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Algo Trading | August 10, 2023

Algo Trading Backtesting Techniques: Ensuring Strategy Robustness

Algorithmic trading, or Algo trading, is a process for traders to make data-driven decisions. It comprises the use of a few computer programs to make decisions based on the market movement. Automated trading offers benefits like speed, accuracy, consistency, and less human errors. However, like any program, there are a few challenges and risks involved, such as technical errors, market changes, regulations, and strategy failures. And this is where algo backtesting comes to rescue.

Algo backtesting allows traders to test their theories and algorithmic trading strategies before going live on the market. It is the process to simulate the performance of a trading strategy using historical data. Algo Backtesting can help the traders evaluate the profitability, risk, and robustness of their strategies under different market conditions before even deploying the strategies into the live market.

However, algo backtesting is not a foolproof method to guarantee success in algo trading. In this article, we will discuss some of the common algo backtesting techniques and methods that Algo traders can use to validate and improve their strategies.

Types of Algo Backtesting Techniques

There are various types of algo backtesting techniques that traders can use to test their strategies. Some of the common types are:

1. Walk-forward backtesting

This is a type of algo backtesting that involves testing a strategy on multiple sets of historical data that are also divided into in-sample and out-of-sample periods. In walk-forward backtesting, the strategy is optimized in the in-sample period and then tested in the out-of-sample period. This process is repeated for different combinations of in-sample and out-of-sample periods. Walk-forward backtesting can provide a more realistic and robust way to evaluate a strategy’s performance in different time periods. However, walk-forward backtesting can also be time-consuming and complex to implement and analyse.

2. Out-of-sample testing

This is a type of algo backtesting that involves testing a strategy on a set of historical data that is not used for optimization or calibration. Out-of-sample testing can provide a way to validate a strategy’s performance on unseen data that is independent of the optimization process. Out-of-sample testing can help to avoid over-fitting and data snooping bias. However, out-of-sample testing can also be insufficient and inaccurate if the out-of-sample data is not large enough or diverse enough to capture future market conditions.

3. Sensitivity Analysis

This is a type of algo backtesting that involves testing a strategy’s performance under different values of its parameters and settings. Sensitivity analysis can provide a way to measure a strategy’s robustness and stability under different market scenarios. Sensitivity analysis can help to identify the optimal and robust values of the strategy’s parameters and settings. However, sensitivity analysis can also be tedious and challenging to perform and interpret.

4. Monte Carlo simulation

This is a type of algo backtesting that involves testing a strategy’s performance under different random scenarios that are generated by a statistical model. Monte Carlo simulation can provide a way to measure a strategy’s performance under various possible outcomes that are not captured by historical data. Monte Carlo simulation can help to estimate the probability and magnitude of the strategy’s returns, risks, and drawdowns. However, Monte Carlo simulation can also be computationally intense and dependent on the quality and validity of the statistical model.

Challenges in Algo Backtesting

Algo backtesting offers numerous benefits but also involves various challenges and limitations for algo traders, such as:

i. Overfitting and data snooping bias

Excessive optimization, inadequate data, or improper testing methods can result in Overfitting and data snooping bias. Creating a strategy that fits the historical data more than it should might fail to produce results in the future. The same historical data for multiple tests and optimizations can lead to false discoveries and spurious results.

ii. Ignoring transaction costs

Transaction costs are the costs associated with executing a trade, such as commissions, fees, slippage, etc. Transaction costs can have a significant impact on the profitability of a strategy, especially for high-frequency or low-margin strategies. Ignoring transaction costs in algo backtesting can lead to unrealistic and inflated results that do not reflect the actual performance of a strategy in the live market. Your trade might be profitable, but the end result might result in a negative balance.

iii.Market assumptions and limitations

Market assumptions are the simplifications or approximations that are used to model the market behaviour and dynamics in algo backtesting. Market assumptions and limitations can affect the accuracy and reliability of the algo backtesting results, as they may not capture the complexity and uncertainty of the real market.

Ensuring Strategy Robustness

Stock market is always volatile and ensuring that a strategy will always work based on such dynamic data would require a set of strategies, not just one. However, here are some set-up parameters to keep in mind:

  • Define clear and conscious rules specifying entry, position sizing, target and stop-loss levers. Clarity in parameters ensures consistency and reproducibility during testing.
  • The choice of market, whether stocks, forex, or commodities, and the timeframe, such as daily, hourly, or minute charts, significantly influences the strategy’s performance and suitability. Create a unique strategy for each market/trade.
  • The shifts or transitions in the market behaviour and dynamics that occur over time can affect the performance of a strategy, as it may not adapt or adjust to the new market conditions.
  • With parameters set, historical data is utilized to identify potential trades. The historical period chosen should align with the intended trading horizon. Trades are then marked based on entry and exit signals generated by the strategy.
  • Algo traders should always include realistic and updated slippage and latency estimates in their algo backtesting process and analysis.
  • Always keep the transaction cost in mind, especially for high-frequency or low-margin strategies. To evaluate profitability, calculate the gross return by tallying all trades, considering both wins and losses. Net return, a more realistic measure, is obtained by deducting commissions and trading costs from the gross returns.

Algo backtesting is an essential step for trading success, as it helps to test and validate the best algo trading strategies before deploying them in the live market. However, it is not a foolproof technique to guarantee a return. Like any other strategy, Algo backtesting also suffers from limitations, bias and inaccurate market comprehension. Updating the strategies regularly as per market dynamics would help investors with better gains.

Now, you may have some questions in mind?


1. How to choose the appropriate data, frequency, and time period for algo backtesting?

Backtesting data and strategies are created according to the market, target stock, and goals. For example, if you are planning to hold for over a month or so to achieve your target, go for a strategy designed for a long-term period. The quality and accuracy of the data play a crucial role. It is important to select high-quality data, that is, data without any errors only from trusted sources for the utmost accuracy.

2. How to compare different algo backtesting methods and software?

The difference lies in multiple factors such as cost, ease of use, features, and capabilities. For example, some algo backtesting software may have more advanced features or support for more asset classes than others. Investors are advised to read reviews and comparisons of different algo backtesting tools to make an informed decision.

3. How to account for transaction costs, slippage, and market impact in algo backtesting?

These factors would influence the end profit/loss. Incorporating these in your strategy in the algo backtesting model would fetch the accuracy. This can be done by including estimates of transaction costs such as commissions and fees in your calculations. You can also model slippage by incorporating the difference between the expected execution price and the actual execution price into your calculations.

4. How to evaluate the performance and robustness of a strategy?

Testing your backtesting strategy on various performance metrics would help you evaluate the end result. This is advised to use various affecting variables such as net profit, desired return, Sharpe ratio, transaction cost and maximum drawdown etc. Then compare the results to see how well the strategy performed under different market conditions and assess its consistency over time.

5. What are the benefits and limitations of algo backtesting?

The idea of algo backtesting is to be able to test a trading strategy without risking any actual capital. A good strategy prepares investors for different market circumstances before investing any money. However, there are also limitations to algo backtesting such as the fact that past performance is not necessarily indicative of future results. There can be multiple events in the real-time market that did not happen in the past.

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