Awesome Agent Trading

A curated directory of AI trading agents, broker integrations, MCP servers, skills, and research tools for US equities and options, global crypto and DeFi, and prediction markets.

Explore tools for market research, strategy development, and order execution. Account and venue availability depend on the integration and jurisdiction.

Use the categories below to distinguish research, backtesting, paper trading, and live execution. Listings describe upstream capabilities, not verified trading performance. Official hosted services, open-source software, and source-available projects are labeled separately where reviewed.

Contributions welcome! See CONTRIBUTING.md. Machine-readable resources: llms.txt and data/projects.json.

Contents

• Trading Agents
• Broker & Exchange Integrations
• MCP Servers & Agent Skills
• Prediction Markets
• Research & Backtesting
• Data, Wallets & Risk Infrastructure
• Machine-Readable Data
• FAQ
• Research & Papers
• Tutorials & Guides
• Communities

Trading Agents

Agent systems for market analysis, decisions, and execution. Check each project's supported mode and venue before choosing an integration.

Emerging execution projects

Broker & Exchange Integrations

Official broker integrations

DEX & On-Chain Trading

Decentralized exchanges and on-chain trading infrastructure for agents.

CEX & Off-Chain Trading

Centralized exchanges with API access for automated trading.

MCP Servers & Agent Skills

MCP servers and agent CLIs

Model Context Protocol servers that provide trading capabilities to AI agents.

Agent Skills

Skills for compatible agent clients. OpenClaw-specific entries remain labeled by their upstream integration; compatibility is not assumed across all clients.

Prediction Markets

Prediction market platforms accessible to AI agents.

Official CLI

Polymarket CLI provides market discovery, JSON output, order and position management, and on-chain operations. Experimental; live trading requires wallet credentials and venue eligibility. MIT is declared in Cargo.toml; a standalone root license file was not found during review.

Research & Backtesting

Research and simulation are distinct from broker execution. Traditional ML/RL and strategy engines below are supporting infrastructure, not necessarily LLM agents.

Data, Wallets & Risk Infrastructure

Wallet and action toolkits

Coinbase AgentKit provides wallets and on-chain actions for agents (Apache-2.0). It supplies execution tools rather than a trading strategy.

General agent runtimes

OpenClaw and ElizaOS can host agent workflows; trading capabilities depend on installed integrations.

Data & Market Intelligence

Data sources and APIs for market analysis by agents.

Agent Identity & Trust

On-chain identity and reputation systems for trading agents.

Payment Protocols

Protocols for agent-to-agent and agent-to-human payments.

Risk controls to evaluate

• Execution permissions: Separate read-only research from order submission and fund transfers.
• Configurable budgets: Enforce position, leverage, concentration, and loss limits in execution code.
• Order lifecycle: Use idempotency, fresh-price checks, fill reconciliation, and explicit cancellation handling.
• Approval and shutdown: Support human review, paper/dry-run modes, and a tested kill switch.
• Exit handling: Document supported stop orders, rejected orders, partial fills, and disconnected sessions.
• Audit trails: Record proposals, approvals, orders, and outcomes; TradeMemory Protocol is one tool to investigate.

Prompt instructions alone are not execution-layer enforcement. A project's documented controls are not an independent security or performance audit.

Machine-Readable Data

data/projects.json is a featured subset, not a complete export of every README entry. It retains the existing fields and adds review metadata to newly reviewed entries: modes, origin, license, license_source, notes, last_verified, verification, and sources. Missing fields on older entries mean not reviewed, not unsupported. A license declaration can come from a README or package manifest; consult license_source and notes for its basis.

modes distinguishes research, backtest, paper, dry-run, and live; dry-run does not imply simulated fills. verification: upstream-documentation means a documentation review, not a runtime test. Hosted services use a null code license. llms.txt provides a compact directory summary.

FAQ

What is agent trading?

Agent trading uses AI agents to research markets, call financial tools, propose decisions, or execute orders under configured controls. Research-only tools and live trading systems have different capabilities.

Where should I start for US equities and options?

Start with Alpaca MCP Server for official paper/live tools, or Robinhood Agentic Trading for an eligible dedicated account. Lumibot provides a Python backtesting and execution runtime. Check account eligibility and data entitlements.

Are these skills limited to OpenClaw?

No. This directory includes MCP servers, CLIs, SDKs, and agent skills. Each upstream project documents compatible clients and setup requirements.

Can AI agents trade on Polymarket or Kalshi?

The venues expose APIs, and this list includes Polymarket CLI, Simmer SDK, Kalshi Trading Bot CLI, and community MCP integrations. Check venue eligibility, credentials, and whether a tool uses simulated or real orders.

Does inclusion mean a strategy is profitable or audited?

No. Entries describe tools and documented capabilities. Backtests, author-reported live results, and independent evaluations are different kinds of evidence.

Research & Papers

• TradingAgents: Multi-Agents LLM Financial Trading Framework — First submitted in 2024; multi-agent financial analysis and trading research.
• R&D-Agent-Quant — 2025; data-centric factor and model optimization.
• LiveTradeBench — 2025; real-time market evaluation of trading agents. Real-time evaluation does not itself imply real-money execution.

Tutorials & Guides

• OpenClaw AI Trading Skills: Complete 2026 Guide — Setup, risks, and real numbers
• How to Build an Autonomous Trading Agent with Python — Dev.to, 2026
• Build a Crypto AI Agent with CoinGecko API — CoinGecko
• How to Build an OpenClaw AI Crypto Trading Agent — CoinGecko + OpenClaw, 4 strategies with backtesting
• OpenClaw Trading: Crypto, DeFi, and Polymarket Skills — BoilerplateHub

Communities

• OpenClaw Discord — Official community
• BankrBot Discord — Trading skill community
• r/algotrading — Algorithmic trading on Reddit
• ERC-8004 Discord — Agent identity standard

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Contributing

See CONTRIBUTING.md for guidelines. PRs welcome!

License

CC0-1.0 — Public domain. Use however you want.