Python / AI / ML Case Study
Bull.AI
Bull.AI combines live market data, technical indicators, news, and machine learning models to turn complex stock analysis into clearer investor-friendly summaries.
- Live and historical stock data aggregation through yfinance.
- ML-based prediction support using XGBoost and ARIMA.
- LLM-generated summaries covering risks, opportunities, and outlooks.
Market data aggregation, technical indicators, and summary generation
yfinance, XGBoost, ARIMA, GPT/Claude, and GCP
Ongoing prototype development
How this case study was approached
The PDF positions Bull.AI as a stock analysis tool created for retail investors who struggle to read charts, fundamentals, and news across multiple sources.
LogicRays combined market data, technical analysis, news feeds, and language models into a single workflow that produces clearer short- and long-term stock summaries.
Project Goals
What the project needed to achieve
The PDF focuses on simplifying stock analysis for retail investors by combining data sources, prediction support, and easier summary generation in one platform.
Bring market data, fundamentals, technical indicators, and news into one analysis flow.
Use ML models to add prediction support for short- and long-term outlooks.
Translate complex stock signals into clearer human-readable summaries for investors.
Solution Approach
How the solution was shaped
The approach joined structured market data with unstructured news so the platform could produce clearer, more balanced stock commentary.
Retail investors were spending too much time interpreting stock charts, fundamentals, and news from disconnected sources.
Bull.AI aggregated market data, applied ML models, calculated technical indicators, and layered in news-based LLM summaries.
The result was a more balanced stock summary flow that made risks and opportunities easier to understand before taking action.
What the work focused on
- Fetched live and historical market data along with key fundamentals such as market cap, ROE, and margins.
- Applied technical indicators and ML models to support better short-term and long-term interpretation.
- Generated human-readable summaries that highlighted pros, cons, outlook, and investment risk.
How delivery stayed structured
- yfinance, XGBoost, ARIMA, and LLM-driven summary generation.
- FastAPI or Flask services backed by MongoDB or Postgres.
- BigQuery, Vertex AI, Cloud Functions, and cloud storage on GCP.
Why this delivery direction matters
The PDF illustrates Bull.AI through a Reliance stock example, where the platform highlighted strong fundamentals but also surfaced short-term overbought risk for a more balanced view.
- Helped avoid a risky short-term entry while preserving a longer-term watch view.
- Highlighted strong fundamentals such as market cap and profit margins.
- Flagged overbought RSI conditions and validated outlook with news analysis.
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