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