What this demo is about
Students explore how inflows, outflows, volatility, and scenario assumptions change the distribution of future liquidity outcomes.
Banking • Colab / Local Python • 20-30 min
Liquidity Management
Choose the run mode that fits your classroom: Colab for quick access, or local Python for full control over the generator and Monte Carlo simulation scripts.
Colab-ready notebook
Local Python workflow
Compute-heavy simulation
Learning objectives
- Interpret daily net-flow variability, not just totals.
- Understand why simulation produces ranges rather than one answer.
- Connect liquidity distributions to treasury policy and buffer decisions.
Run Modes and Expectations
Colab
Best for a classroom walkthrough with Python and charts but without local environment setup friction.
Local Python
Best for changing the generator, simulation count, report logic, or chart behavior directly.
Wait expectation
Monte Carlo runs can take noticeable time. That delay is part of the lesson: simulation depth has computational cost.
How to use it
- Use colab_demo.ipynb for the fastest classroom start.
- Use generate_synthetic_liquidity_data.py and liquidity_monte_carlo_simulation.py for local Python work.
- Explain to students that simulation results arrive as a distribution, not a single deterministic forecast.
This demo is intentionally not forced into a weak browser port yet. The Colab variant keeps the real compute path visible while avoiding local setup pain.