Development

QuantCoderFS is a comprehensive quantitative finance platform that combines AI-powered equity research and algorithmic strategy generation. Built with FastAPI backend and Next.js frontend, it features two primary modules: Market Intelligence for multi-agent stock analysis with BUY/HOLD/SELL recommendations, and Strategy Generator for automated conversion of academic research papers into QuantConnect-compatible trading algorithms with ~90-95% success rate.

Key Features:

• Multi-agent AI stock analysis (fundamentals, news, price, macro)
• BUY/HOLD/SELL signals with conviction scores (1-10)
• Automated research paper to trading algorithm conversion
• QuantConnect Python code generation (5-phase AI workflow)
• Snapshot-based market analysis tracking
• Integration with multiple AI providers (OpenAI, Anthropic, Ollama)

Note: The GitHub repository contains version 1 of QuantCoderFS, prior to the deployment architecture for free and paid tiers. The current production version includes additional features and infrastructure for multi-tier service delivery.

AI-powered document management and query system for maritime conventions including SOLAS, MARPOL, STCW, and other IMO regulations. Uses RAG (Retrieval-Augmented Generation) with vector embeddings to provide accurate, citation-backed answers to regulatory questions. Built with FastAPI backend, React frontend, and Qdrant vector database.

Key Features:

• PDF document indexing with smart chunking (~800 chars)
• Semantic search with OpenAI embeddings (text-embedding-3-small)
• Inline PDF viewer with page navigation and zoom controls
• Chat interface with citation tracking (chapter, section, page)
• Support for 48+ maritime convention PDFs (SOLAS, MARPOL, etc.)
• Background indexing with Telegram notifications