How to Start Algorithmic Trading?

Algorithmic trading means automatically executing trades through the use of computer programs, which are pre-programmed based on specific investment strategies and market conditions. It avoids emotional decisions and can execute trades much quicker than any human. Algo trading needs knowledge of market dynamics, the basics of programming, and risk management principles. This article explains how to start algorithmic trading in India. 

Steps to do Algorithmic Trading 

Here are the steps to start algorithmic trading:  

Step 1: Define the Trading Segment  

Investors begin by identifying the asset class to be traded, such as equities, derivatives, or other market segments.  

Step 2: Selecting the Reliable Broker 

A suitable broker is chosen based on a stable Application Programming Interface (API), dependable platform uptime, and accurate real-time market data. 

Step 3: Prototype the Algorithm 

The trader develops an initial version of the strategy, starting with simpler models and testing the algorithm before any deployment. 

Step 4: Run Backtests and Refinements 

The strategy is backtested using clean and reliable historical data, and adjustments are made until the results demonstrate consistent behaviour. 

Step 5: Paper Trade in Simulations 

The algorithm is tested in a simulated or demo environment to identify technical issues or flawed assumptions before real capital is committed. 

Step 6: Go Live  

The trader deploys the algorithm in the live market with predefined loss limits and capital allocation rules, while maintaining close monitoring and complying with SEBI regulations. 

Risks Involved in Algorithmic Trading 

After understanding how to start algo trading, let’s understand the risks associated with it. 

• Compliance: SEBI strengthened supervision to ensure fairness and transparency in the markets. The traders need to get approval for their algorithms from the exchange and correct labels on the orders placed to avoid fines or suspension of accounts.  
• System Dependencies: Algorithmic trading is reliant on internet connectivity, data feeds, servers, and APIs. Losses can arise unexpectedly if there are connectivity issues, delays in data feeds, or improper API keys. 
• Infrastructure investment: Most traders are finding themselves invested in low-latency servers, VPS, high-quality data feeds, and back-testing tools. These infrastructure investments can decrease profitability, particularly for high-frequency trading strategies. 
• Price Volatility: High volatility may be experienced at times of any budget or tariff announcements. Dynamic risk management in algorithms helps reduce slippage and large drawdowns during turbulent conditions. 
• Limitations of Testing: Backtesting may show high returns that may not be realistic. Optimising strategies solely on historical data may result in poor performance on live markets, while wrong coding logic leads to severe losses. 

Mistakes to Avoid in Algorithmic Trading 

The following are some common mistakes to avoid in algorithmic trading: 

• Poor Data Quality: Testing with invalid data may provide false outcomes of the strategy's performance. Always test high-quality, reliable data sources only. 
• Over-Optimisation: The mistake of building models that look perfect during historical testing but may fail in future markets is very common. Keep the strategies simple to ensure that they will actually work in the real world. 
• Skipping Practice Mode: Live trading without simulation testing should be avoided. Simulation helps to identify weaknesses that need to be addressed before risking one's hard-earned capital in the markets. 
• Insufficient Protection: Trading without proper risk management may result in significant losses. 
• Ignoring Maintenance: Markets are always dynamic and subject to change. If the algorithms are not updated or rereviewed for a few months, they may lose their relevance and effectiveness. 

Conclusion

Understanding how to start algorithmic trading requires a thoughtful approach to planning, systematic implementation, and continuous improvement. Start with appropriate asset selection, choice of reliable brokers, prototyping of simple algorithms, and thorough backtesting before deployment. While algorithmic trading has the advantage of accuracy in speed, traders must always be updated on technical risks, regulatory compliance, and market volatility. Additionally, using a trusted stock trading app ensures secure algorithmic trading.