An Unsupervised Intelligent System for Adaptive Decision Control in Non-Stationary Financial Environments

Intelligent decision systems assume a stable relationship between observations and outcomes. Financial markets violate that assumption, since shifts in volatility, trend and liquidity erode models trained on historical data. This study proposes an unsupervised decision system that restricts trading according to market conditions. Features from multiple time horizons are partitioned by walk-forward clustering into distinct market states, and execution is permitted only when the state satisfies predefined conditions, so that clustering governs when predictions are acted upon rather than how they are formed. The framework is evaluated on gold, Bitcoin, the S&P 500, Nasdaq 100, Dow Jones and the US dollar–Japanese yen exchange rate between January 2024 and December 2025, against a non-gated baseline with identical signals, risk controls and position sizing. Maximum drawdown falls on every asset, by half in several cases, whilst trade frequency declines by a third to two thirds; on the S&P 500 the Sortino ratio rises from 2.57 to 6.63 as maximum drawdown falls from 42.27% to 22.51%. A random-drop ablation shows the gain is not explained by trading less often, and bootstrap resampling indicates higher Sharpe ratios across all six markets, with a combined p-value of 0.0074. Results are gross of transaction costs.