mirror of
https://github.com/wassname/options_backtester.git
synced 2026-06-27 16:30:55 +08:00
Updated example notebooks
This commit is contained in:
Javier Rodríguez Chatruc
@@ -1,549 +0,0 @@
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{
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"cells": [
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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 os\n",
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"import sys\n",
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"\n",
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"BACKTESTER_DIR = os.path.realpath(os.path.join(os.getcwd(), '..', '..'))\n",
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"DATA_DIR = os.path.join(BACKTESTER_DIR, 'data')\n",
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"OPTIONS_DATA = os.path.join(DATA_DIR, 'options_data_clean_v2.h5')\n",
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"STOCKS_DATA = os.path.join(DATA_DIR, 'ivy_5assets.csv')\n",
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"\n",
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"sys.path.append(BACKTESTER_DIR) # Add backtester base dir to $PYTHONPATH"
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/jamoroso/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
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" from pandas.util.testing import assert_frame_equal\n",
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"/Users/jamoroso/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; mutltipliers will not be applied to position notionals.\n",
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" 'Module \"zipline.assets\" not found; mutltipliers will not be applied' +\n"
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]
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}
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],
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"source": [
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"import pyfolio as pf\n",
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"\n",
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"from backtester import Backtest, Type, Direction, Stock\n",
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"from backtester.strategy import Strategy, StrategyLeg\n",
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"from backtester.datahandler import HistoricalOptionsData, TiingoData\n",
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"\n",
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"# Cleaned up data\n",
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"options_data = HistoricalOptionsData(\n",
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" OPTIONS_DATA,\n",
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" key=\"/SPX\",\n",
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" where='quotedate >= \"2012-01-01\" & quotedate <= \"2014-01-01\"')\n",
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"options_schema = options_data.schema"
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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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"data": {
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"text/plain": [
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"Strategy(legs=[StrategyLeg(name=leg_1, type=Type.PUT, direction=Direction.BUY, entry_filter=Filter(query='((type == 'put') & (ask > 0)) & ((underlying == 'SPX') & (dte >= 60))'), exit_filter=Filter(query='(type == 'put') & (dte <= 30)')), StrategyLeg(name=leg_2, type=Type.CALL, direction=Direction.BUY, entry_filter=Filter(query='((type == 'call') & (ask > 0)) & ((underlying == 'SPX') & (dte >= 60))'), exit_filter=Filter(query='(type == 'call') & (dte <= 30)'))], exit_thresholds=(inf, inf))"
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]
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},
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"execution_count": 3,
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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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"put_otm = Strategy(options_schema)\n",
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"\n",
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"leg_1 = StrategyLeg(\"leg_1\", options_schema, option_type=Type.PUT, direction=Direction.BUY)\n",
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"leg_1.entry_filter = (options_schema.underlying == \"SPX\") & (options_schema.dte >= 60)\n",
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"leg_1.exit_filter = (options_schema.dte <= 30)\n",
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"\n",
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"leg_2 = StrategyLeg(\"leg_2\", options_schema, option_type=Type.CALL, direction=Direction.BUY)\n",
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"leg_2.entry_filter = (options_schema.underlying == \"SPX\") & (options_schema.dte >= 60)\n",
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"leg_2.exit_filter = (options_schema.dte <= 30)\n",
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"put_otm.add_legs([leg_1, leg_2])"
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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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"asset_data = TiingoData(STOCKS_DATA)\n",
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"asset_data._data = asset_data.query('date >= \"2012-01-01\" & date <= \"2014-01-01\"')\n",
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"\n",
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"VTI = Stock(\"VTI\", 0.2)\n",
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"VEU = Stock(\"VEU\", 0.2)\n",
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"BND = Stock(\"BND\", 0.2)\n",
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"VNQ = Stock(\"VNQ\", 0.2)\n",
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"DBC = Stock(\"DBC\", 0.2)"
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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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"0% [██████████████████████████████] 100% | ETA: 00:00:00\n",
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"Total time elapsed: 00:00:07\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>total capital</th>\n",
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" <th>cash</th>\n",
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" <th>VTI</th>\n",
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" <th>VEU</th>\n",
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" <th>BND</th>\n",
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" <th>VNQ</th>\n",
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" <th>DBC</th>\n",
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" <th>options qty</th>\n",
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" <th>calls capital</th>\n",
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" <th>puts capital</th>\n",
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" <th>stocks qty</th>\n",
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" <th>options capital</th>\n",
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" <th>stocks capital</th>\n",
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" <th>% change</th>\n",
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" <th>accumulated return</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2010-01-03</th>\n",
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" <th>2012-01-03</th>\n",
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" <th>2012-01-04</th>\n",
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" <tr>\n",
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"</table>\n",
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],
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"text/plain": [
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" total capital cash VTI VEU \\\n",
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"2010-01-03 1.000000e+06 1000000.000000 NaN NaN \n",
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"2012-01-03 9.978700e+05 2233.455073 193990.075633 193976.304225 \n",
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"... ... ... ... ... \n",
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||||
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"\n",
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" BND VNQ DBC options qty \\\n",
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"2010-01-03 NaN NaN NaN NaN \n",
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"2012-01-03 193986.878802 193966.992283 193976.293985 6.0 \n",
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||||
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|
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|
||||
"2012-01-06 194126.638801 191811.435009 193625.396202 6.0 \n",
|
||||
"... ... ... ... ... \n",
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||||
"2013-12-24 242926.387533 245754.530575 246032.446280 7.0 \n",
|
||||
"2013-12-26 242562.316514 245944.449069 246414.781084 7.0 \n",
|
||||
"2013-12-27 242562.316514 246476.220851 247083.866990 7.0 \n",
|
||||
"2013-12-30 242956.726785 246552.188248 245650.111476 7.0 \n",
|
||||
"2013-12-31 242865.709030 245222.758794 245267.776672 7.0 \n",
|
||||
"\n",
|
||||
" calls capital puts capital stocks qty options capital \\\n",
|
||||
"2010-01-03 NaN NaN NaN 0.0 \n",
|
||||
"2012-01-03 25740.0 0.0 24257.0 25740.0 \n",
|
||||
"2012-01-04 25020.0 0.0 24257.0 25020.0 \n",
|
||||
"2012-01-05 25080.0 0.0 24257.0 25080.0 \n",
|
||||
"2012-01-06 23580.0 0.0 24257.0 23580.0 \n",
|
||||
"... ... ... ... ... \n",
|
||||
"2013-12-24 46270.0 0.0 27125.0 46270.0 \n",
|
||||
"2013-12-26 49770.0 0.0 27125.0 49770.0 \n",
|
||||
"2013-12-27 49840.0 0.0 27125.0 49840.0 \n",
|
||||
"2013-12-30 49980.0 0.0 27125.0 49980.0 \n",
|
||||
"2013-12-31 53900.0 0.0 27125.0 53900.0 \n",
|
||||
"\n",
|
||||
" stocks capital % change accumulated return \n",
|
||||
"2010-01-03 0.000000e+00 NaN NaN \n",
|
||||
"2012-01-03 9.698965e+05 -0.002130 0.997870 \n",
|
||||
"2012-01-04 9.677121e+05 -0.002911 0.994966 \n",
|
||||
"2012-01-05 9.654360e+05 -0.002227 0.992749 \n",
|
||||
"2012-01-06 9.628870e+05 -0.004079 0.988700 \n",
|
||||
"... ... ... ... \n",
|
||||
"2013-12-24 1.229700e+06 0.003401 1.276243 \n",
|
||||
"2013-12-26 1.232122e+06 0.004640 1.282165 \n",
|
||||
"2013-12-27 1.234154e+06 0.001639 1.284267 \n",
|
||||
"2013-12-30 1.234553e+06 0.000420 1.284806 \n",
|
||||
"2013-12-31 1.234653e+06 0.003129 1.288826 \n",
|
||||
"\n",
|
||||
"[503 rows x 15 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"allocation = {'cash': 0, 'stocks': 97, 'options': 3}\n",
|
||||
"\n",
|
||||
"bt = Backtest(allocation=allocation)\n",
|
||||
"bt.options_data = options_data\n",
|
||||
"bt.options_strategy = put_otm\n",
|
||||
"\n",
|
||||
"bt.stocks = [VTI, VEU, BND, VNQ, DBC]\n",
|
||||
"bt.stocks_data = asset_data\n",
|
||||
"\n",
|
||||
"bt.run(rebalance_freq=1)\n",
|
||||
"bt.balance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2012-01-03</td></tr>\n",
|
||||
" <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2013-12-31</td></tr>\n",
|
||||
" <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>23</td></tr>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Backtest</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Annual return</th>\n",
|
||||
" <td>13.6%</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Cumulative returns</th>\n",
|
||||
" <td>28.9%</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Annual volatility</th>\n",
|
||||
" <td>14.3%</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Sharpe ratio</th>\n",
|
||||
" <td>0.96</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Calmar ratio</th>\n",
|
||||
" <td>1.14</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Stability</th>\n",
|
||||
" <td>0.83</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Max drawdown</th>\n",
|
||||
" <td>-11.9%</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Omega ratio</th>\n",
|
||||
" <td>1.18</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Sortino ratio</th>\n",
|
||||
" <td>1.37</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Skew</th>\n",
|
||||
" <td>-0.52</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Kurtosis</th>\n",
|
||||
" <td>3.88</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Tail ratio</th>\n",
|
||||
" <td>0.97</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Daily value at risk</th>\n",
|
||||
" <td>-1.7%</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "'numpy.int64' object has no attribute 'to_pydatetime'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-6-ea8302e3d94e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_returns_tear_sheet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreturns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbalance\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'% change'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[0;32m~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/plotting.py\u001b[0m in \u001b[0;36mcall_w_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mset_context\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mplotting_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes_style\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/tears.py\u001b[0m in \u001b[0;36mcreate_returns_tear_sheet\u001b[0;34m(returns, positions, transactions, live_start_date, cone_std, benchmark_rets, bootstrap, turnover_denom, header_rows, return_fig)\u001b[0m\n\u001b[1;32m 502\u001b[0m header_rows=header_rows)\n\u001b[1;32m 503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m \u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_worst_drawdown_periods\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreturns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0mvertical_sections\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/plotting.py\u001b[0m in \u001b[0;36mshow_worst_drawdown_periods\u001b[0;34m(returns, top)\u001b[0m\n\u001b[1;32m 1662\u001b[0m \"\"\"\n\u001b[1;32m 1663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1664\u001b[0;31m \u001b[0mdrawdown_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimeseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen_drawdown_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreturns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1665\u001b[0m utils.print_table(\n\u001b[1;32m 1666\u001b[0m \u001b[0mdrawdown_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Net drawdown in %'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/timeseries.py\u001b[0m in \u001b[0;36mgen_drawdown_table\u001b[0;34m(returns, top)\u001b[0m\n\u001b[1;32m 1006\u001b[0m df_drawdowns.loc[i, 'Peak date'] = (peak.to_pydatetime()\n\u001b[1;32m 1007\u001b[0m .strftime('%Y-%m-%d'))\n\u001b[0;32m-> 1008\u001b[0;31m df_drawdowns.loc[i, 'Valley date'] = (valley.to_pydatetime()\n\u001b[0m\u001b[1;32m 1009\u001b[0m .strftime('%Y-%m-%d'))\n\u001b[1;32m 1010\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecovery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'to_pydatetime'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pf.create_returns_tear_sheet(returns=bt.balance['% change'].dropna())"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -965,7 +965,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.6"
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
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+88
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Reference in New Issue
Block a user