MACD Divergence and Crossover Backtest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MACD Divergence and Crossover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the important libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#portfolio value for allocating using crossover \n",
    "co_portfolio = 1e5\n",
    "\n",
    "#portfolio value for allocating using divergence \n",
    "div_portfolio = 1e5\n",
    "\n",
    "#transaction cost as fraction\n",
    "t_cost = 0.005\n",
    "\n",
    "#list of stocks in portfolio\n",
    "stock_list = ['BHEL.NS','ITC.NS','LUPIN.NS','RELIANCE.NS','IBVENTURES.NS']\n",
    "\n",
    "#calculating the no. of stocks in the portfolio\n",
    "nstocks=0\n",
    "for stock in stock_list:\n",
    "    nstocks+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to read stock data\n",
    "def read_data(ticker):\n",
    "    df = data.DataReader(ticker, 'yahoo',start='1/1/2015')\n",
    "    stocks.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to generate macd line and signal line and related fields in all stock dataframe\n",
    "def MACDLines(df):\n",
    "    #Calculate 12 Day EMA\n",
    "    df['12 Day EMA'] = df['Adj Close'].ewm(span=12).mean()\n",
    "    \n",
    "    #Calculate 26 Dat EMA\n",
    "    df['26 Day EMA'] = df['Adj Close'].ewm(span=26).mean()\n",
    "    \n",
    "    #Calculate MACD value\n",
    "    df['MACD line'] = df['12 Day EMA'] - df['26 Day EMA']\n",
    "    \n",
    "    #Calculate Signal line as 9 day EMA of MACD \n",
    "    df['Signal line'] = df['MACD line'].ewm(span=9).mean()\n",
    "    \n",
    "    # Initialize position based on crossover for each day as 0\n",
    "    df['CO_Position'] = 0\n",
    "    # Initialize position based on divergence for each day as 0\n",
    "    df['Div_Position'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to give positions for each day based on MACD crossover strategy\n",
    "\n",
    "#Entry: If MACD line goes from above to below(yesterday it was above and today it is down) the Signal line then short the stock.\n",
    "#       If MACD line goes from down to above (yesterday it was down and today it is above) the signal line then buy the stock.\n",
    "#Exit: If you are long on the stock and the MACD line is below the signal line then exit the position\n",
    "#      If you are short on the stock and the MACD line is above the signal line then exit the position\n",
    "\n",
    "\n",
    "def MACDCross(df):\n",
    "    for row in range(len(df)):\n",
    "        if (df['CO_Position'].iloc[row-1]==0):\n",
    "            if (df['MACD line'].iloc[row-1]>df['Signal line'].iloc[row-1]) and (df['MACD line'].iloc[row]<df['Signal line'].iloc[row]):\n",
    "                df['CO_Position'].iloc[row] = -1\n",
    "            if (df['MACD line'].iloc[row-1]<df['Signal line'].iloc[row-1]) and (df['MACD line'].iloc[row]>df['Signal line'].iloc[row]):\n",
    "                df['CO_Position'].iloc[row] = 1\n",
    "        \n",
    "        \n",
    "        if (df['CO_Position'].iloc[row-1]==1):\n",
    "            if (df['MACD line'].iloc[row]<df['Signal line'].iloc[row]):\n",
    "                df['CO_Position'].iloc[row]=0\n",
    "            else:\n",
    "                df['CO_Position'].iloc[row] = 1\n",
    "        \n",
    "        \n",
    "        if (df['CO_Position'].iloc[row-1]==-1):\n",
    "            if (df['MACD line'].iloc[row]>df['Signal line'].iloc[row]):\n",
    "                df['CO_Position'].iloc[row]=0\n",
    "            else:\n",
    "                df['CO_Position'].iloc[row] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to give posiitions based on MACD DIvergence and not startegizing\n",
    "# Entry: if the MACD value goes from negetive to positive, long the stock\n",
    "#        if the MACD value goes from positive to negetive teritory short the stock\n",
    "# Exit: if the MACD value again goes to negetive teritory then exit from long position\n",
    "#       if the MACD value again goes to the positive teritory then exit the short position\n",
    "\n",
    "\n",
    "########\n",
    "# REVIEW COMMENTS: This is not actually a divergence strategy, but in fact, a zero line crossover\n",
    "########\n",
    "def MACD_div(df):\n",
    "    for row in range(len(df)):\n",
    "        if (df['Div_Position'].iloc[row-1]==0):\n",
    "            if (df['MACD line'].iloc[row-1]<=0) and (df['MACD line'].iloc[row]>=0):\n",
    "                df['Div_Position'].iloc[row]=1\n",
    "            if (df['MACD line'].iloc[row-1]>=0) and (df['MACD line'].iloc[row]<=0):\n",
    "                df['Div_Position'].iloc[row]=-1\n",
    "                \n",
    "        if (df['Div_Position'].iloc[row-1]==1):\n",
    "            if (df['MACD line'].iloc[row]<0):\n",
    "                df['Div_Position'].iloc[row]=0\n",
    "            else:\n",
    "                df['Div_Position'].iloc[row]=1\n",
    "                \n",
    "        if (df['Div_Position'].iloc[row-1]==-1):\n",
    "            if (df['MACD line'].iloc[row]>0):\n",
    "                df['Div_Position'].iloc[row]=0\n",
    "            else:\n",
    "                df['Div_Position'].iloc[row]=-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "#Initialize the list of stock dataframes\n",
    "stocks = []\n",
    "\n",
    "#Iterate and make the dataframes using the functions\n",
    "i = 0\n",
    "for ticker in stock_list:\n",
    "    \n",
    "    #reading data for each stock\n",
    "    read_data(ticker)\n",
    "    \n",
    "    #MACD line for each stock\n",
    "    MACDLines(stocks[i])\n",
    "    \n",
    "    # taking position by MACD Crossover strategy\n",
    "    MACDCross(stocks[i])\n",
    "    \n",
    "    # taking position by MACD Divergence strategy\n",
    "    MACD_div(stocks[i])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting the dimensions of the weights vector depending on the available data points\n",
    "s = (len(stocks[0]),nstocks) \n",
    "\n",
    "#Inititializing the weight vector of each stock each day to be 0\n",
    "co_weights = np.zeros(s)\n",
    "div_weights = np.zeros(s)\n",
    "\n",
    "#Giving weights for crossover portfolio\n",
    "sumwts=0\n",
    "for row in range(len(stocks[0])):\n",
    "    \n",
    "    # Calculating no. of position taken that day\n",
    "    sumwts = 0\n",
    "    for i in range(nstocks):\n",
    "        sumwts+=np.abs(stocks[i]['CO_Position'].iloc[row]) \n",
    "    \n",
    "    \n",
    "    #Calculating weights for each day\n",
    "    for i in range(nstocks):\n",
    "        if sumwts>0:\n",
    "            co_weights[row][i]=(co_portfolio*stocks[i]['CO_Position'].iloc[row])/((sumwts*stocks[i]['Adj Close'].iloc[row])*(1+t_cost))\n",
    "\n",
    "\n",
    "\n",
    "#Giving weights for divergence portfolio\n",
    "sumwts=0\n",
    "for row in range(len(stocks[0])):\n",
    "    \n",
    "    # Calculating no. of position taken that day\n",
    "    sumwts = 0\n",
    "    for i in range(nstocks):\n",
    "        sumwts+=np.abs(stocks[i]['Div_Position'].iloc[row]) \n",
    "    \n",
    "    \n",
    "    #Calculating weights for each day\n",
    "    for i in range(nstocks):\n",
    "        if sumwts>0:\n",
    "            div_weights[row][i]=(div_portfolio*stocks[i]['Div_Position'].iloc[row])/((sumwts*stocks[i]['Adj Close'].iloc[row])*(1+t_cost))\n",
    "\n",
    "\n",
    "            \n",
    "#Calculating market return\n",
    "for i in range(nstocks):\n",
    "    stocks[i]['Market Return'] = (stocks[i]['Adj Close'] - stocks[i]['Adj Close'].shift(1))\n",
    "\n",
    "    \n",
    "#Calculating portfolio return for crossover\n",
    "stocks[0]['Crossover Strategy Return']=0\n",
    "for i in range(nstocks):\n",
    "    stocks[0]['Crossover Strategy Return']+=stocks[i]['Market Return'] * co_weights[:,i]\n",
    "\n",
    "    \n",
    "    \n",
    "#Calculating portfolio return for divergence\n",
    "stocks[0]['Divergence Strategy Return']=0\n",
    "for i in range(nstocks):\n",
    "    stocks[0]['Divergence Strategy Return']+=stocks[i]['Market Return'] * div_weights[:,i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21799064128>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting both strategy returns\n",
    "stocks[0]['Crossover Strategy Return'].cumsum().plot(figsize=(12,8), label='Crossover Strategy Return')\n",
    "stocks[0]['Divergence Strategy Return'].cumsum().plot(label='Divergence Strategy Return')\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.ylabel('Return')\n",
    "plt.xlabel('Date')\n",
    "plt.title('MACD Crossover and Divergence Strategy PnL')\n",
    "plt.legend()"
   ]
  },
  {
   "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
}