESB_data_plot=np.append(YESB_data_plot,y1+y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Portfolio value using CCI correction trading strategy are plotted against date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Value')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_value=plt.plot(YESB_data1['Date'],YESB_data_plot)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Modified Trading Strategy \n",
    "#Extra parameters used as a constraint:\n",
    "#YESB_per : Difference between current stock price and 50 day moving average of stock price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "YESB_mean=data['Close'].rolling(50).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "YESB_mean=YESB_mean[62:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "YESB_per=((YESB_data1['Close']-YESB_mean)/(YESB_data1['Close']))*1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#YESB_per values are plotted against Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Value')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YESB_data1['Date'],YESB_per)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Modified selling points and buying points are recorded in following array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "selld=np.array([])\n",
    "buyd=np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Modified Trading Strategy implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(62,91):\n",
    "   \n",
    "    if YESB_per[i]<=-30 and YESB_data2['CCI'][i]>=30:\n",
    "        buyd=np.append(buyd,i)\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(91,110):\n",
    "   \n",
    "    if YESB_per[i]>=30 and YESB_data2['CCI'][i]<=30:\n",
    "        selld=np.append(selld,i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(110,209):\n",
    "   \n",
    "    if YESB_per[i]<=-30 and YESB_data2['CCI'][i]>=30:\n",
    "        buyd=np.append(buyd,i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(209,232):\n",
    "  \n",
    "    if YESB_per[i]>=30 and YESB_data2['CCI'][i]<=30:\n",
    "        selld=np.append(selld,i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(232,268):\n",
    "  \n",
    "    if YESB_per[i]<=-30 and YESB_data2['CCI'][i]>=30:\n",
    "        buyd=np.append(buyd,i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(268,461):\n",
    "    \n",
    "    if YESB_per[i]>=30 and YESB_data2['CCI'][i]<=30:\n",
    "        selld=np.append(selld,i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(461,500):\n",
    "    \n",
    "    if YESB_per[i]<=-30 and YESB_data2['CCI'][i]>=30:\n",
    "        buyd=np.append(buyd,i)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([176., 477.])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buyd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([211., 312.])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Final array which contains buying and selling points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalarr=[62,71,176,211,268,312,477]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Profit from new strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "Profit2=YESB_data1.Close[71]-YESB_data1.Close[62]+YESB_data1.Close[211]-YESB_data1.Close[176]+YESB_data1.Close[312]-YESB_data1.Close[268]+YESB_data1.Close[556]-YESB_data1.Close[477]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=YESB_data1.Close[71]-YESB_data1.Close[62]\n",
    "x2=YESB_data1.Close[211]-YESB_data1.Close[176]\n",
    "x3=YESB_data1.Close[312]-YESB_data1.Close[268]\n",
    "x4=YESB_data1.Close[556]-YESB_data1.Close[477]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "YESB_data_plot2=np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,9):\n",
    "    YESB_data_plot2=np.append(YESB_data_plot2,0)\n",
    "for i in range(9,114):\n",
    "    YESB_data_plot2=np.append(YESB_data_plot2,x1)\n",
    "for i in range(114,149):\n",
    "    YESB_data_plot2=np.append(YESB_data_plot2,(YESB_data1.Close[i+62])+x1-YESB_data1.Close[114])\n",
    "for i in range(149,206):\n",
    "    YESB_data_plot2=np.append(YESB_data_plot2,x2+x1)\n",
    "for i in range(206,250):\n",
    "    YESB_data_plot2=np.append(YESB_data_plot2,YESB_data.Close[i+62]+x1+x2-YESB_data1.Close[206])\n",
    "for i in range(250,415):\n",
    "    YESB_data_plot2=np.append(YESB_data_plot2,x1+x2+x3)\n",
    "for i in range(415,495):\n",
    "    YESB_data_plot2=np.append(YESB_data_plot2,YESB_data.Close[i+62]+x1+x2+x3-YESB_data1.Close[477])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#New portfolio value ploted against Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2040f635c88>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YESB_data1['Date'],YESB_data_plot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Profit due to new trading startegy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.47999999999996"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Profit2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initial portfolio value = 0 \n",
    "#1 stock of yes bank is assumed to be bought before start of period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=YESB_data_plot.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Table for strategy 1 i.e total returns vs deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "Table_deviation=pd.DataFrame({'total_returns': YESB_data_plot,\n",
    "              'deviation':YESB_data_plot-mean\n",
    "             })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean2=YESB_data_plot2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Table for strategy 2 i.e total returns vs deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "Table_deviation2=pd.DataFrame({'total_returns': YESB_data_plot2,\n",
    "              'deviation':YESB_data_plot2-mean2\n",
    "             })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>total_returns</th>\n",
       "      <th>deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.305091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-7.35</td>\n",
       "      <td>-14.655091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>59.90</td>\n",
       "      <td>52.594909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>60.78</td>\n",
       "      <td>53.474909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>58.48</td>\n",
       "      <td>51.174909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>62.64</td>\n",
       "      <td>55.334909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>62.32</td>\n",
       "      <td>55.014909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>61.58</td>\n",
       "      <td>54.274909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>57.02</td>\n",
       "      <td>49.714909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>63.22</td>\n",
       "      <td>55.914909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>63.40</td>\n",
       "      <td>56.094909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>57.94</td>\n",
       "      <td>50.634909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>59.73</td>\n",
       "      <td>52.424909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>64.57</td>\n",
       "      <td>57.264909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>62.37</td>\n",
       "      <td>55.064909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>66.89</td>\n",
       "      <td>59.584909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>65.73</td>\n",
       "      <td>58.424909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>68.72</td>\n",
       "      <td>61.414909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>79.03</td>\n",
       "      <td>71.724909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>78.76</td>\n",
       "      <td>71.454909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>78.40</td>\n",
       "      <td>71.094909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>74.27</td>\n",
       "      <td>66.964909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>75.32</td>\n",
       "      <td>68.014909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>70.02</td>\n",
       "      <td>62.714909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>66.70</td>\n",
       "      <td>59.394909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>75.87</td>\n",
       "      <td>68.564909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>78.44</td>\n",
       "      <td>71.134909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>78.23</td>\n",
       "      <td>70.924909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>79.72</td>\n",
       "      <td>72.414909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>81.59</td>\n",
       "      <td>74.284909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>83.05</td>\n",
       "      <td>75.744909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>82.48</td>\n",
       "      <td>75.174909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>495 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_returns  deviation\n",
       "0             0.00  -7.305091\n",
       "1             0.00  -7.305091\n",
       "2             0.00  -7.305091\n",
       "3             0.00  -7.305091\n",
       "4             0.00  -7.305091\n",
       "5             0.00  -7.305091\n",
       "6             0.00  -7.305091\n",
       "7             0.00  -7.305091\n",
       "8             0.00  -7.305091\n",
       "9            -7.35 -14.655091\n",
       "10           -7.35 -14.655091\n",
       "11           -7.35 -14.655091\n",
       "12           -7.35 -14.655091\n",
       "13           -7.35 -14.655091\n",
       "14           -7.35 -14.655091\n",
       "15           -7.35 -14.655091\n",
       "16           -7.35 -14.655091\n",
       "17           -7.35 -14.655091\n",
       "18           -7.35 -14.655091\n",
       "19           -7.35 -14.655091\n",
       "20           -7.35 -14.655091\n",
       "21           -7.35 -14.655091\n",
       "22           -7.35 -14.655091\n",
       "23           -7.35 -14.655091\n",
       "24           -7.35 -14.655091\n",
       "25           -7.35 -14.655091\n",
       "26           -7.35 -14.655091\n",
       "27           -7.35 -14.655091\n",
       "28           -7.35 -14.655091\n",
       "29           -7.35 -14.655091\n",
       "..             ...        ...\n",
       "465          59.90  52.594909\n",
       "466          60.78  53.474909\n",
       "467          58.48  51.174909\n",
       "468          62.64  55.334909\n",
       "469          62.32  55.014909\n",
       "470          61.58  54.274909\n",
       "471          57.02  49.714909\n",
       "472          63.22  55.914909\n",
       "473          63.40  56.094909\n",
       "474          57.94  50.634909\n",
       "475          59.73  52.424909\n",
       "476          64.57  57.264909\n",
       "477          62.37  55.064909\n",
       "478          66.89  59.584909\n",
       "479          65.73  58.424909\n",
       "480          68.72  61.414909\n",
       "481          79.03  71.724909\n",
       "482          78.76  71.454909\n",
       "483          78.40  71.094909\n",
       "484          74.27  66.964909\n",
       "485          75.32  68.014909\n",
       "486          70.02  62.714909\n",
       "487          66.70  59.394909\n",
       "488          75.87  68.564909\n",
       "489          78.44  71.134909\n",
       "490          78.23  70.924909\n",
       "491          79.72  72.414909\n",
       "492          81.59  74.284909\n",
       "493          83.05  75.744909\n",
       "494          82.48  75.174909\n",
       "\n",
       "[495 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table_deviation2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bar graph showing returns due to both strategies\n",
    "#Left one is CCI correction\n",
    "#Right one is CCI correction (modified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=[Profit1,Profit2]\n",
    "X = np.arange(2)\n",
    "plt.bar(X + 0.00, data, color = 'b', width = 0.5)\n",
    "plt.xlabel('Return')\n",
    "plt.ylabel('Portfolio Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}