What is Algo Trading?

Algorithmic trading is nothing but coding pre-defined rules and letting the computer take the trades on your behalf.

Algo trading is transforming the way traders participate in stock markets. It offers speed, efficiency, and automation, but success isn’t guaranteed. Many traders make these common mistakes that impact their profitability.

Let’s break down five critical mistakes traders make in algo trading and how to avoid them:

1. Designing Overly Complex Algo Strategies

Many traders believe that a complex strategy with multiple indicators and rules will perform better. In reality, complicated strategies often lead to overfitting, where the strategy performs well in historical data but fails in live markets. In India, where market conditions change due to RBI policies, budget announcements, and FII flows, simple and robust strategies tend to work better. Traders should focus on understanding market structure and use minimal but effective rules.

2. Over-Optimizing Your Trading Algorithm

Optimization is necessary to fine-tune strategies, but excessive tweaking can lead to curve-fitting. Traders often optimize parameters to achieve the best backtest results, but this does not guarantee future success. In the Indian markets, which are influenced by global trends, election results, and domestic liquidity conditions, rigidly optimized strategies often fail in real time. Instead, traders should stress-test their strategies on out-of-sample data and different time periods.

3. Backtesting

Backtesting is a historical simulation to understand how the strategy could have performed in the past.

It is the backbone of algo trading, but many traders fail to account for real-world conditions like slippage and order execution delays. A perfect backtest on historical data does not always translate into profitability in live markets.

Using platforms that simulate real-market conditions, like AlgoTest, can help traders get a more realistic assessment of their strategies.

But is this enough to go live with the strategy? To understand what more you need to know before you take your trade live, learn from Chintan in his course –where he covers everything from ideation to taking the strategy live in the market.

4. Neglecting Transaction Costs and Slippages

A common mistake traders make is ignoring the impact of brokerage charges, STT (Securities Transaction Tax), GST, stamp duty, and slippage. While backtests might show a profitable system, high-frequency trading strategies can quickly become unprofitable due to these costs. NSE and BSE traders should always factor in brokerage charges and slippages before deploying a strategy. Using brokers that offer zero or low brokerage on F&O trading can be a cost-saving measure.

For better efficiency, accuracy and execution, brokers with an API are another option that you can opt for.

5. Choosing the Wrong Trading Platform

Algo traders need reliable execution platforms, yet many traders select platforms based on cost alone. Execution speed, API stability, uptime, and integration with order management systems (OMS) are critical.

You can also check your broker’s speed and learn how brokers’ speed affects your trading execution and experience.

Traders often face issues like API disconnections or delayed order execution, which can turn a winning strategy into a losing one. Choosing a reliable algo trading platform with proper risk management features can make a significant difference.