Skip to content
All library documents

Structuring an Option Strategy Backtest as an Extensible Package

Article Quant Q&A · Author: Vladimir Nabokov
Read the original

Summary

The document considers how to implement and backtest a strategy that writes call options across a security universe. The suggested first step is to reduce the problem to a manageable baseline: use options with a common expiry, apply an option pricer, choose a moneyness rule, and specify when positions are opened and how they are held. One illustrative trigger is elevated implied volatility, motivated by a mean-reversion hypothesis, with positions held through expiry.

The response favors an object-oriented, documented structure because strategy research tends to require many extensions and calibrations; a single script can become difficult to manage. It offers general software-organization advice rather than a complete backtest design. It does not address transaction costs, exercise and assignment, volatility-surface dynamics, portfolio constraints, or validation methods, so these choices would still need explicit treatment before interpreting results.

Key ideas

  • Begin by simplifying the option strategy with common expiries and a clearly defined option pricer.
  • Specify moneyness, entry triggers, and whether positions are held to expiry.
  • Selling options when implied volatility is unusually high can express a volatility mean-reversion hypothesis.
  • An object-oriented, documented package can accommodate strategy extensions and calibration choices more cleanly than an expanding script.
  • The advice does not specify execution costs or a complete backtest validation process.

Tags

OptionsBacktestingVolatilityMean reversion
Full text
# Option Strategy: Python Implementation Advice


# Option Strategy: Python Implementation Advice












I've been tasked to create and backtest an option strategy. The strategy, in vague terms, is to essentially write call options on securities in a universe, i.e., selling insurance.

I have an idea of what I want to do, but since I'm new to using Python in this vein (I typically use it for automating processes or doing quick on-the-fly data analysis), I'm wondering what is the best approach to doing so?

Given that I have all the data (e.g., underlying security data, implied volatilities, deposit rates, etc.), should I just write a script that loops through the data along time, writing options, priced with a Black-Scholes pricer? Or, should I develop it in an object-orientated fashion?

I'm curious of the pros/cons of both. Please let me know if this not relevant to this site; I thought here would be a better place to ask than Stack Overflow.

Thanks,

VN

## Answer by Attack68 (score 0, accepted)

https://quant.stackexchange.com/a/41468

The scope of "create and backtest an option strategy" is broad. Ok so you narrowed it to a strategy of "selling call options". In order to test it effectively I suggest you start with some very simple assumptions:

- the expiry of all your options are the same

- you have a pricer to price the options

- you assume you write options at the money (or not) at some trigger and hold to expiry, (i.e. vol is above some level - basing your trading strategy that vol is mean reverting so sell when it is higher than average)

Yes, I would write it in an object-oriented way so that you can expand it, and the most important thing about it is the documentation. Not only will this help someone else follow but it will better help you plan the structure of what you are doing. There are so many potential avenues for you to go down and calibrations to be made that having a script will quickly become unmanageable, while a package to import will be much better.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.