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license: apache-2.0
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# MLCryptoForecasterICH.py

H4 model is 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.

**Ichimoku cloud features** (plus the suite of other indicators) really helped the model understand trend context.

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### 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.

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### In all of our logging and modeling:

1 = Uptrend

0 = Downtrend

–1 = Neutral (price inside the Ichimoku cloud)