Machine Learning for Options Trading
Complete Webinar Recording: https://blog.quantinsti.com/machine-learning-options-trading-webinar-19-january-2023/ This session explains the application of machine learning techniques for options trading. It also covers the process of creating options trading strategies using machine learning. Overview: - Need for ML in options trading - ML for options pricing - Trading vertical spreads with ML - Options Trading with Ensemble Classifier - Forecasting and trading Implied Volatility with ML Regressor - Predicting The Returns of an Options Strategy using ML - Predicting The Options Strategy to Deploy using ML - Interactive Q&A Pre-Requisites: - Basic options nomenclature - Basic options trading strategies - Machine learning models ----------------------------------------- Speaker: Varun Kumar Pothula (Quantitative Analyst at QuantInsti) Varun holds a Master's degree in Financial Engineering. He has experience working as a trader, a global macro analyst, and also an algo trading strategist. Currently, working in the Content & Research Team at QuantInsti as a Quantitative Analyst, his contributions help in creating offerings for learners in the domain of algorithmic & quantitative trading. ----------------------------------------- 𝗨𝘀𝗲𝗳𝘂𝗹 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 👨🎓 Machine Learning for Options Trading | FULL Course https://quantra.quantinsti.com/course/machine-learning-options-trading 👨🎓 Learning Track | Machine Learning & Deep Learning in Trading - I https://quantra.quantinsti.com/learning-track/machine-learning-deep-learning-trading-1 👨🎓 Learning Track | Machine Learning & Deep Learning in Trading - II https://quantra.quantinsti.com/learning-track/machine-learning-deep-learning-trading-2 💡 Machine Learning Basics | A guide https://blog.quantinsti.com/machine-learning-basics/ 💡 Basics of Options Trading | Explained https://blog.quantinsti.com/basics-options-trading/ 📜 FREE course | Introduction to Machine Learning for Trading https://quantra.quantinsti.com/course/introduction-to-machine-learning-for-trading 📜 FREE course | Python for Machine Learning in Finance https://quantra.quantinsti.com/course/python-machine-learning ✅ Free Resources to Learn Algorithmic Trading https://blog.quantinsti.com/free-resources-list-compilation-learn-algorithmic-trading/ 📄 Blogs and Tutorials https://blog.quantinsti.com/ 🎞️ Complete webinar recordings https://blog.quantinsti.com/tag/webinars/ ----------------------------------------- This event was conducted on: Thursday, 19th Jan. 2023 8:30 AM ET | 7:00 PM IST | 9:30 PM SGT ----------------------------------------- Like and Follow us on: Our Blog: https://blog.quantinsti.com/ Facebook @ https://www.facebook.com/quantinsti/ Twitter @ https://twitter.com/QuantInsti LinkedIn @ https://www.linkedin.com/school/quantinsti/ Instagram @ https://www.instagram.com/quantinstian/ E-mail us @ sales@quantinsti.com
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Machine Learning for Options Trading
- 1. Machine Learning for Options Trading By Varun Pothula
- 2. Agenda ● Introduction to ML and options trading ● Need for ML in options trading ● Applications of ML in options trading ● Best practices ● Q&A
- 3. Pre-Requisites ● Basic options nomenclature ● Basic options trading strategies ● Machine learning models
- 4. Intro to ML and Options Trading
- 8. Machine Learning - Approaches ● Supervised learning ● Unsupervised learning ● Reinforcement Learning
- 10. Machine Learning Application of ML for Options Trading
- 11. Options Trading ● Buying or selling options contracts ● Choice to buy or sell ○ Set price ○ Specific date ● Hedging ● Income generation ● Speculation
- 13. Need for ML in Options Trading
- 14. Need for ML in Options Trading ● Options trading is very rewarding yet highly risky! ● Highly accurate analysis brings down the risk ● Power of ML ○ Analyses large amounts of data ■ Market data, underlying data ○ Complex and hidden relationships in the data ■ Trends in stock prices, volatility and market indicators
- 15. Applications of ML in options trading
- 16. Applications of ML in options trading 1) Options pricing 2) Trading Strategies 3) Implied volatility forecast 4) Predict the options strategy to deploy
- 17. Options Pricing
- 18. ML for Options Pricing ● Black–Scholes model ○ Commonly used options pricing model ■ Fair value of the options contract ○ Efficiency is limited by its assumptions ○ Assumptions: ■ Constant risk-free rate and the volatility of the underlying asset ■ Underlying asset price follows a lognormal distribution ■ European style options
- 19. ML for Options Pricing Other Models: 1) Derman-Kani model 2) Heston model 3) Cox-Ross-Rubinstein model Limited by input parameters and assumptions Solution: Machine Learning Implementation
- 20. ML for Options Pricing Implied Volatility Interest Rate Days till Expiry Underlying Returns Dividends Fair Price ML Model Features Model Output
- 21. ML for Options Pricing Implied Volatility Interest Rate Days till Expiry Underlying Returns Dividends Fair Price ML Regressors Lasso Regression Decision Trees Random Forest Multi-Layer Perceptron Features Model Output
- 22. ML for Options Pricing
- 23. ML for Options Pricing Fair Price ML Model Select Strike Price Hedging Risk Analysis Identify Mispricing Model Output Application
- 24. ML for Trading Strategies
- 25. ML for Trading Strategies Bullish Bearish Strategy Selection Underlying Analysis Analysis Sentiment Strategy
- 26. ML for Trading Strategies
- 27. ML for Trading Strategies Bullish Bearish Strategy Selection Flawed Analysis —--> Loss Underlying Analysis Analysis Sentiment Strategy
- 28. ML for Trading Strategies Underlying Analysis Bullish Bearish Strategy Selection ML Model Improved Analysis Improved Performance Analysis Sentiment Strategy
- 29. ML for Trading Strategies Bullish Bearish Strategy Selection ML Model Improved Analysis Improved Performance
- 30. ML for Trading Strategies Past Returns RSI ATR Bollinger Bands ML Model Bullish Bearish Features Model Output
- 31. ML for Trading Strategies Past Returns RSI ATR Bollinger Bands ML Model Bullish Bearish Features Model Output
- 32. ML for Trading Strategies
- 33. ML for Trading Strategies
- 34. ML for Trading Strategies Past Returns RSI ATR Bollinger Bands Trading Range ML Regressors Bullish Bearish Bull Call Spread Bear Call Spread Bull Put Spread Bear Put Spread Features Model Output Application
- 35. ML for Implied Volatility Forecast
- 36. ML for Implied Volatility Forecast High Volatility Low Volatility Strategy Selection Underlying Analysis Options Analysis Analysis Volatility Strategy
- 37. ML for Implied Volatility Forecast
- 38. ML for Implied Volatility Forecast Underlying Options ML Model Features Model Output Volatility
- 39. ML for Implied Volatility Forecast ADX DTE ML Model Volatility RSI ATR Bollinger Bands Historical IV
- 40. ML for Implied Volatility Forecast ADX DTE ML Regressor Volatility RSI ATR Bollinger Bands Historical IV Long Straddle Short Straddle Long Strangle Short Strangle
- 41. ML for Implied Volatility Forecast ML Model: RandomForestRegressor SPX EPM Contracts Train→ 2015-mid_2020 Backtest→ mid_2020-Q3_2022
- 42. ML to Predict the Options Strategy to Deploy
- 43. ML to Predict the Options Strategy to Deploy
- 44. ML to Predict the Options Strategy to Deploy Underlying Options ML Model Features Model Output Strategy Options Greeks
- 45. Strategy Universe Buy (1) Sell (-1) No Position (0) Call Put Underlying Strategy 1 Strategy 2 Strategy 3 Strategy n . . . . Positions Contracts Strategy
- 46. Strategy Universe Buy (1) Sell (-1) No Position (0) Call Put Underlying Strategy 1 Strategy 2 Strategy 3 Strategy n . . . . 3 Positions 3 Contracts 27 Strategy 3 3 33
- 48. Strategy Universe At least one position in option contracts
- 49. Strategy Universe Different position in call, put with open position in the underlying
- 50. Strategy Universe Final Strategy Universe: 20 Strategy Combinations
- 51. ML to Predict the Options Strategy to Deploy Underlying Options ML Model Features Model Strategy Universe Strategy Options Greeks
- 52. ML to Predict the Options Strategy to Deploy LSTM Model Features Model Strategy Universe Strategy Underlying H.Returns Momentum Volatility DTE Call, Put LTP Call, Put IV Underlying Returns ATM Strike Δ,γ,V, θ, ρ
- 53. LSTM Architecture Input Layer Hidden Layer Hidden Layer Hidden Layer Hidden Layer Hidden Layer Output Layer Activation : Relu Activation : Softmax Optimizer: ADAM Learning Rate: 10^-4
- 54. LSTM Architecture Train: 2010-Q3_2020 Deploy: Q3_2020-Q3_2022 LSTM Model
- 55. ML to Predict the Options Strategy to Deploy
- 56. Best Practices
- 57. Input Best Practices (only if probability level >0.7) Up Down Use Probability Levels: predict_proba in scikit-learn
- 58. Decision Tree Model Decision Tree Model Decision Tree Model Aggregate Results Output Random Forest Model Best Practices
- 59. Decision Tree Model Logistic Regression XGBoost Model Voting Classifier Output Voting Classifier method Best Practices
- 60. Decision Tree Model Logistic Regression XGBoost Model Machine Learning Model Output Blending and Stacking method Best Practices
- 61. ● Hyper Parameter Tuning ● Cross Validation ● Forward Testing (Paper Trading) Best Practices
- 63. Thank You!