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Architecture Overview

This page describes the overall architecture of the Stock Market Analysis Tool, including the main components, their interactions, and the data flow.

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🔹 High-Level Overview

The application follows a modular structure with clear separation between:

1. Data Layer – fetching, storing, and preprocessing financial data
2. Core Logic Layer – calculation of indicators, portfolio management, network analysis, and prediction
3. Visualization / UI Layer – Streamlit interface and interactive plots
4. Entry Point – main.py, which connects all modules

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🔹 Component Breakdown

1. Data Layer (/data)

• fetch_data.py: Retrieves stock and market data from external APIs (Yahoo Finance).
• /data_saved/: Static CSV or Parquet files for historical data, caching, and precomputed results.

Responsibilities:

• Provide clean, structured data to core modules
• Ensure data is valid and formatted for indicator and visualization modules

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2. Core Logic Layer (/core)

• indicators.py: Computes SMA, EMA, ATR, RSI, MACD, etc.
• market_screener.py: Screens market data, computes correlations, prepares input for heatmaps and network graphs.
• network_graphing.py: Builds interactive correlation networks using Plotly and Louvain clustering.
• portfolio.py: Tracks user-defined portfolios and calculates performance.
• prediction.py: Runs a deterministic forecasting model for price prediction.

Responsibilities:

• Perform all calculations and analyses
• Maintain modularity so each component can be extended independently
• Reference official documentation where applicable

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3. Visualization / UI Layer (/GUI)

• user_interface.py: Creates the Streamlit interface, handles user input, and routes actions to the core logic.
• colour_coding.py: Provides color schemes for heatmaps and other visuals.

Responsibilities:

• Render charts, heatmaps, networks, and tables
• Ensure interactive, user-friendly experience
• Separate UI logic from core calculations

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4. Entry Point (main.py)

• Initializes the Streamlit environment
• Imports core modules and GUI
• Handles user navigation between pages and visualizations

Responsibilities:

• Connect data layer, core logic, and UI
• Serve as the single entry point for developers and users

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🔹 Data Flow

1. User input: Ticker, portfolio data, or parameters via Streamlit UI
2. Data fetch: fetch_data.py retrieves data from Yahoo Finance or loads cached files
3. Processing: Core modules compute indicators, correlations, predictions
4. Visualization: Heatmaps, network graphs, charts, and portfolio results rendered in UI
5. Feedback: Users see results and can adjust input to repeat the cycle

Here you can see how the program files interact with eachother:

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🔹 Notes for Developers

• The modular design allows easy replacement or extension of:
  • Prediction methods
  • Visualization types
  • Data sources
• Each module should operate independently of UI logic
• All modules reference official documentation where applicable for correctness