MLCryptoForecasterICH.py

• 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.

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.

In all of our logging and modeling: