Knowledge Centre

  • Knowledge is power
  • Information is liberating
  • Education is the premise of progress, in every society
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.

Get Started with Algo Trading

Related Blogs

Frequently Asked Questions


Important Message The information contained in this file is provided for informational purposes only, and should not be construed as legal advice on any matter. The content and interpretation of the law addressed herein is subject to revision. We disclaim all liability in respect to actions taken or not taken based on any or all the contents of this file to the fullest extent permitted by law. Every effort is made to avoid errors. In spite of that, errors and discrepancies may creep in. It is expressly stated that neither Findoc Investmart Private Limited nor any of the contributors of updates will be responsible for any damage to anybody on the basis of this document. Readers are, therefore, requested to cross check with the original sources e.g. Government publications, Orders, Judgments etc., before taking any action or making any decision. These services are being provided through our group companies Findoc Capital Mart Pvt Ltd and Findoc Finvest Private Limited

Attention Investors
  • 1. Stock Brokers can accept securities as margin from clients only by way of pledge in the depository system w.e.f. September 1, 2020.
  • 2. Update your mobile number & email Id with your stock broker/depository participant and receive OTP directly from depository on your email id and/or mobile number to create pledge.
  • 3. Pay 20% upfront margin of the transaction value to trade in cash market segment.
  • 4. Check your securities / MF / bonds in the consolidated account statement issued by NSDL/CDSL every month.
  • 5.Investments in securities market are subject to market risks, read all the related documents carefully before investing.
  • 6.The securities are quoted as an example and not as a recommendation.
No need to issue cheques by investors while subscribing to IPO. Just write the bank account number and sign in the application form to authorise your bank to make payment in case of allotment. No worries forrefund as the money remains in investors account.
Prevent Unauthorized Transactions in your demat account --> Update your Mobile Number with your Depository Participant. Receive alerts on your Registered Mobile for all debit and other important transactions in your demat account directly from NSDLon thesame day.....issued in the interest of investors.
KYC is a one-time exercise while dealing in securities markets-once KYC is done through a SEBI registered intermediary (broker, DP, Mutual Fund etc.), you need not undergo the same process again when you approach another intermediary. | (As instructed by SEBI, We hereby declare that we do engage in proprietary trading in all segment across the exchange.)
Effective communication & Speedy redressal of the grievances a. Register on SCORES portal b. Mandatory details for filing complaints on SCORES: i. Name, PAN, Address, Mobile Number, Email ID c. Benefits: i. Effective communication ii. Speedy redressal of the grievances link:
In case of grievances for any of the services rendered by Findoc Investmart Pvt Ltd write an email to
Mandatory updation of certain attributes of KYC of clients - The advisory is also displayed on the Depository website at following link:
1. NSDL:IN-DP-469-2020 2. Findoc Finvest Pvt. LTD. CIN no:U65910CH1995PTC016409 RBI REGISTRATION NO. B-06.00267 3. Findoc Investmart Private Limited CIN no:U74992CH2010PTC035180 SEBI REGISTRATION NO. INZ000164436 4. Findoc Investmart IFSC PVT. LTD CIN no: U65999GJ2017PTC095984 SEBI REGISTRATION NO. INZ000200735 5. INVESTMENT ADVISOR SEBI Registration no. INA100012297

Member I'd | Nse- 14697 | BSE- 6529 | MCX- 55205 | NCDEX- 01152


Registered Office :

1210/1211/1212/1213,1213A, Exchange Plaza, Near Mercury Hotel, Opp. WTC Tower, Gift City, Gandhi Nagar- 382355, Gujarat, India

Corporate Office :

4th Floor, Kartar Bhawan, Near PAU Gate No.1, Ferozepur Road Ludhiana -141001.

Copyright © 2024 FINDOC INVESTMART PVT. LTD. All Rights Reserved.

Developed & Content Powered by Accord Fintech Pvt. Ltd.

Open a Demat Account