Options Backtesting: Strategies and Techniques for Evaluating Trading Performance

Backtest definition

Backtesting is a tool for evaluating cBots by allowing them to trade on historical market data under certain pre-defined conditions.

When backtesting, you can run a cBot instance on past market movements.

Backtesting allows a trader to simulate a trading strategy using historical data to generate results and analyze risk and profitability before risking any actual capital.

A well-conducted backtest that yields positive results assures traders that the strategy is fundamentally sound and is likely to yield profits when implemented in reality. In contrast, a well-conducted backtest that yields suboptimal results will prompt traders to alter or reject the strategy.

Particularly complicated trading strategies, such as strategies implemented by automated trading systems, rely heavily on backtesting to prove their worth, as they are too arcane to evaluate otherwise.

As long as a trading idea can be quantified, it can be backtested. Some traders and investors may seek the expertise of a qualified programmer to develop the idea into a testable form. Typically, this involves a programmer coding the idea into the proprietary language hosted by the trading platform.

The programmer can incorporate user-defined input variables that allow the trader to “tweak” the system. An example of this would be in the simple moving average (SMA) crossover system. The trader would be able to input (or change) the lengths of the two moving averages used in the system. The trader could then backtest to determine which lengths of moving averages would have performed the best on the historical data.

The Ideal Backtesting Scenario

The ideal backtest chooses sample data from a relevant time period of a duration that reflects a variety of market conditions. In this way, one can better judge whether the results of the backtest represent a fluke or sound trading.

The historical data set must include a truly representative sample of stocks, including those of companies that eventually went bankrupt or were sold or liquidated. The alternative, including only data from historical stocks that are still around today, will produce artificially high returns in backtesting.

A backtest should consider all trading costs, however insignificant, as these can add up over the course of the backtesting period and drastically affect the appearance of a strategys profitability. Traders should ensure that their backtesting software accounts for these costs.

Out-of-sample testing and forward performance testing provide further confirmation regarding a system's effectiveness and can show a system's true colors before real cash is on the line. A strong correlation between backtesting, out-of-sample, and forward performance testing results is vital for determining the viability of a trading system.

Backtesting vs. Forward Performance Testing

Forward performance testing, also known as paper trading, provides traders with another set of out-of-sample data on which to evaluate a system. Forward performance testing is a simulation of actual trading and involves following the system's logic in a live market. It is also called paper trading since all trades are executed on paper only; that is, trade entries and exits are documented along with any profit or loss for the system, but no real trades are executed.

An important aspect of forward performance testing is to follow the system's logic exactly; otherwise, it becomes difficult, if not impossible, to accurately evaluate this step of the process. Traders should be honest about any trade entries and exits and avoid behavior such as cherry-picking trades or not including a trade on paper rationalizing that “I would have never taken that trade.” If the trade would have occurred following the system's logic, it should be documented and evaluated.

Backtesting vs. Scenario Analysis

While backtesting uses actual historical data to test for fit or success, scenario analysis makes use of hypothetical data that simulates various possible outcomes. For instance, scenario analysis will simulate specific changes in the values of the portfolio's securities or key factors that take place, such as a change in the interest rate.

Scenario analysis is commonly used to estimate changes to a portfolio's value in response to an unfavorable event and may be used to examine a theoretical worst-case scenario.

Some Pitfalls of Backtesting

For backtesting to provide meaningful results, traders must develop their strategies and test them in good faith, avoiding bias as much as possible. That means the strategy should be developed without relying on the data used in backtesting.

Thats harder than it seems. Traders generally build strategies based on historical data. They must be strict about testing with different data sets from those they train their models on. Otherwise, the backtest will produce glowing results that mean nothing.

Similarly, traders must avoid data dredging, in which they test a wide range of hypothetical strategies against the same set of data, which will also produce successes that fail in real-time markets because there are many invalid strategies that would beat the market over a specific time period by chance.

One way to compensate for the tendency to data dredge or cherry-pick is to use a strategy that succeeds in the relevant, or in-sample, time period and backtest it with data from a different, or out-of-sample, time period. If in-sample and out-of-sample backtests yield similar results, then they are more likely to be proved valid.

Options backtesting software

Amidst the ever-evolving landscape of financial markets, options trading has emerged as a sophisticated yet highly lucrative strategy for investors and traders alike. As the complexity of trading options continues to grow, so does the demand for reliable tools to analyze and backtest strategies effectively.

While some traders rely solely on intuition or technical analysis, many recognize the indispensable role of backtesting platforms in refining and validating their options trading strategies. Whether youre a seasoned professional or a novice exploring the world of options, having access to a robust backtesting platform can make all the difference in achieving consistent success.

ORATS

ORATS, or Option Research & Technology Services, is a financial technology company that specializes in options data and analytics. Their backtesting platform, Options Backtester, offers a comprehensive suite of features for options traders and tops this list for a multitude of reasons.

The platform boasts over 65 million pre-scanned backtests, providing traders with a vast repository of historical data to analyze and learn from. Additionally, traders have the flexibility to customize and create their own backtests, tailoring strategies to their specific preferences and objectives.

With institutional-quality historical end-of-day options data dating back to 2007 for over 5,000 symbols, the platform offers a robust foundation for conducting thorough backtesting analysis. Traders can access detailed options data spanning a wide range of securities, enabling them to evaluate strategies across various market conditions and asset classes.

Each backtest generated on the platform comes complete with a graph of returns, allowing traders to visualize the performance of their strategies over time. Additionally, the platform provides 37 different performance metrics, enabling traders to assess strategy effectiveness from multiple angles. A table of monthly returns and a detailed trade log further enhance the analytical capabilities of the platform.

To guard against overfitting, the platform features a “find similar” button, which helps traders identify and mitigate the risks associated with overly optimized strategies.

One notable feature of the platform is its seamless integration with option scans, allowing traders to swiftly transition from backtesting to implementing trades in live market conditions. This streamlined process enables traders to efficiently translate backtesting insights into actionable trading strategies.

Coming to the platforms pricing, ORATS offers a $29 trial of its backtesting software, allowing traders to explore its features and capabilities before committing to a subscription. Alternatively, traders can trial the platform for free by passing an ORATS knowledge quiz, providing an accessible entry point for those looking to leverage its powerful analytical tools.

In summary, ORATS backtesting platform is a top choice for both beginners starting their options trading journey and professionals seeking to refine and elevate their strategies. With its vast data library, customizable options, and robust analytical tools, ORATS empowers traders at all levels to optimize their trading approach with confidence and precision.

Option Alpha

Option Alpha offers a backtesting platform designed to assist traders in evaluating options trading strategies. One of its primary strengths lies in its user-friendly interface and extensive educational resources. Novice traders can benefit from the platforms straightforward approach to backtesting, with a variety of tools and tutorials available to help them get started.

Let‘s talk about some of the pros of Option Alpha’s Backtester:

Firstly, is its user-friendly interface. Option Alpha‘s platform is known for its intuitive design, having it accessible to traders of all experience levels. The platform’s clean layout and easy-to-navigate menus streamline the backtesting process, allowing traders to focus on refining their strategies.

Secondly, Option Alpha provides a wealth of educational content, including articles, videos, and podcasts, to help traders understand options trading concepts and develop effective strategies. This emphasis on education sets Option Alpha apart as a valuable resource for traders seeking to improve their skills.

Speaking of the platform‘s customization abilities, traders can customize and test a wide range of options trading strategies using Option Alpha’s platform. The ability to adjust parameters such as strike prices, expiration dates, and position sizes enables traders to tailor strategies to their specific goals and risk tolerance.

Now lets come to some of the cons of the platform. There are two considerable drawbacks.

To start with, compared to some other backtesting platforms, Option Alpha may offer a more limited dataset for historical options data. Traders may find that the available data doesnt cover as long of a time period or include as many securities as they would prefer.

While Option Alphas platform is suitable for traders at all levels, more advanced traders may find that it lacks some of the advanced features and customization options offered by other backtesting platforms.

Overall, Option Alphas backtesting platform is a valuable resource for options traders seeking a user-friendly interface and comprehensive educational support. While it may have some limitations in terms of historical data and advanced features, its emphasis on education and accessibility makes it a strong contender for traders looking to refine their strategies and improve their trading performance.

eDeltaPro

eDeltaPro is a comprehensive tool designed to assist options traders in evaluating and optimizing their trading strategies. Positioned as the third platform on the list, eDeltaPro offers a unique set of features and functionalities tailored to meet the needs of traders seeking advanced analytics and customization options.

Pros:

eDeltaPro‘s platform stands out for its advanced analytics and customization capabilities. Traders have access to a wide range of sophisticated tools and metrics to analyze options trading strategies in-depth. The platform’s emphasis on delta hedging and risk management sets it apart, having it particularly suitable for traders focused on managing options exposure effectively.

Another notable strength of eDeltaPros platform is its extensive historical data coverage. Traders can access a comprehensive dataset spanning multiple years, enabling them to backtest strategies across various market conditions and historical scenarios. This rich historical data allows for more robust analysis and validation of trading strategies.

Additionally, eDeltaPro offers a high degree of customization, allowing traders to fine-tune strategies to their specific objectives and risk preferences. Advanced features such as scenario analysis and portfolio optimization further enhance the platforms capabilities, empowering traders to make more informed decisions and optimize their trading performance.

Drawback:

One potential drawback of eDeltaPro‘s platform is its learning curve. Due to its advanced features and analytics tools, new users may require some time to familiarize themselves with the platform’s functionalities fully. Traders who are less experienced or seeking a more straightforward interface may find the learning curve challenging.

In conclusion, eDeltaPros backtesting platform offers advanced analytics, extensive historical data coverage, and customization options tailored to meet the needs of sophisticated options traders. While it may have a learning curve and higher cost compared to some alternatives, its robust feature set makes it a compelling choice for traders seeking in-depth analysis and optimization of options trading strategies.

Options Backtesting and Strategies

Options backtesting is a method used to test and evaluate options trading strategies by simulating trades on historical data. By using backtesting, traders can analyze how a strategy would have performed in various market conditions without risking actual capital. Below is an in-depth look at options backtesting and the associated strategies.

1. What is Options Backtesting？

Options backtesting involves running a trading strategy through historical market data to observe its performance over a specific period. It allows traders to assess the risk and return of various options combinations and adjust their strategies accordingly.

The key steps in options backtesting include:

Define the Strategy: Clearly specify the conditions for buying or selling options, such as long call, long put, or more complex strategies like spreads.

Collect Historical Data: Use past data such as options prices, underlying asset prices, volatility, and interest rates to simulate the trades.

Simulate the Trades: Execute the strategy based on predefined rules at each historical time point.

Analyze the Results: Evaluate the strategy's performance through metrics such as profit/loss, maximum drawdown, and Sharpe ratio over the backtesting period.

2. Common Options Backtesting Strategies

There are various options trading strategies, and each can be backtested to see how it performs under different conditions. Below are some common strategies and how to backtest them.

Long Call Strategy

Strategy Definition: Buy a call option when expecting the underlying asset's price to rise.

Backtesting Considerations:

- Test how call options would have performed based on historical price movements of the underlying asset.

- Evaluate different strike prices and expiration dates to analyze potential gains.

- Assess how intrinsic and time value change over time and impact the call options profitability.

Example: If the underlying asset rose by 20% during a specific time frame, you can backtest to see how much profit would have been made by purchasing a call option at a certain strike price.

3. Long Put Strategy

Strategy Definition: Buy a put option when expecting the underlying asset's price to decline.

Backtesting Considerations:

- Test the performance of put options during historical downturns in the asset price.

- Experiment with different strike prices and expiration dates.

- Analyze the profitability of the strategy during market corrections or bear markets.

Example: Backtest a long put strategy during a past market crash to see how profitable it would have been.

4. Straddle Strategy

Strategy Definition: Buy both a call option and a put option with the same strike price and expiration, typically used when expecting significant volatility but uncertain about the direction.

Backtesting Considerations:

- Evaluate the performance during periods of high volatility, like earnings reports or major economic events.

- Analyze how changes in implied volatility affect the value of both options.

Example: Backtest the straddle strategy around the time of earnings announcements or central bank policy changes to assess its performance during market-moving events.

5. Iron Condor Strategy

Strategy Definition: Involves selling a bear call spread and a bull put spread, and it's typically used in low volatility markets to profit from time decay.

Backtesting Considerations:

- Test the strategy in range-bound markets to evaluate how well it performs when volatility is low.

- Assess the risk of large losses during unexpected market swings and how volatility spikes affect the iron condor's profitability.

Example: Choose a period in history with low volatility and backtest the iron condor to see how well it generated income from time decay.

6. Key Factors to Consider When Backtesting

When backtesting options strategies, several factors can significantly impact the results, so it is essential to account for them:

Slippage and Trading Costs: Options trading involves bid-ask spreads, commissions, and other costs, which can reduce the actual profits compared to backtest results.

Changes in Implied Volatility: Since options prices depend heavily on implied volatility, you need to consider how an increase or decrease in volatility would affect the outcome.

Market Liquidity: Some options may have low liquidity, leading to difficulties in opening or closing positions at desired prices, which could affect the strategy's effectiveness.

Free options backtesting tools

Selecting the Right Tool

Explore popular free backtesting tools and choose one that best fits your requirements.

Popular Free Backtesting Tools:

Tool A

Features: List of features

Pros and Cons

Tool B

Features: List of features

Pros and Cons

Setting Up Your Backtest

Configure the backtesting parameters to align with your trading strategy, selecting the appropriate options and market data.

Interpreting Backtesting Results

Analyze the backtesting results to understand the strategy's performance metrics such as profitability, drawdowns, and win/loss ratios.

Advantages of Free Backtesting

Cost Efficiency: No need to invest in expensive software to test strategies.

Risk Management: Identify potential flaws in a strategy before execution.

Strategy Development: Hone your trading methods without financial risk.

Limitations to Consider

Though free, these tools may lack certain advanced features, which can limit the scope of testing.

Optimizing Your Backtesting Process

Adjusting for Market Volatility

Incorporate market volatility measurements into your backtesting to better simulate real-world trading conditions.

Incorporating Time Decay

Options are time-sensitive; make sure your backtesting accounts for the impact of time decay on strategy performance.

Leveraging Backtesting for Various Options Strategies

Covered Calls

Test how different market conditions can affect the profitability of a covered call strategy.

Spread Strategies

Analyze how spreads perform under varying levels of market volatility and price movements.

Essential Tips for Accurate Backtests

Use realistic trade sizes and costs.

Account for slippage and liquidity.

Regularly update the historical data for relevancy.

Best Forex Backtesting Software

One of the top choices is MetaTrader 4 (MT4) with its Strategy Tester feature. MT4 has been a staple for forex traders, and its backtesting capabilities are powerful. With access to a broad range of historical data and built-in charting tools, traders can simulate various strategies with ease. Moreover, MT4 supports Expert Advisors (EAs), allowing users to automate and backtest their trading strategies programmatically.

TradingView is another excellent option, providing an intuitive web-based platform where traders can backtest forex strategies. TradingView offers a wide range of technical analysis tools, custom scripting via Pine Script, and community-driven strategy sharing, which is perfect for both novice and experienced traders.

For more advanced traders, Forex Tester stands out. It provides comprehensive features, including multi-timeframe testing, manual and automated testing, and a massive library of historical data. Traders can adjust their strategies to specific market conditions and track how small tweaks could have drastically altered past performance.

Algorithmic Trading Tools

Algorithmic trading, also known as algo-trading, is becoming increasingly popular due to its ability to automate complex trading strategies, optimize trade execution, and minimize human error. Traders use algorithmic trading tools to develop, backtest, and implement automated strategies across different asset classes like forex, stocks, and options.

QuantConnect is one of the top algorithmic trading platforms available. It is an open-source platform that allows traders to create and backtest trading algorithms using multiple data sources. QuantConnect supports popular programming languages such as Python and C#, and offers institutional-quality data and advanced research tools, having it an ideal choice for both retail and institutional traders.

Another excellent tool for algorithmic trading is AlgoTrader, which provides end-to-end automation for multiple asset classes. AlgoTrader allows users to design, test, and deploy algorithmic strategies with real-time market data. Its advanced backtesting engine enables users to simulate strategies over a wide range of historical data while factoring in slippage, transaction costs, and market liquidity. It also supports integration with different brokerages for live trading execution.

For forex traders, MetaTrader 5 (MT5) is a popular choice that offers both backtesting and algorithmic trading capabilities. With its built-in strategy tester and support for algorithmic trading via Expert Advisors (EAs), traders can test strategies across multiple timeframes and optimize them for live trading.