Treasury Analytics — Prototype

End-to-end treasury analytics data product: synthetic data warehouse, SSAS semantic layer, Dash web dashboard, and Jupyter notebooks.

Quick Start

Start from the extracted repository root. That folder should contain requirements.txt and the treasury-analytics/ directory.

1. Create and activate a virtual environment

Windows PowerShell:

python -m venv .venv
.\.venv\Scripts\Activate.ps1

macOS/Linux:

python3 -m venv .venv
source .venv/bin/activate

If PowerShell blocks activation, run:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned

2. Install dependencies

pip install -r requirements.txt

3. Generate the synthetic source data

python treasury-analytics/warehouse/treasury_mock_data.py

This creates CSV files under treasury-analytics/data/generated/.

4. Build the DuckDB warehouse

python treasury-analytics/warehouse/build_warehouse.py

This creates the local DuckDB database under treasury-analytics/warehouse/.

5. Start the dashboard

python treasury-analytics/dashboard/app.py

Open the app at:

http://localhost:8050

Notes

• No local .env file is required for the default setup.
• If python does not point to Python 3 on your machine, use python3 instead.
• Run data generation before the warehouse build, because the database loader expects the generated CSV files to exist first.

GitHub Pages

This repository now includes a static landing page at index.html that links to the two dashboard pages:

• Treasury_ALCO_Dashboard.html
• Treasury_Command_Center.html

To publish it with GitHub Pages:

1. Push the repository to GitHub.
2. In the repository, open Settings > Pages.
3. Set the source to deploy from the branch root, or keep the default branch root if your repo uses direct Pages publishing.
4. Wait for GitHub Pages to build, then open the Pages URL GitHub provides.

The .nojekyll file at the repository root prevents GitHub Pages from applying Jekyll processing to the static files.