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A nimble options backtesting library for Python
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NashLea/optopsy
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Optopsy is a nimble backtesting libary for option strategies, it is designed to abstract away the complexities of backtesting large option chain datasets and allows you to focus on what matters, your trading strategy.
The library makes no assumptions on your data and is designed to compose well into your data analysis work. It uses Pandas extensively under the hood and takes Pandas dataframe objects as input and outputs. For example, instead of trying to be a full featured backtester or provide visualizations on your results, it allows the developers to build on top of the backtested results.
This library is currently in development, please use at your own risk
- Use data from any source, just provide a Pandas dataframe with the required columns when calling optopsy functions.
- Optopsy allows you to mix and match different 'filters' to create an option strategy. Results will be returned as Pandas dataframes so that complex strategies can be composed upon.
Entry rules:
- Days to expiration
- Entry Days (Staggered trades)
- Absolute Delta
- Percentage out-of-the-money
- Contract size
Exit rules:
- Days to expiration
- Hold days
- Profit/Stop loss percent
- Spread delta
- Spread price
- Single Calls/Puts
- Vertical Spreads
- Iron Condors
- (Coming Soon) Iron Butterfly
- (Coming Soon) Covered Stock
- (Coming Soon) Combos (Synthetics/Collars)
- (Coming Soon) Diagonal Spreads
- (Coming Soon) Calendar Spreads
- (Coming Soon) Custom Spreads
- (Coming Soon) Strangles
- (Coming Soon) Straddles
You will need Python 3.6.x and Pandas 0.23.1 or newer. It is recommended to installMiniconda3. Seerequirements.txt for full details.
pip install optopsyOptopsy is best used with Jupyter notebooks, however, it is also possible to incorporate it into your python scripts:
The following example usesLevel 2 Historical CSV Data Sample from historicaloptiondata.com.
importosfromdatetimeimportdatetimeimportpandasaspdimportoptopsyasop# Optopsy is a lightweight library, it does not make any# assumptions on the format of your data. Therefore,# you are free to store your data however you like.## To use your data with this library,# convert your data into a pandas DataFrame with# the following list of standard column names:## Column Name Status# ---------------------------# option_symbol Optional# underlying_symbol Required# quote_date Required# expiration Required# strike Required# option_type Required# bid Required# ask Required# underlying_price Required# implied_vol Optional# delta Required# gamma Required# theta Required# vega Required# rho Optionaldefrun_strategy():# grab our data created externallycurr_file=os.path.abspath(os.path.dirname(__file__))file=os.path.join(curr_file,"data","SPX_2014_2018.pkl")data=pd.read_pickle(file)# define the entry and exit filters to use for this strategy, full list of# filters will be listed in the documentation (WIP).filters= {# set the start and end dates for the backtest,# the dates are inclusive, and are python datetime objects."start_date":datetime(2018,1,1),"end_date":datetime(2018,12,31),# filter values can be int, float, or tuple types# tuples are of the following format: (min, ideal, max)"entry_dte": (5,7,9),"leg1_delta":0.50,"leg2_delta":0.30,"contract_size":1,"expr_type":"SPXW" }# strategy functions will return an optopsy dataframe# containing all the simulated tradesspreads=op.long_call_spread(data,filters)spreads.stats()if__name__=="__main__":run_strategy()
Results:
{ 'Initial Balance': 10000, 'Ending Balance': 8560.0, 'Total Profit': -1440.0, 'Total Win Count': 0, 'Total Win Percent': 0.0, 'Total Loss Count': 2, 'Total Loss Percent': 1.0, 'Total Trades': 2}Trades:
entry_date exit_date expiration underlying_symbol dte ratio contracts option_type strike entry_delta entry_stk_price exit_stk_price entry_opt_price exit_opt_price entry_price exit_price costtrade_num0 2018-01-24 2018-01-31 2018-01-31 SPXW 7 1 1 c 2840 0.48 2837.60 2823.89 15.0 -0.00 1500.0 -0.0 1500.00 2018-01-24 2018-01-31 2018-01-31 SPXW 7 -1 1 c 2860 0.28 2837.60 2823.89 -6.4 0.05 -640.0 5.0 -635.01 2018-02-21 2018-02-28 2018-02-28 SPXW 7 1 1 c 2705 0.48 2701.39 2713.78 20.7 -5.80 2070.0 -580.0 1490.01 2018-02-21 2018-02-28 2018-02-28 SPXW 7 -1 1 c 2730 0.30 2701.39 2713.78 -9.2 0.05 -920.0 5.0 -915.0...Total Profit: -1440.0Full Documentation Coming Soon!
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A nimble options backtesting library for Python
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