Core_Abstractions_Base_Components.md

graph LR
    Core_Abstractions_Base_Components["Core Abstractions & Base Components"]
    Portfolio_Optimization["Portfolio Optimization"]
    Financial_Estimators["Financial Estimators"]
    Risk_Measures["Risk Measures"]
    Portfolio_Management_Analysis["Portfolio Management & Analysis"]
    Model_Selection_Pre_processing["Model Selection & Pre-processing"]
    Optimization_Solvers["Optimization Solvers"]
    Utilities_Datasets["Utilities & Datasets"]
    Core_Abstractions_Base_Components -- "provides base classes to" --> Portfolio_Optimization
    Core_Abstractions_Base_Components -- "provides base classes to" --> Financial_Estimators
    Portfolio_Optimization -- "consumes inputs from" --> Financial_Estimators
    Portfolio_Optimization -- "utilizes" --> Risk_Measures
    Financial_Estimators -- "inherits from" --> Core_Abstractions_Base_Components
    Financial_Estimators -- "provides inputs to" --> Portfolio_Optimization
    Risk_Measures -- "provides functions to" --> Portfolio_Optimization
    Portfolio_Management_Analysis -- "uses" --> Risk_Measures
    Portfolio_Management_Analysis -- "receives from" --> Portfolio_Optimization
    Portfolio_Management_Analysis -- "may use" --> Financial_Estimators
    Model_Selection_Pre_processing -- "prepares data for" --> Financial_Estimators
    Model_Selection_Pre_processing -- "influences" --> Portfolio_Optimization
    Optimization_Solvers -- "executes problems for" --> Portfolio_Optimization
    Optimization_Solvers -- "may inherit from" --> Core_Abstractions_Base_Components
    Utilities_Datasets -- "provides support to" --> Core_Abstractions_Base_Components
    Utilities_Datasets -- "provides support to" --> Portfolio_Optimization
    Utilities_Datasets -- "provides support to" --> Financial_Estimators
    Utilities_Datasets -- "provides support to" --> Risk_Measures
    Utilities_Datasets -- "provides support to" --> Portfolio_Management_Analysis
    Utilities_Datasets -- "provides support to" --> Model_Selection_Pre_processing
    Utilities_Datasets -- "provides support to" --> Optimization_Solvers
    Utilities_Datasets -- "supplies data to" --> Financial_Estimators
    click Core_Abstractions_Base_Components href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/skfolio/Core_Abstractions_Base_Components.md" "Details"
    click Financial_Estimators href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/skfolio/Financial_Estimators.md" "Details"

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Details

Overview of skfolio's abstract components and their relationships.

Core Abstractions & Base Components [Expand]

This foundational component defines the abstract base classes and interfaces that ensure skfolio's compatibility with the Scikit-learn API and establish a consistent framework for all financial models, estimators, and optimization algorithms. It provides the architectural backbone for extensibility and modularity.

Related Classes/Methods:

• skfolio.optimization._base
• skfolio.prior._base (1:1)
• skfolio.distribution._base
• skfolio.moments.covariance._base
• skfolio.moments.expected_returns._base (1:1)
• skfolio.portfolio._base
• skfolio.uncertainty_set._base
• skfolio.distance._base (1:1)
• skfolio.typing

Portfolio Optimization

This component implements various portfolio optimization algorithms, allowing users to construct optimal portfolios based on different objectives and constraints. It leverages financial estimators and risk measures to achieve its goals.

Related Classes/Methods:

• skfolio.optimization (1:1)

Financial Estimators [Expand]

This component provides a suite of Scikit-learn compatible estimators for various financial quantities, including expected returns, covariance matrices, distributions, priors, uncertainty sets, and distance metrics. These estimators are crucial for generating inputs for portfolio optimization.

Related Classes/Methods:

• skfolio.moments.covariance (1:1)
• skfolio.moments.expected_returns (1:1)
• skfolio.prior (1:1)
• skfolio.distribution (1:1)
• skfolio.uncertainty_set (1:1)
• skfolio.distance (1:1)

Risk Measures

This component defines and implements various financial risk measures (e.g., VaR, CVaR, volatility) that can be incorporated into portfolio optimization problems or used for standalone risk assessment.

Related Classes/Methods:

• skfolio.risk_measures (1:1)

Portfolio Management & Analysis

This component handles the representation, manipulation, and performance evaluation of financial portfolios. It includes classes for portfolio objects and metrics for assessing portfolio performance.

Related Classes/Methods:

• skfolio.portfolio (1:1)
• skfolio.metrics (1:1)

Model Selection & Pre-processing

This component provides utilities for model selection, such as custom cross-validation strategies, and pre-processing transformers for financial data, ensuring robust and reliable model training and evaluation.

Related Classes/Methods:

• skfolio.model_selection (1:1)
• skfolio.pre_selection (1:1)

Optimization Solvers

This component encapsulates the interfaces and logic for interacting with underlying numerical optimization solvers (e.g., CVXPY, Clarabel). It provides an abstraction layer for different solver backends, promoting flexibility.

Related Classes/Methods:

• skfolio.solvers (1:1)

Utilities & Datasets

This component provides general utility functions, data validation routines, and example financial datasets to facilitate development, testing, and demonstration of the skfolio library.

Related Classes/Methods:

• skfolio.utils (1:1)
• skfolio.datasets (1:1)

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