OpenSourceAGI
ai-broker-investing-agent
Python

๐Ÿ’ฑ Invest with news debate agents, ๐Ÿค‘ algorithmic entry/exit strategies, ๐Ÿ’น execute on Alpaca, ๐Ÿ”ฎ copy trade Polymarket/Kalshi prediction markets

Last updated Jul 3, 2026
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README

Join Discord GitHub Stars GitHub Discussions GitHub last commit Next.js

PRs Welcome

๐Ÿ“‘ Docs ๐ŸŽฏ API ๐Ÿš€ Website

Get it on Google Play

Investment Prediction Agent

AI-powered multi-agent trading system for comprehensive stock & prediction markets analysis and automated trading decisions.

๐Ÿš€ Overview

Auto Investment Broker combines specialized LLM agents to analyze markets, debate strategies, and execute trades. It features real-time data processing, a "Bull vs. Bear" debate engine, and a modern dashboard for visualization.

โœจ Key Features

  • Multi-Agent Architecture: LLM agents including Fundamentals, News, Technical, and Risk Managers.
  • Top Traders Leaderboard: Real-time tracking of top performers from ZuluTrade and Polymarket.
  • Interactive Dashboard: Modern UI with specific agent reports, history tracking, and technical charts.
  • "Bull vs. Bear" Debates: Automated debates to assess risk and reward before every trade.

๐Ÿค– AI Agents & Strategies

| Agent/Team | Role | | :-------------------------- | :-------------------------------------------------------------------------- | | Analyst Team | Gathers data: Fundamentals, Sentiment (Social), News, & Technical Analysis. | | Researcher Team | Conducts "Bull vs. Bear" debates; assesses risk. | | Trader Agent | Synthesizes reports to propose trades. | | Portfolio Manager | Final decision maker; manages risk and position sizing. |

Analysis Team

  • Market Analyst: Technical analysis and liquidity assessment
  • Sentiment Analyst: Social media sentiment and "undiscovered" status
  • News Analyst: Recent events, catalysts, and jurisdiction risks
  • Fundamentals Analyst: Financial scoring and valuation

Research Team

  • Bull Researcher: Advocates for BUY opportunities
  • Bear Researcher: Identifies risks and thesis violations
  • Research Manager: Synthesizes debate and enforces thesis compliance

Execution Team

  • Trader: Proposes execution parameters
  • Risk Team (Risky/Safe/Neutral): Debates position sizing
  • Portfolio Manager: Final authority on all trading decisions

โ˜๏ธ Deploy to Cloudflare Workers

The app runs on Cloudflare Workers via OpenNext, with D1 as the database, better-auth for authentication, and Email Workers for transactional email (verification, password reset, invitations).

# Local dev (Next.js dev server; D1 & email bindings proxied via miniflare)
npm run dev

Apply drizzle migrations to D1

npm run db:migrate:local # local miniflare D1 npm run db:migrate:remote # production D1 (ai-broker-db)

Preview the production worker locally

npm run preview

Deploy

npm run deploy

Setup notes:

  • Bindings are declared in wrangler.jsonc: DB (D1 database ai-broker-db), SEND_EMAIL (Email Workers), cron triggers for the /api/cron/* routes.
  • Secrets: wrangler secret put BETTERAUTHSECRET (likewise GOOGLECLIENTID, GOOGLECLIENTSECRET, STRIPESECRETKEY, STRIPEWEBHOOKSECRET, CRONSECRET, and optionally RESENDAPI_KEY as an email fallback).
  • Email Workers requires Email Routing to be enabled on the zone, with the EMAILFROM sender on a verified domain and recipient destination rules configured; without the binding, sending falls back to Resend (if configured) or console logging.

๐Ÿ“‚ Third Party APIs

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