879 lines
778 KiB
Plaintext
879 lines
778 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Analyse de Portefeuille avec PyFolio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pyfolio as pf\n",
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"import matplotlib.pyplot as plt\n",
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"import empyrical"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Définir un algorithmique de benchmark pour SPY"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def initialize(context):\n",
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" context.spy = sid(8554)\n",
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"\n",
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" \n",
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" set_max_leverage(1.01)\n",
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" \n",
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" schedule_function(rebalance,date_rules.every_day(),time_rules.market_open())\n",
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" \n",
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"def rebalance(context,data):\n",
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" order_target_percent(context.spy,1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100% Time: 0:00:02|##########################################################|\n"
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]
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}
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],
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"source": [
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"# Obtenir les rendements du benchmark\n",
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"benchmark_rets = get_backtest('5ea05f494e836745cf5adb9f')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"bm_returns = benchmark_rets.daily_performance['returns']\n",
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"bm_positions = benchmark_rets.pyfolio_positions\n",
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"bm_transactions = benchmark_rets.pyfolio_transactions"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Utiliser l'algorithme de la vidéo 'Effet de Levier'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100% Time: 0:00:00|##########################################################|\n"
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]
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}
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],
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"source": [
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"# Utiliser le même algorithme que la vidéo 'Effet de Levier'\n",
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"bt = get_backtest('5ea0559950e04245b1a90ff4')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"bt_returns = bt.daily_performance['returns']\n",
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"bt_positions = bt.pyfolio_positions\n",
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"bt_transactions = bt.pyfolio_transactions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f63db9fa128>"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzwAAAHLCAYAAAAN/p1mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na+2bmFm/mD08HPAwTNN
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f63dbacea90>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"bt_returns.plot()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.14140644478845052"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"empyrical.beta(bt_returns,bm_returns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Graphiques PyFolio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"benchmark_rets = bm_returns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+QAAAI0CAYAAACUFOOKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNsLDw0lPT8fj8fDBBx8
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63db5427b8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Rendements cumulés\n",
|
||
|
|
"plt.subplot(2,1,1)\n",
|
||
|
|
"pf.plotting.plot_rolling_returns(bt_returns, benchmark_rets)\n",
|
||
|
|
"\n",
|
||
|
|
"# Rendements non cumulés, quotidiens\n",
|
||
|
|
"plt.subplot(2,1,2)\n",
|
||
|
|
"pf.plotting.plot_returns(bt_returns)\n",
|
||
|
|
"plt.tight_layout()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABCcAAAF3CAYAAACWgyH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4eHhatiwoebMmaMXXnh
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63dbe5d358>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(1)\n",
|
||
|
|
"plt.subplot(1,3,1)\n",
|
||
|
|
"pf.plot_annual_returns(bt_returns)\n",
|
||
|
|
"plt.subplot(1,3,2)\n",
|
||
|
|
"pf.plot_monthly_returns_dist(bt_returns)\n",
|
||
|
|
"plt.subplot(1,3,3)\n",
|
||
|
|
"pf.plot_monthly_returns_heatmap(bt_returns)\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"fig.set_size_inches(15,5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzwAAAHrCAYAAAAKUphQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndOjQ9OjRI9/+9rfz05/
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63dc524240>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plot_return_quantiles(bt_returns);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAHUCAYAAAAA3AjKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCRMmnOpqAQDQ43otILl
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63dd2540f0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plot_rolling_beta(bt_returns, benchmark_rets);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAHUCAYAAAAA3AjKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQzVlyhRFR0crNTVVJ0+
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63dc5241d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plot_rolling_sharpe(bt_returns);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAHUCAYAAAD1IoN6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDwXxVVVi2DovMlqmiTs
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63db2a10b8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plot_drawdown_periods(bt_returns);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0kAAAHUCAYAAADvHmP0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngH3kKBBu0hO7WyhK5d9
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63e3b8e860>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plot_drawdown_underwater(bt_returns);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAHUCAYAAAD1IoN6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n19hR3BH3IZANIdlogYi
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63db4e99b0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plot_gross_leverage(bt_returns, bt_positions);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Top 10 long positions of all time</th>\n",
|
||
|
|
" <th>max</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AMZN-16841</th>\n",
|
||
|
|
" <td>50.99%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Top 10 short positions of all time</th>\n",
|
||
|
|
" <th>max</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>IBM-3766</th>\n",
|
||
|
|
" <td>-52.04%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Top 10 positions of all time</th>\n",
|
||
|
|
" <th>max</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>IBM-3766</th>\n",
|
||
|
|
" <td>52.04%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AMZN-16841</th>\n",
|
||
|
|
" <td>50.99%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAHRCAYAAACl7xLJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO3D8+HFMmjTpnPNLT0/
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63e2ac6c18>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pos_percent = pf.pos.get_percent_alloc(bt_positions)\n",
|
||
|
|
"pf.plotting.show_and_plot_top_positions(bt_returns, pos_percent);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAHUCAYAAAD1IoN6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4FGW+/v+7u5MQlkQIkIRdZA+bCCKI7FlYVRyUIAmI\n26XADxVHPTAM8zsgBrfhcAbBQTbZZRzBUUJYFVxwSTwCUSAQliFECJiNRCFbf//oSUsMIaSh00nV\n+3VddZmu9VNdBebmeeopi91utwsAAAAATMrq6QIAAAAAwJMIRQAAAABMjVAEAAAAwNQIRQAAAABM\njVAEAAAAwNQIRQAAAABMjVAEALhh7du3V0REhIYMGaIBAwboqaee0vfff39d206cOFGHDh3SN998\no/Dw8Aod9x//+Icr5QIAUAKhCABwwywWi1avXq24uDh9+umnuv/++/XUU08pPj6+3G1XrFihDh06\nOPdzvQoLC/Xaa6+5XDMAAMUIRQCAG2a323Xlu8CHDBmiyZMn64033pAkXbp0Sc8++6yGDBmi0NBQ\nvfrqq851Bw0apO+++875+dixY7rrrrtUUFDgnDd16lStXr26xDEfffRRXbx4UcOGDVNKSkqp/RR/\nPnPmjPr27auYmBhFR0dLcrRsffjhhxo1apT69u2rlStXOrdbtWqVhg8frmHDhmny5MlKT0/Xnj17\ndO+995Y4/n333afPP/9cOTk5evHFFxUREaGwsDB98MEHznXat2+vJUuWaOjQoeJd6QBQdRGKAABu\nMXz4cB08eFB5eXlav369fv31V8XFxWnTpk3atGlTiQBzpdatWys4OFifffaZJCkvL09ffPGFhgwZ\nUmK9V155RV5eXoqNjVXTpk2vWUtGRoZCQkJKBKvk5GRt2rRJixYt0vz582W32/X9999rxYoVWrNm\njWJjY9WoUSPNnz9fd999t86ePaszZ85Ikk6fPq1z586pd+/e+utf/yqbzaZt27Zp48aN+tvf/qZj\nx46VOP7WrVsr1AoGAKhchCIAgFvUqVNHhYWFysnJ0cSJE/XWW29Jkvz8/NSmTRudPn26zG2HDx+u\njz/+WJL0+eefKyQkRA0bNnS5lsLCQoWGhpaYd99990mSOnbsqLy8PP3888/as2ePIiIiVK9ePUnS\n6NGj9cUXX8jb21sDBgzQ7t27JUm7du1SWFiYbDab4uLiFBkZKUmqV6+ewsLCtH37dudxBgwY4HLd\nAIDKQSgCALhFSkqKvL295e/vr5MnT2rKlCmKiIjQ0KFDlZiYeM3uZMOGDdMnn3yiS5cuaefOnRo2\nbNgN1WKz2VS7du0S8+rUqSNJslod/yssLCxUenq6/P39nevccsst+vnnnyVJERERzlB0ZU3Z2dl6\n8cUXNWzYMA0dOlQ7d+5Ubm5uiX0AAKo2L08XAAAwpri4OPXs2VNeXl6aM2eOOnXqpLfffluSNHbs\n2Gtu27RpU7Vt21Y7duzQnj179Mc//rHc49lsNhUVFTk/Z2dnV7jmBg0aKDMz0/k5MzNT9evXlyT1\n7dtXf/rTn3Tq1CmdPHlSd911lyQpKChIb731llq3bl3h4wEAqgZaigAAN11cXJxWr16t559/XpL0\n888/q3379pKkL774QqdOnSrRmnI1I0aM0Pz589WuXTsFBASUWu7l5aWioiL98ssvkqSGDRvq8OHD\nkqTY2Fjl5eU51y1vkIPi5f3799eOHTuUlZUlSdqwYYOz+5uPj4/69Omj119/XYMHD3a2MA0aNEjr\n16+XJBUUFCgmJkaHDh265vEAAFULoQgAcMMsFovGjx+voUOHqm/fvnrvvfe0ZMkShYSESJKefvpp\nzZ07VyNGjFB8fLymTJmi//mf/9F3331X5gAEQ4cO1blz58rsOhcYGKg77rhDAwcO1Pfff69JkyZp\n+fLlGjlypI4fP16i5eb3xyjrc5cuXfTEE0/o4Ycf1rBhw5STk6PnnnvOud6QIUO0a9euEjU988wz\nunjxooYMGaKRI0eqqKjIGQAZXAEAqgeL3c1jhL722mv67rvvVFhYqCeffFJhYWHOZV9++aXmz58v\nm82mfv36adKkSZKkmJgY7d+/XxaLRTNmzFDnzp3dWSIAoArKy8vT4MGDtWXLlhLP+QAAcLO59Zmi\nr7/+WsnJydqwYYMyMzM1atSoEqFo7ty5Wr58uQIDAzV27FhFREQoPT1dp06d0oYNG5ScnKzp06dr\n48aN7iwTAFAFrVy5UgMGDCAQAQDczq2hqGfPnurataskx+g7v/76q+x2uywWi06fPq26desqKChI\nkjRw4EDt27dP6enpzmFTW7VqpezsbOXm5pYaNQgAYFxDhw5VgwYN9Le//c3TpQAATMCtochiscjX\n11eStHHjRvXv39/Zv/rChQslHpytX7++Tp8+rYyMDHXq1KnE/AsXLhCKAMBEtm7d6ukSAAAmUilD\ncu/cuVMffPCBli1b5pz3+0eZynq0qbhl6VoSEhJuvEgAAAAAhta9e/erznd7KPrss8+0ZMkSLVu2\nzPmiPMnxXofz5887P587d06BgYHy9vbWhQsXnPPT0tLUoEGDco9T1gnCeBISErjekMS9gJK4H8yJ\n645i3AsoVta9cK2GFLcOyZ2Tk6PXX39db7/9tvz8/Eosa9KkiXJzc5WamqqCggJ9+umnuueee9Sn\nTx9t27ZNkvTjjz8qKChItWrVcmeZAAAAAEzMrS1FsbGxyszM1LPPPuvsBterVy+1bdtWoaGh+stf\n/qJp06ZJcrykr0WLFmrRooU6duyoyMhI2Ww2zZo1y50lAgAAADA5t4aihx56SA899FCZy3v06KEN\nGzaUml8clAAAAOA+drtdhYWFni7jpikoKPB0Cagirmdcgiu5tfscAAAAqq7CwkLDBIni17wADRs2\nrHDYr5TR5wAAAFA1eXl5ycur+v9KaLVaDXEeuHFWa8XbfWgpAgAAAGBqhCIAAAAApkYoAgAAAGBq\ndLwEAACAx5w5c0YjR45Up06dZLfb5ePjo6lTp6pr165lbvPOO++oZ8+eOn78uJKSkvTSSy+Ve5zt\n27crPDz8ZpYOAyEUAQAAwKNuu+02rVq1SpJ0+vRpTZo0SYsXL1bTpk2vuv4TTzwhSTp+/Ph1Dbuc\nkpKijz/+mFCEMtF9DgAAAFVGs2bN9Nhjj2np0qWSpHnz5mncuHEaPXq03n//fUnS9OnTtWfPHkmO\n99G8/vrrio2Nde5j+PDhysrKcn6eM2eO4uPjtWjRIi1cuFBr166VJB09elTR0dGSpPDwcE2bNk3v\nv/++oqOj9fbbb+uRRx7R/fffr7Nnz0qSXn/9dY0dO1ZjxozRhx9+qMOHD2vChAnO4xTvOzk5WRMm\nTNDEiRM1ZcoU5eTk6MyZMxo7dqyeeOIJZ+2oOghFAAAAqFLat2+vY8eOKS8vT02bNtXatWu1du1a\nLViwoNS6FotFDzzwgD755BNJUnJyspo1a6ZbbrnFuc5jjz2mO++8U5MmTbrq9pKjNWny5MkaPXq0\nJMnPz08rV65U3759tX37dsXHx+vYsWNav369Vq5cqbfeekvNmjXT+fPnlZOTI0n65JNPFBYWpjlz\n5mjOnDlasWKF7r77bq1Zs0aSdPjwYb355pvq37//zf3CcMPoPgcAAACnLw+fVfK57Ju2v1ZB/rq7\nfXCFtikoKJDVapWPj48yMzMVGRkpb29vZWRkXP0YrVopNzdX6enp2rVrl+69994K11mzZk21atXK\n+bl79+6SpODgYGVmZioxMVF33nmnc91bb71V//73vzVgwADt3btXd9xxh3x9fRUYGKgDBw5o5syZ\nstvtys/PV+fOnSVJzZs3l7+/f4Vrg/sRigAAAOB0d/vgCoeYmy0xMVEhISH69ttv9fXXX2vdunWy\nWq3OoHI1gwcP1o4dO/TVV19p8eLF13WcgoIC58/e3t4lll35Ili73S6LxSK73e6cV1hYKIvForCw\nMK1du1YZGRnOZ5Zq1ar
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63e2b74390>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plot_turnover(bt_returns, bt_transactions, bt_positions);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA00AAAHUCAYAAADm9cOOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFwhNAAAAAOACoQkAAAA
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63e09f9b00>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.plotting.plot_daily_volume(bt_returns, bt_transactions);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Summary stats</th>\n",
|
||
|
|
" <th>All trades</th>\n",
|
||
|
|
" <th>Short trades</th>\n",
|
||
|
|
" <th>Long trades</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Total number of round_trips</th>\n",
|
||
|
|
" <td>250.00</td>\n",
|
||
|
|
" <td>109.00</td>\n",
|
||
|
|
" <td>141.00</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Percent profitable</th>\n",
|
||
|
|
" <td>0.74</td>\n",
|
||
|
|
" <td>0.45</td>\n",
|
||
|
|
" <td>0.97</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Winning round_trips</th>\n",
|
||
|
|
" <td>186.00</td>\n",
|
||
|
|
" <td>49.00</td>\n",
|
||
|
|
" <td>137.00</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Losing round_trips</th>\n",
|
||
|
|
" <td>64.00</td>\n",
|
||
|
|
" <td>60.00</td>\n",
|
||
|
|
" <td>4.00</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Even round_trips</th>\n",
|
||
|
|
" <td>0.00</td>\n",
|
||
|
|
" <td>0.00</td>\n",
|
||
|
|
" <td>0.00</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>PnL stats</th>\n",
|
||
|
|
" <th>All trades</th>\n",
|
||
|
|
" <th>Short trades</th>\n",
|
||
|
|
" <th>Long trades</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Total profit</th>\n",
|
||
|
|
" <td>$1980690.67</td>\n",
|
||
|
|
" <td>$526009.56</td>\n",
|
||
|
|
" <td>$1454681.10</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Gross profit</th>\n",
|
||
|
|
" <td>$2152761.28</td>\n",
|
||
|
|
" <td>$695137.94</td>\n",
|
||
|
|
" <td>$1457623.34</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Gross loss</th>\n",
|
||
|
|
" <td>$-172070.61</td>\n",
|
||
|
|
" <td>$-169128.38</td>\n",
|
||
|
|
" <td>$-2942.23</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Profit factor</th>\n",
|
||
|
|
" <td>$12.51</td>\n",
|
||
|
|
" <td>$4.11</td>\n",
|
||
|
|
" <td>$495.41</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg. trade net profit</th>\n",
|
||
|
|
" <td>$7922.76</td>\n",
|
||
|
|
" <td>$4825.78</td>\n",
|
||
|
|
" <td>$10316.89</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg. winning trade</th>\n",
|
||
|
|
" <td>$11573.99</td>\n",
|
||
|
|
" <td>$14186.49</td>\n",
|
||
|
|
" <td>$10639.59</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg. losing trade</th>\n",
|
||
|
|
" <td>$-2688.60</td>\n",
|
||
|
|
" <td>$-2818.81</td>\n",
|
||
|
|
" <td>$-735.56</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Ratio Avg. Win:Avg. Loss</th>\n",
|
||
|
|
" <td>$4.30</td>\n",
|
||
|
|
" <td>$5.03</td>\n",
|
||
|
|
" <td>$14.46</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Largest winning trade</th>\n",
|
||
|
|
" <td>$999650.80</td>\n",
|
||
|
|
" <td>$590207.58</td>\n",
|
||
|
|
" <td>$999650.80</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Largest losing trade</th>\n",
|
||
|
|
" <td>$-18940.73</td>\n",
|
||
|
|
" <td>$-18940.73</td>\n",
|
||
|
|
" <td>$-1177.78</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Duration stats</th>\n",
|
||
|
|
" <th>All trades</th>\n",
|
||
|
|
" <th>Short trades</th>\n",
|
||
|
|
" <th>Long trades</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg duration</th>\n",
|
||
|
|
" <td>179 days 00:14:59.524000</td>\n",
|
||
|
|
" <td>168 days 03:03:18.165137</td>\n",
|
||
|
|
" <td>187 days 10:09:59.156028</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Median duration</th>\n",
|
||
|
|
" <td>181 days 01:00:00</td>\n",
|
||
|
|
" <td>167 days 01:00:00</td>\n",
|
||
|
|
" <td>189 days 01:00:00</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Longest duration</th>\n",
|
||
|
|
" <td>364 days 10:28:01</td>\n",
|
||
|
|
" <td>317 days 00:00:00</td>\n",
|
||
|
|
" <td>364 days 10:28:01</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Shortest duration</th>\n",
|
||
|
|
" <td>0 days 13:31:59</td>\n",
|
||
|
|
" <td>0 days 13:31:59</td>\n",
|
||
|
|
" <td>1 days 00:00:00</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Return stats</th>\n",
|
||
|
|
" <th>All trades</th>\n",
|
||
|
|
" <th>Short trades</th>\n",
|
||
|
|
" <th>Long trades</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg returns all round_trips</th>\n",
|
||
|
|
" <td>0.07%</td>\n",
|
||
|
|
" <td>0.15%</td>\n",
|
||
|
|
" <td>0.00%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg returns winning</th>\n",
|
||
|
|
" <td>0.10%</td>\n",
|
||
|
|
" <td>0.19%</td>\n",
|
||
|
|
" <td>0.02%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg returns losing</th>\n",
|
||
|
|
" <td>-0.03%</td>\n",
|
||
|
|
" <td>-0.03%</td>\n",
|
||
|
|
" <td>-0.03%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Median returns all round_trips</th>\n",
|
||
|
|
" <td>0.01%</td>\n",
|
||
|
|
" <td>0.02%</td>\n",
|
||
|
|
" <td>0.00%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Median returns winning</th>\n",
|
||
|
|
" <td>0.02%</td>\n",
|
||
|
|
" <td>0.03%</td>\n",
|
||
|
|
" <td>0.01%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Median returns losing</th>\n",
|
||
|
|
" <td>-0.01%</td>\n",
|
||
|
|
" <td>-0.02%</td>\n",
|
||
|
|
" <td>-0.01%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Largest winning trade</th>\n",
|
||
|
|
" <td>8.37%</td>\n",
|
||
|
|
" <td>8.37%</td>\n",
|
||
|
|
" <td>0.10%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Largest losing trade</th>\n",
|
||
|
|
" <td>-0.19%</td>\n",
|
||
|
|
" <td>-0.13%</td>\n",
|
||
|
|
" <td>-0.19%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Symbol stats</th>\n",
|
||
|
|
" <th>AMZN-16841</th>\n",
|
||
|
|
" <th>IBM-3766</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg returns all round_trips</th>\n",
|
||
|
|
" <td>0.00%</td>\n",
|
||
|
|
" <td>0.15%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg returns winning</th>\n",
|
||
|
|
" <td>0.02%</td>\n",
|
||
|
|
" <td>0.19%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Avg returns losing</th>\n",
|
||
|
|
" <td>-0.03%</td>\n",
|
||
|
|
" <td>-0.03%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Median returns all round_trips</th>\n",
|
||
|
|
" <td>0.00%</td>\n",
|
||
|
|
" <td>0.02%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Median returns winning</th>\n",
|
||
|
|
" <td>0.01%</td>\n",
|
||
|
|
" <td>0.03%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Median returns losing</th>\n",
|
||
|
|
" <td>-0.01%</td>\n",
|
||
|
|
" <td>-0.02%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Largest winning trade</th>\n",
|
||
|
|
" <td>0.10%</td>\n",
|
||
|
|
" <td>8.37%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Largest losing trade</th>\n",
|
||
|
|
" <td>-0.19%</td>\n",
|
||
|
|
" <td>-0.13%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th>Profitability (PnL / PnL total) per name</th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AMZN-16841</th>\n",
|
||
|
|
" <td>73.44%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>IBM-3766</th>\n",
|
||
|
|
" <td>26.56%</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f63df33c668>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pf.create_round_trip_tear_sheet(bt_returns, bt_positions, bt_transactions);"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3.5",
|
||
|
|
"language": "python",
|
||
|
|
"name": "py35"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.5.9"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|