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twiecki/compute_cumulative_portfolio_returns_min.ipynb

Created March 2, 2016 17:53
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compute_cumulative_portfolio_returns_min.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Aidan</th>\n",
" <th>Archelon</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>110</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>105</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>95</td>\n",
" <td>95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>98</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>110</td>\n",
" <td>110</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Aidan Archelon\n",
"0 100 NaN\n",
"1 110 NaN\n",
"2 105 100\n",
"3 95 95\n",
"4 98 80\n",
"5 110 110"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"account_value = pd.DataFrame({'Aidan': [100, 110, 105, 95, 98, 110], \n",
" 'Archelon': [np.nan, np.nan, 100, 95, 80, 110]})\n",
"account_value"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Aidan</th>\n",
" <th>Archelon</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.100000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.045455</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.095238</td>\n",
" <td>-0.050000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.031579</td>\n",
" <td>-0.157895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.122449</td>\n",
" <td>0.375000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Aidan Archelon\n",
"0 NaN NaN\n",
"1 0.100000 NaN\n",
"2 -0.045455 NaN\n",
"3 -0.095238 -0.050000\n",
"4 0.031579 -0.157895\n",
"5 0.122449 0.375000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"account_returns = account_value.pct_change()\n",
"account_returns"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 0.100000\n",
"2 -0.045455\n",
"3 -0.072619\n",
"4 -0.063158\n",
"5 0.248724\n",
"dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"portfolio_returns = account_returns.mean(axis='columns')\n",
"portfolio_returns"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1391489158163266"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cum_portfolio_returns = (1 + portfolio_returns).prod() - 1\n",
"cum_portfolio_returns"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Aidan</th>\n",
" <th>Archelon</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>-2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-3.0</td>\n",
" <td>-2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.2</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-3.0</td>\n",
" <td>-3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Aidan Archelon\n",
"0 2.0 -2.0\n",
"1 -3.0 -2.0\n",
"2 4.0 1.0\n",
"3 0.5 0.5\n",
"4 1.2 0.5\n",
"5 -3.0 -3.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minutely_pnl = pd.DataFrame({'Aidan': [2, -3, 4., 0.5, 1.2, -3], \n",
" 'Archelon': [-2, -2, 1., 0.5, 0.5, -3]})\n",
"minutely_pnl"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"-0.00316247535946\n",
"0.0\n",
"0.000632495071892\n",
"0.00170773669411\n",
"-0.00208723373724\n"
]
}
],
"source": [
"# then this should happen every minute\n",
"daily_pnl = pd.Series({'Aidan': 0, 'Archelon': 0})\n",
"for minute, row in minutely_pnl.iterrows():\n",
" # Start counting the cumulative pnl for this day\n",
" daily_pnl += row\n",
" \n",
" # Calculate current daily return\n",
" daily_return = daily_pnl / account_value.iloc[-1] \n",
" daily_portfolio_return = daily_return.mean()\n",
" daily_total_cumulative_return = cum_portfolio_returns * daily_portfolio_return\n",
" print(\"Cumulative portfolio return on current day on minute {mcumudaily_total_cumulative_return)\n",
" \n",
"assert np.all(daily_return == minutely_pnl.sum() / account_value.iloc[-1])"
]
}
],
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"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"name": "ipython",
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"file_extension": ".py",
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"pygments_lexer": "ipython2",
"version": "2.7.11"
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"nbformat": 4,
"nbformat_minor": 0
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