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Trader – LLM agent for Robinhood with a Rust safety layer and paper trading

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Trader – LLM agent for Robinhood with a Rust safety layer and paper trading
TL;DR · WeSearch summary

Trader is a new LLM-driven trading agent designed for Robinhood that incorporates a Rust safety layer. It allows users to paper trade against live market data while enforcing strict risk limits. The system logs every interaction for auditing purposes, ensuring transparency and safety in trading decisions.

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Record

Original publisherGitHub
Canonical URLhttps://github.com/zhangxd6/Trader/
Publication timeWed, 03 Jun 2026 13:37:56 +0000
Retrieval time2026-06-03T13:42:08.547Z
Last seen2026-06-03T13:42:08.547Z
Headline sourcePublisher (no WeSearch rewrite)
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Excerpt methodFirst ~120 words (~800 chars) of extracted publisher body, fair-use limited.
SummaryWeSearch · cerebras-chat (WeSearch summarizer)
Summary source textcontentText
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ClusterglNkd49TFYr_
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Publisher visitYes — open original
Substitutes article?No — link-out required for full text

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Commercial reuse May the content be reused commercially? Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Opening excerpt (first ~120 words) tap to expand

Trader — LLM-Driven Robinhood Trading Agent A Rust agent that connects an LLM to Robinhood's official agentic trading API, enforces hard risk limits in a typed safety layer, and paper-trades against live market data before you risk a dollar. What it does trader is an agentic loop that: Builds a prompt from your strategy YAML — hard thresholds (stop-loss, position caps, buy filters) and free-text judgment rules. Hands it to an LLM (OpenAI, Anthropic, Groq, Ollama, or any compatible endpoint). The LLM calls Robinhood's MCP tools to read your portfolio, fetch quotes, and place orders. Every order is intercepted by a typed Rust SafetyValidator that enforces your risk limits — rejecting anything that violates them regardless of what the model decided.

Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.

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