Why Algo Trading?

  • Eliminates emotional bias — trades execute strictly by rules.
  • Executes orders at speeds humans can't match (milliseconds).
  • Backtesting allows validation on historical data before risking capital.
  • Improves consistency and helps manage risk with pre-defined stop-loss and sizing rules.

Common Algo Trading Strategies

  • Trend Following — ride persistent trends with moving averages or momentum filters.
  • Mean Reversion — trade when prices revert to a statistical mean.
  • Arbitrage — exploit price differences across markets or instruments.
  • Scalping — capture small moves repeatedly with fast execution.
  • Pairs / Statistical Trading — trade spreads between correlated instruments.

Tools & Platforms

  • Broker APIs like Zerodha Kite Connect, Upstox API, Angel One API for execution.
  • Python with pandas, numpy and TA libraries for development.
  • Backtesting frameworks (Backtrader, Zipline) and TradingView for idea validation.
  • Cloud/VPS hosting for low-latency live systems.

Risk Management & Best Practices

  • Use strict position sizing and limit risk per trade.
  • Backtest across multiple market regimes and forward-test with paper trading.
  • Monitor slippage, transaction costs and latency.
  • Log everything and configure alerts for failures.

How to Build Your First Algo (Step-by-step)

  • Pick a simple idea (e.g., moving-average crossover).
  • Define exact entry/exit and sizing rules.
  • Backtest with quality historical data and analyze metrics.
  • Paper trade, then deploy with small capital and monitor closely.

Further Learning

  • Take structured courses, read quant blogs, and practice on paper accounts.
  • Join communities and study research papers to advance systematically.