Algorithmic trading, commonly known as “algo trading,” has gained attention in India as more traders and investors look for ways to automate their strategies. Instead of manually monitoring price movements and executing trades, an algorithm uses computer programs to make decisions based on predefined rules.
One of the simplest ways to get started with algorithmic trading is by using the trading APIs offered by brokers.
Tips for Developing Algo Strategies Using Trading APIs
Here are some tips to consider when using trading APIs:
Integration of Data
With APIs, you can access tick-by-tick, intraday, and historical data. However, you should verify whether the data includes adjustments for corporate actions like splits and dividends. Look for sources that minimise latency and provide consolidated feeds instead of fragmented exchange-level data.
Delayed or incomplete data will make your strategy ineffective, especially in fast-moving markets. You should also check if the API supports different time intervals for candlesticks, as the strategy may depend on varied timeframes.
Support for Backtesting
Backtesting allows you to simulate trades with historical data to gauge how a strategy would have performed in past market conditions. Check if your API supports slippage, commissions, and order execution delays for a realistic assessment; without these, results may appear profitable but could fail in live markets.
Use APIs that provide both historical and real-time data for a smooth backtesting to live deployment. You must also monitor maximum drawdown, Sharpe ratio, and hit rate to gauge your strategy’s strength beyond raw profits.
Execution Quality
Confirm multiple order types’ availability in your API and use routing techniques to improve execution and minimise price impact. Some APIs allow smart order routing, where trades are executed across different exchanges for the best prices. You should also evaluate how the API manages rejected orders or partial fills.
Control Risk
Define strict risk parameters within your API, including maximum daily loss, position allocation, and exposure per asset. You can also find APIs providing direct options for automated stop-loss and trailing-stop placements during order submission.
Always use rate limits to prevent over-ordering to avoid account freezing because of excessive API calls.
Latency Management
APIs with WebSocket streaming provide faster updates compared to REST APIs, which are request-response based. You should place your trading server close to the exchange data centre to minimise transmission delays, a process called co-location.
Optimize your code to reduce processing bottlenecks and avoid unnecessary API calls that increase execution time. Some APIs also support the FIX protocol, which provides low-latency institutional-grade communication.
Paper Trading
Before you put your real money at risk, use paper trading. For example, you can use the API’s paper algo trading strategy for Banknifty options to test your Banknifty strategy. Paper trading provides a sandbox with simulated execution, where trades happen in real-time using live market data but without financial consequences.
It can also help verify whether your order handling logic, position sizing, and risk rules are functioning correctly. If there are issues with API throttling, connection stability, and error handling, you can immediately detect that as well.
Account Updates
Many APIs provide real-time account updates, order status, and market feeds for dashboards. You can use this information for continuous monitoring of profits, exposure, and drawdowns. The API also provides you with automated alerts via email, SMS, or webhooks in case of unusual activity, such as sudden losses or connection issues.
Conclusion
Building algorithmic strategies using an API can help you automate your trading decisions, manage risks, and capitalise on market opportunities. To maximise benefits, incorporate high-quality data, conduct thorough backtesting, optimise execution, control risk, manage latency, and use paper trading.
Don’t forget to monitor the rules continuously to ensure your algorithm performs as expected in live markets.
