Small demo app and library to simulate ETF prices built from synthetic bond data.

This repository contains a compact Streamlit application and supporting modules to:

• generate synthetic bond data,
• build a simple ETF from a subset of bonds with randomized weights,
• compute ETF price (weighted sum of bond prices), and
• visualize and compare ETF compositions between two dates.

The app is intended as a concise coding interview project illustrating basic data modeling, small domain objects, and a Streamlit UI for interactive exploration.

• Generate 2·N random Bond objects with configurable price ranges.
• Build an ETF from a selected subset of bonds with random weights.
• Compute ETF price and pool value.
• Streamlit dashboard to run simulations and visualize:
  • ETF weights at two dates
  • Weight differences for common bonds
  • Bonds entering or exiting the ETF
  • Pool value comparison

• app.py — Streamlit app (UI). Entry point for interactive simulations and visualizations.
• main.py — Core helper functions: generate_bonds, generate_weights, build_etf, and a simple main() runner for quick testing.
• assets.py — Domain dataclasses: Bond and ETF with price property and extract_data() helper.
• images/ — UI assets (logo) referenced by app.py and the UML diagram generated with PlantUML.
• tests/ — Unit tests for assets.py and main.py to ensure correct behaviour. All tests can be run with pytest -q when the virtual environment is activated.
• requirements.txt — Python dependencies for running the app.

1. Recommended: create and activate a virtual environment.

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

2. Install dependencies:

pip install -r requirements.txt

3. Run the Streamlit app (from the project root):

streamlit run app.py

Open the URL printed by Streamlit (usually http://localhost:8501).

• In the app UI you can choose the number of bonds (N) and a price range for generated bonds.
• The app will generate 2·N bonds but build each ETF from N randomly sampled bonds — this permits comparing two ETF snapshots built from different random samples.

Key functions:

• main.generate_bonds(n, price_range) — returns List[Bond].
• main.build_etf(name, as_of_date, bonds) — returns an ETF instance.
• ETF.price — computed as the weighted sum of underlying bond prices.

Below is a rendered class diagram for the main domain model.

This project is a personal demo by Karl ODJO.