graph LR
Data_Ingestion_Market_Data["Data Ingestion & Market Data"]
Core_Portfolio_Management["Core Portfolio Management"]
Financial_Analysis_Metrics["Financial Analysis & Metrics"]
Portfolio_Optimization["Portfolio Optimization"]
Visualization_Reporting["Visualization & Reporting"]
Data_Ingestion_Market_Data -- "populates" --> Core_Portfolio_Management
Core_Portfolio_Management -- "provides data to" --> Financial_Analysis_Metrics
Financial_Analysis_Metrics -- "updates" --> Core_Portfolio_Management
Core_Portfolio_Management -- "feeds data to" --> Portfolio_Optimization
Portfolio_Optimization -- "sends results to" --> Visualization_Reporting
Financial_Analysis_Metrics -- "sends metrics to" --> Visualization_Reporting
Core_Portfolio_Management -- "sends data to" --> Visualization_Reporting
click Core_Portfolio_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FinQuant/Core_Portfolio_Management.md" "Details"
click Financial_Analysis_Metrics href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FinQuant/Financial_Analysis_Metrics.md" "Details"
click Portfolio_Optimization href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FinQuant/Portfolio_Optimization.md" "Details"
click Visualization_Reporting href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FinQuant/Visualization_Reporting.md" "Details"
The FinQuant architecture is designed as a modular data processing pipeline centered around a robust Portfolio object. Data flows from external sources, through Data Ingestion & Market Data, into the Core Portfolio Management component, which encapsulates the investment portfolio. From this central hub, data is dispatched to specialized modules: Financial Analysis & Metrics for calculating key performance indicators and technical insights, and Portfolio Optimization for advanced risk-return analysis and simulation. The results from these analytical and optimization processes are then consumed by the Visualization & Reporting component, which generates comprehensive plots and visual summaries. This clear separation of concerns, with the Portfolio object acting as the primary data conduit, facilitates a logical and extensible design, ideal for representing as a flow graph where components clearly interact with and transform financial data.
Acquires and prepares raw financial data from external sources (e.g., Quandl, Yahoo Finance) or local dataframes.
Related Classes/Methods:
Core Portfolio Management [Expand]
The central component managing the investment portfolio, holding aggregated financial data, and serving as the primary interface for all analytical and optimization operations.
Related Classes/Methods:
Financial Analysis & Metrics [Expand]
Computes various financial metrics for assets and the overall portfolio, including returns, risk measures, and technical indicators.
Related Classes/Methods:
Portfolio Optimization [Expand]
Implements algorithms for portfolio optimization (e.g., Efficient Frontier) and Monte Carlo simulations to identify optimal risk-return trade-offs and simulate performance.
Related Classes/Methods:
Visualization & Reporting [Expand]
Handles the graphical representation of financial data, analytical results, and optimization outcomes.
Related Classes/Methods: