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Optopsy is a nimble backtesting and statistics library for option strategies, it is designed to answer questions like"How do straddles perform on the SPX?" or "Which strikes and/or expiration dates should I choose to make the most potential profit?"

Use cases for Optopsy:

• Generate option strategies from raw option chain datasets for your own analysis
• Discover performance statistics onpercentage change for various options strategies on a given stock

• Calls/Puts
• Straddles/Strangles
• Vertical Call/Put Spreads

Please see thewiki for API reference.

• Use data from any source, just provide a Pandas dataframe with the required columns when calling optopsy functions.

You will need Python 3.6 or newer and Pandas 0.23.1 or newer and Numpy 1.14.3 or newer.

pip install optopsy==2.0.1

Let's see how long calls perform on the SPX on a small demo dataset on the SPX:

Note: As of July 2024, the link below is broken, however DeltaNeutral still provides free datahere.You should still be able to proceed by mapping the columns according to the current format of the sample data as shown below.

Download the following data sample from DeltaNeutral:http://www.deltaneutral.com/files/Sample_SPX_20151001_to_20151030.csv

This dataset is for the month of October in 2015, lets load it into Optopsy. First create a small helper functionthat returns a file path to our file. We will store it under a folder named 'data', in the same directory as the working python file.

def filepath():    curr_file = os.path.abspath(os.path.dirname(__file__))    return os.path.join(curr_file, "./data/Sample_SPX_20151001_to_20151030.csv")

Next lets use this function to pass in the file path string into Optopsy'scsv_data() function, we will map the columnindices using the defined function parameters. We are omitting thestart_date andend_date parameters in this call becausewe want to include the entire dataset. The numeric values represent the column number as found in the sample file, thenumbers are 0-indexed:

import optopsy as opspx_data = op.csv_data(    filepath(),    underlying_symbol=0,    underlying_price=1,    option_type=5,    expiration=6,    quote_date=7,    strike=8,    bid=10,    ask=11,)

Thecsv_data() function is a convenience function. Under the hood it uses Panda'sread_csv() function to do the import.There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them.

Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simpleand easy to implement into your regular Panda's data analysis workflow. As such, we just need to call thelong_calls() functionto have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here wealso use Panda'sround() function afterwards to return statistics within two decimal places.

long_calls_spx_pct_chgs = op.long_calls(spx_data).round(2)

The function will returned a Pandas DataFrame containing the statistics on thepercentange changes of running long calls in allvalid combinations on the SPX:

There are more customization options for Optopsy's strategy functions, consult the codebase/future documentation to see how it can be used to adjust the results, such as increasing/decreasingthe intervals and other data to be returned.