# NetworkMomentum

A research paper's network-momentum trend-following strategy, implemented
entirely in MQL5.

Companion code for the MQL5 article: https://www.mql5.com/en/articles/23763

## What it does

Momentum does not stay inside one market. If A reliably leads B, then A's trend
carries information about B's next move. The strategy trades that spillover.

Three pieces make it work. Derivative Dynamic Time Warping detects lead-lag
relationships between markets, comparing shapes rather than levels. A convex
optimisation then learns a weighted graph from those relationships, so the
network is fitted rather than hand-drawn. Momentum is propagated across that
graph and turned into a signal.

Returns are volatility-scaled before any of this, so a loud market does not
dominate the network for the wrong reason.

`NM_Native.mq5` is the Expert Advisor. The article covers the Strategy Tester
results, including where the approach struggles.

## Layout

```
Include/NetworkMomentum/NM_Config.mqh        configuration
Include/NetworkMomentum/NM_Matrix.mqh        matrix helpers
Include/NetworkMomentum/NM_Data.mqh          multi-symbol data handling
Include/NetworkMomentum/NM_Symbols.mqh       the basket
Include/NetworkMomentum/NM_Momentum.mqh      volatility scaling and oscillators
Include/NetworkMomentum/NM_DDTW.mqh          derivative dynamic time warping
Include/NetworkMomentum/NM_GraphLearner.mqh  convex graph learning
Include/NetworkMomentum/NM_Engine.mqh        ensemble, propagation, signal
Include/NetworkMomentum/NM_Live.mqh          chart-side layer
Experts/NetworkMomentum/NM_Native.mq5        the Expert Advisor
```

Put every basket symbol in Market Watch before running, since the EA pulls
history for all of them.

## Disclaimer

Educational code. Past behaviour of any model or dataset says nothing about
future results. Test on your own data and broker conditions before drawing
conclusions.