Overfitting occurs when a model:

1. _Becomes too complex_: Fits the training data too closely, memorizing noise and random fluctuations.

2. _Lacks generalization_: Performs poorly on new, unseen data, failing to adapt to changing market conditions.

Bias can arise from:

1. _Data quality issues_: Imbalanced, noisy, or incomplete data can lead to biased models.

2. _Algorithmic flaws_: Inherent biases in algorithms or design choices can affect model performance.

3. _Human prejudices_: Unintentional biases in data selection or labeling can influence model behavior.

To address overfitting and bias:

1. _Regularization techniques_: Use methods like L1/L2 regularization, dropout, or early stopping to prevent overfitting.

2. _Data preprocessing_: Ensure data quality, balance, and diversity to reduce bias.

3. _Ensemble methods_: Combine multiple models to improve generalization and reduce overfitting.

4. _Cross-validation_: Use techniques like k-fold cross-validation to evaluate model performance on unseen data.

5. _Diverse and representative data_: Include diverse market scenarios, assets, and conditions to minimize bias.

6. _Model interpretability_: Analyze and understand model decisions to identify potential biases.

7. _Human oversight and feedback_: Involve human traders and experts to detect and correct biases.

8. _Continuous monitoring and updating_: Regularly update and refine models to adapt to changing markets and minimize overfitting and bias.