alex-jb

alex-jb / orallexa-ai-trading-agent

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Multi-agent AI trading system where Bull and Bear debate before every decision. 9 ML models, LLM strategy evolution, real-time dashboard.

orallexa-ui.vercel.app ai-trading algorithmic-trading art-deco claude desktop-assistant
12
1
100% credibility
Found Apr 08, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Orallexa is an open-source AI trading assistant that uses multiple machine learning models, agent debates, and real-time dashboards to generate stock signals, risk plans, and market intelligence.

How It Works

1
🔍 Discover Orallexa

You find this cool AI trading helper on GitHub with a live demo that analyzes stocks like NVDA instantly.

2
💡 Try the Demo

Click the demo link and see a full AI analysis with bull/bear arguments and a clear buy/sell decision—no setup needed.

3
📥 Get It Running

Download everything and launch the full system on your computer with one simple command.

4
🔗 Connect Your AI Helper

Link your AI account so the system can think and debate like a team of expert traders.

5
📊 Pick a Stock to Analyze

Open the beautiful dashboard, enter a stock ticker, and watch the AI swarm generate signals and risk plans.

6
🤖 Get Smart Insights

See the decision card with confidence score, bull/bear debate, ML rankings, and exact entry/stop/target levels.

7
💰 Test with Paper Trading

Practice trades risk-free using the built-in simulator that tracks your wins and losses.

🚀 Trade with Confidence

Your AI coach now helps you spot opportunities, manage risk, and grow your edge—share insights on social media too!

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Star Growth

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AI-Generated Review

What is orallexa-ai-trading-agent?

Orallexa is a Python multi-agent trading system on GitHub where Bull and Bear agents, powered by Claude LLMs, debate every trade decision before fusing signals from 9 ML models, news sentiment, and technicals into BUY/SELL/WAIT calls with risk plans. It delivers transparent reasoning via a real-time Next.js dashboard, paper trading through Alpaca API, and a voice-enabled desktop coach. Solves opaque AI trading signals by making multi-agent LLM trading observable and executable out-of-the-box.

Why is it gaining traction?

This claude multi agent github project stands out with its Bull/Bear debate in a multi-agent trading framework, cheap dual-tier LLM routing (Haiku for volume, Sonnet for judgment), and walk-forward evals showing positive out-of-sample Sharpe on NVDA/INTC. Live demo at orallexa-ui.vercel.app, Docker one-click deploy, and endpoints like /api/analyze let devs test multi-agent stock trading instantly without setup. Strategy evolver generates and backtests Python code via LLMs, hooking builders of multi agent llm financial trading frameworks.

Who should use this?

Quant hobbyists scripting multi-agent trading bots for personal portfolios. AI devs prototyping claude multi agent systems for financial signals, especially with PPO RL or GNN models. Traders needing a local multi-agent LLM trading dashboard with paper execution and daily intel scans.

Verdict

Grab it if you're into multi-agent trading github experiments—strong live demo, 698 tests, and API docs make prototyping fast. 1.0% credibility and 12 stars mean it's raw; expect tweaks for production, but forkable foundation for agent-based trading.

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