# signal-classification

A skill that builds ML trading signal classifiers on XGBoost and LightGBM, with walk-forward validation, SHAP importance and threshold tuning.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 516 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/signal-classification/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/signal-classification

## What it does

The skill covers the whole pipeline: forward returns become up/down labels with the neutral zone dropped, then XGBoost or LightGBM trains on each window and predictions are stitched together out of sample. Walk-forward matters most here. Plain cross-validation shuffles rows and hands the model its own future, so windows move through time with an embargo gap matching the prediction horizon.

Ready-made pieces ship with it: `create_binary_labels` for labels, `walk_forward_splits` for the windows, an `XGBClassifier` training example, and probability calibration through `CalibratedClassifierCV` with the isotonic method. SHAP is wired in too — `shap.TreeExplainer` gives both global feature importance and a breakdown of a single prediction. Deeper notes live in `references/validation_methods.md` and `references/model_guide.md`.

Raw boosted probabilities are poorly calibrated: a 0.7 output is not a 70% hit rate, so tuning a threshold before calibrating is wasted work. The multi-class setup splits samples across classes, and the skill advises it only with 1000+ samples per class. You need `xgboost` or `lightgbm`, `scikit-learn` and `shap` available.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill signal-classification -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/signal-classification/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/signal-classification/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill signal-classification -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/signal-classification/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill signal-classification -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/signal-classification/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills signal-classification
```

2. **Manually: put the files in place**
- File: `.github/skills/signal-classification/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill signal-classification -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/signal-classification
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/signal-classification/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill signal-classification -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/signal-classification/SKILL.md

## When to use it
- label forward returns as up/down and drop the neutral band
- split history into walk-forward windows with a leakage-blocking gap
- train XGBoost or LightGBM on tabular features without scaling
- calibrate model probabilities before picking a decision threshold
- explain with SHAP which features drive one prediction

## Example prompts
- build labels from forward returns with a 24-bar horizon and a 1% threshold
- run walk-forward validation with a 720-bar train window and a 24-bar gap
- plot a SHAP summary of the top 15 features for the trained model

## Source text · SKILL.md · MIT

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````text
---
name: signal-classification
description: ML trading signal classifiers using XGBoost and LightGBM with walk-forward validation, SHAP feature importance, and threshold optimization
---

# Signal Classification

Predict whether an asset's price will move up or down over a forward horizon using supervised machine learning classifiers. This skill covers the full pipeline: label creation, model training, walk-forward validation, feature importance analysis, and threshold optimization for trading applications.

## Why Tree-Based Models Dominate Trading ML

XGBoost and LightGBM are the workhorses of quantitative trading ML for good reason:

- **Non-linear relationships**: Financial features interact in complex, non-linear ways that trees capture naturally
- **Robust to feature scale**: No need to normalize or standardize inputs — trees split on rank order
- **Built-in feature importance**: Understand which features drive predictions without separate analysis
- **Fast training and inference**: Train on thousands of samples in seconds, predict in microseconds
- **Handle missing values**: Native support for NaN without imputation hacks
- **Regularization built in**: max_depth, min_child_weight, subsample all prevent overfitting

Linear models and deep learning have their place, but for tabular trading features with fewer than 100k samples, gradient-boosted trees consistently outperform alternatives.

## Classification Types

### Binary Classification

The simplest and most common setup. Predict whether forward returns exceed a threshold:

- **Up signal**: forward return > +1%
- **Down signal**: forward return < -1%
- **Neutral (excluded)**: -1% to +1% — drop these from training to create cleaner labels

```python
import numpy as np

def create_binary_labels(
    prices: np.ndarray, horizon: int = 24, threshold: float = 0.01
) -> np.ndarray:
    """Create binary labels from forward returns.

    Args:
        prices: Array of prices.
        horizon: Forward return lookback in bars.
        threshold: Minimum return magnitude for a label.

    Returns:
        Array of labels: 1 (up), 0 (down), NaN (neutral).
    """
    fwd_returns = np.roll(prices, -horizon) / prices - 1
    fwd_returns[-horizon:] = np.nan
    labels = np.where(fwd_returns > threshold, 1,
````
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