Financial series change their behavior over time and contain noise at many scales, which weakens standard linear forecasts. We present WaveESN–RegimeMLP, a modular pipeline that combines (i) wavelet-based multiscale features, (ii) a reservoir network with an elastic-net readout, (iii) a hidden-state model that detects market regimes from residuals, and (iv) genetic search for hyperparameters. Evaluated on daily prices of AAPL, DIA, SPY, and JPM from 1 January 2021 to 1 January 2022, the approach