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27 lines
952 B
Markdown
27 lines
952 B
Markdown
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# How To Use These Templates
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Each folder is a PyP Quant project skeleton. Copy the files into a new PyP Quant project and run training from the dashboard.
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## Required Files
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- `strategy.py` contains `train()` and `predict()`.
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- `quant.config.json` declares pair, timeframe, model family, artifact format, and requirements.
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- `README.md` explains the project intent and tuning knobs.
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## Recommended Workflow
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1. Train the model.
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2. Run PPE simulation.
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3. Inspect trade count, drawdown, profit factor, win rate, and session behavior.
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4. Adjust label thresholds or signal gates.
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5. Train again.
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6. Deploy only after out-of-sample validation.
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## Common Tuning Knobs
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- `threshold`: minimum forward move for UP/DOWN labels.
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- `horizon`: bars ahead used for training labels.
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- `min_confidence`: inference confidence gate.
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- `lookback`: bars required before prediction.
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- `sl_percent` and `tp_percent`: stop-loss and take-profit defaults for simulations.
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