{"id":249316,"date":"2026-07-07T12:12:30","date_gmt":"2026-07-07T16:12:30","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=249316"},"modified":"2026-07-07T12:14:38","modified_gmt":"2026-07-07T16:14:38","slug":"differential-machine-learning-with-twin-networks-in-r-forecasting-bitcoin-with-volatility-proxies","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/differential-machine-learning-with-twin-networks-in-r-forecasting-bitcoin-with-volatility-proxies\/","title":{"rendered":"Differential Machine Learning with Twin Networks in R: Forecasting Bitcoin with Volatility Proxies"},"content":{"rendered":"\n

The article “Differential Machine Learning with Twin Networks in R: Forecasting Bitcoin with Volatility Proxies” was originally posted on DataGeeek<\/a> blog.<\/em><\/p>\n\n\n\n

Introduction<\/h2>\n\n\n\n

Differential Machine Learning (DML), as introduced in the recent arXiv paper (Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps)<\/a><\/em><\/strong>, extends supervised learning by incorporating not only function values but also their derivatives. In financial contexts, this often means sensitivities such as Greeks. However, when direct derivatives are unavailable, we can approximate market dynamics using volatility indicators<\/strong>.<\/p>\n\n\n\n

In this project, we adapt DML to Bitcoin price forecasting. Instead of derivatives, we use RSI, MACD, and Bollinger Bands<\/strong> as proxies for volatility. These indicators capture momentum, trend strength, and price dispersion, providing a practical way to embed uncertainty into the learning process. To implement this, we design a twin-network architecture<\/strong> in Keras: one network learns price dynamics from time-based features, while the other learns volatility signals. Finally, we combine them via a stacking ensemble to achieve robust forecasts with confidence intervals.<\/p>\n\n\n\n

Why Volatility Variables Instead of Derivatives?<\/h2>\n\n\n\n