Algorithmic Options Trading
Introduction to automated options trading systems and backtesting
Learning Objectives
Algorithmic Trading Fundamentals
Systematic approaches to options trading
What is Algorithmic Trading?
Using computer programs to execute trading strategies automatically based on predefined rules and market conditions.
Key Components:
- • Market data feeds and processing
- • Strategy logic and signal generation
- • Risk management and position sizing
- • Order management and execution
- • Performance monitoring and reporting
Advantages for Options Trading
Common Strategy Types
Mean Reversion
Trade against extreme price movements
Volatility Arbitrage
Exploit IV vs RV discrepancies
Delta Neutral
Profit from time decay and Gamma
Statistical Arbitrage
Relative value between options
Backtesting Methodology
Validating strategies with historical data
Data Requirements
High-quality historical data is crucial for meaningful backtests:
- • Option prices, Greeks, and implied volatilities
- • Underlying price data with sufficient granularity
- • Dividend and earnings dates
- • Interest rate data
- • Bid-ask spreads and volume information
Common Backtesting Pitfalls
Survivorship Bias
Only including companies that survived the entire period
Look-Ahead Bias
Using future information not available at trade time
Transaction Cost Ignorance
Underestimating commissions, slippage, and bid-ask spreads
Realistic Assumptions
Strategy Implementation
From backtesting to live trading
Paper Trading Phase
Essential step before risking real capital:
Technology Stack
Programming Languages
• Python (pandas, numpy, scipy)
• R (quantmod, PerformanceAnalytics)
• C++ (high-frequency trading)
• Java (institutional platforms)
Data Providers
• Interactive Brokers API
• TD Ameritrade API
• Alpha Vantage
• Quandl/NASDAQ Data Link
Risk Management Systems
Essential Controls:
- • Maximum position size limits
- • Daily and monthly loss limits
- • Greeks exposure limits
- • Emergency shutdown procedures
- • Real-time P&L monitoring
Advanced Algorithmic Concepts
Machine Learning Applications
AI/ML techniques increasingly used in options trading:
High-Frequency Considerations
Speed advantages in options markets:
- • Delta hedging opportunities
- • Arbitrage between options and underlying
- • Cross-market arbitrage
- • Liquidity provision strategies
Regulatory Considerations
Algorithmic trading subject to various regulations including pattern day trader rules, position limits, and market maker requirements.
Critical Warnings
• Capital Requirements: Algorithmic trading requires significant capital for proper diversification
• Technology Risk: System failures can lead to large losses very quickly
• Model Risk: Strategies can fail when market conditions change
• Overfitting: Strategies optimized to historical data may not work in future
• Liquidity Risk: Automated systems may amplify losses during market stress
• Regulatory Risk: Rules can change, affecting strategy viability