There is one problem that most traders struggle with which is the failing of their strategy when they have set them up perfectly. Our mission at AlgoTest is to ensure that traders understand the reasons and solutions.
If you are someone whose algo trading strategy isn’t delivering expected returns, here are five common reasons why—and how you can fix them.
1. Overfitting to Past Data
The Problem:
Many traders rely on backtesting to fine-tune their strategies. However, excessive optimization can lead to overfitting—where a strategy performs exceptionally well on past data but fails in live markets. This happens because the algo is designed to exploit historical patterns that may not repeat in real trading conditions.
The Fix:
- Use out-of-sample testing to validate your strategy on unseen data.
- Implement walk-forward optimization to test the strategy across different time periods.
- Keep the number of parameters minimal to avoid excessive curve-fitting.
2. Ignoring Market Structure
The Problem:
Factors like liquidity, slippage, impact costs, and circuit breakers can disrupt even the best algo strategies. Many traders backtest on historical prices without accounting for real-world execution challenges.
The Fix:
- Factor in slippage and impact cost while backtesting.
- Use limit orders instead of market orders to control execution price.
- Be aware of trading halts and circuit limits on stocks and indices.
3. Poor Risk Management
The Problem:
Many traders focus only on maximizing profits while ignoring risk. Without proper risk controls, a few bad trades can wipe out months of gains. Algo trading amplifies risks if position sizing and stop-loss mechanisms aren’t well-defined.
The Fix:
- Use position sizing techniques like fixed percentage or volatility-based sizing.
- Implement hard stop-losses and trailing stops in your strategy.
- Diversify across multiple assets to reduce dependency on a single trade.
4. Not Accounting for Changing Market Conditions
The Problem:
Indian markets are highly dynamic, with phases of high and low volatility. A strategy that works well in trending markets might fail in sideways conditions. Many traders set their algo strategies without adapting to these changes.
To learn more about volatility trading, join Raghav, founder of AlgoTest, in his course where he covers how to trade options profitably using IV, RV, and Volatility Risk Premium (VRP).
The Fix:
- Use adaptive strategies that adjust parameters based on volatility and trend strength.
- Incorporate market regime detection techniques to switch between different strategies.
- Regularly review and update your algo to reflect current market conditions.
5. Underestimating Technical Failures
The Problem:
Even automated trading requires human oversight. Issues like server downtime, API failures, brokerage restrictions, and internet lags can cause unintended losses. Moreover, traders often panic and override their strategies during drawdowns, disrupting long-term performance.
You can also check your broker’s speed and learn how brokers’ speed affects your trading execution and experience.
The Fix:
- Use cloud-based execution platforms for stable performance.
- Ensure redundancy in internet connections and power backup.
- Stick to your strategy and avoid emotional interventions unless there’s a clear technical flaw.
