Details

The AlphaPy system is structured around a clear data flow, beginning with the Data Acquisition & Standardization component, which ingests and transforms raw external data into a standardized format. This processed data is then managed and persisted by the Data Frame Management & Persistence component, acting as the central data store and access layer for the entire system. Finally, the standardized data frames are consumed by the Machine Learning Pipeline component, where core machine learning tasks such as model training, evaluation, and optimization are performed, enabling AlphaPy's predictive capabilities. This modular design ensures efficient data handling and a streamlined machine learning workflow.

Data Acquisition & Standardization

This component is the entry point for external data into the AlphaPy system. It is responsible for connecting to various external data sources (e.g., Quandl, Yahoo Finance), retrieving raw data, and transforming it into a standardized pandas DataFrame format. It also performs initial domain-specific enhancements, particularly for market data, ensuring the data is clean and ready for further processing.

Data Frame Management & Persistence

This component manages the internal representation, loading, and persistence of data, primarily as collections of pandas DataFrames. It provides robust mechanisms to load and save these data frames, enforcing consistent naming conventions and facilitating efficient data access throughout the AlphaPy system. It acts as the internal data store and access layer, ensuring data availability and integrity for all other components.

Machine Learning Pipeline

This component encompasses the core machine learning functionalities of AlphaPy. It is responsible for preparing data for model training, defining and training various machine learning models, evaluating their performance, and optimizing model parameters. It leverages standardized data frames from the Data Frame Management & Persistence component to build and deploy predictive models.