GeminiQuant

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for GeminiQuant

I've created a comprehensive project story for GeminiQuant! The file PROJECT_STORY.md covers all the sections you requested:

✅ Inspiration — Solving the black-box problem in quantitative finance using AI-generated explainability

✅ What It Does — Multi-model forecasting (LightGBM, LSTM, TimeXer, PatchTST) + Gemini-powered Q&A + automated PDF reports

✅ How We Built It — Detailed architecture overview, tech stack, and development timeline across 4 phases

✅ Challenges We Ran Into — Including feature leakage with news data, LSTM fusion complexity, time-series evaluation discipline, Gemini function calling, transformer integration, and cold-start deployment

✅ Accomplishments We're Proud Of — End-to-end pipeline, rigorous time-series methodology, LLM explainability at scale, multi-model architecture, automated reports, and solo full-stack development

✅ What We Learned — Key insights about LLMs for explainability, time-series discipline, feature engineering importance, function calling reliability, and deployment constraints

✅ What's Next — Short-term (live market integration, portfolio insights), medium-term (multi-asset, active learning, RL), and long-term (commercial deployment, regulatory compliance, research contributions)

Built With

• and
• data
• manipulation
• numpy
• pandas
• patchtst)
• pytorch-?-deep-learning-framework-(lstm-models)-lightgbm-?-gradient-boosting-for-structured-data-transformers-(hugging-face)-?-pre-trained-models-(includes-timexer