# Yolo WallStreet Model

- **Event:** [AI Engineer World's Fair Hackathon 2026](https://cerebralvalley.ai/e/aiewf-hackathon-2026)
- **When:** Jun 27 at 9:00 AM – Jun 28 at 5:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Kaushik Sivakumar](https://cerebralvalley.ai/u/kaushik07)
- **GitHub:** https://github.com/KaushikSiva/yolo-model
- **Demo video:** https://youtu.be/GUDagEwLa1c
- **Gallery:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/45

YOLO WallStreet is a modular AI trading research system built to forecast short-term stock moves by combining market data, time-series modeling, news ingestion, and large-language-model reasoning. The project is designed around a staged ensemble rather than a single monolithic model, so each component handles a specific part of the prediction problem.

  The core baseline starts with historical OHLCV market data and engineered technical features. A Chronos-based forecasting stage generates forward-looking time-series signals such as expected short-horizon
  returns, directional confidence, and bull/bear scenario estimates. These outputs are then combined with structured market features inside an XGBoost ensemble, which serves as the main numeric prediction
  engine for baseline return forecasting.

  A second pipeline focuses on fresh information. News headlines are collected per ticker through Bright Data, normalized, and stored incrementally so ingestion can resume without repeating completed work.
  These news items are transformed into model-consumable signals through a FinGPT-style feature extraction step. The intent is to detect sentiment, catalysts, novelty, company-specific relevance, and risk
  flags from recent headlines and combine them with the quantitative baseline.

  On top of the baseline forecaster, the system includes an LLM-based adjustment layer using Gemma. Instead of replacing the numeric model, Gemma acts as a bounded adjuster: it reads the baseline prediction,
  recent news context, volatility, and risk indicators, then outputs a constrained adjustment in basis points. This produces a final adjusted return estimate while preserving the baseline model’s stability.

  The repository also includes training scripts for GPU environments, export flows for Mac inference, a FastAPI serving path, model metadata tracking, resumable data pipelines, and production/candidate model
  directories. Overall, YOLO WallStreet is structured as an end-to-end experimental trading stack for building, training, evaluating, and serving a hybrid quantitative plus LLM-driven stock prediction
  system.

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