treynor_usecases.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-02T14:53:40.038068Z",
     "start_time": "2023-06-02T14:53:39.319599Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Loading Daily data for JPM with starting period 2014-01-02.\n",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Loading Daily data for JPM with starting period 2014-01-02.\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Loading Daily data for JPM with starting period 2014-01-02.\n",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Loading Daily data for JPM with starting period 2014-01-02.\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Imports and set up\n",
    "\n",
    "# For today’s newsletter, all you need is data. Import the OpenBB SDK to get it.\n",
    "\n",
    "from openbb_terminal.sdk import openbb\n",
    "# Now grab the data and compute the returns.\n",
    "\n",
    "data_asset = openbb.stocks.load(\n",
    "    symbol= \"JPM\",\n",
    "    start_date=\"2014-01-01\",\n",
    "    end_date=\"2022-12-31\"\n",
    ")\n",
    "\n",
    "data_bm = openbb.stocks.load(\n",
    "    symbol= \"JPM\",\n",
    "    start_date=\"2014-01-01\",\n",
    "    end_date=\"2022-12-31\"\n",
    ")\n",
    "\n",
    "asset = data_asset[\"Adj Close\"]\n",
    "benchmark = data_bm[\"Adj Close\"]\n",
    "\n",
    "asset_returns = asset.pct_change().dropna()\n",
    "benchmark_returns = benchmark.pct_change().dropna()\n",
    "# This code pulls 8 years of stock price history, extracts the price data, and computes the daily returns. I use SPY as the benchmark, but you can use any benchmark you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "                  Open        High         Low       Close   Adj Close  \\\ndate                                                                     \n2014-01-02   45.029848   45.176574   44.767283   44.952621   44.952621   \n2014-01-03   45.014401   45.524086   44.952620   45.300133   45.300133   \n2014-01-06   45.686254   45.925653   45.431414   45.562695   45.562695   \n2014-01-07   45.786654   45.894768   44.875399   45.037571   45.037571   \n2014-01-08   45.207466   45.493198   45.022125   45.462307   45.462307   \n...                ...         ...         ...         ...         ...   \n2022-12-23  128.613174  129.460221  127.687330  129.302628  129.302628   \n2022-12-27  129.588269  130.228473  128.583628  129.755707  129.755707   \n2022-12-28  129.864031  131.400531  129.036672  130.464844  130.464844   \n2022-12-29  130.927769  131.262659  130.317112  131.213409  131.213409   \n2022-12-30  130.809576  132.306686  130.445154  132.080154  132.080154   \n\n              Volume  Dividends  Stock Splits  \ndate                                           \n2014-01-02  15627600       0.38           0.0  \n2014-01-03  14214100       0.00           0.0  \n2014-01-06  17550700       0.00           0.0  \n2014-01-07  17851200       0.00           0.0  \n2014-01-08  14687400       0.00           0.0  \n...              ...        ...           ...  \n2022-12-23   5091900       0.00           0.0  \n2022-12-27   5411000       0.00           0.0  \n2022-12-28   8827700       0.00           0.0  \n2022-12-29   6585200       0.00           0.0  \n2022-12-30   9292500       0.00           0.0  \n\n[2266 rows x 8 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>Dividends</th>\n      <th>Stock Splits</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>45.029848</td>\n      <td>45.176574</td>\n      <td>44.767283</td>\n      <td>44.952621</td>\n      <td>44.952621</td>\n      <td>15627600</td>\n      <td>0.38</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>45.014401</td>\n      <td>45.524086</td>\n      <td>44.952620</td>\n      <td>45.300133</td>\n      <td>45.300133</td>\n      <td>14214100</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>45.686254</td>\n      <td>45.925653</td>\n      <td>45.431414</td>\n      <td>45.562695</td>\n      <td>45.562695</td>\n      <td>17550700</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>45.786654</td>\n      <td>45.894768</td>\n      <td>44.875399</td>\n      <td>45.037571</td>\n      <td>45.037571</td>\n      <td>17851200</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>45.207466</td>\n      <td>45.493198</td>\n      <td>45.022125</td>\n      <td>45.462307</td>\n      <td>45.462307</td>\n      <td>14687400</td>\n      <td>0.00</td>\n      <td>0.0</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    </tr>\n    <tr>\n      <th>2022-12-23</th>\n      <td>128.613174</td>\n      <td>129.460221</td>\n      <td>127.687330</td>\n      <td>129.302628</td>\n      <td>129.302628</td>\n      <td>5091900</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2022-12-27</th>\n      <td>129.588269</td>\n      <td>130.228473</td>\n      <td>128.583628</td>\n      <td>129.755707</td>\n      <td>129.755707</td>\n      <td>5411000</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2022-12-28</th>\n      <td>129.864031</td>\n      <td>131.400531</td>\n      <td>129.036672</td>\n      <td>130.464844</td>\n      <td>130.464844</td>\n      <td>8827700</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2022-12-29</th>\n      <td>130.927769</td>\n      <td>131.262659</td>\n      <td>130.317112</td>\n      <td>131.213409</td>\n      <td>131.213409</td>\n      <td>6585200</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2022-12-30</th>\n      <td>130.809576</td>\n      <td>132.306686</td>\n      <td>130.445154</td>\n      <td>132.080154</td>\n      <td>132.080154</td>\n      <td>9292500</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2266 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_asset"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-02T14:53:50.836844Z",
     "start_time": "2023-06-02T14:53:50.828871Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='date'>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the Treynor ratio\n",
    "\n",
    "# Start by computing beta.\n",
    "\n",
    "bm_cov = benchmark_returns.rolling(\n",
    "    window=30\n",
    ").cov(asset_returns)\n",
    "\n",
    "bm_var = benchmark_returns.rolling(\n",
    "    window=30\n",
    ").var()\n",
    "\n",
    "beta = bm_cov / bm_var\n",
    "# The beta coefficient is the covariance between the returns of the benchmark and the asset divided by the variance of the benchmark. Using pandas makes it easy to compute it.\n",
    "\n",
    "# And finally, compute the Treynor ratio.\n",
    "\n",
    "treynor = (\n",
    "    asset_returns - benchmark_returns\n",
    ") / beta\n",
    "\n",
    "treynor.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-02T14:54:24.970719Z",
     "start_time": "2023-06-02T14:54:24.883928Z"
    }
   }
  },
  {
   "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
}