Random Forest Market Regime Detection with Breadth-Based Position Sizing

Summary

This project describes a framework for classifying market conditions with a Random Forest and adapting capital allocation to the detected regime. It uses historical Nifty 500 data and market breadth features intended to capture cross-stock momentum, trend strength, volatility participation, and the share of stocks above moving averages. Four classes are defined: bull, bear, high volatility, and low volatility. Adaptive thresholds and a persistence filter are used to limit unstable classifications.

The project says it trains and evaluates the classifier with time-series validation, then raises allocation in low-volatility bull conditions and reduces exposure in volatile or bear conditions. It also mentions signal smoothing and transaction costs, and frames the goal as reducing drawdowns and improving risk-adjusted performance. The supplied text provides no sample dates, validation scores, return series, or quantified results, so it does not establish that the framework achieved those aims or would generalize beyond the index and data used.

Key ideas

• The framework classifies four market regimes from Nifty 500 breadth data using a Random Forest.
• Breadth features represent momentum, trend strength, volatility participation, and stocks above moving averages.
• Adaptive thresholds and a persistence filter aim to account for changing conditions and reduce noisy regime changes.
• Capital allocation is increased in low-volatility bull conditions and reduced in bear or high-volatility conditions.
• The project mentions time-series validation, transaction costs, and signal smoothing but gives no quantitative performance evidence.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.