performanceMetrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from empyrical import max_drawdown, alpha_beta, annual_volatility, sharpe_ratio, sortino_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
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      "/Users/Sharonvy/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Comparing Series of datetimes with 'datetime.date'.  Currently, the\n",
      "'datetime.date' is coerced to a datetime. In the future pandas will\n",
      "not coerce, and a TypeError will be raised. To retain the current\n",
      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "  after removing the cwd from sys.path.\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total assets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>162258.97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>158538.15724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>159120.75500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>158674.08724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>156767.42724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            total assets\n",
       "Date                    \n",
       "2016-01-04  162258.97500\n",
       "2016-01-05  158538.15724\n",
       "2016-01-06  159120.75500\n",
       "2016-01-07  158674.08724\n",
       "2016-01-08  156767.42724"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_return = pd.read_csv('~/Downloads/total_asset_history.csv')\n",
    "df_return = df_return.rename(columns = {'Unnamed: 0': 'Date'})\n",
    "df_return['Date'] = pd.to_datetime(df_return['Date'])\n",
    "df_return = df_return.loc[df_return['Date'] >= datetime.date(2016,1,4)]\n",
    "df_return = df_return.set_index(['Date'])\n",
    "df_return.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total assets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>-0.022931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>0.003675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>-0.002807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>-0.012016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            total assets\n",
       "Date                    \n",
       "2016-01-04           NaN\n",
       "2016-01-05     -0.022931\n",
       "2016-01-06      0.003675\n",
       "2016-01-07     -0.002807\n",
       "2016-01-08     -0.012016"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_return.pct_change()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "/Users/Sharonvy/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: Comparing Series of datetimes with 'datetime.date'.  Currently, the\n",
      "'datetime.date' is coerced to a datetime. In the future pandas will\n",
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      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "  \"\"\"\n",
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      "not coerce, and a TypeError will be raised. To retain the current\n",
      "behavior, convert the 'datetime.date' to a datetime with\n",
      "'pd.Timestamp'.\n",
      "  \n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SVENY01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>0.000263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>0.000264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>0.000256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>0.000252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>0.000258</td>\n",
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      ],
      "text/plain": [
       "             SVENY01\n",
       "Date                \n",
       "2016-01-04  0.000263\n",
       "2016-01-05  0.000264\n",
       "2016-01-06  0.000256\n",
       "2016-01-07  0.000252\n",
       "2016-01-08  0.000258"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_rate = pd.read_csv('~/Downloads/feds200628.csv', skiprows = 9)\n",
    "zero_rate['Date'] = pd.to_datetime(zero_rate['Date'])\n",
    "\n",
    "zero = zero_rate[['Date', 'SVENY01']]\n",
    "zero = zero.loc[zero['Date'] >= datetime.date(2016,1,4)]\n",
    "zero = zero.loc[zero['Date'] <= datetime.date(2019,12,31)]\n",
    "zero = zero.set_index(['Date'])\n",
    "zero['SVENY01'] = zero['SVENY01']/3000\n",
    "zero.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "quotes = df.join(zero, how = 'inner')\n",
    "quotes['excess_return'] = quotes['total assets'] - quotes['SVENY01']\n",
    "\n",
    "returns = np.array(quotes['total assets'].values)\n",
    "benchmark = np.array(quotes['SVENY01'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxdd = max_drawdown(returns)\n",
    "alpha, beta = alpha_beta(returns, benchmark)\n",
    "ann_vol = annual_volatility(returns)\n",
    "sharpe = sharpe_ratio(returns, annualization = 252)\n",
    "sortino = sortino_ratio(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======Performance Metrics for Linear Regression=======\n",
      "Maximum Drawdown: -16.2044%\n",
      "Alpha:  20.0352%\n",
      "Beta:  -0.24371795816040437\n",
      "Annual Volatility:  0.12406190454974238\n",
      "Sharpe Ratio:  1.1968728861741635\n",
      "Sortino Ratio:  1.7190341979682902\n"
     ]
    }
   ],
   "source": [
    "print(\"=======Performance Metrics for Linear Regression=======\")\n",
    "print('Maximum Drawdown: {0: .4%}'.format(maxdd))\n",
    "print('Alpha: {0: .4%}'.format(alpha))\n",
    "print('Beta: ', beta)\n",
    "print('Annual Volatility: ', ann_vol)\n",
    "print('Sharpe Ratio: ', sharpe)\n",
    "print('Sortino Ratio: ', sortino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>0.999993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>0.999230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>1.000550</td>\n",
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      ],
      "text/plain": [
       "               asset\n",
       "Date                \n",
       "2016-01-04  1.000000\n",
       "2016-01-05  1.000000\n",
       "2016-01-06  0.999993\n",
       "2016-01-07  0.999230\n",
       "2016-01-08  1.000550"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_return = pd.read_csv('~/Downloads/6pair.csv')\n",
    "df_return = df_return.rename(columns = {'Unnamed: 0': 'Date', 'date,total asset': 'asset'})\n",
    "df_return['Date'] = pd.to_datetime(df_return['Date'])\n",
    "df_return = df_return.set_index(['Date'])\n",
    "df_return.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <td>2016-01-04</td>\n",
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       "      <td>2016-01-06</td>\n",
       "      <td>-0.000007</td>\n",
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       "      <td>2016-01-07</td>\n",
       "      <td>-0.000763</td>\n",
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       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>0.001321</td>\n",
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      ],
      "text/plain": [
       "               asset\n",
       "Date                \n",
       "2016-01-04       NaN\n",
       "2016-01-05  0.000000\n",
       "2016-01-06 -0.000007\n",
       "2016-01-07 -0.000763\n",
       "2016-01-08  0.001321"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_return.pct_change()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "quotes = df.join(zero, how = 'inner')\n",
    "quotes['excess_return'] = quotes['asset'] - quotes['SVENY01']\n",
    "\n",
    "returns = np.array(quotes['asset'].values)\n",
    "benchmark = np.array(quotes['SVENY01'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxdd = max_drawdown(returns)\n",
    "alpha, beta = alpha_beta(returns, benchmark)\n",
    "ann_vol = annual_volatility(returns)\n",
    "sharpe = sharpe_ratio(returns, annualization = 252)\n",
    "sortino = sortino_ratio(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======Performance Metrics for Linear Regression with Kalman Filter=======\n",
      "Maximum Drawdown: -3.6690%\n",
      "Alpha:  6.3747%\n",
      "Beta:  0.2064687060852846\n",
      "Annual Volatility:  0.027584512250742585\n",
      "Sharpe Ratio:  3.147801330517112\n",
      "Sortino Ratio:  5.235870759830302\n"
     ]
    }
   ],
   "source": [
    "print(\"=======Performance Metrics for Linear Regression with Kalman Filter=======\")\n",
    "print('Maximum Drawdown: {0: .4%}'.format(maxdd))\n",
    "print('Alpha: {0: .4%}'.format(alpha))\n",
    "print('Beta: ', beta)\n",
    "print('Annual Volatility: ', ann_vol)\n",
    "print('Sharpe Ratio: ', sharpe)\n",
    "print('Sortino Ratio: ', sortino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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