Common Mistakes to Avoid in Algo Trading in India

If you’re a trader navigating the complex financial markets, you must have heard about algo trading. It is the latest revolution where trades are executed with the help of computer “algorithms” or automated systems. If manual trading feels like a big hassle now due to your busy schedules, it's time to consider algo trading! There are primarily two ways to participate in algo trading in India:

1. Complete DIY (do-it-yourself): In this approach, you need to create a trading strategy from scratch, code your algorithm (yes, it requires extensive technical knowledge), test it, and deploy it on your own.

2. Use dedicated platforms: Various algo trading platforms in India offer built-in strategies and tools to test & execute trades seamlessly.

But regardless of the methods you practice, errors are not just inevitable— they're an essential part of the learning process. In this article, we will discuss various mistakes traders make while practising algo trading. We will also see how you can avoid those mistakes to have a more profitable trading journey. 

What Are The Common Mistakes Algo Traders Make?

If you choose the DIY approach, here are some common mistakes you could make while practising algo trading:

1. Poor backtesting

Backtesting is the process of testing the trading strategy/algorithm using historical data to predict its effectiveness in live markets. It’s like rewinding the clock and watching your strategy play out, trade by trade! 

Poor backtesting can mislead traders, causing them to overestimate a strategy's real-world performance. Having limited historical data or ignoring noisy data (​​those containing errors, missing values, or irrelevant information) could lead to losses or sub-optimal results. Moreover, excessive testing or under-testing may also create problems during live trading. 

Traders often expect backtesting results to work out exactly as planned. However, such results often fail to capture the full complexity of live markets, including unexpected events, sudden shifts in liquidity, or changes in market sentiment.

To learn more about backtesting, please go through this article: Backtesting Algo Trading Strategies.

How to Avoid This?

• Focus on the key principles of backtesting like defining objectives and choosing appropriate parameters & indicators. Ensure your historical data is accurate and complete (use reliable data sources from reputable providers).

• Backtesting for longer periods (>3-5 years) helps build confidence that the strategy will perform well in various real-world market cycles.

• Don't use all your data for backtesting. Divide it into two sets: in-sample (for developing the strategy) and out-of-sample (for testing its performance on unseen data). This method will help you understand how well the strategy adapts to new market conditions.

2. Neglecting transaction costs and slippages

While backtesting, many traders forget to account for slippages or transaction costs like brokerage charges, taxes, and commissions. Traders need to incorporate these estimates into their models while backtesting. Neglecting this can lead to overestimating profits. Strategies can look extremely powerful and profitable on paper, but missing transaction fees can lead to reduced profits or even losses. 

[A "slippage" occurs when the price at which your order is executed does not match the price at which it was requested.]

How to Avoid This?

• Include and modify the trading algorithm to incorporate these costs to ensure the results will give more realistic net profits. Estimate slippage based on historical data, trade size, etc.

• Test with different cost conditions to figure out the perfect strategy. Look out for latency (delays) in order placement and execution!

3. Over-optimisation

Also known as curve fitting, this occurs when the algorithms are too excessively fine-tuned to historical data. The algorithm might fail to figure out new market conditions due to over-optimisation.

For example, consider an algo trader developing a moving average crossover strategy for the HDFC stock. Starting with a simple 50-day and 200-day moving average, he optimises his strategy extensively using 12 years of historical data (2010-2022). 

Then, he adjusts parameters, adds filters for volatility & volume, and eventually creates a complex strategy that uses 73-day and 187-day moving averages. This strategy promised an impressive 40% annual return in backtests. However, when deployed in 2023, it quickly lost 17% in the first three months, failing to adapt to new market conditions. This strategy fell victim to overfitting by being too precisely calibrated to past data, ignoring market noise, and becoming overly complex!

How to Avoid This?

• Start with a basic strategy with limited parameters. Look for reasonable strategy performance with acceptable risk metrics. Only add new conditions or features if they deliver better results over time.

• Use out-of-sampling testing and maintain cross-validation techniques.
  [Out-of-sample testing is a kind of testing you do on unknown data to know whether a backtested strategy is strong enough to work in a live market environment. Cross-validation is used to assess how well a trading strategy or algorithm will perform on new, unseen data.] 

4. Incomplete technological knowledge

Ignoring the technical aspects of algo trading can lead to errors, bugs, or faulty trade executions. Investors must have excellent coding skills (unless they use a third-party platform). Additionally, they need to be well-versed in data handling and all the software required for algo trading.

How to Avoid This?

• Stay updated with all the latest technological advancements in the field of financial markets & algo trading. Practice coding and improve your skills regularly.

• Look for alternate platforms where you can use their in-built strategies or strategy builder for creating and deploying. Eg: marketfeed, AlgoBulls.
  
• Consult experts in the areas you lack and attend various lectures, talks and seminars on algo trading.

5. Insufficient risk management

Risk management plays a very crucial role in algo trading. Failing to set stop-loss parameters or having a faulty algorithm could even wipe out your entire trading capital. Concentrating risk in a single strategy, asset class, or market sector increases vulnerability to specific market events. Moreover, many traders fail to account for low-probability, high-impact events (black swans), which can lead to significant losses when these events occur.

How to Avoid This?

• Use position-sizing (allocating a predetermined percentage of your capital to each trade), portfolio diversification, and set stop loss (SL) rules to reduce the potential risks and implement operational risk management strategies like recovery and backup systems, backup power sources, etc.

• Make sure your algo trading strategies adapt to changing market conditions and economic events. Keep track of news and trends which can create market volatility.

6. Poor Trade Execution

We feel trade execution is the most important step in algo trading. A well-designed strategy can identify profitable opportunities. But if execution is poor, you might not capture those gains. For example, an algorithm aiming to buy at a specific price point might miss the opportunity entirely if execution is slow or inefficient.

How to Avoid This?

• Invest in high-performance hardware and software, co-location services, and low-latency network solutions to improve the infrastructure.

• Improve order execution by carefully selecting appropriate order types, and setting stop-loss orders and developing effective order routing algorithms. 

7. Lack of monitoring

A trader's work is not completed after the algorithm is deployed, they can't just relax. You could incur losses if you fail to keep track of your algorithm or conduct regular checks for glitches. Failing to monitor the trade performance and not optimising the technical indicator parameters/conditions could cause severe issues in the long run. Not having a backup plan or alerting systems can result in losses or low profits.

How to Avoid This?

• Use real-time risk management in trading, implement monitoring tools and set up alerts to evaluate real-time performance.

• Implement regular reviews, and performance assessments and make necessary timely changes to optimise the probability of making profits.

Mistakes to Avoid While Using Third-Party Platforms

One of the biggest mistakes algo traders make is falling victim to mis-selling. This mostly comes from the platforms that offer trading strategies or algorithms. In most cases, this comes as:

• Inflated performance claims: Most of these platforms or vendors will exaggerate the historical return of their algorithms by presenting overly optimistic back-test results that do not include real-world factors.

• Lack of transparency: Sellers rarely disclose the exact methodology of their algorithm, which might make it difficult for buyers to check and validate certain claims or even know the risks involved.

• Not considering individual suitability: Most buyers never check whether the pre-made algorithm fits their risk tolerance, capital, or trading goals.

• Underestimating challenges in implementation: Most traders underestimate the technical expertise required to properly implement and maintain the purchased algorithms.

Traders should properly research all algo trading platforms, demand complete documentation of their services & costs, and verify the performance claims independently.

Conclusion

In the world of algo trading, mistakes are inevitable and part of the learning process. However, true growth comes from recognising these errors and avoiding them in the future. The path to becoming a successful trader can be challenging, but with the right mindset and tools, you can confidently navigate the complexities of the market. We've highlighted several common mistakes traders often encounter and practical solutions to overcome them. Remember, knowledge is power, but the application of that knowledge leads to success!

How will you use these insights to refine your trading approach and build a more robust strategy? Let us know in the comments down below!