xsa-dev
commented
2 months ago Upon initial inspection, it may not be immediately visible, but all of these elements must be effectively executed.
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Data Collection and Processing:
- Development of modules for integration with data sources, including APIs for getting real-time signals.
- Data cleaning and normalization to eliminate gaps, noise, and anomalies.
- Time series processing and feature engineering based on historical data.
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Modeling and Training:
- Selection and integration of a machine learning library (e.g., scikit-learn, TensorFlow, PyTorch).
- Implementation of automated model hyperparameter selection and tuning (GridSearch, Random Search, Bayesian Optimization).
- Support for multiple types of models (linear regression, decision trees, neural networks, etc.) and the ability to easily compare them.
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Validation and Testing:
- Implementation of a cross-validation mechanism and data splitting into training and test sets.
- Automated model testing on historical data to assess its accuracy and robustness.
- Visualization of test results and output of key metrics (MAE, RMSE, R^2, etc.).
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Deployment and Monitoring:
- Creation of infrastructure for deploying trained models on real-time data.
- Setting up a system for monitoring model performance and alerting in case of prediction quality degradation.
- Support for model retraining on new data according to a schedule or in response to market condition changes.
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Interface and Management:
- Development of a user interface for easy management of the model training and monitoring process.
- Integration with version control systems to track changes in models and data.
- Support for a sandbox mode where users can experiment with different models and settings without risking production systems.
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Documentation and Security:
- Preparation of documentation for developers and end-users.
- Implementation of security measures, including access control, data encryption, and protection against unauthorized use.
virtualpeer
Biotiger-34
Feature to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, this aims would be a sandbox for easily deploying robust machine learning libraries on real-time data.