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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)
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