# BOSS

Bag-of-SFA-Symbols classifier written from scratch in pure MQL5. Turns price
windows into words, and bags of words into a regime classifier.

Companion code for the MQL5 article: https://www.mql5.com/en/articles/23491

## What it does

Symbolic Fourier Approximation converts a price window into a short string:
the window goes through a Fourier transform, the low coefficients are kept, and
each is binned into a letter. Similar shapes become the same word, and noise
that does not change the shape drops out.

BOSS then counts words rather than comparing series point by point. That is
what makes it fast, and it is why one classifier is not enough: a single window
length sees one time scale, so the ensemble spans several.

Regimes are labelled without hand-labelling, which matters because hand-labels
are where this kind of study usually goes wrong.

Benchmarked against Dynamic Time Warping on BTCUSD, the ensemble wins on clean
accuracy and runs roughly twenty times faster. `BOSSvsDTWBenchmark.mq5`
reproduces that comparison, and `BOSSNoiseSweep.mq5` is the noise robustness
sweep.

## Layout

```
Include/BOSS/SFA.mqh                  Symbolic Fourier Approximation
Include/BOSS/FourierTransform.mqh     the transform behind SFA
Include/BOSS/BOSS.mqh                 bag-of-words model and ensemble
Include/BOSS/RegimeLabeler.mqh        unsupervised regime labels
Indicators/BOSS/BOSSRegime.mq5        the regime indicator
Scripts/BOSS/BOSSSelfTest.mq5         correctness checks
Scripts/BOSS/BOSSvsDTWBenchmark.mq5   accuracy and speed against DTW
Scripts/BOSS/BOSSNoiseSweep.mq5       robustness under added noise
```

Run `BOSSSelfTest.mq5` first. If it fails, nothing downstream is worth reading.

## 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.