MACD_Zero,_Signal_crossover_and_Divergence_Strategies_Backtesting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E_QaT3ddHy86"
   },
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   "source": [
    "#Importing required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_datareader import data\n",
    "import requests\n",
    "from math import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "%matplotlib inline\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "plotly.tools.set_credentials_file(username='sudsawal', api_key='2Y21xZQ1sHCU8JSbrWIp')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uXUl8iJQHy8-"
   },
   "outputs": [],
   "source": [
    "portfolio = 1e5 #Portfolio total worth of 1 lakh\n",
    "tscost = 0.005 #Per trade transaction cost of 0.5%, so if two stocks of 30 each are bought, (1+0.005)*2*30 is charged in total\n",
    "nstocks = 0\n",
    "\n",
    "stock_list = ['BHEL.NS','ITC.NS','LUPIN.NS','RELIANCE.NS','IBVENTURES.NS'] #Stock list, to add a stock simply\n",
    "                                                                           #add the ticker according to Yahoo! Finance\n",
    "for stock in stock_list:\n",
    "    nstocks+=1          #Calculates number of stocks according to input stock list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IJnCQYTXHy9A"
   },
   "outputs": [],
   "source": [
    "#Function to read Data\n",
    "def readData(ticker, n):\n",
    "    stocks.append(data.DataReader(ticker, 'yahoo',start='1/1/2015')) #Taking backtesting period as roughly 5 years\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WrTdcN7iHy9B"
   },
   "outputs": [],
   "source": [
    "#Defines MACD of each ticker\n",
    "def MACD(df1):\n",
    "    df1['EMA_small'] = df1['Adj Close'].ewm(span=12).mean()\n",
    "    df1['EMA_large'] = df1['Adj Close'].ewm(span=26).mean()\n",
    "    df1['MACD_line'] = df1['EMA_small'] - df1['EMA_large']\n",
    "    df1['Signal_line'] = df1['MACD_line'].ewm(span=9).mean()\n",
    "    \n",
    "    df1['Position'] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ypesnW4yHy9D"
   },
   "outputs": [],
   "source": [
    "#Defines the Position based on a MACD Zero crossover strategy as defined below:\n",
    "\n",
    "########################################################################################################################\n",
    "#Entry:  - When MACD line crosses zero line(>0), then long/buy the stock. Similar for squaring off the short position. #\n",
    "#                                                                                                                      #\n",
    "#Exit: -   When MACD line crosses zero line(<0), then short/sell the stock. Similar for squaring off the long position.#                           \n",
    "#                                                                                                                      #\n",
    "########################################################################################################################\n",
    "\n",
    "\n",
    "def ZeroCrossover(df1):\n",
    "    for row in range(len(df1)):\n",
    "        if(df1['Position'].iloc[row-1]==0):\n",
    "            if (df1['MACD_line'].iloc[row] < 0):\n",
    "                df1['Position'].iloc[row] = -1\n",
    "\n",
    "            if (df1['MACD_line'].iloc[row] > 0):\n",
    "                df1['Position'].iloc[row] = 1\n",
    "\n",
    "            \n",
    "        elif (df1['Position'].iloc[row-1]==1):\n",
    "            if (df1['MACD_line'].iloc[row] < 0):\n",
    "                df1['Position'].iloc[row] = 0\n",
    "            if (df1['MACD_line'].iloc[row] > 0):\n",
    "                df1['Position'].iloc[row] = 1\n",
    "\n",
    "        else:\n",
    "            if (df1['MACD_line'].iloc[row] > 0):\n",
    "                df1['Position'].iloc[row] = 0\n",
    "            if (df1['MACD_line'].iloc[row] < 0):\n",
    "                df1['Position'].iloc[row] = -1\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YlbglKzrGNrl"
   },
   "outputs": [],
   "source": [
    "#Defines the Position based on a MACD Signal Crossover strategy as defined below:\n",
    "\n",
    "########################################################################################################################\n",
    "#Entry: -When MACD line crosses signal line and greater than it, then long/buy the stock.                              #\n",
    "#       - Similar for squaring off the short position.                                                                 #\n",
    "#Exit:  -When MACD line crosses signal line and less than it, then short/sell the stock.                               #\n",
    "#       -Similar for squaring off the long position.                                                                   #                           \n",
    "#                                                                                                                      #\n",
    "########################################################################################################################\n",
    "\n",
    "\n",
    "def SignalCrossover(df1):\n",
    "    for row in range(len(df1)):\n",
    "        if(df1['Position'].iloc[row-1]==0):\n",
    "            if (df1['MACD_line'].iloc[row] < df1['Signal_line'].iloc[row]):\n",
    "                df1['Position'].iloc[row] = -1\n",
    "\n",
    "            if (df1['MACD_line'].iloc[row] > df1['Signal_line'].iloc[row]):\n",
    "                df1['Position'].iloc[row] = 1\n",
    "\n",
    "            \n",
    "        elif (df1['Position'].iloc[row-1]==1):\n",
    "            if (df1['MACD_line'].iloc[row] < df1['Signal_line'].iloc[row]):\n",
    "                df1['Position'].iloc[row] = 0\n",
    "            if (df1['MACD_line'].iloc[row] > df1['Signal_line'].iloc[row]):\n",
    "                df1['Position'].iloc[row] = 1\n",
    "\n",
    "        else:\n",
    "            if (df1['MACD_line'].iloc[row] > df1['Signal_line'].iloc[row]):\n",
    "                df1['Position'].iloc[row] = 0\n",
    "            if (df1['MACD_line'].iloc[row] < df1['Signal_line'].iloc[row]):\n",
    "                df1['Position'].iloc[row] = -1\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "btYb4wFVI5TO"
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   "outputs": [],
   "source": [
    "#Defines the Position based on a MACD Divergence(in a given window) strategy as defined below:\n",
    "\n",
    "########################################################################################################################\n",
    "#Entry: -When closing price records a higher high, but the MACD records a lower high. then long/buy the stock.         #\n",
    "#       -Similar for squaring off the short position.                                                                  #\n",
    "#Exit:  -When closing price records a lower low, but the MACD records a higher low. Then short/sell the stock.         #\n",
    "#       -Similar for squaring off the long position.                                                                   #                           \n",
    "#                                                                                                                      #\n",
    "########################################################################################################################\n",
    "\n",
    "def Divergence(df1):\n",
    "    for row in range(1,len(df1)):\n",
    "        if(df1['Position'].iloc[row-1]==0):\n",
    "            if (df1['Adj Close'].rolling(20).max()[row] > df1['Adj Close'].rolling(20).max()[row-1]) and (df1['MACD_line'].rolling(20).max()[row] <= df1['MACD_line'].rolling(20).max()[row-1]):\n",
    "                df1['Position'].iloc[row] = 1\n",
    "\n",
    "            if (df1['Adj Close'].rolling(20).min()[row] < df1['Adj Close'].rolling(20).min()[row-1]) and (df1['MACD_line'].rolling(20).min()[row] >= df1['MACD_line'].rolling(20).min()[row-1]):\n",
    "                df1['Position'].iloc[row] = -1\n",
    "\n",
    "            \n",
    "        elif (df1['Position'].iloc[row-1]==1):\n",
    "            if (df1['Adj Close'].rolling(20).max()[row] > df1['Adj Close'].rolling(20).max()[row-1]) and (df1['MACD_line'].rolling(20).max()[row] <= df1['MACD_line'].rolling(20).max()[row-1]):\n",
    "                df1['Position'].iloc[row] = 1\n",
    "            if (df1['Adj Close'].rolling(20).min()[row] < df1['Adj Close'].rolling(20).min()[row-1]) and (df1['MACD_line'].rolling(20).min()[row] >= df1['MACD_line'].rolling(20).min()[row-1]):\n",
    "                df1['Position'].iloc[row] = 0\n",
    "\n",
    "        else:\n",
    "            if (df1['Adj Close'].rolling(20).min()[row] < df1['Adj Close'].rolling(20).min()[row-1]) and (df1['MACD_line'].rolling(20).min()[row] >= df1['MACD_line'].rolling(20).min()[row-1]):\n",
    "                df1['Position'].iloc[row] = -1\n",
    "            if (df1['Adj Close'].rolling(20).max()[row] > df1['Adj Close'].rolling(20).max()[row-1]) and (df1['MACD_line'].rolling(20).max()[row] <= df1['MACD_line'].rolling(20).max()[row-1]):\n",
    "                df1['Position'].iloc[row] = 0\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Gz2jiRz-u_6"
   },
   "outputs": [],
   "source": [
    "def computer():\n",
    "  s = (len(stocks[0]),nstocks) #setting the dimensions of the weights vector depending on the available data points\n",
    "  weights = np.zeros(s) \n",
    "  sumwts = 0\n",
    "\n",
    "  for row in range(len(stocks[0])):\n",
    "      i = 0\n",
    "      for i in range(nstocks):\n",
    "          sumwts+=np.abs(stocks[i]['Position'].iloc[row]) #checking how many positions were taken on that day\n",
    "      for i in range(nstocks):\n",
    "          if sumwts>0:\n",
    "              #weights assigned as:\n",
    "              #weights = (portfolio*position)/(no.of positions * adj close on that day * (1+transac cost))\n",
    "              weights[row][i]=(portfolio*stocks[i]['Position'].iloc[row])/((sumwts*stocks[i]['Adj Close'].iloc[row])*(1+tscost))\n",
    "          else:\n",
    "              weights[row][i]=0\n",
    "  i = 0\n",
    "\n",
    "\n",
    "  for i in range(nstocks):\n",
    "      stocks[i]['Market Return'] = (stocks[i]['Adj Close'] - stocks[i]['Adj Close'].shift(1)) #Absolute market return\n",
    "\n",
    "    \n",
    "  #Calculating portfolio return\n",
    "  stocks[0]['Strategy Return']=0\n",
    "  i = 0\n",
    "  for i in range(nstocks):\n",
    "      stocks[0]['Strategy Return']+=stocks[i]['Market Return'] * weights[:,i] #multiplying market return of each\n",
    "                                                                              #ticker with its weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wH6IFRECgLG_"
   },
   "source": [
    "# **Zero Crossover**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WSTwcAtPHy9G"
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-238b6e757485>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mZeroCrossover\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstocks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mi\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mcomputer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-8-4075ca167191>\u001b[0m in \u001b[0;36mcomputer\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m       \u001b[0mi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m       \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnstocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m           \u001b[0msumwts\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstocks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Position'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#checking how many positions were taken on that day\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m       \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnstocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[0msumwts\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1477\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1478\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1479\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1480\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   2100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2101\u001b[0m             \u001b[1;31m# validate the location\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2102\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2104\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   2007\u001b[0m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2008\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2009\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2010\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2011\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "#Iterating over all the tickers to read data, generate MACD and zero crossover conditions and generate positions based on strategy\n",
    "i = 0\n",
    "stocks = []\n",
    "for ticker in stock_list:\n",
    "    readData(ticker,i)\n",
    "    MACD(stocks[i])\n",
    "    ZeroCrossover(stocks[i])\n",
    "    i+=1\n",
    "computer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "colab_type": "code",
    "id": "FN006IqdNl9R",
    "outputId": "830ef671-f6b7-452d-ea02-034b42cd4c3b"
   },
   "outputs": [],
   "source": [
    "trace = go.Scatter(\n",
    "  text = ((100 * (stocks[0]['Strategy Return'][64:].cumsum())/portfolio).values),\n",
    "  x = stocks[0]['Strategy Return'][64:].cumsum().index,\n",
    "  y = (stocks[0]['Strategy Return'][64:].cumsum())\n",
    ")\n",
    "data1 = [trace]\n",
    "layout = go.Layout(\n",
    "    title='Hover over the points to see the text'\n",
    ")\n",
    "fig1 = go.Figure(data=data1, layout=layout)\n",
    "py.iplot(data1, filename='basic-line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cRNVQzxsgXRf"
   },
   "source": [
    "# **Signal Line Crossover**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g9n2vhIZD34e"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (1107,) (1106,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-7789fc333864>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mSignalCrossover\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstocks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mi\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mcomputer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-8-4075ca167191>\u001b[0m in \u001b[0;36mcomputer\u001b[1;34m()\u001b[0m\n\u001b[0;32m     26\u001b[0m   \u001b[0mi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m   \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnstocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m       \u001b[0mstocks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Strategy Return'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mstocks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Market Return'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#multiplying market return of each\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m                                                                               \u001b[1;31m#ticker with its weight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(left, right)\u001b[0m\n\u001b[0;32m   1064\u001b[0m             \u001b[0mrvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1065\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1066\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msafe_na_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1067\u001b[0m         return construct_result(left, result,\n\u001b[0;32m   1068\u001b[0m                                 index=left.index, name=res_name, dtype=None)\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36msafe_na_op\u001b[1;34m(lvalues, rvalues)\u001b[0m\n\u001b[0;32m   1028\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1029\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1030\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mna_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1031\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1032\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m   1007\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1008\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1009\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meval_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1010\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1011\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(op, op_str, a, b, use_numexpr, **eval_kwargs)\u001b[0m\n\u001b[0;32m    203\u001b[0m     \u001b[0muse_numexpr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0muse_numexpr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0m_bool_arith_check\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0muse_numexpr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0meval_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    206\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[1;34m(op, op_str, a, b, truediv, reversed, **eval_kwargs)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\pandas\\core\\computation\\expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[1;34m(op, op_str, a, b, **eval_kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[0m_store_test_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (1107,) (1106,) "
     ]
    }
   ],
   "source": [
    "#Iterating over all the tickers to read data, generate MACD and give the positions based on strategy\n",
    "i = 0\n",
    "stocks = []\n",
    "for ticker in stock_list:\n",
    "    readData(ticker,i)\n",
    "    MACD(stocks[i])\n",
    "    SignalCrossover(stocks[i])\n",
    "    i+=1\n",
    "computer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "colab_type": "code",
    "id": "bYEms2ABHFaQ",
    "outputId": "d48138f7-fd3e-4d66-8cdb-e6f25073c47f"
   },
   "outputs": [],
   "source": [
    "trace = go.Scatter(\n",
    "  text = ((100 * (stocks[0]['Strategy Return'][64:].cumsum())/portfolio).values),\n",
    "  x = stocks[0]['Strategy Return'][64:].cumsum().index,\n",
    "  y = (stocks[0]['Strategy Return'][64:].cumsum())\n",
    ")\n",
    "data2 = [trace]\n",
    "layout = go.Layout(\n",
    "    title='Hover over the points to see the text'\n",
    ")\n",
    "fig2 = go.Figure(data=data2, layout=layout)\n",
    "py.iplot(data2, filename='basic-line2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bcR1-Uj5gbbK"
   },
   "source": [
    "# **MACD Divergence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K-3Qa6dUHiab"
   },
   "outputs": [],
   "source": [
    "#Iterating over all the tickers to read data, generate MACD and generate positions based on Divergence strategy\n",
    "i = 0\n",
    "stocks = []\n",
    "for ticker in stock_list:\n",
    "    readData(ticker,i)\n",
    "    MACD(stocks[i])\n",
    "    Divergence(stocks[i])\n",
    "    i+=1\n",
    "computer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "colab_type": "code",
    "id": "GCMImZ2sL4gX",
    "outputId": "bbb4e7fd-cbb7-4a8b-b8fa-d2ebf0958fcc"
   },
   "outputs": [],
   "source": [
    "trace = go.Scatter(\n",
    "  text = ((100 * (stocks[0]['Strategy Return'][64:].cumsum())/portfolio).values),\n",
    "  x = stocks[0]['Strategy Return'][64:].cumsum().index,\n",
    "  y = (stocks[0]['Strategy Return'][64:].cumsum())\n",
    ")\n",
    "data3 = [trace]\n",
    "layout = go.Layout(\n",
    "    title='Hover over the points to see the text'\n",
    ")\n",
    "fig3 = go.Figure(data=data3, layout=layout)\n",
    "py.iplot(data3, filename='basic-line3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hdRJ3Li-bAWX"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "MACD Zero, Signal crossover and Divergence Strategies Backtesting.ipynb",
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  "kernelspec": {
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