Running the Example Notebooks¶

One runnable Marimo notebook is included in the repo. No QuantConnect account is required.

Prerequisites¶

Python 3.11+
Git

Setup¶

Clone the repo and install notebook dependencies:

git clone https://github.com/WolfpackOfOne/Q-agent.git
cd Q-agent
python -m venv infrastructure/marimo/venv
source infrastructure/marimo/venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r infrastructure/marimo/requirements.txt

Election & Industry Returns¶

Explores the relationship between Trump 2024 election probability (Polymarket) and US sector/industry ETF returns (yfinance: XLE, XLF, XLV, XLI, XLK, XLP, XLY, XLU, XLB, XLRE, XLC, plus Trump-themed slices XOP, ITA, KBE, IBB, ICLN, TAN, GDX, ITB).

No setup beyond the venv is required. The Trump-probability series is read from the committed MyProjects/ElectionIndustryBeta/data/trump_prob.csv, and the ETF prices are fetched live from yfinance.

source infrastructure/marimo/venv/bin/activate
marimo run infrastructure/marimo/notebooks/election_industry_returns.py --port 2719

Open: http://localhost:2719

Notebook setup walkthrough

Sample outputs¶

Industry & Sector ETF Sensitivity to Trump Election Probability

OLS betas for 19 ETFs regressed on daily ΔP(Trump win). Green = Trump-benefiting, red = Trump-hurting. Stars indicate 5% significance.

Industry betas bar chart

Factor ETF Sensitivity to ΔP(Trump Win)

Monthly betas for 8 US equity factor ETFs. Faded bars have p > 0.10. Scatter insets show the top-3 ETFs by absolute t-stat.

Factor ETF betas