A Simulated Quantum Reservoir for Adaptive Trading Signals

Summary

The article presents an MQL5 trading system based on Quantum Reservoir Computing. A fixed four-qubit reservoir maps market features into a larger state representation, while learning updates the output layer using a buffer of recent examples. The proposed system uses Monte Carlo simulation rather than specialized quantum hardware, extracts normalized price, volatility, momentum, time-cycle, moving-average deviation, and return features, and converts predictions into directional trades. Position size depends on confidence and risk limits; the design also describes prediction review, anomaly filters, and reduced sizing after poor results.

The article reports a broker test on EURUSD at M15 over 2017–2025, using generated tick data from OHLC, and gives performance figures including a Sharpe ratio and win rate. These are author-reported results for a particular configuration, not independent validation. The proposed quantum benefits are claims rather than demonstrated comparisons against classical models, and the supplied evidence does not establish robustness across instruments, periods, costs, or live execution. Its adaptive learning design and test setup therefore warrant careful out-of-sample replication.

Key ideas

• The proposed reservoir is fixed while the output layer learns from a rolling, weighted experience buffer.
• Market features are normalized and encoded into a simulated four-qubit state representation.
• The trading system uses prediction confidence for position sizing and limits itself to one open position.
• The article reports one EURUSD M15 test using generated tick data, but does not provide independent validation or broad robustness evidence.
• Claims of quantum advantage require comparison with suitable classical baselines and out-of-sample testing.

Tags

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