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AlphaAnalyst Open Source Autonomous Equity Research Agent — Data skill for Claude Code

Data community

Enter a US stock ticker, get an analyst grade memo with DCF valuation, peer comparables, news sentiment, and earnings call tone analysis.

How to install AlphaAnalyst Open Source Autonomous Equity Research Agent

This entry records only its repository, not the path inside it, so there is no exact command to give. Open kbhujbal/AlphaAnalyst-open-source-autonomous-equity-research-agent and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What AlphaAnalyst Open Source Autonomous Equity Research Agent does

Enter a US stock ticker, get an analyst grade memo with DCF valuation, peer comparables, news sentiment, and earnings call tone analysis. Multi agent LLM pipeline (Claude + GPT-4o devil's advocate) with strict citation validation. FastAPI + Next.js + pgvector RAG. No hallucinated numbers

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README

AlphaAnalyst

AlphaAnalyst is an open-source autonomous equity research agent. Give it a US stock ticker and it produces a research memo: executive summary, financial snapshot, recent catalysts, DCF + comparables valuation, earnings-call tone shift, bull/bear cases, and risks — every numerical claim cited back to its primary source.

The pipeline runs ten data fetchers concurrently (SEC EDGAR, Polygon, FMP, Finnhub, MarketAux, Google News, FRED, Voyage embeddings, sec-api XBRL, FMP transcripts), indexes the long-form documents into pgvector, then runs six LLM agents — five "constructive" agents plus a Devil's Advocate forced to use a different model family for genuine independence. A pure-Python DCF in `decimal.Decimal` and a peer-comparable multiples engine produce the valuation. A synthesizer with a hard schema and a citation validator writes the final memo.

Two design choices anchor the project: **the LLM is a writer, not a knower** (numbers come from APIs; the synthesizer downgrades any section whose numerical claims aren't tagged to a real source) and **valuation is pure Python** (`decimal.Decimal` everywhere; no LLM ever touches an arithmetic operator). The result is a memo you can hand to an analyst and have them audit every number to a 10-K page.

Architecture

                     ┌──────────────┐
  ticker (e.g. TSLA) │   Frontend   │  Next.js 14 + TanStack Query
  ──────────────────►│  (App Router)│  Types codegen'd from /openapi.json
                     └──────┬───────┘
                            │ POST /api/v1/analyze
                            ▼
                     ┌──────────────┐
                     │   FastAPI    │  Lifespan, CORS, exception handlers
                     │ Orchestrator │  Per-step Redis progress writes
                     └──────┬───────┘
                            │ asyncio.gather
       ┌────────────────────┼─────────────────────────┐
       ▼                    ▼                         ▼
  ┌─────────┐         ┌──