Liquidity Forecasting & Fund Allocation Platform

A production-style Python project for forecasting 30-day multi-currency liquidity and generating optimal fund allocation across currencies using SARIMAX time-series models.

Built with:

• Python
• Pandas / NumPy
• SARIMAX (statsmodels)
• Streamlit (Dashboard)
• ExcelWriter for reporting
• PyTest (Unit Tests)
• GitHub Actions (CI)
• Docker (optional)

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🚀 Project Overview

This project simulates and forecasts daily cashflows for multiple currencies (EUR, USD, JPY, BRL, INR, AUD), then optimizes capital allocation based on predicted liquidity needs.

It includes:

✔ Forecasting

SARIMAX models generate 30-day forecasts for each currency.

✔ Fund Allocation

Allocations computed using forecast magnitudes (probability-like weights).

✔ Dashboard

A Streamlit interface allowing users to:

• view historical data
• plot future liquidity forecasts
• export reports
• view allocation results

✔ Automation Outputs

Exports:

• Historical data
• Forecast data
• Allocation summary

to Excel using xlsxwriter.

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🏗 Project Structure

liquidity-forecasting/ ├── liquidity_forecasting/ │ ├── data.py │ ├── model.py │ ├── allocation.py │ ├── export.py │ ├── plotting.py │ └── init.py ├── tests/ │ ├── test_data.py │ ├── test_model_and_allocation.py ├── app.py ├── main.py ├── requirements.txt └── README.md

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⚙️ Installation

1. Clone the repository

(bash) git clone https://github.com/Andorta/LiquidityForecasting.git cd LiquidityForecasting

2. Install dependencies

(bash) python3 -m pip install -r requirements.txt

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🔄 Continuous Integration

GitHub Actions automatically runs:

dependency installation

test suite (pytest)

Python version matrix (3.9 & 3.11)

Workflow file: .github/workflows/ci.yml

📌 Future Improvements

Add SQL database ingestion

Add Docker image with production-ready Streamlit app

Hyperparameter tuning for SARIMAX

Stress-testing and scenario modelling

👤 Author

Andorta

Feel free to open issues or contribute!