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)