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Coursera HW1 thoughts from someone with no financial background
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## HomeWork 1 Further thoughts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": "markdown",
"metadata": {},
"source": [
"## 2. Functions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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\n",
"\n",
"def optimizer2(dt_start, dt_end, ls_symbols, data, verbose=False, quiet=False):\n",
"\n",
"\tbestSharpeRatio = -1\n",
" for x in data:\n",
" sharpe = simulate2(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])\n",
"\n",
"def simulate2(dt_start, dt_end, ls_symbols, ls_allocations, initial_allocation=1):\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",
" fund_stats = np.zeros((num_tradingDays, 3)) # Number of trading days * 3\n",
" \n",
" for i in range(num_tradingDays):\n",
" for j in range(len(na_price[i,:])):\n",
"\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",
" fund_stats[i,1] = ((fund_stats[i,0]/initial_allocation) * 100)\n",
" if i != 0:\n",
" fund_stats[i,2] = ((fund_stats[i,0]/fund_stats[i-1,0]) - 1 )\n",
"\n",
" avgDailyReturn = np.average(fund_stats[:,2])\n",
" sqStdDev = np.std(fund_stats[:,2])\n",
" sharpeRatio = (num_tradingDays**(1/2.0))*(avgDailyReturn/sqStdDev)\n",
" cumReturn = fund_stats[-1,0]\n",
" return sqStdDev, avgDailyReturn, sharpeRatio, cumReturn, ls_allocations \n",
"\n",
"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",
"\n",
"\n",
"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]\n",
"\n",
"def fundStats2(dt_start, dt_end, ls_symbols, ls_allocations,initial_allocation=1):\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, 3)) # 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",
" fund_stats[i,1] = ((fund_stats[i,0]/initial_allocation) * 100)\n",
" if i != 0:\n",
" fund_stats[i,2] = ((fund_stats[i,0]/fund_stats[i-1,0]) - 1 )\n",
"\n",
" return fund_stats\n",
"\n",
"def numTradingDays(dt_start, dt_end):\n",
"\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",
" return num_tradingDays"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": null,
"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": "markdown",
"metadata": {},
"source": [
"## 3 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": [
"## 4 Homework One Check"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.85933151455268364, [0.0, 0.0, 0.0, 1.0]]\n",
"[0.90941221874571065, [0.0, 0.2, 0.0, 0.8]]\n",
"[0.95628709092442132, [0.0, 0.4, 0.0, 0.6]]\n",
"[0.9969636534553471, [0.0, 0.6, 0.0, 0.4]]\n",
"[1.0288936268139, [0.0, 0.8, 0.0, 0.2]]\n",
"[1.0505648136725501, [0.0, 1.0, 0.0, 0.0]]\n",
"[1.0743896534257054, [0.2, 0.6, 0.0, 0.2]]\n",
"[1.1026130725634706, [0.2, 0.8, 0.0, 0.0]]\n",
"[1.1211091802014921, [0.4, 0.6, 0.0, 0.0]]\n",
" \n",
"Final Results:\n",
"vol (sqStdDev): 0.0174466092415\n",
"Avg Daily Return: 0.00123458808756\n",
"Sharpe Ratio 1.1211091802\n",
"Cumulative Return 1.31223266453\n",
"ls_allocations [0.4, 0.6, 0.0, 0.0]\n"
]
}
],
"source": [
"formatSimulate(simulate2(*optimizer2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['BRCM', 'TXN', 'AMD', 'ADI'], \n",
" datagen(step=2))))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"251"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" numTradingDays(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note the above. The value hard-coded into simulate is 252** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5 Homework One Detailed"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fund_stats_hw1=fundStats2(*optimizer2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),['BRCM', 'TXN', 'AMD', 'ADI'], \n",
" datagen(step=2), quiet=True))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fund_stats_hw1;"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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C80RkGhCsPfgMaedctbJqVeRhrMV16WLJ+Fau9GalNwK3cJqEsjjnXMrEalYKJwKnnQbT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\nYc4cuOIKCw433WTzDoLzHKK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"text/plain": [
"<matplotlib.figure.Figure at 0x7f9f7df0f710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_hw1[:,[0]])\n",
"plt.legend(['Normalized Price'])\n",
"plt.ylabel('Normalized Price of Fund')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OhsaN4aST4MtA5q1Fi6ymEfTqq3DKKZbRM9j0NG+eXRF7jaL45s+3RXrOOw9+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\nlQcK55xzUf0/xHshufADeN0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9f7e02ee10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_hw1[:,[1]])\n",
"plt.legend(['Normalized Price (%)'])\n",
"plt.ylabel('Normalized Price of Fund (%)')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Kpbc3X2LJwmlum8ImJ6NNDObgp8ELWdra05jC7ATJRjaFaT/s7xfV4iKWZgs3jiKWu+8G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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9f7e1d6dd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_hw1[:,[2]])\n",
"plt.ylabel('Daily Returns of Fund (%)')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0012345880875578715"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avgDailyReturn_hw1 = np.average(fund_stats_hw1[:,2])\n",
"avgDailyReturn_hw1"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.017446609241508617"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stdDev_hw1 = np.std(fund_stats_hw1[:,2])\n",
"stdDev_hw1"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.1211091802014921"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numTradingDays_hw1 = numTradingDays(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31))\n",
"sharpeRatio_hw1 = (numTradingDays_hw1 **(1/2.0))*(avgDailyReturn_hw1/stdDev_hw1)\n",
"sharpeRatio_hw1 "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.3122326645349758"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cumReturn_hw1 = fund_stats_hw1[-1,0]\n",
"cumReturn_hw1 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6 Calculate Sharpe Ratio for Fund"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the Sharpe ratio for 2010 for a fund consisting of 'AAPL', 'BRCM', 'TXN', 'ADI', giving each stock a weighting factor of 0.25, assuming an initial allocation of 1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fund_stats_example = fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),\n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], [0.25, 0.25, 0.25, 0.25] ,initial_allocation=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fund_stats_example;"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(fund_stats_example[:,[0]])\n",
"plt.legend(['Normalized Price'])\n",
"plt.ylabel('Normalized Price of Fund')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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KKSk2n+LgQejY0YLE+PHwwguJLXNBvf++dRyXK2fbf/iDneA+/TSx5Vi92gJu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\niP4fKyNr7YgMLQQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9f7ed0ac90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(fund_stats_example[:,[1]])\n",
"plt.legend(['Normalized Price (%)'])\n",
"plt.ylabel('Normalized Price of Fund (%)')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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B3/42zwcDig83FgixmCZtuS95D83y7thRfYolaub9UWUrRQWhh/aW4ryPUizi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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9f7e224150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(fund_stats_example[:,[2]])\n",
"plt.ylabel('Daily Returns of Fund (%)')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0013124233375213211"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avgDailyReturn_example = np.average(fund_stats_example[:,2])\n",
"avgDailyReturn_example"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.015786402937725121"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stdDev_example = np.std(fund_stats_example[:,2])\n",
"stdDev_example"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.3171269057934021"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numTradingDays_example= numTradingDays(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31))\n",
"sharpeRatio_example = (numTradingDays_example **(1/2.0))*(avgDailyReturn_example/stdDev_example)\n",
"sharpeRatio_example"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.3472453878341921"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cumReturn_example = fund_stats_example[-1,0]\n",
"cumReturn_example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Calculation of Sharpe Ratio for Fund**\n",
"\n",
"So the Sharpe ratio is calulated by the above formula using the daily returns **of the fund**. \n",
"The daily return of the fund is the found by multipying the daily return for each stock by \n",
"the weighting factor allocated to that stock (ls_allocation),\n",
"and the summing all weighted daily returns (to give the fund daily returns) [column 3 of fund_stats]. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"$$Sharpe\\ Ratio = {\\sqrt{Number\\ Trading\\ Days}\\ \\times\\ {{Average\\ Daily\\ Return}\\over {Standard\\ Deviation}}}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BUT THIS SHOULD SURELY BE ????"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$Sharpe\\ Ratio = {\\sqrt{Number\\ Stocks}\\ \\times\\ {{Average\\ Daily\\ Return}\\over {Standard\\ Deviation}}}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So that when the number of stocks is 1 the above formula simplifies to "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$Sharpe\\ Ratio = {{Average\\ Daily\\ Return}\\over {Standard\\ Deviation}}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That is, *k* should be 4 in simulate????"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.16627262622126457"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numberOfStocks = 4\n",
"sharpeRatio_example_alt = (numberOfStocks**(1/2.0))*(avgDailyReturn_example/stdDev_example)\n",
"sharpeRatio_example_alt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another possibility is that the value under the square root symbol should be\n",
"**the number of stocks with a weighting factor greater than zero**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take an example of a portfolio with just one stock. AAPL in 2010"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fund_stats_one_stock = fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),\n",
" ['AAPL'], [1] ,initial_allocation=1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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5222J0/z0UytxRHPUURYcShs+3NY6TuUGGdarlzWeR4oVHLp1s/EL7dvDBx+U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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb80a965a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_one_stock[:,[0]])\n",
"plt.legend(['AAPL'])\n",
"plt.ylabel('Normalized Price of Fund')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0017905981418011325"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avgDailyReturn_one_stock = np.average(fund_stats_one_stock[:,2])\n",
"avgDailyReturn_one_stock"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.01683159433616141"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stdDev_one_stock = np.std(fund_stats_one_stock[:,2])\n",
"stdDev_one_stock"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.6854261764221505"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numTradingDays_one_stock = numTradingDays(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31))\n",
"sharpeRatio_one_stock = (numTradingDays_one_stock **(1/2.0))*(avgDailyReturn_one_stock/stdDev_one_stock)\n",
"sharpeRatio_one_stock"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also calulate the Sharpe Ratio 'by hand' for \n",
"a single stock using the well-known formula"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"aapl_raw_2010 = getData(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), ['AAPL'])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ELPtq0bZzVQseoaafM8+05pV4vf02FB38FksfxfLl0KlT/K8Xrelpy5bCNZRe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\nzjnnovJA4ZxzLioPFM4556L6fzGrNHfTimFdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb80170ff10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(aapl_raw_2010)\n",
"plt.legend(['AAPL'])\n",
"plt.ylabel('Price of Fund')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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WqoOA9zBUuhtFhWXatPDG+LvfBb797ejjVJKwqMVS7GgivYdjTVhcV5imSCrkGIUIS9wY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\nSWrKxCJJasrEIklqysQiSWrqE4z82agYv5jCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb80170f6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(dailyrets_aapl_2010)\n",
"plt.ylabel('Daily Returns of Fund (%)')\n",
"plt.xlabel('Trading Days')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The formula for daily returns is given [here](http://wiki.quantsoftware.org/index.php?title=Gallery)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dailyrets_aapl_2010 = (aapl_raw_2010[1:,:]/aapl_raw_2010[0:-1,:]) - 1"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.10659812376067525"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sharpe_aapl_2010_byHand = np.average(dailyrets_aapl_2010)/ np.std(dailyrets_aapl_2010)\n",
"Sharpe_aapl_2010_byHand "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So why do the two numbers not agree?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, they do (almost) if I multiply the above by the square root of the number of trading days."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.6888318913714755"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sharpe_aapl_2010_byHand *(numTradingDays(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31)) **(1/2.0)) "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.6854261764221505"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sharpeRatio_one_stock"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**I think you will need to talk to someone with a financial background about this. **\n",
"\n",
"**Or, Coursera may have the answer**"
]
}
],
"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
}
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