How to Backtest Options Strategies Without Getting Fooled by Overfitting

Backtesting is a common process used in options trading to test the effectiveness of a specific strategy, using historical market data to determine how it would have performed in a similar situation in the past. It can be a super powerful tool in trading, but readers need to be aware of a hidden risk in backtesting known as “overfitting,” where traders can get a sense of false confidence in their current trading plan.

Our guide will talk more about backtesting option strategies in the correct manner, and to avoid the dangers of overfitting the data to fit what you’re hoping to accomplish in each situation. The goal of good backtesting is to achieve results that accurately translate to the live market. Overfitting can also be a big problem for multi-leg spreads or volatility plays, which we will cover in the guide as well.

Avoid the false sense of confidence from “perfect” historical performance and learn about quality backtesting today!

What Is Overfitting in Backtest Options Strategies?

When traders find a strategy model that performs perfectly in backtests, they likely overfit the data to achieve the desired outcome they were looking for. Overfitting can happen easily, especially when traders tailor a strategy too closely to data from the past. It results in a strategy that looks phenomenal on paper, but doesn’t translate well to the live markets. Often, these overfitted strategies can fall apart quickly, leaving inexperienced traders stunned.

Curve-fitting historical data creates illusions of profitability, but these are often faulty trade setups that can fall apart quickly, even with little changes in the markets. You might be wondering if you’ve been overfitting data to reach your desired result in backtesting—you can know for sure if you’ve been doing this because there are some signals that you’re likely falling into this bad habit.

Common Signs You’re Overfitting

• Perfect Sharpe Ratios: The Sharpe Ratio refers to the measurement of a strategy’s risk-adjusted return, which indicates the profit generated compared to the risk taken in the trade. If you’re backtesting and this ratio keeps coming out perfectly high, it means that there’s a hidden risk in the strategy, and it’s due to the overfitting of data. Unrealistic profit factors are also in the same vein as achieving a perfect Sharpe ratio during backtests.
• Extreme Win/Loss Ratios: If you’re seeing extremely high win rates in your backtests, this can be a sign that you’re overfitting the data, especially if the backtest has little to no drawdowns.
• Overuse of Strategy Filter: When the filters are fine-tuned to work exceptionally well on historical data, this can be a big sign of overfitting. It can create false confidence in the trader, and it leads to poor real-world performance. The problem with this is that the backtest is rooted in noise from historical data and not signals.
• Overly Complex Entry Rules: Forming a strategy that uses complex entry rules can mistakenly take cues from random price fluctuations in the past and treat them as something that can be heavily relied on in the future.
• Backtests That Fall Apart When Moved Forward: If the backtest performs well, but then suddenly performs poorly when the time frame is shifted to a period by even a few months, this could indicate that the trader is overfitting the data to achieve the result they want to see in the live market.

Why Options Strategies Are Especially Vulnerable

Options require a lot of active management due to the fact that there are so many factors that can be adjusted to each contract to suit each trader’s financial goals. These can create a ton of opportunities for profitability, but this opens the door for traders to tweak the terms of the option contract to fit the result they’d like to see, which can lead to overfitting a plan.

Why Options Are Susceptible to Overfitting

• High Degrees of Freedom: There are so many elements that a trader can adjust with an options contract, like the strike price, the expiration date, volatility levels, skew, and the Greek symbols. While customization is a big perk, the high degree of freedom that options deliver can cause traders to develop a faulty trading plan.
• Market Conditions Shift: This happens quite often in options where certain events can create rapid shifts in the stock prices or the value of the underlying—it includes events like IV crush, earnings cycles, or a regime change. It can lead to traders developing a strategy that might work well in a bear market, but it can collapse when a bull market emerges or an earnings announcement hits with some positive news and a corresponding rise in the stock’s value.

There are a few reasons why options lead so many traders to develop strategies where overfitting becomes a problem, and they include the following:

• Volatility can cause markets and stock prices to shift wildly in value or direction.
• Backtests don’t always account for factors in options trading like slippage or bid-ask spreads.
• Options Greeks by nature change non-linearly.
• Traders cannot always account for the impact of dividends, macro events, or earnings dates when they’re backtesting strategies.

Example

The ultimate example of an overfitted strategy falling apart is when a trader puts together a calendar spread that looks great in low IV. As long as the market conditions remain the same, the calendar spread should perform well, but it can completely collapse or fall apart due to volatility spikes. The important lesson here is not to overfit and factor in other elements about the market or the underlying that can give you hints as to how the investment will possibly develop.

Smart Techniques to Avoid Overfitting

To keep away from overfitting a strategy and having it completely fall apart in a live market, you should follow some of these smart techniques that allow you to backtest in a way where you’re developing a sound strategy that doesn’t have any big blind spots.

• Out-of-Sample Testing: Traders can do this by splitting their data sets into two parts. One side is for developing your strategy, while the other side is for testing it. Traders can know that they’re overfitting if the backtest only works on the training set.
• Walk-Forward Analysis: It is a good practice to rotate your test periods forward in time instead of testing a huge piece of data all at once. Walk-forward analysis offers a better simulation of live trading because it leaves the trader unsure as to what could be coming next, much like it is in the real world.
• Parameter Robustness: To ensure a trading strategy that is robust, traders need to use parameters that accommodate deviations and fluctuations in the markets. It’s important to avoid strategies where delta = 0.43, expiration = 21 DTE, and IV rank = 72%. This is too narrow a window for a viable strategy to perform well in—these narrow, curve-fitted variables are a strong sign that you’re overfitting.
• Monte Carlo Simulations: One of the best methods for finding out how resilient your trading strategy is is through introducing randomness to your trades in the form of permutations. This acts as a stress test for your strategy, and you can tell quickly if your strategy is overfitted if the equity curve collapses when randomization kicks in.

Tools for Realistic Backtesting

Doing quality backtesting that delivers realistic results that can withstand the pressure and unpredictability of a live market requires using the right tools and a platform that has the proper features. Your backtesting platform needs to be able to replicate real-world trading as much as possible, so you’ll want to look for the following features whenever you go to choose a website or mobile app.

Key Features

• Bid-Ask Modeling: The process of analyzing and forecasting the bid-ask spread in financial markets, which can be a key indicator of elements like trading costs and liquidity.
• Slippage Simulation: Modeling and accounting for the difference between the expected price of a trade and the actual price at which it is executed. Slippage can occur due to many different factors, such as market liquidity, order size, or volatility.
• Volatility Surfaces: These refer to three-dimensional representations that are used in options trading to visualize how implied volatility can change due to factors like the strike price and the time until expiration.
• Awareness of Events: Traders need to use a platform that takes macroeconomic events into account, as well as aspects of the options market like the impact of earnings report releases or ex-dividend dates.

Recommended Tools

Check out a few of our favorite recommendations in terms of platforms that provide quality backtesting.

• QuantConnect: This tool has a strong focus on advanced algorithmic trading capabilities, provides an open-source LEAN engine for extensibility and flexibility, and supports a wide range of asset classes.
• OptionNet Explorer: Traders will enjoy this platform due to its strengths when it comes to backtesting or analyzing complicated options strategies, and its intuitive user interface, which makes it easy for beginners to learn.
• ORATS (Options Research & Technology Services): This one comes with strong backtesting capabilities, and there is a big emphasis on options data and analytics that are more comprehensive than most other platforms.

Case Study—Why This “Perfect” Strategy Failed Live

Let’s check out a hypothetical real-world example of how a strategy that seemed perfect at the time when it was backtested ultimately fell apart when a different market regime took over. The lesson in this coming example is that traders need to keep away from overfitting when backtesting and account for all kinds of market events to find out how stable their strategy might be.

It’s 2019, and a trader puts together a put credit spread strategy because they had a neutral to slightly bullish outlook on the underlying asset they were trading. This particular trade was backtested and showed that it had a 90% win rate, all while maintaining a reward-to-risk ratio of 4:1. The trade performed well from 2019 to 2022, but then it hit a problem in 2023.

What Went Wrong?

The issue that arose in 2023 was that the volatility in the markets began to spike for this particular underlying asset, and the strategy that seemed to work well for years suddenly began to unravel. The root cause of the live trades beginning to fail in 2023 was the fact that the backtest had been overfitted to accommodate a low-volatility regime. But the minute that volatility began to become super present in 2023, the strategy started breaking down and not delivering the returns it once did.

In this case study, you can see that the trade should have simulated additional scenarios in their backtests, including those that take place in bear markets. They didn’t test it across enough volatility regimes or changing market conditions, and the strategy cracked under the pressure of the bull market working its way into a bear market.

Final Checklist Before You Go Live

Remember to ask yourself the following questions before going live with a backtested strategy. You can consider this the official checklist you should be completing to make sure that you’re not taking to the markets with an overfitted strategy, which could break apart in the future.

• Did you test across multiple market regimes (bear, bull, or sideways markets)?
• Are results consistent across different tickers/timeframes?
• Is your strategy too reliant on one parameter (e.g., IV rank threshold)?
• Does the strategy still work with minor parameter changes?
• Is the performance stable across different symbols?
• Did you include commissions, slippage, or bid-ask spreads into your assessment?

If you find yourself answering “no” to any of the questions we have put in the checklist, there’s a good chance that you’re dealing with an overfitted strategy. At this point, you should go back and continue to backtest the strategy to improve its resiliency.

Backtest Correctly and Avoid Overfitting

Backtesting is a powerful tool—but only when used wisely. It’s important to have the mindset that this isn’t a crystal ball that ensures complete success in all your trades, but it can be an essential tool for figuring out the general success rate of the strategy you end up using. Backtesting helps you to learn how a strategy behaves, not to confirm it’ll “definitely work.”

During this process, traders should keep their focus on robustness, not perfection. You would be doing yourself a disservice if you ignore real-world constraints that could present challenges to your investment and not include these factors in your backtesting process. Focus on robustness and simplicity, but factor in real-world challenges too to make sure that your strategy can deliver a strong performance in the live markets and not just on paper.