python 2 function for calculating the Sharpe Ratio

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calcSharpe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1 Description **\n",
    "\n",
    "    calcSharpe(start_date, end_date, ls_allocations, initial_allocation) \n",
    "    calculates the _annualized_ Sharpe ratio for an investment fund or a single stock\n",
    "    using the following formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Sharpe\\ Ratio = {\\sqrt{Number\\ Trading\\ Days}\\ \\times\\ {{Average\\ Daily\\ Return}\\over {Standard\\ Deviation}}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2 Usage **\n",
    "\n",
    "To calculate the annualized Sharpe Ratio for Apple ('aapl') for the year 2010, \n",
    "with an initial allocation of 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6854261764221505"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcSharpe(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), ['AAPL'], [1],initial_allocation=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**  Usage**\n",
    "\n",
    "To calculate the annualized Sharpe ratio for 2010 for a fund consisting of four stocks, \n",
    "AAPL, BRCM, TXN,ADI, using a weighting factor of 0.25 for each fund, and an\n",
    "initial allocation of 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3171269057934021"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcSharpe(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), ['AAPL', 'BRCM', 'TXN', 'ADI'], [0.25, 0.25, 0.25, 0.25],initial_allocation=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**  Usage**\n",
    "\n",
    "To calculate the annualized Sharpe Ratio of Apple (AAPL) for each \n",
    "year from 2000 to 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.93904334765251041,\n",
       " 0.97277269499193475,\n",
       " -0.80979975389743264,\n",
       " 1.1659624486930709,\n",
       " 2.9658383823909986,\n",
       " 2.3150902550685979,\n",
       " 0.52024589363877627,\n",
       " 2.507595011527894,\n",
       " -1.1037245228671726,\n",
       " 2.7114807424428973,\n",
       " 1.6854261764221505,\n",
       " 0.91996571936467764]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_sharpe = []\n",
    "for i in range(2000, 2012,1):    \n",
    "    aapl_sharpe.append(\n",
    "    calcSharpe(dt.datetime(i, 1, 1), dt.datetime(i, 12, 31),\n",
    "                                ['AAPL'], [1],initial_allocation=1))\n",
    "aapl_sharpe "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**  3 Dependencies**\n",
    "\n",
    "  The [QTSK package](http://wiki.quantsoftware.org/index.php?title=QSToolKit_Installation_Guide), and the **getData()** function from that package. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**  4 Notes**\n",
    "\n",
    "* The sum of the weighting factors (ls_allocations) should always equal 1.  Thus for a single stock use a weighting factor of 1.\n",
    "* The default value for the initial allocation is 1\n",
    "* calcSharpe() is a modification of the function simulate() from the [QTSK package](http://wiki.quantsoftware.org/index.php?title=QSToolKit_Installation_Guide)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions and Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thomas/anaconda2/lib/python2.7/site-packages/QSTK/qstkutil/qsdateutil.py:36: FutureWarning: TimeSeries is deprecated. Please use Series\n",
      "  return pd.TimeSeries(index=dates, data=dates)\n"
     ]
    }
   ],
   "source": [
    "import QSTK.qstkutil.qsdateutil as du\n",
    "import QSTK.qstkutil.tsutil as tsu\n",
    "import QSTK.qstkutil.DataAccess as da\n",
    "# Third Party Imports\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "def getData(dt_start, dt_end, ls_symbols):\n",
    "\n",
    "    # The Time of Closing is 1600 hrs \n",
    "    dt_timeofday = dt.timedelta(hours=16)\n",
    "    ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)\n",
    "\n",
    "    # Creating an object of the dataaccess class with Yahoo as the source.\n",
    "    c_dataobj = da.DataAccess('Yahoo', cachestalltime=0)\n",
    "\n",
    "    # Keys to be read from the data, it is good to read everything in one go.\n",
    "    ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']\n",
    "\n",
    "    # Reading the data, now d_data is a dictionary with the keys above.\n",
    "    # Timestamps and symbols are the ones that were specified before.\n",
    "    ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)\n",
    "    d_data = dict(zip(ls_keys, ldf_data))\n",
    "    \n",
    "     # Filling the data for NAN\n",
    "    for s_key in ls_keys:\n",
    "        d_data[s_key] = d_data[s_key].fillna(method='ffill')\n",
    "        d_data[s_key] = d_data[s_key].fillna(method='bfill')\n",
    "        d_data[s_key] = d_data[s_key].fillna(1.0)\n",
    "\n",
    "    # Getting the numpy ndarray of close prices.\n",
    "    na_price = d_data['close'].values\n",
    "    \n",
    "    # returning the closed prices for all the days    \n",
    "    return na_price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calcSharpe(dt_start, dt_end, ls_symbols, ls_allocations,initial_allocation=1):\n",
    "    \n",
    "    # This function returns the annualized Sharpe ratio for an investment fund \n",
    "\n",
    "    na_price =  getData(dt_start, dt_end, ls_symbols)\n",
    "    dt_timeofday = dt.timedelta(hours=16)\n",
    "    ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)\n",
    "    num_tradingDays = (len(ldt_timestamps))\n",
    "    \n",
    "    arr_firstdayPrices = na_price[0,:]\n",
    "    \n",
    "    fund_stats = np.zeros((num_tradingDays, 2)) # Number of trading days * 3\n",
    "\n",
    "    for i in range(num_tradingDays):\n",
    "        for j in range(len(na_price[i,:])):\n",
    "            fund_stats[i,0] += ((na_price[i,j]/arr_firstdayPrices[j]) * ( initial_allocation * ls_allocations[j]) )\n",
    "\n",
    "    for i in range(num_tradingDays):\n",
    "        if i != 0:\n",
    "            fund_stats[i,1] = ((fund_stats[i,0]/fund_stats[i-1,0]) - 1 )\n",
    "            \n",
    "    sharpe = (num_tradingDays **(1/2.0))*(np.average(fund_stats[:,[1]]) / np.std(fund_stats[:,[1]])) \n",
    "\n",
    "    return sharpe\n",
    " "
   ]
  }
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