Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+Your H4 model is now performing quite well:
+
+- **Neutral (inside cloud, –1)**:  
+  - Precision 0.69 — when it predicts “neutral,” it’s correct 69% of the time.  
+  - Recall 0.43 — it only catches 43% of the actual neutral periods (you might tighten this).
+
+- **Downtrend (0)**:  
+  - Precision 0.81, Recall 0.93 — it’s very good at spotting bearish moves.
+
+- **Uptrend (1)**:  
+  - Precision 0.90, Recall 0.94 — excellent at capturing rallies.
+
+- **Overall accuracy: 84%** — a big jump from ~50% on 15 min data.
+
+This tells us that **Ichimoku cloud features** (plus the suite of other indicators) really helped the model understand trend context.
+
+---
+
+### Next steps you might consider:
+
+1. **Feature Importance**  
+   Plot a bar chart of your model’s feature importances to see which indicators are driving predictions.
+
+2. **Backtesting**  
+   Build a simple backtester that uses your predicted signals to simulate entry/exit and evaluate net returns.
+
+3. **Hyperparameter Tuning**  
+   Run a grid search or randomized search to squeeze out even more performance.
+
+4. **Visualization**  
+   Overlay your up/down/neutral predictions on a price+cloud chart for visual validation.
+
+Would you like to start with a **feature importance plot**? 🚀