Details

The AlphaPy system is architected as a modular, pipeline-driven machine learning framework, designed to streamline quantitative finance and sports analytics workflows. At its core, the Workflow Orchestration component (alphapy/__main__.py, alphapy/market_flow.py, alphapy/sport_flow.py) orchestrates the entire process, initiating specific analytical pipelines. Data ingress is managed by the Data Management component (alphapy/data.py, alphapy/frame.py), which handles acquisition and loading. This raw data then flows into Feature Engineering (alphapy/features.py, alphapy/transforms.py) for transformation into a rich feature set. These features are consumed by Model Building & Optimization (alphapy/estimators.py, alphapy/optimize.py) to train and select machine learning models. Trained models are then passed to Model Persistence & Prediction (alphapy/model.py) for saving and generating predictions. The generated predictions are critical input for Backtesting & Strategy Simulation (alphapy/portfolio.py, alphapy/system.py), where trading strategies are rigorously evaluated. Finally, Reporting & Visualization (alphapy/plots.py, alphapy/analysis.py) provides comprehensive insights by consuming data and results from all key stages, offering visual and statistical analysis of the pipeline's performance. This clear component separation and sequential data flow are ideal for visual representation in a flow graph, highlighting the end-to-end ML pipeline.

Workflow Orchestration

The central coordinator for AlphaPy's machine learning pipelines. It manages the overall execution flow, including loading configurations, sequencing pipeline stages, and initiating specific market or sport analysis flows.

Data Management

Handles all aspects of data acquisition, loading, and persistence. This component is responsible for interacting with external data sources and managing the internal representation of data, primarily as pandas DataFrames.

Feature Engineering

Transforms raw or ingested data into a structured and enriched set of features suitable for machine learning models. This includes creating numerical, statistical, and interaction features, as well as applying various data transformations.

Model Building & Optimization

Encompasses the selection, configuration, training, and initial evaluation of various machine learning models. It includes functionalities for hyperparameter optimization to improve model performance.

Model Persistence & Prediction

Manages the lifecycle of trained machine learning models, including saving models to disk, loading them for later use, and generating predictions based on new input data.

Backtesting & Strategy Simulation

Simulates trading strategies based on the predictions generated by the machine learning models. This component manages portfolio positions, executes simulated trades, and tracks the performance of the trading system over historical data.

Reporting & Visualization

Generates comprehensive plots, charts, and reports for in-depth analysis of data characteristics, model performance metrics, and backtesting results. It provides visual insights into the effectiveness and behavior of the entire ML pipeline.