tomGdow/gist:8291a2ba46f9d72bc9ea

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HomeWork 1 Attempt"
   ]
  },
  {
   "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": false
   },
   "outputs": [],
   "source": [
    "def datagen(step=1):\n",
    "        myarr =[]\t\n",
    "\tfor i in range (0, 11, step):\n",
    "\t\tfor j in range (0, 11, step):\n",
    "\t\t\tfor k in range (0, 11, step):\n",
    "\t\t\t\tfor l in range (0, 11, step):\n",
    "\t\t\t\t\tif (i+j+k+l) == 10:\n",
    "\t\t\t\t\t\tls_allocation = [i/10.0, j/10.0, k/10.0, l/10.0]\n",
    "                                                myarr.append(ls_allocation) \n",
    "        return myarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def optimizer(dt_start, dt_end, ls_symbols, data, verbose=False, quiet=False):\n",
    "\n",
    "\tbestSharpeRatio = -1\n",
    "        for x in data:\n",
    "                sharpe= simulate(dt_start, dt_end, ls_symbols, x)[2] \n",
    "                if verbose and not quiet:\n",
    "                        print x\n",
    "                if sharpe > bestSharpeRatio:\n",
    "                        bestSharpeRatio = sharpe\n",
    "                        mylist = [bestSharpeRatio, x]\n",
    "                        if not quiet:\n",
    "                                print mylist\n",
    "        return (dt_start, dt_end, ls_symbols, mylist[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def simulate(dt_start, dt_end, ls_symbols, ls_allocations):\n",
    "\n",
    "        na_price =  getData(dt_start, dt_end, ls_symbols)\n",
    "\n",
    "        initial_allocation=1\n",
    "        \n",
    "        k = 252 # for sharpe ratio\n",
    "\t\n",
    "\t# The Time of Closing is 1600 hrs \n",
    "\tdt_timeofday = dt.timedelta(hours=16)\n",
    "\t\n",
    "\t# Get a list of trading days between the start and the end.\n",
    "\tldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)\n",
    "\t\n",
    "\t# Number of Trading days\n",
    "\tnum_tradingDays = (len(ldt_timestamps))\n",
    "    \n",
    "\t# The first day prices of the quities\n",
    "\tarr_firstdayPrices = na_price[0,:]\n",
    "\t\n",
    "\t# The cumulative statistics of the overall fund, which will be updated while incrementally parsing over the prices for the equity\n",
    "\t# It'll contain the \n",
    "\t## [0] fund invest - The sum of the invested money \n",
    "\t## [1] fund's cumulative return\n",
    "\t## [2] fund's daily returns. The daily \n",
    "\tfund_stats = np.zeros((num_tradingDays, 3)) # Number of trading days * 3\n",
    "\t\n",
    "\t# For all the trading days\n",
    "\tfor i in range(num_tradingDays):\n",
    "\t\t# For all the equities\n",
    "\t\tfor j in range(len(na_price[i,:])):\n",
    "\t\t\t# Do\n",
    "\t\t\tfund_stats[i,0] += ((na_price[i,j]/arr_firstdayPrices[j]) * ( initial_allocation * ls_allocations[j]) )\n",
    "\t\t\t\n",
    "\tfor i in range(num_tradingDays):\n",
    "\t\tfund_stats[i,1] = ((fund_stats[i,0]/initial_allocation) * 100)\n",
    "\t\tif i != 0:\n",
    "\t\t\tfund_stats[i,2] = ((fund_stats[i,0]/fund_stats[i-1,0]) - 1 )\n",
    "\n",
    "\tavgDailyReturn = np.average(fund_stats[:,2])\n",
    "\tsqStdDev = np.std(fund_stats[:,2])\n",
    "\tsharpeRatio = (k**(1/2.0))*(avgDailyReturn/sqStdDev)\n",
    "\tcumReturn = fund_stats[-1,0]\n",
    "\treturn sqStdDev, avgDailyReturn, sharpeRatio, cumReturn, ls_allocations \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "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",
    "    \n",
    "    # Get a list of trading days between the start and the end.\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": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def formatSimulate(arg):\n",
    "    print \" \"\n",
    "    print \"Final Results:\"\n",
    "    print \"vol (sqStdDev): \", arg[0]\n",
    "    print \"Avg Daily Return: \", arg[1]\n",
    "    print \"Sharpe Ratio \", arg[2]\n",
    "    print \"Cumulative Return \", arg[3]\n",
    "    print \"ls_allocations \", arg[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Homework One"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86104162871091428, [0.0, 0.0, 0.0, 1.0]]\n",
      "[0.91122199609543864, [0.0, 0.2, 0.0, 0.8]]\n",
      "[0.95819015169413413, [0.0, 0.4, 0.0, 0.6]]\n",
      "[0.99894766268827129, [0.0, 0.6, 0.0, 0.4]]\n",
      "[1.0309411783452129, [0.0, 0.8, 0.0, 0.2]]\n",
      "[1.0526554919865365, [0.0, 1.0, 0.0, 0.0]]\n",
      "[1.0765277444030112, [0.2, 0.6, 0.0, 0.2]]\n",
      "[1.1048073296045644, [0.2, 0.8, 0.0, 0.0]]\n",
      "[1.1233402454533965, [0.4, 0.6, 0.0, 0.0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.017446609241508617,\n",
       " 0.0012345880875578715,\n",
       " 1.1233402454533965,\n",
       " 1.3122326645349758,\n",
       " [0.4, 0.6, 0.0, 0.0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulate(*optimizer(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['BRCM', 'TXN', 'AMD', 'ADI'], datagen(step=2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Final Results:\n",
      "vol (sqStdDev):  0.0174466092415\n",
      "Avg Daily Return:  0.00123458808756\n",
      "Sharpe Ratio  1.12334024545\n",
      "Cumulative Return  1.31223266453\n",
      "ls_allocations  [0.4, 0.6, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "formatSimulate((0.017446609241508617,\n",
    " 0.0012345880875578715,\n",
    " 1.1233402454533965,\n",
    " 1.3122326645349758,\n",
    " [0.4, 0.6, 0.0, 0.0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some fun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import four stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "importData = getData(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['BRCM', 'TXN', 'AMD', 'ADI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 31.34  24.67   9.7   29.27]\n",
      " [ 30.93  24.53   9.71  29.22]\n",
      " [ 30.6   24.35   9.57  29.16]\n",
      " ..., \n",
      " [ 42.42  31.16   8.09  35.69]\n",
      " [ 42.65  31.42   8.08  35.78]\n",
      " [ 42.96  31.41   8.14  36.08]]\n"
     ]
    }
   ],
   "source": [
    "print importData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Import and Plot Apple Stock for 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "importDataTwo = getData(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['AAPL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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RqplE8+CDNlM5tA53NOvX242xLF27WlqL8Gvs2WNDTS+6KLGyR0q0RvHYYzB6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\nCuecczF5oHDOORfT/wOrPpYQbIpFLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa93828d190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.plot(importDataTwo)\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('AAPL (2010)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and Plot API Stock for 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "importDataThree = getData(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['ADI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6LQZ4662xn1ZD8QTBy1JKlYVw2mmRZ7zLJguhSRMts33BBTpAM96g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\nwzAAEwTDMAwjwP8HOr97uFR5NWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa91478df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(importDataThree)\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('ADI (2010)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and Plot BRCM Stock for 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "importDataFour = getData(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['BRCM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q4GqtR5jQ22/DWWfB1lvDb34Dhx0Ge+0FVVXQJNL4PWUK9OoFt98O48blt7yNUW1NRiPx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa91478d190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(importDataFour)\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('ADI (2010)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apple Stock Over a Period of 10 Years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "importDataAAPL10 = getData(dt.datetime(2000, 1, 1), dt.datetime(2010, 12, 31),['BRCM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8nG23Lf74CRPcZ81yv/fesD1iRHKejg73l15yP+CAsP3AA+4//an7V7/qvuee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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa914836ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(importDataAAPL10)\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('AAPL (2000 - 2010)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2766"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numTradingDays(dt.datetime(2000, 1, 1), dt.datetime(2010, 12, 31))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Some new functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def numTradingDays(dt_start, dt_end):\n",
    "\n",
    "\t# The Time of Closing is 1600 hrs \n",
    "\tdt_timeofday = dt.timedelta(hours=16)\n",
    "\t\n",
    "\t# Get a list of trading days between the start and the end.\n",
    "\tldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)\n",
    "\t\n",
    "\t# Number of Trading days\n",
    "\tnum_tradingDays = (len(ldt_timestamps))\n",
    "\n",
    "\treturn num_tradingDays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "251"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numTradingDays(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fundStats(dt_start, dt_end, ls_symbols, ls_allocations):\n",
    "\n",
    "        na_price =  getData(dt_start, dt_end, ls_symbols)\n",
    "\n",
    "        initial_allocation=1\n",
    "        \n",
    "        k = 252 # for sharpe ratio\n",
    "\t\n",
    "\t# The Time of Closing is 1600 hrs \n",
    "\tdt_timeofday = dt.timedelta(hours=16)\n",
    "\t\n",
    "\t# Get a list of trading days between the start and the end.\n",
    "\tldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)\n",
    "\t\n",
    "\t# Number of Trading days\n",
    "\tnum_tradingDays = (len(ldt_timestamps))\n",
    "    \n",
    "\t# The first day prices of the quities\n",
    "\tarr_firstdayPrices = na_price[0,:]\n",
    "\t\n",
    "\t# The cumulative statistics of the overall fund, which will be updated while incrementally parsing over the prices for the equity\n",
    "\t# It'll contain the \n",
    "\t## [0] fund invest - The sum of the invested money \n",
    "\t## [1] fund's cumulative return\n",
    "\t## [2] fund's daily returns. The daily \n",
    "\tfund_stats = np.zeros((num_tradingDays, 3)) # Number of trading days * 3\n",
    "\t\n",
    "\t# For all the trading days\n",
    "\tfor i in range(num_tradingDays):\n",
    "\t\t# For all the equities\n",
    "\t\tfor j in range(len(na_price[i,:])):\n",
    "\t\t\t# Do\n",
    "\t\t\tfund_stats[i,0] += ((na_price[i,j]/arr_firstdayPrices[j]) * ( initial_allocation * ls_allocations[j]) )\n",
    "\t\t\t\n",
    "\tfor i in range(num_tradingDays):\n",
    "\t\tfund_stats[i,1] = ((fund_stats[i,0]/initial_allocation) * 100)\n",
    "\t\tif i != 0:\n",
    "\t\t\tfund_stats[i,2] = ((fund_stats[i,0]/fund_stats[i-1,0]) - 1 )\n",
    "\n",
    "\treturn fund_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An Example of Fund Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86104162871091428, [0.0, 0.0, 0.0, 1.0]]\n",
      "[0.91122199609543864, [0.0, 0.2, 0.0, 0.8]]\n",
      "[0.95819015169413413, [0.0, 0.4, 0.0, 0.6]]\n",
      "[0.99894766268827129, [0.0, 0.6, 0.0, 0.4]]\n",
      "[1.0309411783452129, [0.0, 0.8, 0.0, 0.2]]\n",
      "[1.0526554919865365, [0.0, 1.0, 0.0, 0.0]]\n",
      "[1.0765277444030112, [0.2, 0.6, 0.0, 0.2]]\n",
      "[1.1048073296045644, [0.2, 0.8, 0.0, 0.0]]\n",
      "[1.1233402454533965, [0.4, 0.6, 0.0, 0.0]]\n"
     ]
    }
   ],
   "source": [
    "fundstats = fundStats(*optimizer(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['BRCM', 'TXN', 'AMD', 'ADI'], datagen(step=2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.99136213,  0.98277247,  0.97847622,  0.98852229,\n",
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       "        0.93854786,  0.93159332,  0.92347591,  0.88576552,  0.90373686,\n",
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       "        0.9085486 ,  0.91562369,  0.93145932,  0.94111329,  0.96641001,\n",
       "        0.96406938,  0.97019077,  0.97279376,  0.96391764,  0.9456499 ,\n",
       "        0.96759487,  0.95918106,  0.95475506,  0.96633557,  0.9561661 ,\n",
       "        0.95102961,  0.95582149,  0.9664483 ,  0.96383822,  0.95420624,\n",
       "        0.97838123,  0.96939947,  0.96233643,  0.96062191,  0.9891215 ,\n",
       "        0.99562082,  0.99596543,  0.98349646,  0.99748496,  1.01542723,\n",
       "        0.99168123,  0.9919365 ,  0.9858463 ,  0.9877849 ,  0.98371768,\n",
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       "        0.99239172,  1.00464655,  1.01923421,  1.02898461,  1.0678678 ,\n",
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       "        0.9820372 ,  0.95748428,  0.95762609,  0.9526775 ,  0.9871276 ,\n",
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       "        1.03690636,  1.05441042,  1.04786785,  1.0541431 ,  1.02552297,\n",
       "        1.05844147,  1.03713752,  1.02290084,  1.05721124,  1.06195279,\n",
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       "        1.02139051,  1.01962777,  1.02641355,  1.02887612,  1.0262944 ,\n",
       "        1.0207509 ,  1.0004843 ,  1.00750124,  1.05351482,  1.04742251,\n",
       "        1.06624899,  1.06675739,  1.07726862,  1.07164783,  1.0746316 ,\n",
       "        1.11238523,  1.10194348,  1.10124293,  1.11894695,  1.127833  ,\n",
       "        1.13035445,  1.12487904,  1.12810813,  1.14359206,  1.1313663 ,\n",
       "        1.11605326,  1.12249018,  1.11992051,  1.14007862,  1.1468644 ,\n",
       "        1.1411018 ,  1.20847894,  1.20754226,  1.20536967,  1.20182503,\n",
       "        1.20712036,  1.2236374 ,  1.25351684,  1.26876676,  1.26860793,\n",
       "        1.26240216,  1.26635877,  1.24082096,  1.24409827,  1.23910146,\n",
       "        1.22450599,  1.22742664,  1.2647981 ,  1.29093083,  1.30896032,\n",
       "        1.30160373,  1.33157252,  1.31972966,  1.32113362,  1.30437336,\n",
       "        1.34212985,  1.35496257,  1.34440524,  1.34489875,  1.35265241,\n",
       "        1.3722929 ,  1.36796545,  1.34749253,  1.33853203,  1.33409113,\n",
       "        1.3218916 ,  1.3230644 ,  1.31334519,  1.30859656,  1.31460237,\n",
       "        1.31303462,  1.30691323,  1.30791018,  1.29926025,  1.30851927,\n",
       "        1.31223266])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fundstats[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         96.06219067,   98.91214963,   99.56208163,   99.5965429 ,\n",
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       "        102.07508998,  100.04842996,  100.75012371,  105.35148194,\n",
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       "        110.12429287,  111.89469472,  112.78329986,  113.03544503,\n",
       "        112.4879035 ,  112.81081301,  114.35920584,  113.13663006,\n",
       "        111.60532559,  112.24901825,  111.9920513 ,  114.0078623 ,\n",
       "        114.68644047,  114.11018035,  120.84789418,  120.75422637,\n",
       "        120.53696671,  120.18250349,  120.71203576,  122.36373998,\n",
       "        125.35168371,  126.8766764 ,  126.86079349,  126.24021642,\n",
       "        126.63587692,  124.08209553,  124.40982682,  123.91014616,\n",
       "        122.45059935,  122.74266392,  126.47981046,  129.09308294,\n",
       "        130.89603183,  130.16037347,  133.15725199,  131.97296593,\n",
       "        132.11336159,  130.43733634,  134.21298472,  135.49625704,\n",
       "        134.4405243 ,  134.48987516,  135.26524081,  137.22929006,\n",
       "        136.79654528,  134.749253  ,  133.85320306,  133.4091126 ,\n",
       "        132.18915983,  132.3064399 ,  131.33451929,  130.85965633,\n",
       "        131.46023748,  131.30346224,  130.6913233 ,  130.79101834,\n",
       "        129.92602545,  130.85192699,  131.22326645])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fundstats[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "         9.56146172e-03,  -7.79160074e-03,   3.67083193e-04,\n",
       "         5.76523436e-03,   1.45199849e-02,  -3.15344324e-03,\n",
       "        -1.49659648e-02,  -6.64975812e-03,  -3.31774247e-03,\n",
       "        -9.14444858e-03,   8.87213967e-04,  -7.34598112e-03,\n",
       "        -3.61567515e-03,   4.58950578e-03,  -1.19256776e-03,\n",
       "        -4.66201679e-03,   7.62828322e-04,  -6.61354962e-03,\n",
       "         7.12637472e-03,   2.83786008e-03])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fundstats[:,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Average Daily Return from Above example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00123458808756\n"
     ]
    }
   ],
   "source": [
    "averageDailyReturn =np.average(fundstats[:,2])\n",
    "print averageDailyReturn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Standard Deviation  from Above example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017446609241508617"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standardDeviationForSharpe = np.std(fundstats[:,2])\n",
    "standardDeviationForSharpe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Sharpe  from Above example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "251"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numberOfTradingDays =numTradingDays(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31))\n",
    "numberOfTradingDays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1211091802014921"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sharpeRatio = (numberOfTradingDays**(1/2.0))*(averageDailyReturn/standardDeviationForSharpe )\n",
    "sharpeRatio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Cumulative Return from Above Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3122326645349758"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumReturn = fundstats[-1,0]\n",
    "cumReturn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Plots of the Above Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fundstats[:,[1]];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XX/4ysf1TJRM1ijVroo/wChesURQXJ69PxqVeyk7GqjoRKI/Y/CPgqcDtp4Af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\n8kDhnHMurv8Heg7o8kC+ta0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa914557290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fundstats[:,[1]])\n",
    "plt.ylabel('What am I?')\n",
    "plt.xlabel('What is this?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uZ5WYwqpVLI8/zj4oH6Q9QHD+cYpFKjy7bdu2rby2GFCuWAAejFavjm/7xESg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\nB+Cf1OWqsEl2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa914413650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fundstats[:,[2]])\n",
    "plt.ylabel('What am I?')\n",
    "plt.xlabel('What is this?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cTCWxYoX9vGtXYhPx1q+3ZHT/+Y9ddE4PLFqbaM2hqj4HsOAQrDmcd571J5x3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\n55yL4MHBOedcBA8OzjnnIvx/66PHqxQWhCgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa91443ce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fundstats[:,[0]])\n",
    "plt.ylabel('What am I?')\n",
    "plt.xlabel('What is this?')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}