{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instructions\n",
    "In this assingment, you will be creating your very own trading algorithim by using signals to determine when you should buy or sell a stock. The goal of this assignment is to see your capabilities in coding. In the first part of the assignment, you will be writing the code for the RSI signal and another signal of your own choice. In the second part, you will use these signals to buy or sell the stock. At the end, we will see how much money you have made. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1\n",
    "\n",
    "Download the following packages (just run the boxes below). Do not change the code below. Note that we will be using an API package to download data from yahoo. The data is in dataframe format. You will be given the  High Price, Low Price, Close Price, Adjusted Close Price, and Volume of the stock traded on a particular day. Your task is to use the data to generate signals, which can be used to determine when to buy or sell the stock. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install yahoo_historical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "import datetime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = \"GILD\"\n",
    "from yahoo_historical import Fetcher\n",
    "data = Fetcher(ticker, [2010,1,4], [2019,6,14]) #Year, Month, Date\n",
    "data = data.getHistorical()\n",
    "\n",
    "# if isinstance(data, pd.DataFrame):\n",
    "#     print(\"Hello\")\n",
    "# print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "date_object = datetime.strptime(data.iloc[0, 0], \"%Y-%m-%d\")\n",
    "for x in data.index[:]:\n",
    "    data.iloc[x,0] = datetime.strptime(data.iloc[x,0], \"%Y-%m-%d\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2\n",
    "In this step, you will be coding the RSI signal along with an additional signal of your choice. RSI readings range from 0 to 100, with readings above 70 generally interpreted as indicating overbought conditions and readings below 30 indicating oversold conditions. When deciding which other signal to choose, try to consider which signal would go well with the RSI signal. \n",
    "\n",
    "Please try to give plenty of comments. Also use the adjusted closing price in the data when calculating for RSI and the second signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Create RSI Column '''\n",
    "\n",
    "# Define RSI period\n",
    "rsi_period = 14\n",
    "\n",
    "# Calculate Adj Close difference\n",
    "change = data['Adj Close'].diff(1) \n",
    "gain = change.mask(change<0,0)\n",
    "\n",
    "# Create new column \"Gain\"\n",
    "data['Gain'] = gain\n",
    "\n",
    "# Calculate Loss difference\n",
    "loss = change.mask(change>0,0)\n",
    "\n",
    "# Create new column \"Loss\"\n",
    "data['Loss'] = loss\n",
    "\n",
    "# Calculate average gain and average loss\n",
    "avg_gain = gain.ewm(com = rsi_period - 1,min_periods = rsi_period).mean()\n",
    "avg_loss = loss.ewm(com = rsi_period - 1,min_periods = rsi_period).mean()\n",
    "\n",
    "# Create new column for average gain and average loss\n",
    "data['avg_gain'] = avg_gain\n",
    "data['avg_loss'] = avg_loss\n",
    "\n",
    "# Calculate rs value\n",
    "rs = abs(avg_gain / avg_loss)\n",
    "\n",
    "# Calculate rsi value\n",
    "rsi = 100 - (100/(1+rs))     # (RSI > 70) = overbought , (RSI < 30) = oversold\n",
    "\n",
    "# Create new column to store RSI\n",
    "data['rsi'] = rsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the rsi_signal\n",
    "def RSI(data, period):\n",
    "    rsi_signal = []\n",
    "    for i in range(data.shape[0]):\n",
    "        current_rsi = data.iloc[i].rsi\n",
    "        if (current_rsi) > 70:                     # Overbought\n",
    "            rsi_signal.append(-1)\n",
    "        elif (current_rsi) < 30:                   # Oversold\n",
    "            rsi_signal.append(1)\n",
    "        else:\n",
    "            rsi_signal.append(0)\n",
    "    return rsi_signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define second_signal\n",
    "def MovingAvg(data):\n",
    "    data['MA5'] = data['Adj Close'].rolling(5).mean() # Create a new column containing 5 Moving Average values\n",
    "    data['MA125'] = data['Adj Close'].rolling(125).mean() # Create a new column containing 125 Moving Average values\n",
    "\n",
    "    data.dropna() # Remove empty data\n",
    "    \n",
    "    shares = []\n",
    "    \n",
    "    for i in data.index:\n",
    "        if data.loc[i, 'MA5'] > data.loc[i, 'MA125']: # Buy if 5 MA graph value is larger than 125 MA graph\n",
    "            shares.append(1)\n",
    "        else:  # Sell if 5 MA graph value is smaller than 125 MA graph\n",
    "            shares.append(-1)\n",
    "            \n",
    "    return shares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3\n",
    "After finishing step 2, you can now create your own trading algorithim. The singals you have created should be used in this part. The output of the signals may be an array of values, which you may use to determine if you should buy the stock or not. You may assume that you can only make a trade after the market has closed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' \\n\\nMy trading strategy: \\n    \\n    Always buy when (5 MA > 125 MA) OR oversold, always sell when (5 MA > 125 MA) or when overbought, \\n    \\n'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal1 = RSI(data, rsi_period)\n",
    "signal2 = MovingAvg(data)\n",
    "\n",
    "trades = []\n",
    "\n",
    "for ie in data.index:\n",
    "    if signal1[ie] == 1 or signal2[ie] == 1: # Always buy when one of them is buy\n",
    "        trades.append(1)\n",
    "    elif signal1[ie] == -1 or signal1[ie] == -1: #Always sell when one of them is sell\n",
    "        trades.append(-1)\n",
    "    else:\n",
    "        trades.append(1)\n",
    "\n",
    "''' \n",
    "\n",
    "My trading strategy: \n",
    "    \n",
    "    Always buy when (5 MA > 125 MA) OR oversold, always sell when (5 MA > 125 MA) or when overbought, \n",
    "    \n",
    "'''\n",
    "\n",
    "# # Show RSI graph\n",
    "# myrsi = pd.DataFrame(data, columns = [\"rsi\"])\n",
    "# myrsi.plot(figsize = (20,10))\n",
    "# plt.title(\"rsi\")\n",
    "# plt.show()\n",
    "# print(signal1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your code does not run, you will get a zero. We will not debug your code. I hope you have a fun time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of Algorithim\n",
    "We have created a compute_pnl() and compute performance() function to calculate the overall profitability and performance of your trading strategy. Please do not change the underlying code. Also add the details of your performance to your report. Please include the charts that have been generated below. Note each trade costs $0.1. Also at the end of the period, all unsettled positions will be liquidated based on the final closing price of the stock. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_pnl(long, short, r_pnl, data, day):\n",
    "    d_pnl = r_pnl\n",
    "    if long:\n",
    "        for x in long:\n",
    "            d_pnl = d_pnl + data.iloc[day, 5] - x\n",
    "    else:\n",
    "        for x in short:\n",
    "            d_pnl = d_pnl + x - data.iloc[day, 5]\n",
    "    \n",
    "    return d_pnl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "import statistics\n",
    "def compute_performance(x):\n",
    "    long = deque()\n",
    "    short = deque()\n",
    "    \n",
    "    r_pnl = 0\n",
    "    realized_pnl = []\n",
    "    d_pnl = 0\n",
    "    daily_pnl = []\n",
    "    \n",
    "    num_long_positions = 0;\n",
    "    num_short_positions = 0;\n",
    "    long_position = []\n",
    "    short_position = []\n",
    "\n",
    "    for day in data.index[:]:\n",
    "        if trades[day] == -1:\n",
    "            if long:\n",
    "                r_pnl = r_pnl + data.iloc[day,5]- long[0] - 0.1\n",
    "                long.popleft()\n",
    "                num_long_positions = num_long_positions - 1\n",
    "            else :\n",
    "                r_pnl = r_pnl - 0.1\n",
    "                short.append(data.iloc[day,5]) \n",
    "                num_short_positions = num_short_positions + 1\n",
    "        \n",
    "        elif trades[day] == 1:\n",
    "            if short:\n",
    "                r_pnl = r_pnl + short[0] - data.iloc[day,5] - 0.1\n",
    "                short.popleft()\n",
    "                num_short_positions = num_short_positions - 1\n",
    "            else :\n",
    "                r_pnl = r_pnl - 0.1\n",
    "                long.append(data.iloc[day,5])\n",
    "                num_long_positions = num_long_positions + 1\n",
    "        else :\n",
    "            pass\n",
    "        \n",
    "        daily_pnl.append(compute_pnl(long, short, r_pnl, data, day))\n",
    "        realized_pnl.append(r_pnl)\n",
    "        long_position.append(num_long_positions)\n",
    "        short_position.append(num_short_positions)\n",
    "        \n",
    "    while long:\n",
    "        r_pnl = r_pnl + data.iloc[-1,5] - long[0] - 0.1\n",
    "        long.popleft()\n",
    "        num_long_positions = num_long_positions - 1 \n",
    "        \n",
    "    while short:\n",
    "        r_pnl = r_pnl + short[0] - data.iloc[-1,5]  - 0.1\n",
    "        short.popleft()\n",
    "        num_short_positions = num_short_positions - 1\n",
    "    \n",
    "    daily_pnl.append(r_pnl)\n",
    "    realized_pnl.append(r_pnl)\n",
    "    long_position.append(num_long_positions)\n",
    "    short_position.append(num_short_positions)\n",
    "        \n",
    "    return r_pnl, realized_pnl, daily_pnl, long_position, short_position\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_pnl, realized_pnl, daily_pnl, long_position, short_position = compute_performance(trades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "realized_pnl_df = pd.DataFrame(realized_pnl, columns = [\"realized_pnl\"])\n",
    "realized_pnl_df.plot(figsize = (20,5))\n",
    "plt.title(\"realized_pnl\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total earnings 24891.617415003897\n",
      "stdev: 24118.361439026317\n"
     ]
    }
   ],
   "source": [
    "daily_pnl_df = pd.DataFrame(daily_pnl, columns = [\"daily_pnl\"])\n",
    "daily_pnl_df.plot(figsize = (20,5))\n",
    "plt.title(\"daily_pnl\")\n",
    "plt.show()\n",
    "print(\"total earnings \" + str(r_pnl))\n",
    "print(\"stdev: \" + str(statistics.stdev(daily_pnl)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "long_position_df = pd.DataFrame(long_position, columns = [\"long_position\"])\n",
    "long_position_df.plot(figsize = (20,5))\n",
    "plt.title(\"long_position\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "short_position_df = pd.DataFrame(short_position, columns = [\"short_position\"])\n",
    "short_position_df.plot(figsize = (20,5))\n",
    "plt.title(\"short_position\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adj_close_price_df = pd.DataFrame(data.iloc[:,5])\n",
    "adj_close_price_df.plot(figsize = (20,5))\n",
    "plt.title(\"adj_close_price_df\")\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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}