FinWhale Trading Platform

Overview

The project involved designing and shipping FinWhale, an AI-native financial research platform that fuses a multi-tool conversational agent with structured market-data tools. The platform combines a streaming agent chat backed by LangGraph Cloud, a tabular Data Lens heatmap for cross-ticker quantitative analysis, candlestick Charts with bidirectional daily/intraday linking, and Custom ETF construction. The agent autonomously performs SEC EDGAR filing extraction, SQL queries against years of OHLC market history, web research, and Python execution inside isolated E2B sandboxes - turning hours of manual research into a single conversation.

Achievements

Took FinWhale from spec to a production-grade V1 covering authentication, agent chat, code execution, agent memory, Data Lens, Charts, custom ETFs, cross-surface symbol navigation, and search across chat history. The agent operates on a TimescaleDB warehouse of millions of split-adjusted OHLC bars with sub-second SQL responses, while the chat surface streams tokens, tool calls, and citations in real time through a backend proxy on AWS EC2. Per-user usage tracking and an LLM fallback chain (Claude Sonnet → Gemini → GPT) keep cost and reliability predictable under a $200/month per-user token cap.

Responsibilities

• Architected a multi-tool LangGraph Cloud agent with specialized capabilities for SEC filing extraction, SQL over TimescaleDB, web research, and sandboxed Python — all surfaced to the user as a real-time execution trail.
• Designed the TimescaleDB market warehouse with split-adjusted daily and intraday continuous aggregates, and enforced read-only schema-scoped access so the SQL agent can never mutate production data.
• Built the E2B sandbox layer with a custom Python template so each chat thread gets an isolated, package-preloaded execution environment with file uploads, artifact persistence, and best-effort cleanup on deletion.
• Implemented the Data Lens — a tabular heatmap with per-ticker filters, computed columns, and cross-ticker correlation analysis — wired into the agent via "Open in Data Lens" from any thread's detected tickers.
• Built the streaming chat end-to-end: SSE proxy on AWS EC2, inline ticker extraction and search indexing, citations with hover tooltips and a Sources panel, agent interrupt, and Supabase Auth with magic-link onboarding.

Technologies Used