feat: add PyP quant strategy templates

docs/how-to-use.md

@@ -0,0 +1,26 @@
+# How To Use These Templates
+
+Each folder is a PyP Quant project skeleton. Copy the files into a new PyP Quant project and run training from the dashboard.
+
+## Required Files
+
+- `strategy.py` contains `train()` and `predict()`.
+- `quant.config.json` declares pair, timeframe, model family, artifact format, and requirements.
+- `README.md` explains the project intent and tuning knobs.
+
+## Recommended Workflow
+
+1. Train the model.
+2. Run PPE simulation.
+3. Inspect trade count, drawdown, profit factor, win rate, and session behavior.
+4. Adjust label thresholds or signal gates.
+5. Train again.
+6. Deploy only after out-of-sample validation.
+
+## Common Tuning Knobs
+
+- `threshold`: minimum forward move for UP/DOWN labels.
+- `horizon`: bars ahead used for training labels.
+- `min_confidence`: inference confidence gate.
+- `lookback`: bars required before prediction.
+- `sl_percent` and `tp_percent`: stop-loss and take-profit defaults for simulations.