Algorithmic Stock Trading

Algorithmic trading uses computer programs to execute trades based on predefined rules.

Why Algorithmic Trading?

Advantages

• Removes emotional decisions
• Executes faster than humans
• Can monitor multiple stocks simultaneously
• Backtestable and measurable
• Consistent execution

Limitations

• Cannot adapt to unprecedented events
• Requires technical skills
• Overfitting is common
• Technology infrastructure needed

Common Strategies

Mean Reversion

• RSI extreme reversals
• Bollinger Band bounces
• Z-score based entries
• Works well in range-bound markets

Momentum/Trend Following

• Moving average crossovers
• Channel breakouts
• Relative strength ranking
• Best in trending markets

Statistical Arbitrage

• Pairs trading
• Sector-neutral strategies
• Correlation-based
• Market-neutral approach

Market Making

• Provide liquidity
• Earn bid-ask spread
• Requires high-frequency capability
• Capital intensive

Building a Trading Bot

Step 1: Strategy Design

• Define clear entry and exit rules
• Determine position sizing
• Set risk parameters
• Document everything

Step 2: Backtesting

• Test on 10+ years of data
• Out-of-sample validation
• Walk-forward optimization
• Account for transaction costs

Step 3: Paper Trading

• Run in real-time without real money
• Compare to backtest results
• Monitor for issues
• 3-6 months minimum

Step 4: Live Trading

• Start with minimal capital
• Scale up gradually
• Monitor continuously
• Have kill switch ready

Tools and Platforms

Programming

• Python (most popular)
• Pine Script (TradingView)
• QuantConnect, Alpaca API

Key Metrics

• Sharpe Ratio > 1.5
• Maximum Drawdown < 15%
• Profit Factor > 1.5
• Minimum 200 trades for significance

Key Rules

1. Simple strategies beat complex ones
2. Out-of-sample testing is mandatory
3. Costs matter (commissions, slippage)
4. Markets change, strategies must adapt
5. Risk management is non-negotiable