kbhujbal

kbhujbal / AlphaAnalyst-open-source-autonomous-equity-research-agent

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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

ai-agents anthropic-claude autonomous-agent dcf equity-research
22
1
100% credibility
Found May 03, 2026 at 22 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AlphaAnalyst is an open-source web app that generates detailed equity research memos for US stocks, pulling data from filings, news, and markets to produce cited summaries, DCF valuations, and downloadable reports.

How It Works

1
🔍 Discover the tool

You find AlphaAnalyst, a free helper that creates stock reports just by typing a company name.

2
💻 Get it running

Follow simple steps to start the web page on your computer so you can use it anytime.

3
📈 Enter a stock ticker

Type something like TSLA and press go – that's all it takes to start your report.

4
⚙️ Watch it work

See the progress bar fill up as it gathers news, numbers, and insights automatically.

5
📋 Review your memo

Read the full report with summaries, risks, values, and every fact linked to its source.

✅ Download and decide

Save the PDF or editable spreadsheet to guide your stock picks with confidence.

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Star Growth

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AI-Generated Review

What is AlphaAnalyst-open-source-autonomous-equity-research-agent?

Enter a US stock ticker like TSLA into the Next.js frontend, and this Python FastAPI app delivers a full equity research memo: executive summary, financial snapshot, catalysts, DCF valuation with sensitivity grid, peer comps, news sentiment, earnings call tone analysis, bull/bear cases, and risks. Every number traces to a primary source via strict citations—no hallucinations. Exports include PDF reports and live-formula Excel models for entering stock prices or tweaking assumptions.

Why is it gaining traction?

The multi-agent pipeline pairs constructive LLMs with a GPT-4o devil's advocate for balanced views, while pure Python handles valuations to avoid LLM math errors. pgvector RAG indexes filings and transcripts for precise retrieval, and real-time job polling shows progress. Devs love the docker-compose setup (Postgres + Redis) and OpenAPI-typed frontend—fork the GitHub repo URL, enter stock symbols, and run evals to verify 99% numerical accuracy.

Who should use this?

Equity researchers or traders entering stock tickers for quick memos before entering the stock market. Devs building fintech tools who need an agent baseline for stock name or ISIN code analysis. Quant hobbyists tweaking DCF in Excel exports or scripting via API.

Verdict

Solid v0.1 for autonomous research—docs guide setup, pytest covers ~80%, evals enforce quality—but 22 stars and 1.0% credibility signal early maturity. Enter GitHub code to prototype; production needs multi-period DCF and backtesting first.

(198 words)

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