deonmenezes

deonmenezes / opentradex

Public

Dashboard-first agent harness for AI-assisted trading workflows.

discord.ggrFdwJC8z agent-harness ai codex-cli dashboard kalshi
10
3
69% credibility
Found Apr 07, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

OpenTradex is an open-source dashboard and AI agent for monitoring news, scanning prediction markets like Kalshi and Polymarket, and simulating or executing trades based on reasoned opportunities.

How It Works

1
🔍 Discover OpenTradex

You hear about a smart helper that watches news and markets to spot trading opportunities on prediction sites.

2
🚀 Set up your workspace

Run a quick welcome guide that creates your personal trading space and prepares everything with simple choices.

3
🔗 Pick your markets and news

Choose favorite trading spots like event markets and news sources so your helper knows where to look for edges.

4
📊 Launch the live dashboard

Open a beautiful screen showing your portfolio, positions, markets, and news feeds in real time.

5
🤖 Let the AI agent run

Submit a trading idea or start auto cycles, and watch it research news, check prices, and suggest moves.

✅ Enjoy smart trading

Your helper manages positions, tracks profits, and grows your edge while you watch from the dashboard.

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Star Growth

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AI-Generated Review

What is opentradex?

OpenTradex is a dashboard-first harness for agent-driven, AI-assisted trading workflows. It onboards you with a single CLI command (`opentradex onboard`), wires market APIs like Kalshi for live execution and Polymarket for discovery, pulls news from RSS/Twitter/Reddit, and spins up a Next.js dashboard to monitor agent cycles. Developers get a ready-to-run loop where local AI agents reason over data, estimate edges, and trade without hardcoded strategies.

Why is it gaining traction?

It skips the boilerplate of building agent runtimes and API glue, letting you focus on strategy via persistent memory files and a clean six-lane prompt system (markets, feeds, risk). The real hook is watching agents self-discover arb opportunities in real-time on the dashboard, with half-Kelly sizing and hard risk caps baked in. TypeScript frontend pairs smoothly with Python CLI tools for hybrid workflows.

Who should use this?

Quant devs prototyping prediction market bots on Kalshi/Polymarket, AI tinkerers automating news-to-trade pipelines, or traders wanting a dashboard to test theses without full bot infrastructure. Ideal for those bridging LLMs to live execution without deep infra work.

Verdict

Grab it if you're experimenting with AI-assisted trading—early stars (10) and 0.7% credibility score mean it's raw, but solid docs and onboard flow make it viable for local runs. Polish tests and add broker rails to hit production.

(187 words)

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