StockAI Terminal
An ML + LLM prototype for stock analysis: AdaBoost signals, walk-forward validated, explained by an LLM fed structured model output so it cites rather than hallucinates — and never gives buy/sell advice.
- AdaBoost
- Walk-forward CV
- Llama 3.3 · Groq
- GitHub Actions
- Streamlit
What it is
StockAI Terminal is an end-to-end ML + LLM prototype for stock analysis. I want to be precise about that word: it's a well-engineered prototype, not a production system. The value is in the explainability and the grounded LLM layer, not in the raw prediction accuracy.
The model
The signal model is AdaBoost, evaluated with walk-forward validation — train on the past, test on the next window, roll forward — across 31 stocks, 4 sectors, ~10 years, and roughly 74,000 datapoints.
Accuracy lands around 57%. I'm stating that plainly because it's the honest number: a few points above a coin flip on a genuinely hard problem. The point of the project isn't to beat the market — it's to make the model's reasoning legible.
What the model learned
The feature importances are the interesting part:
- —Volatility — 41.7%
- —PE ratio — 17.7%
- —MACD histogram — 16.9%
- —RSI — roughly 0%
Volatility dominates, while the much-loved RSI contributes essentially nothing. Surfacing that kind of finding is exactly what the explainability layer is for.
The LLM layer
A Llama 3.3 70B model, served via Groq, turns the model output into a readable analysis. Crucially, it's fed the structured model output — the predictions and feature importances — so it cites those numbers rather than hallucinating its own.
It never gives buy or sell advice. It explains what the model saw and why, and stops there.
Automation
The model retrains weekly via GitHub Actions, so the terminal stays current without manual intervention.