Open-source trading repos for GrokBot

Arcvale @itsarcvaleOriginally posted 2026.10.24

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THIS IS PURE F*CKING GOLD FOR GROK BOT TRADERS beyond the 1,894 public Grok Bot shares people pass around, there are 20 open-source trading repos with 622K+ GitHub stars you can point Grok Bot or Claude Code at. completely FREE. and most traders have never opened one full trading firms made of agents, bull and bear researchers who argue before every trade, risk teams that attack the decision, a foundation model trained on candlesticks, and engines that run the same strategy in backtest and live. all open source 20 GitHub projects worth opening first: 01 hypergrok-trading-desk → 7 role profiles and 17 skills for research, risk, execution and review on Hyperliquid. starts on public data with no wallet, runs in Grok Bot and Claude Code https://github.com/galleonlabs/hypergrok-trading-desk 02 awesome-grokbot → 1,894 live Grok Bot shares, every link status-checked and filterable. search it for a trading setup before writing your own instructions https://github.com/kydlikebtc/awesome-grokbot 03 TradingAgents → a trading firm made of LLMs: 4 analysts, bull and bear researchers who debate, a trader, a risk team and a portfolio manager who approves or kills the call https://github.com/TauricResearch/TradingAgents 04 ai-hedge-fund → investor agents that run paper funds on real market days with fake money, plus backtests against a benchmark. works with Anthropic, xAI and more. never places a real trade https://github.com/virattt/ai-hedge-fund 05 OpenBB → an open data platform for analysts, quants and AI agents. one place to pull market data before any agent forms an opinion https://github.com/OpenBB-finance/OpenBB 06 alpaca-mcp-server → Alpaca's official MCP server. stocks, ETFs, crypto and options through plain language from Claude, Cursor or VS Code https://github.com/alpacahq/alpaca-mcp-server 07 Kronos → the first open-source foundation model for candlesticks, trained on data from 45+ global exchanges https://github.com/shiyu-coder/Kronos 08 qlib → Microsoft's AI-oriented quant platform: supervised learning, market dynamics modeling and RL, from first idea to production https://github.com/microsoft/qlib 09 RD-Agent → Microsoft's agent that automates the research loop. its quant version, R&D-Agent-Quant, was accepted to NeurIPS 2025 https://github.com/microsoft/RD-Agent 10 FinGPT → open-source financial LLMs, including a forecaster and sentiment models released on Hugging Face https://github.com/AI4Finance-Foundation/FinGPT 11 freqtrade → a free crypto trading bot in Python with backtesting, plotting, money management and strategy optimization. control it from Telegram or a web UI https://github.com/freqtrade/freqtrade 12 hummingbot → automated strategies on centralized and decentralized exchanges. its Condor harness connects LLM decisions to deterministic execution, and paper mode needs no API keys https://github.com/hummingbot/hummingbot 13 ccxt → one trading API for 100+ crypto exchanges and prediction markets, in JavaScript, Python, C#, PHP, Go, Java and Rust https://github.com/ccxt/ccxt 14 nautilus_trader → a production-grade, Rust-native engine. research, deterministic simulation and live execution in one event-driven architecture https://github.com/nautechsystems/nautilus_trader 15 Lean → QuantConnect's algorithmic trading engine. write the strategy in Python or C#, backtest it, then take the same code live https://github.com/QuantConnect/Lean 16 jesse → research, backtest, optimize and live-trade your own crypto strategies. multiple timeframes and symbols without look-ahead bias, fully self-hosted https://github.com/jesse-ai/jesse 17 vectorbt → test thousands of trading ideas fast, then inspect trades, fees, drawdowns and portfolio performance in interactive charts https://github.com/polakowo/vectorbt 18 http://backtesting.py → pip install, write one Strategy class, run one Backtest. the shortest path from an idea to a result on history https://github.com/kernc/backtesting.py 19 FinRL → the original reinforcement learning framework for trading. built for education, benchmarks and research prototypes https://github.com/AI4Finance-Foundation/FinRL 20 machine-learning-for-trading → the code of the book, 3rd edition. 27 chapter guides from data sourcing to live execution https://github.com/stefan-jansen/machine-learning-for-trading start with one repo you can actually read. give Grok Bot the link, have it read the README and run it on paper first read every agent's output and check one trade by hand before anything goes on a schedule then write the risk limits in yourself: position size, max loss per day, and a human yes before any real order 20 repos. 622K stars. $0 in software. none of them know your risk limits until you write them in a new stack like this every night the code is free. the tokens to run it through Grok Bot aren't. the article below shows 7 fixes that turn 7 usable results into 70 on the same budget ↓

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