Predicting EUR/USD With Hanning Windows

In the autumn of 2019 I was an undergrad with a few weekends free and the quiet conviction that I could find a small edge on EUR/USD. The screenshots were flattering: the prediction (blue) hugged the actual rate (green) in a way that looked like skill. A linear regression in the frequency domain, dressed up. I did not trade real money with it, and that restraint is the only thing about the project that aged well.

The pipeline:

• Smooth the input series.
• Differentiate.
• Short-time Fourier transform with overlapped, Hanning-windowed frames.
• Extrapolate the frequency-domain coefficients.
• Invert everything back to a predicted price series.

A Python server (NumPy, SciPy, Flask) ran the model. An MQL4 client on a broker terminal called the server and would have placed trades if I’d dared.

What I actually learned: even a naive model can show a sometimes-profitable backtest, and that’s the trap. The real game is built by people with co-located servers, microsecond ticks, and millions in infrastructure. This project taught me how far my edge wasn’t.