1353 lines
173 KiB
Plaintext
1353 lines
173 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Sharpe Ratio and Portfolio Values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import quandl"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Create a Portfolio"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"start = pd.to_datetime('2012-01-01')\n",
|
||
"end = pd.to_datetime('2017-01-01')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Grabbing a bunch of tech stocks for our portfolio\n",
|
||
"aapl = quandl.get('WIKI/AAPL.11',start_date=start,end_date=end)\n",
|
||
"cisco = quandl.get('WIKI/CSCO.11',start_date=start,end_date=end)\n",
|
||
"ibm = quandl.get('WIKI/IBM.11',start_date=start,end_date=end)\n",
|
||
"amzn = quandl.get('WIKI/AMZN.11',start_date=start,end_date=end)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Alternative\n",
|
||
"# aapl = pd.read_csv('AAPL_CLOSE',index_col='Date',parse_dates=True)\n",
|
||
"# cisco = pd.read_csv('CISCO_CLOSE',index_col='Date',parse_dates=True)\n",
|
||
"# ibm = pd.read_csv('IBM_CLOSE',index_col='Date',parse_dates=True)\n",
|
||
"# amzn = pd.read_csv('AMZN_CLOSE',index_col='Date',parse_dates=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"aapl.to_csv('AAPL_CLOSE')\n",
|
||
"cisco.to_csv('CISCO_CLOSE')\n",
|
||
"ibm.to_csv('IBM_CLOSE')\n",
|
||
"amzn.to_csv('AMZN_CLOSE')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Normalize Prices\n",
|
||
"\n",
|
||
"This is the same as cumulative daily returns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"53.063217800140997"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Example\n",
|
||
"aapl.iloc[0]['Adj. Close']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"for stock_df in (aapl,cisco,ibm,amzn):\n",
|
||
" stock_df['Normed Return'] = stock_df['Adj. Close']/stock_df.iloc[0]['Adj. Close']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Adj. Close</th>\n",
|
||
" <th>Normed Return</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-03</th>\n",
|
||
" <td>53.063218</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-04</th>\n",
|
||
" <td>53.348386</td>\n",
|
||
" <td>1.005374</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-05</th>\n",
|
||
" <td>53.940658</td>\n",
|
||
" <td>1.016536</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-06</th>\n",
|
||
" <td>54.504543</td>\n",
|
||
" <td>1.027162</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-09</th>\n",
|
||
" <td>54.418089</td>\n",
|
||
" <td>1.025533</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Adj. Close Normed Return\n",
|
||
"Date \n",
|
||
"2012-01-03 53.063218 1.000000\n",
|
||
"2012-01-04 53.348386 1.005374\n",
|
||
"2012-01-05 53.940658 1.016536\n",
|
||
"2012-01-06 54.504543 1.027162\n",
|
||
"2012-01-09 54.418089 1.025533"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"aapl.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Adj. Close</th>\n",
|
||
" <th>Normed Return</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-23</th>\n",
|
||
" <td>115.547742</td>\n",
|
||
" <td>2.177549</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-27</th>\n",
|
||
" <td>116.281568</td>\n",
|
||
" <td>2.191378</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-28</th>\n",
|
||
" <td>115.785740</td>\n",
|
||
" <td>2.182034</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-29</th>\n",
|
||
" <td>115.755990</td>\n",
|
||
" <td>2.181473</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-30</th>\n",
|
||
" <td>114.853583</td>\n",
|
||
" <td>2.164467</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Adj. Close Normed Return\n",
|
||
"Date \n",
|
||
"2016-12-23 115.547742 2.177549\n",
|
||
"2016-12-27 116.281568 2.191378\n",
|
||
"2016-12-28 115.785740 2.182034\n",
|
||
"2016-12-29 115.755990 2.181473\n",
|
||
"2016-12-30 114.853583 2.164467"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"aapl.tail()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Allocations\n",
|
||
"\n",
|
||
"Let's pretend we had the following allocations for our total portfolio:\n",
|
||
"\n",
|
||
"* 30% in Apple\n",
|
||
"* 20% in Google/Alphabet\n",
|
||
"* 40% in Amazon\n",
|
||
"* 10% in IBM\n",
|
||
"\n",
|
||
"Let's have these values be reflected by multiplying our Norme Return by out Allocations"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"for stock_df,allo in zip([aapl,cisco,ibm,amzn],[.3,.2,.4,.1]):\n",
|
||
" stock_df['Allocation'] = stock_df['Normed Return']*allo"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Adj. Close</th>\n",
|
||
" <th>Normed Return</th>\n",
|
||
" <th>Allocation</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-03</th>\n",
|
||
" <td>53.063218</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.300000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-04</th>\n",
|
||
" <td>53.348386</td>\n",
|
||
" <td>1.005374</td>\n",
|
||
" <td>0.301612</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-05</th>\n",
|
||
" <td>53.940658</td>\n",
|
||
" <td>1.016536</td>\n",
|
||
" <td>0.304961</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-06</th>\n",
|
||
" <td>54.504543</td>\n",
|
||
" <td>1.027162</td>\n",
|
||
" <td>0.308149</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-09</th>\n",
|
||
" <td>54.418089</td>\n",
|
||
" <td>1.025533</td>\n",
|
||
" <td>0.307660</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Adj. Close Normed Return Allocation\n",
|
||
"Date \n",
|
||
"2012-01-03 53.063218 1.000000 0.300000\n",
|
||
"2012-01-04 53.348386 1.005374 0.301612\n",
|
||
"2012-01-05 53.940658 1.016536 0.304961\n",
|
||
"2012-01-06 54.504543 1.027162 0.308149\n",
|
||
"2012-01-09 54.418089 1.025533 0.307660"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"aapl.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Investment\n",
|
||
"\n",
|
||
"Let's pretend we invested a million dollars in this portfolio"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"for stock_df in [aapl,cisco,ibm,amzn]:\n",
|
||
" stock_df['Position Values'] = stock_df['Allocation']*1000000"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Total Portfolio Value"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"portfolio_val = pd.concat([aapl['Position Values'],cisco['Position Values'],ibm['Position Values'],amzn['Position Values']],axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Position Values</th>\n",
|
||
" <th>Position Values</th>\n",
|
||
" <th>Position Values</th>\n",
|
||
" <th>Position Values</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-03</th>\n",
|
||
" <td>300000.000000</td>\n",
|
||
" <td>200000.000000</td>\n",
|
||
" <td>400000.000000</td>\n",
|
||
" <td>100000.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-04</th>\n",
|
||
" <td>301612.236461</td>\n",
|
||
" <td>203864.734300</td>\n",
|
||
" <td>398368.223296</td>\n",
|
||
" <td>99150.980283</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-05</th>\n",
|
||
" <td>304960.727573</td>\n",
|
||
" <td>203113.258186</td>\n",
|
||
" <td>396478.797638</td>\n",
|
||
" <td>99206.836843</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-06</th>\n",
|
||
" <td>308148.724558</td>\n",
|
||
" <td>202361.782072</td>\n",
|
||
" <td>391926.999463</td>\n",
|
||
" <td>101999.664861</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-09</th>\n",
|
||
" <td>307659.946988</td>\n",
|
||
" <td>203650.026838</td>\n",
|
||
" <td>389887.278583</td>\n",
|
||
" <td>99737.474166</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Position Values Position Values Position Values Position Values\n",
|
||
"Date \n",
|
||
"2012-01-03 300000.000000 200000.000000 400000.000000 100000.000000\n",
|
||
"2012-01-04 301612.236461 203864.734300 398368.223296 99150.980283\n",
|
||
"2012-01-05 304960.727573 203113.258186 396478.797638 99206.836843\n",
|
||
"2012-01-06 308148.724558 202361.782072 391926.999463 101999.664861\n",
|
||
"2012-01-09 307659.946988 203650.026838 389887.278583 99737.474166"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"portfolio_val.columns = ['AAPL Pos','CISCO Pos','IBM Pos','AMZN Pos']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>AAPL Pos</th>\n",
|
||
" <th>CISCO Pos</th>\n",
|
||
" <th>IBM Pos</th>\n",
|
||
" <th>AMZN Pos</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-03</th>\n",
|
||
" <td>300000.000000</td>\n",
|
||
" <td>200000.000000</td>\n",
|
||
" <td>400000.000000</td>\n",
|
||
" <td>100000.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-04</th>\n",
|
||
" <td>301612.236461</td>\n",
|
||
" <td>203864.734300</td>\n",
|
||
" <td>398368.223296</td>\n",
|
||
" <td>99150.980283</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-05</th>\n",
|
||
" <td>304960.727573</td>\n",
|
||
" <td>203113.258186</td>\n",
|
||
" <td>396478.797638</td>\n",
|
||
" <td>99206.836843</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-06</th>\n",
|
||
" <td>308148.724558</td>\n",
|
||
" <td>202361.782072</td>\n",
|
||
" <td>391926.999463</td>\n",
|
||
" <td>101999.664861</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-09</th>\n",
|
||
" <td>307659.946988</td>\n",
|
||
" <td>203650.026838</td>\n",
|
||
" <td>389887.278583</td>\n",
|
||
" <td>99737.474166</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" AAPL Pos CISCO Pos IBM Pos AMZN Pos\n",
|
||
"Date \n",
|
||
"2012-01-03 300000.000000 200000.000000 400000.000000 100000.000000\n",
|
||
"2012-01-04 301612.236461 203864.734300 398368.223296 99150.980283\n",
|
||
"2012-01-05 304960.727573 203113.258186 396478.797638 99206.836843\n",
|
||
"2012-01-06 308148.724558 202361.782072 391926.999463 101999.664861\n",
|
||
"2012-01-09 307659.946988 203650.026838 389887.278583 99737.474166"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"portfolio_val['Total Pos'] = portfolio_val.sum(axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>AAPL Pos</th>\n",
|
||
" <th>CISCO Pos</th>\n",
|
||
" <th>IBM Pos</th>\n",
|
||
" <th>AMZN Pos</th>\n",
|
||
" <th>Total Pos</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-03</th>\n",
|
||
" <td>300000.000000</td>\n",
|
||
" <td>200000.000000</td>\n",
|
||
" <td>400000.000000</td>\n",
|
||
" <td>100000.000000</td>\n",
|
||
" <td>1.000000e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-04</th>\n",
|
||
" <td>301612.236461</td>\n",
|
||
" <td>203864.734300</td>\n",
|
||
" <td>398368.223296</td>\n",
|
||
" <td>99150.980283</td>\n",
|
||
" <td>1.002996e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-05</th>\n",
|
||
" <td>304960.727573</td>\n",
|
||
" <td>203113.258186</td>\n",
|
||
" <td>396478.797638</td>\n",
|
||
" <td>99206.836843</td>\n",
|
||
" <td>1.003760e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-06</th>\n",
|
||
" <td>308148.724558</td>\n",
|
||
" <td>202361.782072</td>\n",
|
||
" <td>391926.999463</td>\n",
|
||
" <td>101999.664861</td>\n",
|
||
" <td>1.004437e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2012-01-09</th>\n",
|
||
" <td>307659.946988</td>\n",
|
||
" <td>203650.026838</td>\n",
|
||
" <td>389887.278583</td>\n",
|
||
" <td>99737.474166</td>\n",
|
||
" <td>1.000935e+06</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" AAPL Pos CISCO Pos IBM Pos AMZN Pos \\\n",
|
||
"Date \n",
|
||
"2012-01-03 300000.000000 200000.000000 400000.000000 100000.000000 \n",
|
||
"2012-01-04 301612.236461 203864.734300 398368.223296 99150.980283 \n",
|
||
"2012-01-05 304960.727573 203113.258186 396478.797638 99206.836843 \n",
|
||
"2012-01-06 308148.724558 202361.782072 391926.999463 101999.664861 \n",
|
||
"2012-01-09 307659.946988 203650.026838 389887.278583 99737.474166 \n",
|
||
"\n",
|
||
" Total Pos \n",
|
||
"Date \n",
|
||
"2012-01-03 1.000000e+06 \n",
|
||
"2012-01-04 1.002996e+06 \n",
|
||
"2012-01-05 1.003760e+06 \n",
|
||
"2012-01-06 1.004437e+06 \n",
|
||
"2012-01-09 1.000935e+06 "
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"%matplotlib inline"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.text.Text at 0x23286633518>"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAHYCAYAAAAmg26+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCImwxLEAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x23286463358>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val['Total Pos'].plot(figsize=(10,8))\n",
|
||
"plt.title('Total Portfolio Value')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x23286aa1908>"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEICAYAAACj2qi6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnKrxTMNxHMepmv8BJQvYZQBpK9IAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x23286463048>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val.drop('Total Pos',axis=1).plot(kind='line')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>AAPL Pos</th>\n",
|
||
" <th>CISCO Pos</th>\n",
|
||
" <th>IBM Pos</th>\n",
|
||
" <th>AMZN Pos</th>\n",
|
||
" <th>Total Pos</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-23</th>\n",
|
||
" <td>653264.617079</td>\n",
|
||
" <td>377469.015679</td>\n",
|
||
" <td>407359.955612</td>\n",
|
||
" <td>424839.412389</td>\n",
|
||
" <td>1.862933e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-27</th>\n",
|
||
" <td>657413.396830</td>\n",
|
||
" <td>379323.596496</td>\n",
|
||
" <td>408410.671112</td>\n",
|
||
" <td>430877.506563</td>\n",
|
||
" <td>1.876025e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-28</th>\n",
|
||
" <td>654610.167268</td>\n",
|
||
" <td>376108.989746</td>\n",
|
||
" <td>406089.322915</td>\n",
|
||
" <td>431285.259454</td>\n",
|
||
" <td>1.868094e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-29</th>\n",
|
||
" <td>654441.973495</td>\n",
|
||
" <td>376603.544631</td>\n",
|
||
" <td>407091.167926</td>\n",
|
||
" <td>427386.471541</td>\n",
|
||
" <td>1.865523e+06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2016-12-30</th>\n",
|
||
" <td>649340.095692</td>\n",
|
||
" <td>373636.215323</td>\n",
|
||
" <td>405600.618032</td>\n",
|
||
" <td>418851.589119</td>\n",
|
||
" <td>1.847429e+06</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" AAPL Pos CISCO Pos IBM Pos AMZN Pos \\\n",
|
||
"Date \n",
|
||
"2016-12-23 653264.617079 377469.015679 407359.955612 424839.412389 \n",
|
||
"2016-12-27 657413.396830 379323.596496 408410.671112 430877.506563 \n",
|
||
"2016-12-28 654610.167268 376108.989746 406089.322915 431285.259454 \n",
|
||
"2016-12-29 654441.973495 376603.544631 407091.167926 427386.471541 \n",
|
||
"2016-12-30 649340.095692 373636.215323 405600.618032 418851.589119 \n",
|
||
"\n",
|
||
" Total Pos \n",
|
||
"Date \n",
|
||
"2016-12-23 1.862933e+06 \n",
|
||
"2016-12-27 1.876025e+06 \n",
|
||
"2016-12-28 1.868094e+06 \n",
|
||
"2016-12-29 1.865523e+06 \n",
|
||
"2016-12-30 1.847429e+06 "
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val.tail()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Portfolio Statistics\n",
|
||
"### Daily Returns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"portfolio_val['Daily Return'] = portfolio_val['Total Pos'].pct_change(1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Cumulative Return"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Our return 84.74285181665459 was percent!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"cum_ret = 100 * (portfolio_val['Total Pos'][-1]/portfolio_val['Total Pos'][0] -1 )\n",
|
||
"print('Our return {} was percent!'.format(cum_ret))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Avg Daily Return"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.0005442330716215298"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val['Daily Return'].mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Std Daily Return"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.010568287769162561"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val['Daily Return'].std()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x232869912e8>"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x232869a7208>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val['Daily Return'].plot(kind='kde')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Sharpe Ratio\n",
|
||
"\n",
|
||
"The Sharpe Ratio is a measure for calculating risk-adjusted return, and this ratio has become the industry standard for such calculations. \n",
|
||
"\n",
|
||
"Sharpe ratio = (Mean portfolio return − Risk-free rate)/Standard deviation of portfolio return\n",
|
||
"\n",
|
||
"The original Sharpe Ratio\n",
|
||
"\n",
|
||
"Annualized Sharpe Ratio = K-value * SR\n",
|
||
"\n",
|
||
"K-values for various sampling rates:\n",
|
||
"\n",
|
||
"* Daily = sqrt(252)\n",
|
||
"* Weekly = sqrt(52)\n",
|
||
"* Monthly = sqrt(12)\n",
|
||
"\n",
|
||
"Since I'm based in the USA, I will use a very low risk-free rate (the rate you would get if you just put your money in a bank, its currently very low in the USA, let's just say its ~0% return). If you are in a different country with higher rates for your trading currency, you can use this trick to convert a yearly rate with a daily rate:\n",
|
||
"\n",
|
||
"daily_rate = ((1.0 + yearly_rate)**(1/252))-1\n",
|
||
"\n",
|
||
"Other values people use are things like the 3-month treasury bill or [LIBOR](http://www.investopedia.com/terms/l/libor.asp).\n",
|
||
"\n",
|
||
"Read more: Sharpe Ratio http://www.investopedia.com/terms/s/sharperatio"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"SR = portfolio_val['Daily Return'].mean()/portfolio_val['Daily Return'].std()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.05149680662647732"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"SR"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"ASR = (252**0.5)*SR"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.8174864618858524"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"ASR"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.010568287769162561"
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val['Daily Return'].std()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.0005442330716215298"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val['Daily Return'].mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x23286445e80>"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x23285da5cf8>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"portfolio_val['Daily Return'].plot('kde')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.axes._subplots.AxesSubplot at 0x232885a1ef0>"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcHGd95/HPr6u7+j6m55Y0I9k6sB0wtpGNwY4TgwEH\nk9jkYJMAcQKJwyvHJiQEnGQ3L0g2hGSzBDabTeIQsk5gc+KAF+wQWwRjEhtbBmNsy5ZkyTpnNPf0\n9N1d9ewf1SNLsqRpjfrQdP3er9e8uru6uupXmtF853mq6nnEGINSSin/CnS7AKWUUt2lQaCUUj6n\nQaCUUj6nQaCUUj6nQaCUUj6nQaCUUj6nQaCUUj6nQaCUUj6nQaCUUj4X7HYBzRgYGDCbNm3qdhlK\nKbWmPPHEEzPGmMGV1lsTQbBp0yZ27tzZ7TKUUmpNEZEDzaynXUNKKeVzGgRKKeVzGgRKKeVzGgRK\nKeVzGgRKKeVzGgRKKeVzGgRKKeVzGgRKVQvw2F9AZanblSjVFRoESn39j+C+D8DDH+92JUp1hQaB\nUvse8h5f2NHdOpTqEg0Cpaaf9x6PPQNOrbu1KNUFGgTK38qLUFmEocvArcPCwW5XpFTHaRAof1s4\n5D1e9D3e49y+7tWiVJdoECh/WzzsPV50g/eoQaB8SINA+Vth2nscvgwCQVia6G49SnVB24NARCwR\n+ZaIfLHxOisiD4jInsZjX7trUOqMSnPeY6wfEsOwdKy79SjVBZ1oEfwSsOuE13cCO4wxW4EdjddK\ndUdpHgIhsBNeEOQ1CJT/tDUIRGQDcAvwqRMW3wrc3Xh+N3BbO2tQ6qyKcxDtAxENAuVb7W4RfAL4\nIOCesGzYGLPcETsJDJ/ugyJyh4jsFJGd09PTbS5T+VZp3gsCgKQGgfKntgWBiLwNmDLGPHGmdYwx\nBjBneO8uY8x2Y8z2wcEV515WanVK8xDLes8TI1CY0ZvKlO+0c/L664AfEJG3AhEgJSKfAY6JyKgx\nZkJERoGpNtag1NmV5iEz7j2PDwAGSguQ0D8+lH+0rUVgjPl1Y8wGY8wm4EeBrxhj3gXcC9zeWO12\n4AvtqkGpFRXnINpoESx3EZXmu1ePUl3QjfsIPga8SUT2ADc1XivVHeVFiKS85xoEyqfa2TV0nDHm\nq8BXG89ngTd2Yr9KnZUxUCuCHfdeRzPeowaB8hm9s1j5V60EGAjFvNfaIlA+pUGg/KtW8h6Ptwg0\nCJQ/aRAo/6oVvMflFkE4DQiUF7pWklLdoEGg/Kta9B5DUe8xEPDOE2iLQPmMBoHyr0aLoJZ3OPgz\nd5B/+OsQ0SBQ/tORq4aUuiA1WgQLDzxO4eGHqR05QuK2Pg0C5TvaIlD+VfOCoLhrPwDVF1/EDaa9\nm8yU8hENAuVfVa9rqHp4kkAsBq5LZTEElaUuF6ZUZ2kQKP+qFXFqQn16juT33QxAZdbVIFC+o0Gg\n/KtapLrknSaLv/71EAhQzTkaBMp3NAiUf9UK1EvefwF7fJzg8DD1XN27msh1ulycUp2jQaD8q1qk\nXrIACA4OEhoZobZQbryX72JhSnWWBoHyr1qRejUMQDCbJTQ6Sm2+cbexdg8pH9EgUP5VLVCv2Fh9\nfYhtExwZoT6Xxxg0CJSvaBAo/6qVqFeCBBtToQb7+zG1Om5dNAiUr2gQKP+qFXDKAYID/QBYWW+m\nMqcc0CBQvqJBoPyrWsSpgJXxJqQJ9jeCoKJBoPxFg0D5V62IUzYE0mkArD4vCOoaBMpnNAiUb5lK\nHqfsYjWCYLlFUC9bGgTKVzQIlG+5hSIYsFKNFkFWu4aUP2kQKN9y8t5UlcstgkAkQiAWw6naUNUg\nUP6hQaB8yyk0giCTPr7M6u+nXrO1RaB8RYNA+ZZbqAAvtQgArGwfTiWoQaB8RYNA+ZNTwym7wMlB\nEMz04VT1HIHyFw0C5U/VgvcLHwikXgqCQDqFUwEqOuic8g8NAuVPteLxILDSqeOLrVTaaynoyWLl\nIxoEyp+qXhCIHSQQiRxfbKXTuBUXU9IgUP6hQaD8qVbArQqBePSkxcutAydf6EZVSnWFBoHyp1oJ\ntx7Aip0aBN75Ajdf7EZVSnWFBoHyp2oBpyYEYrGTFgdSjRZBsQpOvRuVKdVxGgTKn2pF3LoQiJ8c\nBMstAqca0OkqlW9oECh/qhZxawECieRJi18KAtEgUL6hQaD8qVbArQmBROKkxdZy11A1oPcSKN/Q\nIFD+VG10DSVSJy1enpvA1a4h5SMaBMqfal7XkJXKnLQ4YNtI2NZhJpSvaBAoXzKlJYwrBJLJl71n\nJRN6slj5igaB8iV3KQdAIB5nrlBlz7GX/vq30invZLGeI1A+0bYgEJGIiDwmIt8WkWdE5CON5VkR\neUBE9jQe+9pVg1Jn4ix5v/glFufH/+JR3vRHX+OJA/NAY5gJbREoH2lni6ACvMEY82rgCuBmEbkW\nuBPYYYzZCuxovFaqo9yC90v+haLhuUkvFP7mkRcBCGSyeo5A+UrbgsB4lv+kCjW+DHArcHdj+d3A\nbe2qQakzcQveWEK7cw4Ab7hkiK/vncUYg5XJ4NS0RaD8o63nCETEEpEngSngAWPMN4BhY8xEY5VJ\nYLidNSh1Om7Rm6Zy16LD5sE4b7x0iJl8hRdni95Q1HofgfKRtgaBMcYxxlwBbACuEZFXnvK+wWsl\nvIyI3CEiO0Vk5/T0dDvLVD60HAQvFAyvGEly+XrvMtLnJnJYmTSmLrjFXDdLVKpjOnLVkDFmAfg3\n4GbgmIiMAjQep87wmbuMMduNMdsHBwc7UabyEbfozVe8v2jY1B9ny1ACEdh9LH984Dl3YaGbJSrV\nMe28amhQRDKN51HgTcBzwL3A7Y3Vbge+0K4alDoTt+wFwVIgzEUDcaK2xVhfjN1TS1iNqSudJW0R\nKH8ItnHbo8DdImLhBc4/GGO+KCKPAP8gIu8FDgDvaGMNSp2WU64CQYrBMBv6vBFItw0n2HNsCWus\nMd7Qkp4jUP7QtiAwxjwFXHma5bPAG9u1X6Wa4ZbrELRxAxYjaW+qys1DCb62ewYSo946OjmN8gm9\ns1j5j+viVhyM7f0dNJQMA7AxG6fquMxbXjA4+VLXSlSqkzQIlP/US7h1wQmFSIaDxMNeIIxnvS6i\no3XvtVMod61EpTpJg0D5T2NSmqodYrjRLQQvBcGBqgWAU6x1pTylOk2DQPlPY1KaihViOBU+vng0\nE8EKCIdyVSQcxK0aqFe6WKhSnaFBoPynWsSpC0UrxHDypRZByAqwLhPh4FwRKx7Ru4uVb2gQKP9p\nTEqzFLDpT9gnvTWejTWCINaYt1gHnlO9T4NA+U+1gFsXClaYTOzlQXBorkggGfOGotYWgfIBDQLl\nP7Uibk0oBCOkoqGT3hrLxpjJV5F4QkcgVb6hQaB8x5SXME6ApVCUZCTAnQ/fydu/8Hb2LexjrHGX\ncSWa0FnKlG9oECjfcXOLAOStGIfL3+RL+77E3oW9fOKbn2B9XxSAYiTemKVMzxGo3qdBoHzHXfJG\nFV0Mxnhi7l/pj/Tzk9/1kzx0+CEiYa8FsGgncOsBjA5FrXxAg0D5znKLYDEU59n5J7hx/EZuufgW\nXOOyJ/dN7GCA2UAcAGdhtpulKtURGgTKd5bnK16KVinWC1w1dBXb+raRCWd47NhjrM9EOULCW3d+\nrpulKtURGgTKd5wlr9+/Eve6iK4YvIKABLhy6Eqemn6K9Zkoh13vjmMnp5PTqN6nQaB8Z3nieie5\nRCwYY0NyAwCXZi/lQO4AIxnhYLUx8NziYtfqVKpTNAiU77gFb56BanKBi9MXIyIAbMtuw2CIxqY5\nUvcGnnN1chrlAxoEyneWJ64vx2bZnNl8fPkl2UsAqIeOkA95l5E6S4XOF6hUh2kQKN9xS948A/lI\njk3pTceXr4uvIxlKsuQeIG97N5Y5eQ0C1fs0CJTvuKUKbgDqQWFDYsPx5SLC5sxm5mqHqVohsAS3\noMNQq96nQaB8xylVqYe88wLrEutOem88Nc5k8RBWQCAcwClqEKje11QQiMg9InKLiGhwqDXPrdSo\n2KcPgo2pjUyXphlOC44dxCnVu1GiUh3V7C/2/w38OLBHRD4mIq9oY01KtZVbdijZQlBs+iP9J703\nnhoHYLBviZodxClrEKje11QQGGMeNMa8E7gKeBF4UET+Q0R+SkRCZ/+0UhcWt+pQtCEdGj5+6eiy\njcmNACSSC5SCIdyyAWO6UaZSHdN0V4+I9AM/Cfw08C3gk3jB8EBbKlOqHYzBrRryYeiPjLzs7eUW\nQSg8SyFoe0NR14qdrlKpjgo2s5KI/DPwCuBvgO83xkw03vp7EdnZruKUarl6Gbcm5COG4ejLgyAe\nijMYHaRuTVMIRbzJaSp5sONdKFapzmgqCIC/MMbcd+ICEQkbYyrGmO1tqEup9qgWcZaDID502lXG\nU+MslibJhyK4VcGUc0hyuMOFKtU5zXYN/bfTLHuklYUo1RHVPE4tQMmGdYnT/3LfmNrITOUI+VAM\nENz5qc7WqFSHnbVFICIjwHogKiJXAstn1lJArM21KdVyprQEjlAMC69KnT4IxpJjLFTmmLcvBsCZ\nm8LqZJFKddhKXUNvwTtBvAH4+AnLl4DfaFNNSrWNu+hNNFO2YfwsQQBQTnsXxDlzM50pTqkuOWsQ\nGGPuBu4WkR8yxnyuQzUp1TbuojfRTDEMQ2c6R5D0rhwqZxojkC7Md6Y4pbpkpa6hdxljPgNsEpFf\nOfV9Y8zHT/MxpS5YTs77pV6yhWwke9p1llsEpb7GZxZ0ljLV21bqGlq+Zi7R7kKU6gS3MeNYNRTG\nCpy+5z9hJ8hGshSSNQDqizpLmeptK3UN/Xnj8SOdKUep9lqeuN6xk2ddb0NyAwtVb1Ka8rzOUqZ6\nW7ODzv2BiKREJCQiO0RkWkTe1e7ilGo1dynnPUbTZ11vPDnOjDUNYqgs5DpRmlJd0+x9BG82xuSA\nt+GNNbQF+LV2FaVUu7gF7698iQ2cdb2x5BjztWnEhtqSDjGheluzQbDchXQL8I/GGG0rqzWptrQE\nQOgMN5MtG0uOYTDUbXDzpU6UplTXNBsEXxSR54DXADtEZBAot68spdojn/eCIJFeOQgAyuEApqST\n06je1uww1HcCrwe2G2NqQAG4tZ2FKdUO+XyecggG4mcPguVRSIuRAOicBKrHNTvoHMAlePcTnPiZ\nvz7TyiIy1nh/GDDAXcaYT4pIFvh7YBPe+YZ3GGP0jh3VEaVSmboNw4nT30y2rC/cRzwUJxcrMrio\nQaB6W7NXDf0N8IfA9cDVja+VRh2tA79qjLkMuBb4eRG5DLgT2GGM2QrsaLxWqiNq5SqlMKxfIQhE\nhPHkOItRgaqL0clpVA9rtkWwHbjMnMP/hsacBRON50sisgtvALtbge9trHY38FXgQ81uV6nz4ZTr\nFG3YkDp7EIB3L8Fs7Dmowky+wmAy0oEKleq8Zk8WPw28fBaPJonIJuBK4BvA8AkT20zidR0p1RlV\nl6otDCZWvll+PDnOdMQBIxyd0GEmVO9qtkUwADwrIo8Bxy+hMMb8wEofFJEE8Dngl40xuRPniDXG\nGBE5bStDRO4A7gAYHx9vskylzi5QdalFA6RjK0+1PZYc48VGI2Dy8CSv3rauzdUp1R3NBsGHV7Px\nxsT2nwM+a4y5p7H4mIiMGmMmRGQUOO2sH8aYu4C7ALZv364dtKolrCrU7AAJe+Uf/fHUOPlGECwc\nPoQ3RbdSvafZy0cfwrvCJ9R4/jjwzbN9Rrw//f8S2HXKKKX3Arc3nt8OfOEca1Zq1UJVcEIWgYCs\nuO5YcoxCIwgKxybbXJlS3dPsVUM/A/wT8OeNReuBz6/wseuAdwNvEJEnG19vBT4GvElE9gA3NV4r\n1XbGGMJVcEIrdwsBDMWGqEa9EUqrMzpdpepdzXYN/TxwDd7JXowxe0TkrJddGGO+zktTW57qjU1X\nqFSLVJbmCLqAbTe1fkACpFNZ4BjO/Gxba1Oqm5q9aqhijKkuv2jcVKb99mpNmZvYDYCxm59ue2Rg\nAwCS0zkJVO9qNggeEpHfwJvE/k3APwL/r31lKdV688deAEBiqaY/Mz60GReIlBcpVPQOY9Wbmg2C\nO4Fp4DvAzwL3Af+lXUUp1Q6L04cAsOJ9TX9mS3YrxQiEnRzTSzr4nOpNTZ0jMMa4IvJ54PPGmOk2\n16RUWyzNTtAH2E3cVbxsS/ZS9kcg7BSYWqqwaSC+8oeUWmPO2iIQz4dFZAZ4Hni+MTvZb3WmPKVa\nJz83A0C0b0PTnxnLbiUfBbtaYWpJR15XvWmlrqH3410GerUxJmuMyQKvBa4Tkfe3vTqlWqi04J3w\njQ2ub/ozVjiJGzZEqjWmcto1pHrTSkHwbuDHjDH7lxcYY/YB7wJ+op2FKdVqy7OT2YOjzX9IBCsC\nkbLLlJ4jUD1qpSAIGWNmTl3YOE/Q3F05Sl0gTLFMzTJE+89tnMNoNECibDia04HnVG9aKQiqq3xP\nqQuOlKpUw5BMps/pc+loiGgVji7sblNlSnXXSlcNvVpEcqdZLoAOzq7WlGCpTt2Gvnj4nD7XH49S\npkp5cU+bKlOqu84aBMYYq1OFKNVONbeGXTE4YeiLNTfExLJsKslRFpHiwTZVp1R3NXtDmVJr2nx5\nnnjZ4IaESOjcfuyDaa8rKVKZpFp321GeUl2lQaB8YbY0S7wMJmxx4uRIzbAyXhCknTlm8nrlkOo9\nGgTKF2bLXhAQanbA3ZdY2SwAqUqZPbM6L4HqPRoEyhdmizPEKxAIn9uJYoBgth+AZAmemX6+1aUp\n1XUaBMoXFuYnCBgIRpofgnqZxLNI0CVZMuyZ1yuHVO/RIFC+kGt06VixxLl/OJIiGHZJFiwO5fe1\nuDKluk+DQPlCYWYCAJNsfi6C48JJrLBLphhkqnKgxZUp1X0aBMoXCtONk7ypzLl/OJzygqAk5JxD\nGKOT86neokGgfKHWmHN4+QqgcxJOYtkuqbLBocxkQa8cUr1Fg0D1PGMMZtEbedQ6l5FHl0W8FkG8\n5E1VuXdhbyvLU6rrNAhUz1uqLREtOQCER8bOfQPhNJbtEqrWsRzDwSUdakL1Fg0C1fOmi9MkSwY3\nZIhlm5+m8rhwkmDYG1oikbfZO79/hQ8otbZoEKied6x4jGQJiLgkMoPnvoGgjRXzxl+M5zLsW9Ar\nh1Rv0SBQPW+6OE2yCEHbJZUdWNU2rGQUgORSgkPaNaR6jAaB6nlTxSkSJUPIdglFkqvaRjAVByCd\nizBTnqTm1FpZolJdpUGget5UcYpUCSRswTmOPLosmPVuRMssBTG4HMkfaWWJSnWVBoHqedOlaVIl\ncMPnPvLoskAqhViQKXpBolcOqV6iQaB63kxukkgVnNjqZ1eVSBorKgw1piM4kNMTxqp3aBConpeb\n9rpx3Pi5jzx6XCRNMGoYrJaxiHIwpy0C1Ts0CFRPK9fL1BfmATDJ1Z0oBiDaRzBco6+Sx3IGtWtI\n9RQNAtXTJgoTJIveIHGB9CoGnFsW7SNoV0kVF3Gr/doiUD1Fg0D1tIn8BOmC99waWMXNZMtiWayI\nS7SQo1rIMFGYoObqJaSqN2gQqJ52tHCUTCMI7OFVDDi3LNZPMOogxhBdjOEYh8m8jkKqeoMGgepp\nR/NHyRQBMcRHzyMIolmCEW+8ofSSN+/xoaVDLahQqe7TIFA97WjhKAOFkDfVZP/5tAhODALvfoTD\n+cOtKFGprtMgUD1tIj9BthDAirjY6eHVbyiaJRjxhrLuKzoEJaQtAtUzNAhUTzuSP0I673q/xOPn\nd7J4uUXQV8mTCg5rEKie0bYgEJFPi8iUiDx9wrKsiDwgInsaj33t2r9SNafGdGmaZMHxfonHVjfy\nKADBMIFYDLEt+itLRANDGgSqZ7SzRfB/gJtPWXYnsMMYsxXY0XitVFsczh/GdR3iRQeiAQitfogJ\nAGJZQimbddUcQWeAQ0s6kb3qDW0LAmPM14C5UxbfCtzdeH43cFu79q/UgdwBolUIOgYTj57/BmNZ\nQqkAI+UFTK2fUr3EbHn2/LerVJd1+hzBsDFmovF8EjiPs3dKnd2B3AFSjXsISCXOf4PRLMGYS19x\ngXLJ69U8vKRXDqm1r2sni43Xpj5ju1pE7hCRnSKyc3p6uoOVqV5xMHeQ4bI30JyVbcHpqMQwoXCZ\nRGGR0qIXLHqeQPWCTgfBMREZBWg8Tp1pRWPMXcaY7caY7YOD53G1h/KtA0sHGC2lAYiOtKDxmRwm\nFMohxhA45iCItghUT+h0ENwL3N54fjvwhQ7vX/nIgdwB+he9E8TxDRvOf4PJUUIRb0KCvqUcg1G9\nckj1hnZePvq3wCPAK0TksIi8F/gY8CYR2QPc1HitVMuV62UmC5Ok51wkYLCG1p//RhPDBGPeTWUD\npQWGous1CFRPWP3cfSswxvzYGd56Y7v2qdSy5V/Q6fkqwZiDJFrQvZgcIRTzbiobKi6wGBzmudxj\n579dpbpM7yxWPWnf4j4AMoslQlEH4udxM9my5AiBoEHiUQZLC0QCQ8yWZynWiue/baW6SINA9aQX\nFl5AEPoLJa87J7nu/DeaGAEglI0xWJpHal646OBzaq3TIFA9ae/CXtLBYRLlRosgOXL+G7VjEE5j\np4KMVHLH7yXQ8wRqrdMgUD3phYUX6C8MYbkGK2FBJN2aDSeHCSUMQ4U5FnPeHMh6Cala6zQIVM+p\nOTUO5g5iT3jDSoT6UyDSmo2nN2DHykRqZQoTBVJ2SlsEas3TIFA950DuAHVTx5r0LooLDbXwhsTM\nOKGgN4SWOXqYseSYBoFa8zQIVM/Zu7gXgMFF78c7eD5TVJ4qM47dCIL+xRmGouu0a0iteRoEque8\nsPACIGwqOohlCI6OtW7jmY2E4nUARgszJK0RjuaPUnfrrduHUh2mQaB6zvOzezDVfq6QJexEHUm1\ntkUQCIL0pRgpzBF0B6ibOhOFiZU/q9QFSoNA9Zynpp6jXh5mZGmKUKIOydYGAUB4KMlocZZK0buX\nYP/i/tbtQ6kO0yBQPSVfzTNbPULcjBOYnsJOtOgegmXxIbDChPtCbCjOMb/o3UvgdUcptTZpEKie\n8s3JZwB4Q+oiTKXmtQhSLRhwblkgAJkx7HidTGmRo0crDEWH2Luwt3X7UKrDNAhUT7l31+MA3Jbx\nhpSwEw6kWzAE9Yky44TCSwCUDh7k4sxmbRGoNU2DQPWUnRPfQZwkl7necNH2cBaC4dbuJDOOHfDm\nVBrITTMU2ci+xX24xm3tfpTqEA0C1TNy5RrTlf2MRLdQ278fAhBa3+LWAED/FkIh716CkcIcIWeE\nUr3E0fzR1u9LqQ7QIFA94+E9E0j4GFcOfxfl3bsJ9wnSv7H1O+rfimUbAvEoG4qz5HL9gJ4wVmuX\nBoHqGf+y+1uIuHzPpiuo7N5DOFGCTAtvJls2sBURsIfSbHEWOTCRAuC5uedavy+lOkCDQPWMnRNP\nAvDq2EXUJyYIp6uQbkMQZDZCIITdF2L90jTPHKmwKbWJp2efbv2+lOoADQLVE44slJh3d5Ow+skc\nzQMQTtegrw1dQ1YQshdhJ2vE56dwyhXWR7fx7Myzrd+XUh2gQaB6wtf3TGPFDnD54BVUd+8BIJKp\nQ/+W9uywfyt2ZBExhnWFGUxlA1OlKaaKU+3Zn1JtpEGgesKDe54nEFrkhrGrKT/7LIFoiGAy2J6u\nIYCBLYQD3lVC14UKHJr0hpp4eka7h9Tao0Gg1jzXNTw+8QQAVw1fRenb3yY6GkYGNkPAas9OB7Zh\nx8sAXBPM8dyBBJZYGgRqTdIgUGvesxM5StZuwoEYm+11VPbsIdpfbl+3EMDIqwgEDcGBDFvKs7iu\nzVD4Ir419a327VOpNtEgUGvew3umCSae55qRa6k9/Sy4LtH4DAxsbd9OBy+FQIjwQJj45GE29EVx\nipv59vS3KdfL7duvUm2gQaDWvAf3PkkglOOmTd9D6UnvEtJotgQD29q306ANQ5diJ6tU9+/n+y8f\n5eDRddTcGk9OP9m+/SrVBhoEak0r1xyezT0CwHXrrqPwjccIjw1h2QZGLm/vzkcvJxycwi0Weduo\nRa2wiQAWjxx9pL37VarFNAjUmvbY/lkk8S02Jy9nUJKUnniC+JY0BCPtbREArLuKcHwBgLG5w2zu\nzxJxtvGVg19p736VajENArWm/dPTj2KFp3nHJbdSeOwxTK1GfCgPQ5d5N36106bvJpKug0Dl2V28\nY/sYc9PbeDH3IvsW9rV330q1kAaBWrNc1/DwsXsIEOFtW26m8PV/RyIRYqEXYLTN3UIAA1sJ9A1h\nD0Qp79rFD161AZN/JSDct/++9u9fqRbRIFBr1gN7nqUW/RbXDryVZDDB0oMPEn/N5QTqC7DuyvYX\nIOK1ClIFys88w2AyzE3btiKlV3DPnnuou/X216BUC2gQqDXrfzzxh2CCfPB1P0vpySepT06SenVj\nfuLx13WmiK1vJprOUZ+cpHr4MO+8dpzizNVMl6b1XIFaMzQI1Jp07577mKjtZKt9G5uz68jddz9i\n2yRGFiCabf+J4mXb3kJ81JuZrPjoo1y/ZYBL0q8lUB/iT578E5zGTGlKXcg0CNSaczB3kI88+mGc\n4jgfeO0duJUKuS9+kcSNN2JNPg7j13rdNp0QzWBfcR1WFAqPPIqI8ItveAWFyZvYt7iPL+3/Umfq\nUOo8aBCoNaXqVPnVhz5AtS5cZN7H6zcPsfTlL+MsLNB3yw0w9wJs+u6O1iSv/EESowXyX9mBWyzy\n5suGuWrgBqiM8d8f/0PmynMdrUepc6VBoNaUjz/xcZ6b20XxyA/zwZuuBWDuM5/F3riRWOKYt9Ir\nbu5sUZfdRmYruKUySw88gIjw0bdfTm3yh1ks5/jtR34HY0xna1LqHGgQqDVjx8EdfHbXZ6nPX89N\nG2/khq0D5B96iPJTT5F9z3uQXZ+HwUsge3FnCwsniN74A9hph5k//VNMrcaWoSS/e8ubKU+9mR0H\nH+TTT3+6szUpdQ40CNSacCR/hN98+L8SqI6RKNzK79z2SkypxLGP/h6hjeNkXr8ZDj0KV76rK/XJ\n1e9h6PJDdynfAAAKQUlEQVQFqi8eYPqP/xcAP/SaDbz3VT9FLXc5n/jmJ7l/3/1dqU2plWgQqAte\nqV7iZ+7/zxSqNQLTP8Hdt1/NwEMf4cjbr6Z28CCjP34N8qVfgmgfXPHO7hS5/ioS3/u9ZLbVmL3r\nLo79/h/gFot88OZLePuG91MvbuRDD3+Izzz7We0mUhcc6cYPpYjcDHwSsIBPGWM+drb1t2/fbnbu\n3NmR2tSFJV8p8yP3/ByHKjsZKb2Pv7rxzUT/+GdZeOQgtWKQ4RuCZNcdhFAcfuSvYNtbulfs1C7M\nn93I5FODLDxdJxCPEL/iUsJXXss/LYb5O7mPufX7uCT9Gn7hil/kmnWvJhKykE5d4aR8R0SeMMZs\nX3G9TgeBiFjAbuBNwGHgceDHjDFnnPlbg6C31B2XZ44uciR3DJn4F9KH/pV0cZrh0WtIvOansMde\nQ6lU5v6vPMBnv/PnVEIHuG7+et45Vaf07/8OBqLb1jH4Gx8l/tprYOGA1xqIpLt9aLDvIbjv1yg+\nt4+FvXGK0za1wktjHj2/Lsv//e4yuy6q4FRGcIvbCNXHyIbXsTU7xmVD6/mu9WkuG02xoS/akpBw\nq1UCtn3e21Frz4UcBK8DPmyMeUvj9a8DGGN+70yf0SA4P8YYKnWXpXKdfKVOpe4QCwWJhS3idpCQ\nW6NwcB+FF/dRPnyA6uwMNQuq6Sjl9f3khpMsRQ0lp0zSTpKNZMmEM8RDcayARa40x8LiASbn9jO5\ndJTJepVpt8JiLUesUmWwbIjnqlSrdaakykSwiF0usW7G5aJjhnWzULegGoRM2TAyD9kFsE750QzG\nIL0pT+a978e+5Ve684/ZrFoZCtMw8zzOd+6n+tiXKe6bZ/b5OE7ZIjdi89XLwzw8XmY66RKqQ6wC\n8aKFVYszF+4jF8kSS6cZTKTIRCPEQjZx2yYWsglaIUKBEEEiBCVCgDCFaoF8bYHg3BH69uxjaPdh\nNuyeJTtXY7E/wuyrNuBe9xr6rruBjYNbWRdfh7XCVJ5utYozv4CzsIBbLGAlk9QSEeZCVWbqCyxW\nFlmsLmKMIR6Kkwqn6Av30Rfpoy/ch4hQcSpUnApVp0rNqWEFLJJ2kngoTkC0d7qdLuQg+GHgZmPM\nTzdevxt4rTHmF870mdUGwZ99+8+4f//9GF46xhOPd65Q4dKn8/ynh5e82k7zbyGNRSf9XXbCanLi\nwlPWPd1nTlwm5uUryOm2fcoywaxYj5zm23qmZZHqy3/pnqoahFwUHAscATcAARcC5oQv19ueZSDo\nGiIVCJiz/0XrBgMURoZwXAcpl6iGasxlYD5rkegLcW15inS5QCjhELn0UuTmj8JFnb1PoCWMgdm9\nuM89wOLnP8/cwwepLq78134lCEtRLyhdAdP4ArBc7yvgvvTcrkOk5r1fCsPh9TA9IKRnDRcfgmgV\n6gFvm5VQ4yShNH6ExPv+heoQcIRwzRzf1umUbMhHvO2snpzwBaf8r2maJUIg0Lkutm+8NsMj1/V1\nZF+/9brf4qrhq1b12WaDoM3j9K6eiNwB3AEwPj6+qm0MRgfZktmyvL2Xtt34YXvRFLFix5ganGT5\nB9Cc9PN48g+WOc0yTlh00q/n0zTpzfKyk96Sl3/+dD/PJ2wvWZ9FjHvK/k+IJDnpjcbbgsjJ/w6m\n8eXYAYqZEOVkiErCxsRsIq6QzBuS8zXiC3Wi+TrRsotjXOqug4M5HghGQrjBCKFQgqCdJBJwCJkS\nEnQpxfvIJ0YIj25kXX+ahFPHLVcJjYxib9lK+OKLkNBZfpO4Dsy/CHYckiNnXu9CJ+KNVnr9Vvqu\n/zkyxlB9/mnKj32N+pEXkFoOy13ACjkQACdfpb5Uo56vMVis4zoujmNwjIvjen/auAFwlr8sCAUC\n2JZFpC+CNRQllrVJuV5rsOa4uI7L/ESByrEitXKNquNQX/4rwuD9wSTgWMJCuI9KKEQhalOM2hSi\nYaq2RbTskioHSZYDJEoQK9exay7enycOhjoudQw1XLxB94QA3v+6AF70uBgcXONgcBqvvZ/GE/9o\nOxfRkEUk1LnWRXx4hIszox3ZVzQYbfs+tGtIKaV6VLMtgm500D0ObBWRi0TEBn4UuLcLdSillKIL\nXUPGmLqI/ALwZbzLRz9tjHmm03UopZTydOUcgTHmPkCncFJKqQuAXrullFI+p0GglFI+p0GglFI+\np0GglFI+p0GglFI+15XRR8+ViEwDB7pdx1kMADPdLqLN9Bh7gx5jb2j2GDcaYwZXWmlNBMGFTkR2\nNnP33lqmx9gb9Bh7Q6uPUbuGlFLK5zQIlFLK5zQIWuOubhfQAXqMvUGPsTe09Bj1HIFSSvmctgiU\nUsrnNAhWQUSyIvKAiOxpPL5sqiIRGRORfxORZ0XkGRH5pW7UulrNHGNjvU+LyJSIPN3pGldDRG4W\nkedFZK+I3Hma90VE/mfj/adEZHVTQ3VRE8d4iYg8IiIVEflAN2o8X00c4zsb37/viMh/iMiru1Hn\n+WjiGG9tHOOTIrJTRK5f9c6MMfp1jl/AHwB3Np7fCfz+adYZBa5qPE8Cu4HLul17K4+x8d4NwFXA\n092uuYljsoAXgIsBG/j2qd8T4K3A/XgTu10LfKPbdbfhGIeAq4HfBT7Q7ZrbdIyvB/oaz7+vR7+P\nCV7q3r8ceG61+9MWwercCtzdeH43cNupKxhjJowx32w8XwJ2Aes7VuH5W/EYAYwxXwPmOlXUeboG\n2GuM2WeMqQJ/h3ecJ7oV+GvjeRTIiEhn5iRsjRWP0RgzZYx5HDjLjMQXtGaO8T+MMfONl48CGzpc\n4/lq5hjzppECQBxWOc8n2jW0WsPGmInG80lg+Gwri8gm4ErgG+0tq6XO6RjXiPXAoRNeH+bl4dzM\nOheytV5/M871GN+L18pbS5o6RhF5u4g8B3wJeM9qd3bBTl7fbSLyIHC62dJ/88QXxhgjImdMYhFJ\nAJ8DftkYk2ttleenVceo1IVKRG7EC4LV959fwIwx/wz8s4jcAPwOcNNqtqNBcAbGmDP+g4rIMREZ\nNcZMNLoNps6wXggvBD5rjLmnTaWuWiuOcY05Aoyd8HpDY9m5rnMhW+v1N6OpYxSRy4FPAd9njJnt\nUG2tck7fR2PM10TkYhEZMMac8zhL2jW0OvcCtzee3w584dQVRESAvwR2GWM+3sHaWmXFY1yDHge2\nishFImIDP4p3nCe6F/iJxtVD1wKLJ3SRrQXNHONat+Ixisg4cA/wbmPM7i7UeL6aOcYtjd8zNK5u\nCwOrC7xunx1fi19AP7AD2AM8CGQby9cB9zWeX4938uYp4MnG11u7XXsrj7Hx+m+BCbwTj4eB93a7\n9hWO6614V3C9APxmY9n7gPc1ngvwJ433vwNs73bNbTjGkcb3KgcsNJ6nul13i4/xU8D8Cf/3dna7\n5jYc44eAZxrH9whw/Wr3pXcWK6WUz2nXkFJK+ZwGgVJK+ZwGgVJK+ZwGgVJK+ZwGgVJK+ZwGgVJK\n+ZwGgVJK+ZwGgVJK+dz/ByeGYKkYu7p7AAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x232885a7b38>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"aapl['Adj. Close'].pct_change(1).plot('kde')\n",
|
||
"ibm['Adj. Close'].pct_change(1).plot('kde')\n",
|
||
"amzn['Adj. Close'].pct_change(1).plot('kde')\n",
|
||
"cisco['Adj. Close'].pct_change(1).plot('kde')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"12.699606293110037"
|
||
]
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"np.sqrt(252)* (np.mean(.001-0.0002)/.001)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Great Job!"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.1"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|