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Building an Options Backtester: Data, Pricing, and Validation Challenges

Article Quant Q&A · Author: user20664

Summary

The discussion describes what makes options strategy backtesting more demanding than testing a simple underlying asset. A credible simulation needs historical option chains, including contract terms and market prices, alongside underlying prices used to generate signals. It must also account for expiration, exercise, missing or stale quotes, and the changing sensitivities of positions. Historical options data may be costly or require gathering it over time, making data availability an early design constraint.

The practical advice is to begin with a simple structure such as a spread, then explain simulated profit and loss through exposures such as delta, gamma, and theta before adding complexity. More advanced tests must define entry, exit, and roll decisions and support arbitrary structures. The answers offer practitioner experience and implementation considerations, rather than a complete backtesting specification or quantitative validation. Results will depend heavily on data quality, pricing assumptions, execution modeling, and how the strategy handles contracts that did not trade on a given date.

Key ideas

  • Options backtests require historical option chains as well as underlying market data.
  • Contract expiration, exercise, and sparse or absent trading need explicit handling.
  • Start with a simple options structure and check whether its simulated profit and loss has a sensible explanation.
  • Add strategy complexity gradually, including rules for exits and rolling positions.
  • Data cost and quality, along with pricing and execution assumptions, limit the reliability of results.

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Full text
# Writing an Options Strategy Backtester


# Writing an Options Strategy Backtester












I've been doing some digging, and this question has been asked many times in various forms over the years -

- Backtesting Options Strategies in R

- Are there any good tools for backtesting options strategies?

- Backtesting on historical options data

- Papers about backtesting option trading strategies

In particular I am interested in spread trading. From these I've gathered backtesting these strategies is pretty much relegated to commercial tools, or professionals writing their own. I understand the basic idea of backtesting, and I'd like to make my own. Partially because right now I'm just a hobbyist who doesn't yet have the capital to afford a really nice tool, and partially because I would like to learn exactly how it works from the inside.

One post mentions that an options backtester is not much different than an equity backtester. It's possible that this is true - but I don't understand how. For options backtesting, we'd need historical options data (to get the bid/ask, strike, expiration, delta, imp. vol., etc), and also historical data for the underlying contract in order to generate signals. We would also need to properly expire the options. Perhaps I am over-complicating it.

Is there any resources that really dive in depth to how their backtester was made? Some of the papers in the 4th link go into it a little bit in the methodology section, but it seemed to me there wasn't too much to chew on in most of them. Most likely because discussion of the backtester itself is tangential to the actual paper.

## Answer by Ariel Silahian (score 3, accepted)

https://quant.stackexchange.com/a/27829

Alright this is a good question. I've been there before. As you said, backtesting options will be almost the same as stocks, but with more data to play with (Greeks, volatility, theoretical prices, etc)

The most important thing here will be your historical data. Your source of data. In order to backtest options, usually you need to have the whole historical option chain.

You won't find this on Internet for free (Don't even try it) However, there are some companies offering this, and let me tell you is not cheap at all.

Personally, I ended up writing another software that periodically will scan the whole option market, collecting the data from the chains. This is a "huge" database that I hold for my backtesting projects.

So I suggest you do the same. First focus on the data.

Good luck

## Answer by jason m (score 4)

https://quant.stackexchange.com/a/54544

For whoever stumbles upon this, as I did.

The dimensionality of the problem is so much larger. If someone tells you backtesting options is the same as backtesting stocks or any delta-1 underlying, they are entirely missing the point.

- Data - Option data is expensive. The biggest favor you can do yourself is to start gathering data as early as possible. There are various ways to do this either as a student or as a practitioner, or both. Scrape, get vendor data for backfill or just be patient.

- Read books about how to think about options. Options are a different mindset and different dynamic than underlying (delta-1).

- Start by building a backtest on a simple structure (spread, etc). Find ways to explain your PnL to yourself in the backtest (is it due to delta, gamma, theta, etc). Does your math make sense? Can you convince yourself that you are correct?

- Build a more complicated structure and repeat step 3.

- Build ways to test any arbitrarily complex structure and find ways to determine what `predicts` the outcome of your structure. When do you exit, when do you roll, etc. This, part 5, is incredibly difficult and takes extreme amounts of time and care.

I think this is an unfortunately very difficult problem.

To make some of the above easier, you may want to use open source solutions such as quantlib but even that is not free (time and learning curve, etc).

These are all quite hard problems that may take you months to solve.

I am glad to discuss this further if you reply on this answer.

## Answer by Motopixman (score 2)

https://quant.stackexchange.com/a/43309

In terms of computational difficulty, I do not see Options backtesting in the same league as equity backtesting. There are more variables involved, and the dataset is much larger. But the bottom line though is that Options Backtesting will always cost money whether you purchase the data and develop the model yourself or use a third party solution. I find backtesting to be an integral part of my Options Trading. To me, it's an essential part of the process. So, together with my business partner, we developed a tool to backtest Options. We then thought that other traders might find it useful too and began promoting it as a commercial tool. We would love for you to try it out and have your feedback. Here is an invite for special beta testing - no cost- so you can post here your impressions.

I find backtesting to be an integral part of my Options Trading. To me, it's an essential part of the process. So, together with my business partner, we developed a tool to backtest Options. We then thought that other traders might find it useful too and began promoting it as a commercial tool. We would love for you to try it out and have your feedback. Here is an invite for special beta testing - no cost- so you can post here your impressions.

We would love for you to try it out and have your feedback. Here is an invite for special beta testing - no cost- so you can post here your impressions.

## Answer by sirnfs (score 1)

https://quant.stackexchange.com/a/39579

I recently opened sourced an options backtester written in Python. It may provide what you're looking for.

## Answer by Marco Aurélio Guerra (score 0)

https://quant.stackexchange.com/a/61666

I have a project in my github here i try this for brazilian stock market, the data is public available. It's quite a challenge the data is raw , you have to ajust prices, deal with pricing for options not traded in that day, exercise options ... unfortunaly i wrote the code in portuguese perphaps one day i translate to english.

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