FLSD Financial Dashboard

A financial data processing pipeline and dashboard system.

Features

• API-Based Data Submission: Upload CSV files via API endpoint
• Smart Data Processing: Automatic processing based on data type
• Interactive Dashboard: Visualize and explore processed data
• Type-Specific Visualizations: Different visualizations for financial, market, and forecast data

Getting Started

Prerequisites

• Python 3.9+
• Virtual environment (recommended)

Installation

1. Clone the repository

2. Create and activate a virtual environment:

   python -m venv venv
   # On Windows
   .\venv\Scripts\activate
   # On Linux/Mac
   source venv/bin/activate
   

3. Choose one of these installation methods:

   Option 1: Install using requirements.txt (Recommended for Development)

   pip install -r requirements.txt
   

   Option 2: Install as a package

   pip install -e .
   

   This will install the project as an editable package with command-line tools:

   • flsd-run: Run both API and dashboard
   • flsd-api: Run only the API server
   • flsd-dashboard: Run only the dashboard
   • flsd-pipeline: Run the nightly update pipeline

Running the Application

Run both the API server and dashboard:

python -m src.run_services

This will start:

• FastAPI server on http://localhost:8000
• Streamlit dashboard on http://localhost:8501

Data Pipeline

File Naming Convention

Files should follow this naming pattern:

{type}_{description}_{date}.csv

Where:

• type: Determines processing pipeline (financial, market, forecast)
• description: Brief data description
• date: Date in YYYYMMDD format

Examples:

• financial_quarterly_20231231.csv
• market_stocks_20240415.csv
• forecast_revenue_20240630.csv

API Usage

Upload a file:

curl -X POST -F "file=@your_file.csv" http://localhost:8000/upload/

Or use the Swagger UI at http://localhost:8000/docs

Expected Data Formats

Financial Data

• Required columns: date, amount
• Optional columns: any additional financial metrics

Market Data

• Required columns: date, price
• Optional OHLC data: open, high, low, close

Forecast Data

• Required columns: date, prediction
• Should include both historical and future dates

Dashboard

The dashboard automatically visualizes the latest data with type-specific visualizations:

• Financial: Line charts for amounts and running totals
• Market: Price charts, percent change analysis, and OHLC if available
• Forecast: Combined historical and forecast visualizations

For details on downloading nightly processed data and sharing the dashboard publicly, see docs/streamlit_deploy.md.

Directory Structure

flsd/
  ├── data/
  │   ├── raw/        # Raw uploaded CSV files
  │   └── processed/  # Processed data files
  ├── scripts/
  │   └── nightly_update.py  # Script for running nightly updates
  ├── src/
  │   ├── utils/      # Utility functions
  │   ├── api.py      # FastAPI server
  │   ├── dashboard.py # Streamlit dashboard
  │   ├── pipeline.py # Data processing logic
  │   └── run_services.py # Run both API and dashboard
  ├── requirements.txt
  ├── setup.py        # Package installation configuration
  └── README.md

Package Installation

The project can be installed as a package, which provides command-line tools for easier usage. To install it in development mode:

pip install -e .

This will make the following commands available:

• flsd-run: Run both API and dashboard
• flsd-api: Run only the API server
• flsd-dashboard: Run only the dashboard
• flsd-pipeline: Run the nightly update pipeline

Contributing

1. Fork the repository
2. Create your feature branch
3. Submit a pull request

License

This project is licensed under the MIT License.