From cdca57012a3c7ac7bcef351c64a36e20f77a0942 Mon Sep 17 00:00:00 2001 From: Andras Schmelczer Date: Sun, 12 Jul 2026 14:53:50 +0100 Subject: [PATCH] no forex --- .../foreign-exchange-prediction-experiment.md | 21 ------------------- 1 file changed, 21 deletions(-) delete mode 100644 src/content/work/foreign-exchange-prediction-experiment.md diff --git a/src/content/work/foreign-exchange-prediction-experiment.md b/src/content/work/foreign-exchange-prediction-experiment.md deleted file mode 100644 index 9debca2..0000000 --- a/src/content/work/foreign-exchange-prediction-experiment.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -title: Predicting EUR/USD With Hanning Windows -description: A weekend frequency-domain experiment that did a passable job on EUR/USD. I would not have trusted it with my money, and I didn't. -date: 2026-05-03 -period: 'Autumn 2019' -thumbnail: - src: ./_assets/forex.jpg - alt: Chart comparing predicted and actual EUR/USD exchange rates. -article: - tags: ['systems', 'tools'] - stack: ['Python', 'NumPy', 'SciPy', 'Flask', 'MQL4'] - outcome: A prediction server, an MQL4 trading client, and a clearer view of how far my edge wasn't -project: - title: Foreign Exchange Prediction Experiment ---- - -In the autumn of 2019 I was an undergrad with a few free weekends and the quiet conviction that I could find a small edge on EUR/USD. The screenshots that survive are flattering: the predicted rate in blue hugging the actual rate in green closely enough to look like skill. It was a linear extrapolation in the frequency domain wearing a nice coat. - -The pipeline: smooth the price series, differentiate it, run a short-time Fourier transform over overlapped Hanning-windowed frames, extrapolate the frequency-domain coefficients forward, then invert everything back into a predicted price. A Python server (NumPy, SciPy, Flask) served the model; an MQL4 client sitting in a broker terminal called it and stood ready to place trades, if I'd dared. I never dared, and that restraint is the part of the project that aged best. - -What the weekends actually bought me was a working understanding of the trap: even a naive model will hand you a sometimes-profitable backtest, and a sometimes-profitable backtest is the most persuasive wrong evidence there is. The people playing this game for real have co-located servers, microsecond ticks, and teams whose whole job is the thing I was doing between lectures. I didn't learn how to predict currencies. I learned, precisely and cheaply, how far my edge wasn't, which I've come to think is the best possible return on a project like this.