options_backtester/backtester/examples/2legs_example.ipynb

In [1]:

import os
import sys

BACKTESTER_DIR = os.path.realpath(os.path.join(os.getcwd(), '..', '..'))
DATA_DIR = os.path.join(BACKTESTER_DIR, 'data')
OPTIONS_DATA = os.path.join(DATA_DIR, 'options_data_clean_v2.h5')
STOCKS_DATA = os.path.join(DATA_DIR, 'ivy_5assets.csv')

sys.path.append(BACKTESTER_DIR) # Add backtester base dir to $PYTHONPATH

In [2]:

import pyfolio as pf

from backtester import Backtest, Type, Direction, Stock
from backtester.strategy import Strategy, StrategyLeg
from backtester.datahandler import HistoricalOptionsData, TiingoData

# Cleaned up data
options_data = HistoricalOptionsData(
        OPTIONS_DATA,
        key="/SPX",
        where='quotedate >= "2012-01-01" & quotedate <= "2014-01-01"')
options_schema = options_data.schema

/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.
  from pandas.util.testing import assert_frame_equal
/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.
  'Module "zipline.assets" not found; mutltipliers will not be applied' +

In [3]:

put_otm = Strategy(options_schema)

leg_1 = StrategyLeg("leg_1", options_schema, option_type=Type.PUT, direction=Direction.BUY)
leg_1.entry_filter = (options_schema.underlying == "SPX") & (options_schema.dte >= 60)
leg_1.exit_filter = (options_schema.dte <= 30)

leg_2 = StrategyLeg("leg_2", options_schema, option_type=Type.CALL, direction=Direction.BUY)
leg_2.entry_filter = (options_schema.underlying == "SPX") & (options_schema.dte >= 60)
leg_2.exit_filter = (options_schema.dte <= 30)
put_otm.add_legs([leg_1, leg_2])

Out [3]:

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))

In [4]:

asset_data = TiingoData(STOCKS_DATA)
asset_data._data = asset_data.query('date >= "2012-01-01" & date <= "2014-01-01"')

VTI = Stock("VTI", 0.2)
VEU = Stock("VEU", 0.2)
BND = Stock("BND", 0.2)
VNQ = Stock("VNQ", 0.2)
DBC = Stock("DBC", 0.2)

In [5]:

allocation = {'cash': 0, 'stocks': 97, 'options': 3}

bt = Backtest(allocation=allocation)
bt.options_data = options_data
bt.options_strategy = put_otm

bt.stocks = [VTI, VEU, BND, VNQ, DBC]
bt.stocks_data = asset_data

bt.run(rebalance_freq=1)
bt.balance

Out [5]:

0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:07

	total capital	cash	VTI	VEU	BND	VNQ	DBC	options qty	calls capital	puts capital	stocks qty	options capital	stocks capital	% change	accumulated return
2010-01-03	1.000000e+06	1000000.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.000000e+00	NaN	NaN
2012-01-03	9.978700e+05	2233.455073	193990.075633	193976.304225	193986.878802	193966.992283	193976.293985	6.0	25740.0	0.0	24257.0	25740.0	9.698965e+05	-0.002130	0.997870
2012-01-04	9.949656e+05	2233.455073	194079.321182	193451.915101	194080.052134	190650.750322	195450.064670	6.0	25020.0	0.0	24257.0	25020.0	9.677121e+05	-0.002911	0.994966
2012-01-05	9.927494e+05	2233.455073	194942.028158	191259.015126	194010.172135	192441.520981	192783.241526	6.0	25080.0	0.0	24257.0	25080.0	9.654360e+05	-0.002227	0.992749
2012-01-06	9.887005e+05	2233.455073	194495.800412	188827.756460	194126.638801	191811.435009	193625.396202	6.0	23580.0	0.0	24257.0	23580.0	9.628870e+05	-0.004079	0.988700
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2013-12-24	1.276243e+06	273.097958	248730.685735	246256.243708	242926.387533	245754.530575	246032.446280	7.0	46270.0	0.0	27125.0	46270.0	1.229700e+06	0.003401	1.276243
2013-12-26	1.282165e+06	273.097958	249907.026341	247293.424036	242562.316514	245944.449069	246414.781084	7.0	49770.0	0.0	27125.0	49770.0	1.232122e+06	0.004640	1.282165
2013-12-27	1.284267e+06	273.097958	249750.180927	248281.214826	242562.316514	246476.220851	247083.866990	7.0	49840.0	0.0	27125.0	49840.0	1.234154e+06	0.001639	1.284267
2013-12-30	1.284806e+06	273.097958	249828.603634	249565.342851	242956.726785	246552.188248	245650.111476	7.0	49980.0	0.0	27125.0	49980.0	1.234553e+06	0.000420	1.284806
2013-12-31	1.288826e+06	273.097958	250743.535215	250553.133640	242865.709030	245222.758794	245267.776672	7.0	53900.0	0.0	27125.0	53900.0	1.234653e+06	0.003129	1.288826

503 rows × 15 columns

In [6]:

pf.create_returns_tear_sheet(returns=bt.balance['% change'].dropna())

Start date	2012-01-03
End date	2013-12-31
Total months	23
	Backtest
Annual return	13.6%
Cumulative returns	28.9%
Annual volatility	14.3%
Sharpe ratio	0.96
Calmar ratio	1.14
Stability	0.83
Max drawdown	-11.9%
Omega ratio	1.18
Sortino ratio	1.37
Skew	-0.52
Kurtosis	3.88
Tail ratio	0.97
Daily value at risk	-1.7%

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-ea8302e3d94e> in <module>
----> 1 pf.create_returns_tear_sheet(returns=bt.balance['% change'].dropna())

~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/plotting.py in call_w_context(*args, **kwargs)
     50         if set_context:
     51             with plotting_context(), axes_style():
---> 52                 return func(*args, **kwargs)
     53         else:
     54             return func(*args, **kwargs)

~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/tears.py in create_returns_tear_sheet(returns, positions, transactions, live_start_date, cone_std, benchmark_rets, bootstrap, turnover_denom, header_rows, return_fig)
    502                              header_rows=header_rows)
    503 
--> 504     plotting.show_worst_drawdown_periods(returns)
    505 
    506     vertical_sections = 11

~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/plotting.py in show_worst_drawdown_periods(returns, top)
   1662     """
   1663 
-> 1664     drawdown_df = timeseries.gen_drawdown_table(returns, top=top)
   1665     utils.print_table(
   1666         drawdown_df.sort_values('Net drawdown in %', ascending=False),

~/.local/share/virtualenvs/backtester_options-33KCFJeg/lib/python3.7/site-packages/pyfolio/timeseries.py in gen_drawdown_table(returns, top)
   1006         df_drawdowns.loc[i, 'Peak date'] = (peak.to_pydatetime()
   1007                                             .strftime('%Y-%m-%d'))
-> 1008         df_drawdowns.loc[i, 'Valley date'] = (valley.to_pydatetime()
   1009                                               .strftime('%Y-%m-%d'))
   1010         if isinstance(recovery, float):

AttributeError: 'numpy.int64' object has no attribute 'to_pydatetime'