Algorithmic Trading Cheat Sheet

What is Algorithmic Trading?

Algorithmic trading (Algo trading) uses computer programs and algorithms to automate the process of buying and selling securities based on predefined rules, market data, and timing.

Popular Algorithm Types

• Trend Following: Buy when price trends upward, sell in downtrends.
• Mean Reversion: Assumes asset price will return to average price over time.
• Arbitrage: Exploits price differences between markets or instruments.
• Market Making: Continuously quotes buy and sell prices to capture bid-ask spread.

Common Technical Indicators

• Simple Moving Average (SMA): Average price over a set period.
• Exponential Moving Average (EMA): Weighted moving average giving more weight to recent prices.
• Relative Strength Index (RSI): Measures overbought or oversold conditions, on a scale of 0-100.
• Moving Average Convergence Divergence (MACD): Shows trend direction and momentum.

Risk Management Techniques

• Position Sizing: Define how much capital to risk on each trade.
• Stop-Loss Orders: Automatically sell to limit losses if price moves unfavorably.
• Diversification: Spread investments across assets to reduce risk.
• Maximum Drawdown Limit: Set maximum acceptable portfolio loss.

Backtesting Best Practices

• Use quality historical data matching your trading frequency.
• Test your strategies on out-of-sample data to avoid overfitting.
• Include transaction costs and slippage to simulate real market conditions.
• Analyze risk-adjusted returns such as Sharpe and Sortino ratios.

Common Tools and Languages

• Python: Popular for data analysis and algorithmic trading libraries (Pandas, NumPy, backtrader, Zipline).
• R: Useful for statistical modeling and trading systems.
• C++ / Java: Preferred for high-frequency trading for speed advantage.
• Broker APIs: Interactive Brokers, Alpaca, Zerodha Kite APIs for automated trade execution.

Tips for Developing Successful Strategies

• Start simple and gradually add complexity.
• Focus on rules that can be automated and thoroughly backtested.
• Avoid curve-fitting strategies just to past data.
• Regularly monitor and update strategies as market conditions change.
• Balance risk and reward carefully in all trades.

Resources for Learning

• Books: Algorithmic Trading by Ernie Chan, Quantitative Trading by Ernest Chan.
• Online communities: QuantConnect, Quantopian (community).
• GitHub open-source algo trading repositories.