Finance Tools MCP

# Model Evaluation Findings and Development Guide ## Overview This report summarizes the evaluation of the stock market prediction model, focusing on the performance metrics under both default and F2-score optimized thresholds. The objective is to document the findings and provide guidance for future development. ## Model Performance Summary ### Default Threshold (0.5) The model, when evaluated with a default classification threshold of 0.5, demonstrated a balanced performance across key metrics: - **Accuracy:** 0.8065 - **Confusion Matrix:** - True Positives (TP): 40 - True Negatives (TN): 35 - False Positives (FP): 9 - False Negatives (FN): 9 - **Classification Report:** - Precision (Class 0): 0.80 - Recall (Class 0): 0.80 - F1-score (Class 0): 0.80 - Precision (Class 1): 0.82 - Recall (Class 1): 0.82 - F1-score (Class 1): 0.82 This configuration provides a good balance between correctly identifying positive cases (recall) and minimizing false alarms (precision), making it suitable for general applications where overall accuracy is a priority. ### Tuned Threshold (Optimized for F2-score: 0.1922) To optimize for F2-score (which weights recall twice as much as precision), the threshold was tuned to 0.1922. This resulted in: - **Maximum F2-score:** 0.9038 - **Corresponding Precision:** 0.7344 - **Corresponding Recall:** 0.9592 When the model was re-evaluated with this tuned threshold: - **Accuracy:** 0.7957 (slightly lower than default) - **Confusion Matrix:** - True Positives (TP): 47 - True Negatives (TN): 27 - False Positives (FP): 17 - False Negatives (FN): 2 - **Classification Report:** - Precision (Class 0): 0.93 - Recall (Class 0): 0.61 - F1-score (Class 0): 0.74 - Precision (Class 1): 0.73 - Recall (Class 1): 0.96 - F1-score (Class 1): 0.83 This tuning successfully increased recall for the positive class (Class 1) significantly, reducing false negatives. However, it came at the cost of increased false positives and a slight decrease in overall accuracy. ## Feature Importance The analysis of feature importances revealed that technical indicators are the primary drivers of the model's predictions. The top features include: - `MACD_Volume_Interaction` - `EMV_MA` - `MACD_Hist` - `Close_Avg_10D` - `Volume_Avg_10D` This suggests that the model is effectively leveraging established technical analysis patterns to make predictions. ## Conclusion and Development Guide Based on the user's preference for higher accuracy, the **default threshold (0.5) is recommended for future model deployment and usage.** This setting provides a robust and balanced performance, minimizing both false positives and false negatives. ### Future Development Considerations: 1. **Model Calibration:** The low optimal threshold (0.1922) for F2-score optimization suggests that the model's raw probability outputs might be conservative. Investigating and implementing probability calibration techniques (e.g., Platt scaling, isotonic regression) could improve the interpretability of the predicted probabilities, even if the default threshold is preferred for decision-making. 2. **Business Objectives Alignment:** Continuously review the business objectives to ensure the chosen threshold (default 0.5) aligns with the risk tolerance and desired outcomes (e.g., minimizing missed opportunities vs. minimizing incorrect trades). 3. **Feature Engineering Enhancement:** While current features are effective, explore additional features or combinations that could further enhance predictive power, especially those related to market sentiment, macroeconomic indicators, or alternative data sources. 4. **Model Monitoring:** Implement continuous monitoring of model performance in production to detect any degradation over time and trigger retraining or recalibration as needed.