Python for Trading on Technical - RSI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date        Open        High         Low       Close   Adj Close  \\\n",
      "0  2018-01-01  518.849976  522.250000  515.000000  516.775024  474.472595   \n",
      "1  2018-01-02  518.625000  521.000000  511.500000  514.849976  472.705139   \n",
      "2  2018-01-03  514.250000  515.799988  509.299988  510.649994  468.848938   \n",
      "3  2018-01-04  510.500000  510.500000  504.799988  507.700012  466.140472   \n",
      "4  2018-01-05  507.649994  513.200012  503.000000  506.000000  464.579559   \n",
      "\n",
      "     Volume  \n",
      "0   5431340  \n",
      "1   6112248  \n",
      "2   6846552  \n",
      "3   8947614  \n",
      "4  11025976  \n"
     ]
    }
   ],
   "source": [
    "#Import data from file saved on system\n",
    "Infy = pd.read_csv('Infy-His-data2018-20.csv')\n",
    "print (Infy.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date        Close      delta         up       down    roll_up  \\\n",
      "0    2018-01-01   516.775024        NaN        NaN        NaN        NaN   \n",
      "1    2018-01-02   514.849976  -1.925049   0.000000  -1.925049        NaN   \n",
      "2    2018-01-03   510.649994  -4.199982   0.000000  -4.199982        NaN   \n",
      "3    2018-01-04   507.700012  -2.949982   0.000000  -2.949982        NaN   \n",
      "4    2018-01-05   506.000000  -1.700012   0.000000  -1.700012        NaN   \n",
      "..          ...          ...        ...        ...        ...        ...   \n",
      "732  2020-12-23  1253.050049  32.550049  32.550049   0.000000  11.967869   \n",
      "733  2020-12-24  1236.050049 -17.000000   0.000000 -17.000000  11.382150   \n",
      "734  2020-12-28  1240.300049   4.250000   4.250000   0.000000  11.053580   \n",
      "735  2020-12-29  1250.300049  10.000000  10.000000   0.000000  11.064296   \n",
      "736  2020-12-30  1246.800049  -3.500000   0.000000  -3.500000   9.503584   \n",
      "\n",
      "     roll_down        RS        RSI  \n",
      "0          NaN       NaN        NaN  \n",
      "1          NaN       NaN        NaN  \n",
      "2          NaN       NaN        NaN  \n",
      "3          NaN       NaN        NaN  \n",
      "4          NaN       NaN        NaN  \n",
      "..         ...       ...        ...  \n",
      "732   2.925005  4.091572  80.359700  \n",
      "733   4.139291  2.749783  73.331787  \n",
      "734   4.139291  2.670404  72.755045  \n",
      "735   4.139291  2.672993  72.774248  \n",
      "736   4.389291  2.165175  68.406172  \n",
      "\n",
      "[737 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Get just the close in file 'T1'\n",
    "T1 = pd.DataFrame({\"Date\": Infy[\"Date\"], \"Close\": Infy[\"Close\"]})\n",
    "\n",
    "# Window length for moving average\n",
    "window_length = 14\n",
    "\n",
    "# Get the difference in price from previous step\n",
    "T1['delta'] = T1['Close'].diff()\n",
    "\n",
    "\n",
    "# Make the positive gains (up) and negative gains (down) Series\n",
    "T1['up'] = T1['delta']\n",
    "T1['down'] = T1['delta']\n",
    "\n",
    "for x in range (len(T1)):\n",
    "    if T1.up[x] < 0:\n",
    "        T1.up[x] = 0\n",
    "    if T1.down[x] > 0:\n",
    "        T1.down[x] = 0\n",
    "\n",
    "# Calculate the SMA\n",
    "T1['roll_up'] = T1['up'].rolling(window_length).mean()\n",
    "T1['roll_down'] = T1['down'].abs().rolling(window_length).mean()\n",
    "\n",
    "# Calculate the RSI based on SMA\n",
    "T1['RS'] = T1['roll_up'] / T1['roll_down']\n",
    "T1['RSI'] = 100.0 - (100.0 / (1.0 + T1['RS']))\n",
    "\n",
    "print (T1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x96477f0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "T1['positions_long'] = np.nan\n",
    "for x in range (len(T1)):\n",
    "    if T1.RSI[x] < 30:\n",
    "        T1['positions_long'][x] = 1\n",
    "    if T1.RSI[x] >= 50:\n",
    "        T1['positions_long'][x] = 0\n",
    "T1.positions_long = T1.positions_long.fillna(method='ffill')  \n",
    "\n",
    "T1['positions_short'] = np.nan\n",
    "for x in range (len(T1)):\n",
    "    if T1.RSI[x] > 70:\n",
    "        T1['positions_short'][x] = -1\n",
    "    if T1.RSI[x] <= 50:\n",
    "        T1['positions_short'][x] = 0\n",
    "T1.positions_short = T1.positions_short.fillna(method='ffill')  \n",
    "\n",
    "T1['positions'] = T1.positions_long + T1.positions_short \n",
    "T1['price_difference']= T1.Close - T1.Close.shift(1)\n",
    "T1['pnl'] = T1.positions.shift(1) * T1.price_difference\n",
    "T1['cumpnl'] = T1.pnl.cumsum()\n",
    "\n",
    "# Calculate the max drawdown in the past window days for each day \n",
    "T1['rolling_max'] = T1['cumpnl'].rolling(250, min_periods=1).max()\n",
    "T1['daily_drawdown'] = T1['cumpnl']-T1['rolling_max']\n",
    "\n",
    "# Calculate the minimum (negative) daily drawdown\n",
    "T1['max_daily_drawdown'] = T1['daily_drawdown'].rolling(250, min_periods=1).min()\n",
    "\n",
    "fig, axs = plt.subplots(2,figsize=(15,8))\n",
    "axs[0].plot(T1['cumpnl'])\n",
    "axs[1].plot(T1['max_daily_drawdown'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Trade count 27\n",
      "           Date        Close        RSI      cumpnl  Trade  Trade_Return\n",
      "25   2018-02-06   553.875000  45.673362   17.849976    1.0           NaN\n",
      "30   2018-02-14   548.875000  25.918083   17.849976    1.0      0.000000\n",
      "36   2018-02-22   580.375000  54.958559   49.349976   -1.0      5.427525\n",
      "62   2018-04-04   562.099976  27.827288   49.349976    1.0      0.000000\n",
      "69   2018-04-13   585.724976  52.716058   72.974976   -1.0      4.033463\n",
      "108  2018-06-08   630.025024  70.616844   72.974976   -1.0      0.000000\n",
      "179  2018-09-21   705.299988  48.659653   -2.299988    1.0    -10.672758\n",
      "199  2018-10-23   657.599976  27.908687   -2.299988    1.0      0.000000\n",
      "213  2018-11-13   665.700012  52.969220    5.800049   -1.0      1.216770\n",
      "219  2018-11-21   619.099976  20.559953    5.800049    1.0      0.000000\n",
      "225  2018-11-30   667.450012  53.313879   54.150085   -1.0      7.243994\n",
      "233  2018-12-12   678.500000  72.706423   54.150085   -1.0      0.000000\n",
      "239  2018-12-20   667.049988  49.859837   65.600098    1.0      1.716515\n",
      "249  2019-01-04   661.049988  28.612182   65.600098    1.0      0.000000\n",
      "252  2019-01-09   676.099976  56.830593   80.650085   -1.0      2.226000\n",
      "254  2019-01-11   683.500000  77.960954   80.650085   -1.0      0.000000\n",
      "280  2019-02-19   724.299988  48.039195   39.850098    1.0     -5.633023\n",
      "298  2019-03-18   710.200012  23.169296   39.850098    1.0      0.000000\n",
      "300  2019-03-20   738.950012  52.002169   68.600098   -1.0      3.890656\n",
      "306  2019-04-01   755.099976  71.132003   68.600098   -1.0      0.000000\n",
      "316  2019-04-15   727.500000  46.766791   96.200073    1.0      3.793811\n",
      "338  2019-05-21   709.299988  29.377154   96.200073    1.0      0.000000\n",
      "343  2019-05-28   728.099976  54.554911  115.000061   -1.0      2.582061\n",
      "352  2019-06-11   754.900024  76.982212  115.000061   -1.0      0.000000\n",
      "364  2019-06-27   730.549988  44.242437  139.350098    1.0      3.333110\n",
      "371  2019-07-08   717.599976  27.567176  139.350098    1.0      0.000000\n",
      "376  2019-07-15   779.349976  62.833676  201.100098   -1.0      7.923270\n",
      "378  2019-07-17   794.150024  76.722691  201.100098   -1.0      0.000000\n",
      "390  2019-08-02   775.599976  47.865683  219.650146    1.0      2.391703\n",
      "410  2019-09-04   821.299988  77.816039  219.650146   -1.0      0.000000\n",
      "421  2019-09-20   805.000000  49.049238  235.950134    1.0      2.024843\n",
      "440  2019-10-22   643.549988  24.423550  235.950134    1.0      0.000000\n",
      "454  2019-11-13   691.349976  64.789597  283.750122   -1.0      6.914007\n",
      "456  2019-11-15   703.900024  72.162961  283.750122   -1.0      0.000000\n",
      "462  2019-11-25   698.099976  45.273187  289.550171    1.0      0.830834\n",
      "481  2019-12-20   731.549988  72.759186  289.550171   -1.0      0.000000\n",
      "492  2020-01-07   727.900024  48.668646  293.200134    1.0      0.501437\n",
      "506  2020-01-27   778.099976  72.551630  293.200134   -1.0      0.000000\n",
      "511  2020-02-03   767.400024  44.728435  303.900085    1.0      1.394312\n",
      "535  2020-03-09   704.450012  24.428411  303.900085    1.0      0.000000\n",
      "553  2020-04-07   639.000000  56.424706  238.450073   -1.0    -10.242568\n",
      "568  2020-04-30   715.500000  70.473417  238.450073   -1.0      0.000000\n",
      "577  2020-05-14   658.150024  44.773913  295.800049    1.0      8.713815\n",
      "593  2020-06-08   720.849976  71.116041  295.800049   -1.0      0.000000\n",
      "597  2020-06-12   692.049988  49.889058  324.600037    1.0      4.161547\n",
      "612  2020-07-03   762.700012  71.246815  324.600037   -1.0      0.000000\n",
      "643  2020-08-17   957.500000  47.519708  129.800049    1.0    -20.344646\n",
      "654  2020-09-01   914.150024  29.373103  129.800049    1.0      0.000000\n",
      "663  2020-09-14   978.400024  61.887929  194.050049   -1.0      6.566844\n",
      "666  2020-09-17  1011.000000  71.786041  194.050049   -1.0      0.000000\n",
      "694  2020-10-28  1076.550049  46.251387  128.500000    1.0     -6.088899\n",
      "697  2020-11-02  1072.300049  27.769116  128.500000    1.0      0.000000\n",
      "706  2020-11-13  1124.199951  52.495222  180.399902   -1.0      4.616608\n",
      "729  2020-12-18  1189.800049  75.569440  180.399902   -1.0      0.000000\n"
     ]
    }
   ],
   "source": [
    "T1['Trade'] = T1['positions'].diff()\n",
    "T2 = T1.where((T1.Trade != 0)) \n",
    "T2 = T2.dropna()\n",
    "T2.drop([\"delta\", \"up\", \"down\", \"roll_up\", \"roll_down\", \"RS\", \"positions_long\", \"positions_short\", \"positions\", \"price_difference\", \"pnl\", \"rolling_max\", \"daily_drawdown\", \"max_daily_drawdown\"], axis = 1, inplace = True)\n",
    "T2['Trade_Return'] = (T2['cumpnl'].diff()/T2['Close'])*100\n",
    "print (\"Number of Trade count\", round(len(T2)/2))\n",
    "print (T2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot trade retruns\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(T2['Date'], T2['Trade_Return'])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}