Regime-Aware Short-Term Trading Strategy Using Hidden Markov Models and Monte Carlo Simulation

Abstract

This paper presents a new short term trading strategy for financial markets that considers market regimes. It uses Hidden Markov Models (HMMs) to identify market states dynamically and Monte Carlo simulation for short term price forecasting. By modeling underlying market conditions (such as bull, bear, or stagnation) through a Gaussian HMM, the strategy adjusts its trading signals according to current market conditions. We also apply Monte Carlo simulation, specifically Geo- metric Brownian Motion (GBM), to predict 1,000 future 5 day price paths. This approach quantifies uncertainty and enables buy/sell decisions based on price percentiles. The strategy has been thoroughly backtested using daily adjusted closing prices of the NIFTY 50 index (NSEI) from January 2018 to December 2024. Backtesting results show a Sharpe Ratio of 1.0461, a Sortino Ratio of 1.5119, and a Cumulative Return of 44.83%. This significantly outperforms a traditional buy-and-hold strategy, which produced a Sharpe Ratio of about 0.67, a Cumulative Return of around 26.44%, and a Maximum Drawdown of roughly -38.44% during the same time, while achieving similar annualized returns but with notably lower volatility and drawdowns. This framework improves signal accuracy and risk management, offering a solid approach to short- term trading.