miniAladdin

Inspiration

I built miniAladdin because I wanted to make financial research less overwhelming. Markets produce huge amounts of data, but it is often difficult to understand what actually matters and why a stock receives a particular signal.

What it does

miniAladdin is an AI-powered equity research platform that brings stock analysis into one place. It analyzes momentum, risk, trends, analyst expectations, news sentiment and macroeconomic conditions, then combines them into an explainable verdict. It also includes portfolio tracking, investigations and a Factor Lab for testing whether the signals actually have predictive value.

How I built it

I built miniAladdin as a full-stack application using Next.js and React for the frontend and FastAPI with Python for the backend. I used Pandas and NumPy for financial analysis and integrated market, macroeconomic and news data through APIs including Yahoo Finance, FRED and Alpha Vantage. I also built the factor research and validation pipeline to evaluate the signals instead of treating them as black boxes. The application is deployed using Vercel and Render with Supabase for persistence and authentication.

Challenges I ran into

The biggest challenge was working with real financial data and making the analysis reliable. I had to handle different data sources, missing data, point-in-time information, forward returns and avoid look-ahead bias. Deployment also introduced dependency and Python version issues that required debugging the backend environment.

Accomplishments that I'm proud of

I am proud that I built the entire platform from the ground up as a solo project. It went beyond a simple AI demo and became a working research terminal with live data, stock analysis, portfolio features, investigations and a dedicated Factor Lab for statistical validation.

What I learned

I learned that building financial AI is about much more than adding an AI model. Data quality, statistical validation, avoiding leakage and explaining results are equally important. I also gained experience building, testing and deploying a complete full-stack application with multiple real-world data sources.

What's next for miniAladdin

I want to make miniAladdin a much stronger AI research companion by adding deeper natural-language research, better portfolio-level insights, stronger factor testing and more intelligent explanations. The goal is to help users go from a market question to clear, evidence-backed research much faster.

Built With

• alpha-vantage
• fastapi
• fred-api
• github
• groq
• html
• javascript
• newsapi
• next.js
• numpy
• pandas
• postgresql
• pyarrow
• python
• react
• render
• rest-apis
• supabase
• tailwind-css
• typescript
• uvicorn
• vercel
• yfinance