Forecast-Integrated Trading Algorithms with Adaptive Risk Management: Multi-Asset Empirical Evaluation
TL;DR
The results indicate that the value of machine learning forecasts in trading depends less on raw predictive accuracy than on the risk-management logic wrapped around it, and that this advantage is statistically robust across assets, regimes, and specification choices rather than an artifact of a single favorable sample.
Abstract
Predictive accuracy is often treated as a sufficient condition for profitable algorithmic trading, yet whether machine learning forecasts translate into trading performance that holds up across different market regimes remains contested. This study develops and stress-tests a prediction-driven, dynamically optimized trading framework across twelve instruments spanning equities, commodities, foreign exchange, and cryptocurrencies, and three structurally distinct regimes: a calm market (2018), the COVID-19 crisis (2020), and the Russian–Ukrainian geopolitical shock (2022). Daily price forecasts from RNN, LSTM, GRU, and hybrid architectures feed a rule-based framework that opens long or short positions from the divergence between predicted and observed prices, applies volatility-adjusted stop-loss and take-profit thresholds optimized by Sharpe-ratio grid search, and is extended with a rolling-MAPE confidence filter and an error-based dynamic position-sizing rule. Across the resulting 36 asset-period combinations, the prediction-based strategies outperformed the buy-and-hold benchmark in 86–92% of cases on cumulative return and 92% of cases on the Sharpe ratio; a one-sided binomial sign test rejects the null of no systematic advantage at p < 0.001 for every strategy variant, and the pattern is stable when each of the three regimes is examined separately (10–11 of 12 assets per period). Excluding crude oil—the only instrument where active strategies persistently underperformed, plausibly reflecting structural breaks such as the negative 2020 futures prices—raises the win rate to roughly 97%. Median outperformance reached about 15 percentage points in cumulative return and close to two Sharpe-ratio points, with maximum drawdowns falling in almost every case. The rolling-MAPE filter was the most effective mechanism for containing losses in turbulent regimes, while the position-sizing variant delivered the strongest average risk-adjusted returns. These results indicate that the value of machine learning forecasts in trading depends less on raw predictive accuracy than on the risk-management logic wrapped around it, and that this advantage is statistically robust across assets, regimes, and specification choices rather than an artifact of a single favorable sample.