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63 lines
2.7 KiB
Markdown
63 lines
2.7 KiB
Markdown
# PyP Quant Trading Strategy Templates
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Open-source Python quant trading strategy templates for PyP Quant Mode.
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This repository is a public, educational starter library for traders who want to build quantitative trading strategies with Python, validate them with PyP PPE simulation, and deploy live signals through the PyP platform.
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These examples are intentionally safe starter projects. They are not financial advice, not live performance claims, and not recommendations to trade any instrument.
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## What Is Inside
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Each template follows the PyP Quant Mode contract:
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```python
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def train(data, config):
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return model, metrics
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def predict(model, market_data, config):
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return {"signal": "UP|DOWN|HOLD", "confidence": 0.0, "metadata": {}}
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```
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Every project includes:
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- `strategy.py`
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- `quant.config.json`
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- `README.md`
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## Templates
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| Template | Pair | Timeframe | Model family | Use case |
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| --- | --- | --- | --- | --- |
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| `eurusd-logistic-15m` | EURUSD | 15m | sklearn | Baseline directional classifier |
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| `eurusd-xgboost-1h` | EURUSD | 1h | XGBoost | Feature-rich trend classifier |
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| `gbpusd-breakout-rf` | GBPUSD | 30m | sklearn RandomForest | Range breakout classifier |
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| `usdjpy-mean-reversion` | USDJPY | 1h | sklearn | Mean reversion baseline |
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| `xauusd-regime-xgboost-v11` | XAUUSD | 1h | XGBoost | Less restrictive Au-79-style gold model |
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| `xauusd-atr-breakout` | XAUUSD | 15m | custom Python | ATR breakout rules |
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| `btcusdt-lightgbm-1h` | BTCUSDT | 1h | LightGBM | Crypto trend classifier |
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| `ethusdt-volatility-classifier` | ETHUSDT | 30m | sklearn | Volatility regime classifier |
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| `solusdt-scalp-baseline` | SOLUSDT | 1m | custom Python | High-volatility scalp baseline |
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| `onnx-export-sklearn-starter` | EURUSD | 1h | sklearn to ONNX | ONNX export starter |
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| `statsmodels-arima-direction` | EURUSD | 1h | statsmodels | Statistical direction baseline |
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| `lightgbm-fx-multifeature` | EURUSD | 30m | LightGBM | Multi-feature FX classifier |
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## Use With PyP
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1. Open PyP Quant Mode:
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https://pyp.stanlink.online/projects/quant/new
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2. Create a new quant project.
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3. Copy a template's `strategy.py` and `quant.config.json`.
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4. Run a training job.
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5. Validate with PPE simulation.
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6. Deploy only after the strategy produces acceptable out-of-sample behavior.
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## PyP Links
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- Quant landing page: https://pyp.stanl.ink/for-quant-traders
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- Quant docs: https://pyp.stanl.ink/docs/quant/what-is-quant-mode
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- Create a quant project: https://pyp.stanlink.online/projects/quant/new
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## Risk Disclaimer
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Trading foreign exchange, CFDs, crypto, and leveraged products involves substantial risk. These templates are educational examples only. Past performance and simulated results do not guarantee future performance.
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