How to build algo strategies to trade in stock market?

Algo trading (algorithmic trading) uses mathematical models + computer automation to execute trades at high speed based on predefined rules.

Some dependencies and approaches are listed below for building an algorithmic strategy and trade execution. 

1. Stock Market Data Feeds

These provide real-time information that the algorithm reads:

• Price ticks (bid/ask)
• Volume, order book depth
• News feeds & sentiment (optional)
• Economic indicators
  Tech: FIX protocol, WebSocket APIs, REST APIs from brokers/exchanges.

2. Algorithm Engine (Strategy Logic Layer)

The brain of the system where your strategy runs.

• Technical indicators (EMA, VWAP, RSI, MACD, ATR)
• Statistical models (mean reversion, pairs trading)
• Machine learning models (trend prediction, volatility prediction)
• Event-driven logic (buy when X happens)

Tech:

• Python: pandas, NumPy, TA-Lib, scikit-learn, TensorFlow
• C++/Rust/Java: high-speed systems for low latency

3. Trading Platforms / Backtesting Systems

Used to test and run strategies:

• MetaTrader, Amibroker, TradingView (retail)
• QuantConnect, AlgoTrader (cloud)
• Custom Python/C++ systems (advanced users)

Tech:

• Backtesting engines
• Historical database
• Optimization frameworks

4. Order Execution Systems

Handles the actual buying/selling:

• Smart Order Routers (SOR)
• Direct Market Access (DMA)
• Co-location with exchange servers (for low latency)
• FIX engines for institutional execution

Tech:

• FIX 4.2 / 4.4 protocol
• C++ and FPGA for ultra-low latency
• High-speed networking (10–40Gbps)

5. Risk Management Layer

Monitors overall exposure:

• Position limits
• Max drawdown
• Stop-loss automation
• Volatility filter
• Margin check

Tech:

• Real-time rule-based engines
• Portfolio management systems

6. Broker API Integration

To send orders to real markets:

• Zerodha Kite Connect (India)
• Upstox API (India)
• Interactive Brokers API (USA)
• Alpaca, TD Ameritrade, Robinhood APIs

Tech: REST APIs, WebSockets, FIX, gRPC

7. High-Performance Infrastructure

For top-tier algo trading (hedge funds, HFT):

• Multi-core low-latency servers
• FPGA acceleration (hardware logic trading)
• Co-located servers near exchanges
• Millisecond or microsecond execution

Tech:

• C++/FPGA
• RDMA networking
• Custom OS kernels (Linux low-latency builds)

8. Machine Learning / AI Layer (Optional)

Modern algos use AI to:

• Predict price direction
• Detect patterns
• Analyze order book behaviour
• Optimize strategy parameters

Tech:

• Deep Learning (LSTM, Transformers)
• Reinforcement Learning (RL)
• Feature engineering pipelines

FindNex own developed trading tool is an advanced tool built using Python/C++ for greater performance with low-latency to perform better compared to cloud based algo trading tools. This trading tool integrated with few brokers (i.e. Zerodha) for trading in NSE stocks, which has been extensively tested for reliability and performance in analysing data and executing orders.