Discussing the article: "Variational Mode Decomposition: Building the Solver, and Measuring What Hindsight Buys"
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: Variational Mode Decomposition: Building the Solver, and Measuring What Hindsight Buys.
A from‑scratch VMD library for MetaTrader 5: radix‑2 transform, ADMM updates, mirrored windows, and a result type that reports solve quality and mode separation. We verify the solver to full double precision against a reference and test causality by sabotage. The article quantifies repaint from batch versus causal runs and compares it to volatility‑matched random walks. You get a causal indicator, usable diagnostics, and settings that avoid common pitfalls.
Price is a sum of things happening at different speeds: a multi-month trend, a multi-week swing, an intraday wobble, and noise that means nothing. Every trader draws that decomposition by eye on every change of timeframe. The question here is whether a machine can draw it, and what the drawing is worth once drawn.
The standard answers are unsatisfying. A moving average extracts a trend but lags it, throws the rest away, and needs you to pick the lookback. A Fourier transform assumes the frequencies never change, which is false for any market. Empirical Mode Decomposition is adaptive and popular, but it is a recipe rather than a stated problem and it suffers from mode mixing, one component quietly absorbing part of another.
Variational Mode Decomposition (Dragomiretskiy and Zosso, 2014) states a clear optimization: find K components that sum to the signal, each as narrow as possible around its own center frequency. It is solved by the alternating direction method of multipliers (ADMM) and yields clean bands with directly readable center frequencies. There is no VMD implementation on this site, so this article writes one.
The library is the first half; the second half decided how it had to be built. VMD is a batch algorithm: hand it a series and it optimizes over the whole thing at once, so a naive implementation decomposes the full chart history and plots the modes. The result looks convincing, fitting most historical turning points, and it is worthless, because every historical bar's mode value came from a window containing that bar's own future. This article measures how much of that picture is hindsight, on three real symbol/timeframe combinations against matched random walks.
Author: Hammad Dilber