trading_strat_ex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:06.419970Z",
     "end_time": "2023-04-06T16:32:07.611956Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# Get the data\n",
    "tlt = yf.download(\"TLT\", start=\"2002-01-01\", end=\"2023-04-05\")\n",
    "# Prepare the data\n",
    "tlt[\"log_return\"] = np.log(tlt[\"Adj Close\"] / tlt[\"Adj Close\"].shift(1))\n",
    "\n",
    "# Then we’ll add a column for the calendar day of the month (1 – 31) and a column for the year.\n",
    "\n",
    "tlt[\"day_of_month\"] = tlt.index.day\n",
    "\n",
    "tlt[\"year\"] = tlt.index.year"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:10.032439Z",
     "end_time": "2023-04-06T16:32:10.472414Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'Mean Log Returns by Calendar Day of Month'}, xlabel='day_of_month'>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate Our Hypothesis\n",
    "# We expect there to be positive returns in TLT toward the end of the month. We expect this because we think fund managers buy TLT at the end of the month. We expect there to be negative returns in TLT toward the beginning of the month. This is when fund managers sell their high-quality assets and go back to buying meme stocks.\n",
    "\n",
    "grouped_by_day = tlt.groupby(\"day_of_month\").log_return.mean()\n",
    "grouped_by_day.plot.bar(title=\"Mean Log Returns by Calendar Day of Month\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:11.347879Z",
     "end_time": "2023-04-06T16:32:11.536625Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Build A Simple Trading Strategy\n",
    "\n",
    "Let’s build a naive strategy to test our hypothesis:\n",
    "\n",
    "- Buy and hold TLT during the last week of the month\n",
    "- Short and hold TLT during the first week of the month\n",
    "Simple.\n",
    "\n",
    "This code creates 3 new columns:\n",
    "\n",
    "first_week_returns – the daily log return if it’s between the 1st and 7th day of the month, otherwise 0\n",
    "last_week_returns – the daily log return if it’s on or after the 23rd day of the month, otherwise 0\n",
    "last_week_less_first_week – the difference between last_week_returns and first_week_returns\n",
    "last_week_less_first_week represents the returns from our naive strategy. It’s basically saying “go long TLT the last week” and “go short TLT the first week”."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n2002-07-30   81.750000   81.900002   81.519997   81.519997   40.925713   \n2002-07-31   81.949997   82.800003   81.900002   82.529999   41.432766   \n2002-08-01   82.540001   83.019997   82.540001   83.000000   41.668743   \n2002-08-02   83.160004   84.099998   82.879997   83.849998   42.095478   \n2002-08-05   84.040001   84.440002   83.849998   84.220001   42.281223   \n...                ...         ...         ...         ...         ...   \n2023-03-29  103.699997  104.449997  103.599998  104.320000  104.056183   \n2023-03-30  104.459999  105.050003  104.269997  104.800003  104.534973   \n2023-03-31  105.349998  106.559998  105.080002  106.370003  106.101006   \n2023-04-03  105.879997  107.099998  105.699997  106.599998  106.599998   \n2023-04-04  105.779999  107.800003  105.750000  107.129997  107.129997   \n\n              Volume  log_return  day_of_month  year  first_week_returns  \\\nDate                                                                       \n2002-07-30      6100         NaN            30  2002            0.000000   \n2002-07-31     29400    0.012313            31  2002            0.000000   \n2002-08-01     25000    0.005679             1  2002            0.005679   \n2002-08-02     52800    0.010189             2  2002            0.010189   \n2002-08-05     61100    0.004403             5  2002            0.004403   \n...              ...         ...           ...   ...                 ...   \n2023-03-29  16804400   -0.002011            29  2023            0.000000   \n2023-03-30  14315800    0.004591            30  2023            0.000000   \n2023-03-31  25393200    0.014870            31  2023            0.000000   \n2023-04-03  18234800    0.004692             3  2023            0.004692   \n2023-04-04  18052400    0.004960             4  2023            0.004960   \n\n            last_week_returns  last_week_less_first_week  \nDate                                                      \n2002-07-30                NaN                        NaN  \n2002-07-31           0.012313                   0.012313  \n2002-08-01           0.000000                  -0.005679  \n2002-08-02           0.000000                  -0.010189  \n2002-08-05           0.000000                  -0.004403  \n...                       ...                        ...  \n2023-03-29          -0.002011                  -0.002011  \n2023-03-30           0.004591                   0.004591  \n2023-03-31           0.014870                   0.014870  \n2023-04-03           0.000000                  -0.004692  \n2023-04-04           0.000000                  -0.004960  \n\n[5207 rows x 12 columns]",
      "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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>log_return</th>\n      <th>day_of_month</th>\n      <th>year</th>\n      <th>first_week_returns</th>\n      <th>last_week_returns</th>\n      <th>last_week_less_first_week</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2002-07-30</th>\n      <td>81.750000</td>\n      <td>81.900002</td>\n      <td>81.519997</td>\n      <td>81.519997</td>\n      <td>40.925713</td>\n      <td>6100</td>\n      <td>NaN</td>\n      <td>30</td>\n      <td>2002</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2002-07-31</th>\n      <td>81.949997</td>\n      <td>82.800003</td>\n      <td>81.900002</td>\n      <td>82.529999</td>\n      <td>41.432766</td>\n      <td>29400</td>\n      <td>0.012313</td>\n      <td>31</td>\n      <td>2002</td>\n      <td>0.000000</td>\n      <td>0.012313</td>\n      <td>0.012313</td>\n    </tr>\n    <tr>\n      <th>2002-08-01</th>\n      <td>82.540001</td>\n      <td>83.019997</td>\n      <td>82.540001</td>\n      <td>83.000000</td>\n      <td>41.668743</td>\n      <td>25000</td>\n      <td>0.005679</td>\n      <td>1</td>\n      <td>2002</td>\n      <td>0.005679</td>\n      <td>0.000000</td>\n      <td>-0.005679</td>\n    </tr>\n    <tr>\n      <th>2002-08-02</th>\n      <td>83.160004</td>\n      <td>84.099998</td>\n      <td>82.879997</td>\n      <td>83.849998</td>\n      <td>42.095478</td>\n      <td>52800</td>\n      <td>0.010189</td>\n      <td>2</td>\n      <td>2002</td>\n      <td>0.010189</td>\n      <td>0.000000</td>\n      <td>-0.010189</td>\n    </tr>\n    <tr>\n      <th>2002-08-05</th>\n      <td>84.040001</td>\n      <td>84.440002</td>\n      <td>83.849998</td>\n      <td>84.220001</td>\n      <td>42.281223</td>\n      <td>61100</td>\n      <td>0.004403</td>\n      <td>5</td>\n      <td>2002</td>\n      <td>0.004403</td>\n      <td>0.000000</td>\n      <td>-0.004403</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-03-29</th>\n      <td>103.699997</td>\n      <td>104.449997</td>\n      <td>103.599998</td>\n      <td>104.320000</td>\n      <td>104.056183</td>\n      <td>16804400</td>\n      <td>-0.002011</td>\n      <td>29</td>\n      <td>2023</td>\n      <td>0.000000</td>\n      <td>-0.002011</td>\n      <td>-0.002011</td>\n    </tr>\n    <tr>\n      <th>2023-03-30</th>\n      <td>104.459999</td>\n      <td>105.050003</td>\n      <td>104.269997</td>\n      <td>104.800003</td>\n      <td>104.534973</td>\n      <td>14315800</td>\n      <td>0.004591</td>\n      <td>30</td>\n      <td>2023</td>\n      <td>0.000000</td>\n      <td>0.004591</td>\n      <td>0.004591</td>\n    </tr>\n    <tr>\n      <th>2023-03-31</th>\n      <td>105.349998</td>\n      <td>106.559998</td>\n      <td>105.080002</td>\n      <td>106.370003</td>\n      <td>106.101006</td>\n      <td>25393200</td>\n      <td>0.014870</td>\n      <td>31</td>\n      <td>2023</td>\n      <td>0.000000</td>\n      <td>0.014870</td>\n      <td>0.014870</td>\n    </tr>\n    <tr>\n      <th>2023-04-03</th>\n      <td>105.879997</td>\n      <td>107.099998</td>\n      <td>105.699997</td>\n      <td>106.599998</td>\n      <td>106.599998</td>\n      <td>18234800</td>\n      <td>0.004692</td>\n      <td>3</td>\n      <td>2023</td>\n      <td>0.004692</td>\n      <td>0.000000</td>\n      <td>-0.004692</td>\n    </tr>\n    <tr>\n      <th>2023-04-04</th>\n      <td>105.779999</td>\n      <td>107.800003</td>\n      <td>105.750000</td>\n      <td>107.129997</td>\n      <td>107.129997</td>\n      <td>18052400</td>\n      <td>0.004960</td>\n      <td>4</td>\n      <td>2023</td>\n      <td>0.004960</td>\n      <td>0.000000</td>\n      <td>-0.004960</td>\n    </tr>\n  </tbody>\n</table>\n<p>5207 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build A Simple Trading Strategy\n",
    "tlt[\"first_week_returns\"] = 0.0\n",
    "tlt.loc[tlt.day_of_month <= 7, \"first_week_returns\"] = tlt[\n",
    "    tlt.day_of_month <= 7\n",
    "].log_return\n",
    "\n",
    "tlt[\"last_week_returns\"] = 0.0\n",
    "tlt.loc[tlt.day_of_month >= 23, \"last_week_returns\"] = tlt[\n",
    "    tlt.day_of_month >= 23\n",
    "].log_return\n",
    "\n",
    "tlt[\"last_week_less_first_week\"] = tlt.last_week_returns - tlt.first_week_returns\n",
    "tlt"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:13.618250Z",
     "end_time": "2023-04-06T16:32:13.684581Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'Mean Log Strategy Returns by Year'}, xlabel='year'>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the return\n",
    "(\n",
    "    tlt.groupby(\"year\")\n",
    "    .last_week_less_first_week.mean()\n",
    "    .plot.bar(title=\"Mean Log Strategy Returns by Year\")\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:15.310880Z",
     "end_time": "2023-04-06T16:32:15.435074Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'Cumulative Sum of Returns By Year'}, xlabel='year'>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative returns by years\n",
    "(\n",
    "    tlt.groupby(\"year\")\n",
    "    .last_week_less_first_week.sum()\n",
    "    .cumsum()\n",
    "    .plot(title=\"Cumulative Sum of Returns By Year\")\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:25.878019Z",
     "end_time": "2023-04-06T16:32:25.943883Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'Cumulative Sum of Returns By Day'}, xlabel='Date'>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative returns by day\n",
    "tlt.last_week_less_first_week.cumsum().plot(title=\"Cumulative Sum of Returns By Day\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:28.189351Z",
     "end_time": "2023-04-06T16:32:28.319857Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'center':'Cumulative Returns By Day'}, xlabel='Date'>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative returns by day\n",
    "np.exp(tlt.last_week_less_first_week.cumsum()).plot(\n",
    "    title=\"Cumulative Returns By Day\"\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:30.512158Z",
     "end_time": "2023-04-06T16:32:30.620089Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9940028008255136"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute sharpe ratio for the log return\n",
    "tlt.last_week_less_first_week.mean() / tlt.last_week_less_first_week.std()\n",
    "# Compute sharpe ratio for the return\n",
    "np.exp(tlt.last_week_less_first_week.mean()) / np.exp(\n",
    "    tlt.last_week_less_first_week.std()\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-06T16:32:34.200743Z",
     "end_time": "2023-04-06T16:32:34.224657Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}