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@tomGdow
Created February 13, 2016 18:06
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Fund Manager example
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Murtaza The Fund Manager"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Let's say I have a fund consisting of four stocks, AAPL, BRCM, TXN, ADI and I want to retrospectively optimize\n",
"their performance by maximizing the Sharpe Ratio for the years 2000 - 2016,\n",
"with a view to critically accessing the Sharpe ratio as an indicator of fund performance. \n",
"\n",
"* Let's start with 2010, just to get things started.\n",
"\n",
"* We can use simulate2() to calculate the overall fund performance giving each stock a weighting \n",
"factor of 0.25 (ls_allocations = [0.25, 0.25, 0.25, 0.25])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n",
"Final Results:\n",
"vol (sqStdDev): 0.0157864029377\n",
"Avg Daily Return: 0.00131242333752\n",
"Sharpe Ratio 1.31712690579\n",
"Cumulative Return 1.34724538783\n",
"ls_allocations [0.25, 0.25, 0.25, 0.25]\n"
]
}
],
"source": [
"formatSimulate(\n",
" simulate2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], [0.25, 0.25, 0.25, 0.25], \n",
" initial_allocation=1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Just to satisfy my curiosity that all is well, I'll calculate the Sharpe ratio using \n",
"calcSharpe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.3171269057934021"
]
},
"execution_count": 8,
"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": [
"* But my initial 'guess' of weighting factors (ls_allocations) of 0.25 for each stock may\n",
"not be the best. Is there any way to optimize that? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* But of course! That's where optimize() comes in. We can use datagen() to systematically create\n",
"a whole array of possibilities that optimize2() may use to loop through each one, calculate the Sharpe ratio for each, and (finally) tell us which is best."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* For example:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[[0.0, 0.0, 0.0, 1.0],\n",
" [0.0, 0.0, 1.0, 0.0],\n",
" [0.0, 1.0, 0.0, 0.0],\n",
" [1.0, 0.0, 0.0, 0.0]]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datagen(10) "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[[0.0, 0.0, 0.0, 1.0],\n",
" [0.0, 0.0, 0.5, 0.5],\n",
" [0.0, 0.0, 1.0, 0.0],\n",
" [0.0, 0.5, 0.0, 0.5],\n",
" [0.0, 0.5, 0.5, 0.0],\n",
" [0.0, 1.0, 0.0, 0.0],\n",
" [0.5, 0.0, 0.0, 0.5],\n",
" [0.5, 0.0, 0.5, 0.0],\n",
" [0.5, 0.5, 0.0, 0.0],\n",
" [1.0, 0.0, 0.0, 0.0]]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datagen(5)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"datagen(2);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Remove semi-colon from above command and evalute to see output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* So let's optimize, using a step-size of 0.2 (initially)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(datetime.datetime(2010, 1, 1, 0, 0),\n",
" datetime.datetime(2010, 12, 31, 0, 0),\n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'],\n",
" [1.0, 0.0, 0.0, 0.0])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myoptimized = optimizer2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),\n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], datagen(2),quiet = True)\n",
"myoptimized"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The important bit is in the last line.\n",
"I can now use this result **directly** in the simulate2() function to get a better\n",
"fund performance for 2010"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n",
"Final Results:\n",
"vol (sqStdDev): 0.0168315943362\n",
"Avg Daily Return: 0.0017905981418\n",
"Sharpe Ratio 1.68542617642\n",
"Cumulative Return 1.51234162365\n",
"ls_allocations [1.0, 0.0, 0.0, 0.0]\n"
]
}
],
"source": [
"formatSimulate(simulate2(*myoptimized))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* So, by using weighting factors of [1.0, 0.0, 0.0, 0.0], I can INCREASE the Sharpe ratio \n",
" to 1.685"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The above may my done in a single command"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n",
"Final Results:\n",
"vol (sqStdDev): 0.0168315943362\n",
"Avg Daily Return: 0.0017905981418\n",
"Sharpe Ratio 1.68542617642\n",
"Cumulative Return 1.51234162365\n",
"ls_allocations [1.0, 0.0, 0.0, 0.0]\n"
]
}
],
"source": [
"formatSimulate(simulate2(\n",
" *optimizer2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),\n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], datagen(2),quiet = True)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* It is possible that using a small step size a further improvement may be made. \n",
"As this is slow, I'll leave that up to you! "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Can you show me the difference?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* But of course!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Let's use fundStats2() to get the normalized daily prices and daily returns of \n",
"the fund under the two sets of conditions"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fund_stats_guess=fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], [0.25, 0.25, 0.25, 0.25])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fund_stats_optimized=fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], [1, 0, 0, 0])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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NAq6W52LXI4TwEwXxakKIHkA9XdBPo6nZTN0wlQfXPkijBo1qlGIAuO8+6NkT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\nZ7qD2vJEQQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdff6beb810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_guess[:,[0]])\n",
"plt.plot(fund_stats_optimized[:,[0]])\n",
"plt.legend(['Guess', 'Optimized'], loc=2)\n",
"plt.ylabel('Normalized Price of Fund')\n",
"plt.xlabel('Trading Days (2010)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ogoPhq6/g3XehQ4db7VZWMHhw7YpPgP4YxZeFH4QQB4ycexlQeivl+1LKLlLK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\ngOASunc3O8WAYlxSTo7pxR9/3AzN4W+6RRm0168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P+dIdx3FKlaiqJxH5C5acLxforKr3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdff6b32710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_guess[:,[1]])\n",
"plt.plot(fund_stats_optimized[:,[1]])\n",
"plt.legend(['Guess', 'Optimized'], loc=2)\n",
"plt.ylabel('Normalized Price of Fund (%)')\n",
"plt.xlabel('Trading Days (2010)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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VVUgPP1y7n2ku/mJsNcfS3d14Esp2OBZdE0hvkcl74RDxS47FMJoLi1slLLbn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\nnmKFxWKxWCw95f8DcchlrkNCDokAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdff6c10210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_guess[:,[2]])\n",
"plt.plot(fund_stats_optimized[:,[2]])\n",
"plt.legend(['Guess', 'Optimized'], loc=1)\n",
"plt.ylabel('Daily Returns')\n",
"plt.xlabel('Trading Days (2010)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sugestion\n",
"\n",
"**Modify optimize() to give the WORST ls_allocations based on smallest Sharpe Ratio, and include this on above plots as well.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Caution. Slow!**"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.85933151455268364, [0.0, 0.0, 0.0, 1.0]]\n"
]
},
{
"data": {
"text/plain": [
"(datetime.datetime(2010, 1, 1, 0, 0),\n",
" datetime.datetime(2010, 12, 31, 0, 0),\n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'],\n",
" [0.0, 0.0, 0.0, 1.0])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myWorstCase = deoptimizer(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31),\n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], datagen(2))\n",
"myWorstCase"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n",
"Final Results:\n",
"vol (sqStdDev): 0.018527929613\n",
"Avg Daily Return: 0.00100496461527\n",
"Sharpe Ratio 0.859331514553\n",
"Cumulative Return 1.23266142808\n",
"ls_allocations [0.0, 0.0, 0.0, 1.0]\n"
]
}
],
"source": [
"formatSimulate(simulate2(*myWorstCase))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fund_stats_worst=fundStats2(dt.datetime(2010, 1, 1), dt.datetime(2010, 12, 31), \n",
" ['AAPL', 'BRCM', 'TXN', 'ADI'], [0, 0, 0, 1])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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E3GzczCGqyYiKAl9fzWFtKH36QMuWsHYt2Nho0VGurtCpEzz3HNx3n2bKGjQI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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdff6c778d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.plot(fund_stats_guess[:,[1]])\n",
"plt.plot(fund_stats_optimized[:,[1]])\n",
"plt.plot(fund_stats_worst[:,[1]])\n",
"plt.legend(['Guess', 'Optimized', 'Worst'], loc=2)\n",
"plt.ylabel('Normalized Price of Fund (%)')\n",
"plt.xlabel('Trading Days (2010)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"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\n",
"\n",
"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",
" "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def deoptimizer(dt_start, dt_end, ls_symbols, data, verbose=False, quiet=False):\n",
"\n",
"\tworstSharpeRatio = 100\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 < worstSharpeRatio:\n",
" worstSharpeRatio = sharpe\n",
" mylist = [worstSharpeRatio, x]\n",
" if not quiet:\n",
" print mylist\n",
" return (dt_start, dt_end, ls_symbols, mylist[1])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
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},
"nbformat": 4,
"nbformat_minor": 0
}
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