[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-georgezouq--awesome-ai-in-finance":3,"tool-georgezouq--awesome-ai-in-finance":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",155373,2,"2026-04-14T11:34:08",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":79,"owner_url":80,"languages":79,"stars":81,"forks":82,"last_commit_at":83,"license":84,"difficulty_score":85,"env_os":86,"env_gpu":87,"env_ram":87,"env_deps":88,"category_tags":91,"github_topics":92,"view_count":32,"oss_zip_url":79,"oss_zip_packed_at":79,"status":17,"created_at":105,"updated_at":106,"faqs":107,"releases":108},7537,"georgezouq\u002Fawesome-ai-in-finance","awesome-ai-in-finance","🔬 A curated list of awesome LLMs & deep learning strategies & tools in financial market.","awesome-ai-in-finance 是一个精心整理的开源资源库，专注于汇集金融市场中应用大语言模型（LLM）与深度学习策略的优质工具、代码及研究论文。面对全球金融市场每天产生的海量交易数据，人类往往难以快速洞察规律，而该项目旨在通过整合前沿人工智能技术，帮助使用者实现智能化分析与自动交易，从而更有效地应对市场挑战。\n\n这份清单内容极其丰富，涵盖了从多智能体交易框架（如 FinRobot、ATLAS）、生成式市场模拟引擎（MarS），到高频交易、投资组合管理及加密货币策略等细分领域。其独特亮点在于不仅收录了理论论文和课程，还提供了可落地的实战系统、数据源以及交易所 API 接口，甚至包含了让 AI 代理使用真实资金进行实盘竞技的基准测试项目。\n\n无论是希望构建量化交易系统的开发者、探索金融 AI 前沿的科研人员，还是对智能投资感兴趣的金融机构从业者，都能从中找到极具价值的参考素材。它打破了理论与工程的壁垒，为利用 AI 技术在金融领域创新提供了坚实的一站式基础支持。","# Awesome AI in Finance [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome) [![Community](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F733027681184251937.svg?style=flat&label=Join%20Community&color=7289DA)](https:\u002F\u002Fdiscord.gg\u002FcqaUf47)\n\nThere are millions of trades made in the global financial market every day. Data grows very quickly and people are hard to understand.\nWith the power of the latest artificial intelligence research, people analyze & trade automatically and intelligently. This list contains the research, tools and code that people use to beat the market.\n\n[[中文资源](.\u002Fchinese.md)]\n\n## Contents\n\n- [Agents](#agents)\n- [LLMs](#llms)\n- [Papers](#papers)\n- [Courses & Books](#courses--books)\n- [Strategies & Research](#strategies--research)\n  - [Time Series Data](#time-series-data)\n  - [Portfolio Management](#portfolio-management)\n  - [High Frequency Trading](#high-frequency-trading)\n  - [Event Drive](#event-drive)\n  - [Crypto Currencies Strategies](#crypto-currencies-strategies)\n  - [Technical Analysis](#technical-analysis)\n  - [Lottery & Gamble](#lottery--gamble)\n  - [Arbitrage](#arbitrage)\n- [Data Sources](#data-sources)\n- [Research Tools](#research-tools)\n- [Trading System](#trading-system)\n- [TA Lib](#ta-lib)\n- [Exchange API](#exchange-api)\n- [Articles](#articles)\n- [Others](#others)\n\n## Agents\n\n- 🌟🌟 [nofx](https:\u002F\u002Fgithub.com\u002FNoFxAiOS\u002Fnofx) - A multi-exchange Al trading platform with multi-Ai competition self-evolution, and real-time dashboard.\n- [TradingAgents](https:\u002F\u002Fgithub.com\u002FTauricResearch\u002FTradingAgents) - Multi-Agents LLM Financial Trading Framework.\n- 🌟 [FinRobot](https:\u002F\u002Fgithub.com\u002FAI4Finance-Foundation\u002FFinRobot) - An Open-Source AI Agent Platform for Financial Analysis using LLMs.\n- [AgentFund](https:\u002F\u002Fgithub.com\u002FRioBot-Grind\u002Fagentfund) - Decentralized crowdfunding platform for AI agents with milestone-based escrow on Base blockchain.\n- 🌟 [ATLAS](https:\u002F\u002Fgithub.com\u002Fchrisworsey55\u002Fatlas-gic) - Self-improving AI trading system with 25 agents, Karpathy-style autoresearch, Darwinian selection, autonomous agent spawning, and multi-cohort meta-weighting.\n- [InvicTrade](https:\u002F\u002Finvictrade.com) - AI-powered trading signals with 74% historical win rate, combining strategies from legendary investors using multi-model AI intelligence.\n- [OpenFinClaw](https:\u002F\u002Fgithub.com\u002FcryptoSUN2049\u002FopenFinclaw) - AI-native one-person hedge fund platform. Expert agent teams turn natural language into quant strategies in 60s. Multi-market (US\u002FHK\u002FCN\u002FCrypto), self-evolving strategy pipeline with community leaderboard.\n- [ProfitPlay Agent Arena](https:\u002F\u002Fgithub.com\u002Fjarvismaximum-hue\u002Fprofitplay-starter) - Open prediction market arena where AI agents compete in real-time BTC\u002FETH\u002FSOL prediction games. Python and Node.js SDKs, 9 live markets, REST + WebSocket APIs.\n\n## LLMs\n\n- 🌟🌟🌟 [Nof1](https:\u002F\u002Fthenof1.com\u002F) - Benchmark designed to measure AI's investing abilities. Each model is given $10,000 of real money, in real markets, with identical prompts and input data.\n- 🌟 [AI Hedge Fund](https:\u002F\u002Fgithub.com\u002Fvirattt\u002Fai-hedge-fund) - Explore the use of AI to make trading decisions.\n- 🌟🌟 [MarS](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FMarS) - A Financial Market Simulation Engine Powered by Generative Foundation Model.\n- 🌟🌟 [Financial Statement Analysis with Large Language Models](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4835311) - GPT-4 can outperform professional financial analysts in predicting future earnings changes, generating useful narrative insights, and resulting in superior trading strategies with higher Sharpe ratios and alphas, thereby suggesting a potential central role for LLMs in financial decision-making.\n- [FinRpt](https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.07322) - Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation.\n- [PIXIU](https:\u002F\u002Fgithub.com\u002Fchancefocus\u002FPIXIU) - An open-source resource providing a financial large language model, a dataset with 136K instruction samples, and a comprehensive evaluation benchmark.\n- [FinGPT](https:\u002F\u002Fgithub.com\u002FAI4Finance-Foundation\u002FFinGPT) - Provides a playground for all people interested in LLMs and NLP in Finance.\n- [MACD + RSI + ADX Strategy (ChatGPT-powered) by TradeSmart](https:\u002F\u002Fwww.tradingview.com\u002Fscript\u002FGxkUyJKW-MACD-RSI-ADX-Strategy-ChatGPT-powered-by-TradeSmart\u002F ) - Asked ChatGPT on which indicators are the most popular for trading. We used all of the recommendations given.\n- [A ChatGPT trading algorithm delivered 500% returns in stock market. My breakdown on what this means for hedge funds and retail investors](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FChatGPT\u002Fcomments\u002F13duech\u002Fa_chatgpt_trading_algorithm_delivered_500_returns\u002F)\n- [Use chatgpt to adjust strategy parameters](https:\u002F\u002Ftwitter.com\u002F0xUnicorn\u002Fstatus\u002F1663413848593031170)\n- [Hands-on LLMs: Train and Deploy a Real-time Financial Advisor](https:\u002F\u002Fgithub.com\u002Fiusztinpaul\u002Fhands-on-llms) - Train and deploy a real-time financial advisor chatbot with Falcon 7B and CometLLM.\n- [ChatGPT Strategy by OctoBot](https:\u002F\u002Fblog.octobot.online\u002Ftrading-using-chat-gpt) - Use ChatGPT to determine which cryptocurrency to trade based on technical indicators.\n- [LLMs Meet Finance](https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.13125) - A three-stage fine-tuning pipeline (SFT → DPO → synthetic-data RL) that adapts Qwen2.5 and DeepSeek-R1 to financial tasks on the Open FinLLM Leaderboard, with findings on cross-task transfer and data scaling laws in finance.\n\n## Skills\n\n- [XVARY Stock Research](https:\u002F\u002Fgithub.com\u002Fxvary-research\u002Fclaude-code-stock-analysis-skill) — Claude Code skill for public SEC EDGAR + market data: `\u002Fanalyze`, `\u002Fscore`, `\u002Fcompare`. MIT.\n\n## Papers\n\n- [The Theory of Speculation L. Bachelier, 1900](http:\u002F\u002Fwww.radio.goldseek.com\u002Fbachelier-thesis-theory-of-speculation-en.pdf) - The influences which determine the movements of the Stock Exchange are.\n- [Brownian Motion in the Stock Market Osborne, 1959](http:\u002F\u002Fm.e-m-h.org\u002FOsbo59.pdf) - The common-stock prices can be regarded as an ensemble of decisions in statistical equilibrium.\n- [An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain, 2015](http:\u002F\u002Fwww.doc.ic.ac.uk\u002Fteaching\u002Fdistinguished-projects\u002F2015\u002Fj.cumming.pdf)\n- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.10059.pdf)\n- [Reinforcement Learning for Trading, 1994](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1551-reinforcement-learning-for-trading.pdf)\n- [Dragon-Kings, Black Swans and the Prediction of Crises Didier Sornette](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0907.4290.pdf) - The power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems. \n- [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.02787.pdf) - Deep reinforcement learning provides a framework toward end-to-end training of such trading agent.\n- [Machine Learning for Trading](https:\u002F\u002Fcims.nyu.edu\u002F~ritter\u002Fritter2017machine.pdf) - With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case.\n- [Ten Financial Applications of Machine Learning, 2018](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=3197726) - Slides review few important financial ML applications.\n- [FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, 2020](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.09607) - Introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.\n- [Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, 2020](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=3690996) - Propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return.\n\n## Courses & Books & Blogs\n\n- 🌟 [QuantResearch](https:\u002F\u002Fgithub.com\u002Fletianzj\u002FQuantResearch) - Quantitative analysis, strategies and backtests https:\u002F\u002Fletianzj.github.io\u002F\n- [NYU: Overview of Advanced Methods of Reinforcement Learning in Finance](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fadvanced-methods-reinforcement-learning-finance\u002Fhome\u002Fwelcome)\n- [Udacity: Artificial Intelligence for Trading](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fai-for-trading--nd880)\n- [AI in Finance](https:\u002F\u002Fcfte.education\u002F) - Learn Fintech Online.\n- [Advanced-Deep-Trading](https:\u002F\u002Fgithub.com\u002FRachnog\u002FAdvanced-Deep-Trading) - Experiments based on \"Advances in financial machine learning\" book.\n- [Advances in Financial Machine Learning](https:\u002F\u002Fwww.amazon.com\u002FAdvances-Financial-Machine-Learning-Marcos-ebook\u002Fdp\u002FB079KLDW21\u002Fref=sr_1_1?s=books&ie=UTF8&qid=1541717436&sr=1-1) - Using advanced ML solutions to overcome real-world investment problems.\n- [Build Financial Software with Generative AI](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fbuild-financial-software-with-generative-ai?ar=false&lpse=B&) - Book about how to build financial software hands-on using generative AI tools like ChatGPT and Copilot.\n- [Financial AI in Practice](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Ffinancial-ai-in-practice) - A book about creating profitable, regulation-compliant financial applications.\n- [Investing for Programmers](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Finvesting-for-programmers) - A book about maximizing your portfolio, analyzing markets, and making data-driven investment decisions using Python and generative AI\n- [Mastering Python for Finance](https:\u002F\u002Fgithub.com\u002Fjamesmawm\u002Fmastering-python-for-finance-second-edition) - Sources codes for: Mastering Python for Finance, Second Edition.\n- [MLSys-NYU-2022](https:\u002F\u002Fgithub.com\u002Fjacopotagliabue\u002FMLSys-NYU-2022\u002Ftree\u002Fmain) - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.\n- [Train and Deploy a Serverless API to predict crypto prices](https:\u002F\u002Fgithub.com\u002FPaulescu\u002Fhands-on-train-and-deploy-ml) - In this tutorial you won't build an ML system that will make you rich. But you will master the MLOps frameworks and tools you need to build ML systems that, together with tons of experimentation, can take you there.\n- [KeepRule](https:\u002F\u002Fkeeprule.com) - AI-powered investment discipline platform with principles from 26 legendary investors including Buffett, Munger, and Dalio.\n\n## Strategies & Research\n\n### Time Series Data\n\nPrice and Volume process with Technology Analysis Indices\n\n- 🌟🌟 [stockpredictionai](https:\u002F\u002Fgithub.com\u002Fborisbanushev\u002Fstockpredictionai) - A complete process for predicting stock price movements.\n- 🌟 [Personae](https:\u002F\u002Fgithub.com\u002FCeruleanacg\u002FPersonae) - Implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.\n- 🌟 [Ensemble-Strategy](https:\u002F\u002Fgithub.com\u002FAI4Finance-LLC\u002FDeep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020) - Deep Reinforcement Learning for Automated Stock Trading.\n- [FinRL](https:\u002F\u002Fgithub.com\u002FAI4Finance-LLC\u002FFinRL-Library) - A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance.\n- [AutomatedStockTrading-DeepQ-Learning](https:\u002F\u002Fgithub.com\u002Fsachink2010\u002FAutomatedStockTrading-DeepQ-Learning) - Build a Deep Q-learning reinforcement agent model as automated trading robot.\n- [tf_deep_rl_trader](https:\u002F\u002Fgithub.com\u002Fmiroblog\u002Ftf_deep_rl_trader) - Trading environment(OpenAI Gym) + PPO(TensorForce).\n- [trading-gym](https:\u002F\u002Fgithub.com\u002F6-Billionaires\u002Ftrading-gym) - Trading agent to train with episode of short term trading itself.\n- [trading-rl](https:\u002F\u002Fgithub.com\u002FKostis-S-Z\u002Ftrading-rl) - Deep Reinforcement Learning for Financial Trading using Price Trailing.\n- [deep_rl_trader](https:\u002F\u002Fgithub.com\u002Fmiroblog\u002Fdeep_rl_trader) - Trading environment(OpenAI Gym) + DDQN (Keras-RL).\n- [Quantitative-Trading](https:\u002F\u002Fgithub.com\u002FCeruleanacg\u002FQuantitative-Trading) - Papers and code implementing Quantitative-Trading.\n- [gym-trading](https:\u002F\u002Fgithub.com\u002Fhackthemarket\u002Fgym-trading) - Environment for reinforcement-learning algorithmic trading models.\n- [zenbrain](https:\u002F\u002Fgithub.com\u002Fcarlos8f\u002Fzenbrain) - A framework for machine-learning bots.\n- [DeepLearningNotes](https:\u002F\u002Fgithub.com\u002FAlphaSmartDog\u002FDeepLearningNotes) - Machine learning in quant analysis.\n- [stock_market_reinforcement_learning](https:\u002F\u002Fgithub.com\u002Fkh-kim\u002Fstock_market_reinforcement_learning) - Stock market trading OpenAI Gym environment with Deep Reinforcement Learning using Keras.\n- [Chaos Genius](https:\u002F\u002Fgithub.com\u002Fchaos-genius\u002Fchaos_genius) - ML powered analytics engine for outlier\u002Fanomaly detection and root cause analysis..\n- [mlforecast](https:\u002F\u002Fgithub.com\u002FNixtla\u002Fmlforecast) - Scalable machine learning based time series forecasting.\n- [patternity](https:\u002F\u002Fgithub.com\u002Fquantium-ai\u002Fpatternity) - Deterministic algorithm for stock price prediction, focusing on pattern recognition in historical data.\n- [Quantium Research](https:\u002F\u002Fgithub.com\u002Fquantium-ai\u002Fresearch) - Research experiments exploring uncommon quant techniques.\n\n### Portfolio Management\n\n- [Deep-Reinforcement-Stock-Trading](https:\u002F\u002Fgithub.com\u002FAlbert-Z-Guo\u002FDeep-Reinforcement-Stock-Trading) - A light-weight deep reinforcement learning framework for portfolio management.\n- [qtrader](https:\u002F\u002Fgithub.com\u002Ffilangel\u002Fqtrader) - Reinforcement Learning for portfolio management.\n- [PGPortfolio](https:\u002F\u002Fgithub.com\u002FZhengyaoJiang\u002FPGPortfolio) - A Deep Reinforcement Learning framework for the financial portfolio management problem.\n- [DeepDow](https:\u002F\u002Fgithub.com\u002Fjankrepl\u002Fdeepdow) - Portfolio optimization with deep learning.\n- [skfolio](https:\u002F\u002Fgithub.com\u002Fskfolio\u002Fskfolio) - Python library for portfolio optimization built on top of scikit-learn.\n\n### High Frequency Trading\n\n- [High-Frequency-Trading-Model-with-IB](https:\u002F\u002Fgithub.com\u002Fjamesmawm\u002FHigh-Frequency-Trading-Model-with-IB) - A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion.\n- 🌟 [SGX-Full-OrderBook-Tick-Data-Trading-Strategy](https:\u002F\u002Fgithub.com\u002Frorysroes\u002FSGX-Full-OrderBook-Tick-Data-Trading-Strategy) - Solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.\n- [HFT_Bitcoin](https:\u002F\u002Fgithub.com\u002Fghgr\u002FHFT_Bitcoin) - Analysis of High Frequency Trading on Bitcoin exchanges.\n\n### Event Drive\n\n- 🌟🌟 [stockpredictionai](https:\u002F\u002Fgithub.com\u002Fborisbanushev\u002Fstockpredictionai) - Complete process for predicting stock price movements.\n- 🌟 [trump2cash](https:\u002F\u002Fgithub.com\u002Fmaxbbraun\u002Ftrump2cash) - A stock trading bot powered by Trump tweets.\n\n### Crypto Currencies Strategies\n\n- [LSTM-Crypto-Price-Prediction](https:\u002F\u002Fgithub.com\u002FSC4RECOIN\u002FLSTM-Crypto-Price-Prediction) - Predicting price trends in crypto markets using an LSTM-RNN for trading.\n- [tforce_btc_trader](https:\u002F\u002Fgithub.com\u002Flefnire\u002Ftforce_btc_trader) - TensorForce Bitcoin trading bot.\n- [Tensorflow-NeuroEvolution-Trading-Bot](https:\u002F\u002Fgithub.com\u002FSC4RECOIN\u002FTensorflow-NeuroEvolution-Trading-Bot) - A population model that trade cyrpto and breed and mutate iteratively.\n- [gekkoga](https:\u002F\u002Fgithub.com\u002Fgekkowarez\u002Fgekkoga) - Genetic algorithm for solving optimization of trading strategies using Gekko.\n- [Gekko_ANN_Strategies](https:\u002F\u002Fgithub.com\u002Fmarkchen8717\u002FGekko_ANN_Strategies) - ANN trading strategies for the Gekko trading bot.\n- [gekko-neuralnet](https:\u002F\u002Fgithub.com\u002Fzschro\u002Fgekko-neuralnet) - Neural network strategy for Gekko.\n- [bitcoin_prediction](https:\u002F\u002Fgithub.com\u002FllSourcell\u002Fbitcoin_prediction) - Code for \"Bitcoin Prediction\" by Siraj Raval on YouTube.\n\n### Technical Analysis\n\n- [QTradeX](https:\u002F\u002Fgithub.com\u002FsquidKid-deluxe\u002FQTradeX-Algo-Trading-SDK) - A powerful and flexible Python framework for designing, backtesting, optimizing, and deploying algotrading bots\n- [quant-trading](https:\u002F\u002Fgithub.com\u002Fje-suis-tm\u002Fquant-trading) - Python quantitative trading strategies.\n- [Gekko-Bot-Resources](https:\u002F\u002Fgithub.com\u002Fcloggy45\u002FGekko-Bot-Resources) - Gekko bot resources.\n- [gekko_tools](https:\u002F\u002Fgithub.com\u002Ftommiehansen\u002Fgekko_tools) - Gekko strategies, tools etc.\n- [gekko RSI_WR](https:\u002F\u002Fgithub.com\u002Fzzmike76\u002Fgekko) - Gekko RSI_WR strategies.\n- [gekko HL](https:\u002F\u002Fgithub.com\u002Fmounirlabaied\u002Fgekko-strat-hl) - Calculate down peak and trade on.\n- [EthTradingAlgorithm](https:\u002F\u002Fgithub.com\u002FPhilipid3s\u002FEthTradingAlgorithm) - Ethereum trading algorithm using Python 3.5 and the library ZipLine.\n- [gekko_trading_stuff](https:\u002F\u002Fgithub.com\u002Fthegamecat\u002Fgekko-trading-stuff) - Awesome crypto currency trading platform.\n- [forex.analytics](https:\u002F\u002Fgithub.com\u002Fmkmarek\u002Fforex.analytics) - Node.js native library performing technical analysis over an OHLC dataset with use of genetic algorithmv.\n- [Bitcoin_MACD_Strategy](https:\u002F\u002Fgithub.com\u002FVermeirJellen\u002FBitcoin_MACD_Strategy) - Bitcoin MACD crossover trading strategy backtest.\n- [crypto-signal](https:\u002F\u002Fgithub.com\u002FCryptoSignal\u002Fcrypto-signal) - Automated crypto trading & technical analysis (TA) bot for Bittrex, Binance, GDAX, and more.\n- [Gekko-Strategies](https:\u002F\u002Fgithub.com\u002FxFFFFF\u002FGekko-Strategies) - Strategies to Gekko trading bot with backtests results and some useful tools.\n- [gekko-gannswing](https:\u002F\u002Fgithub.com\u002Fjohndoe75\u002Fgekko-gannswing) - Gann's Swing trade strategy for Gekko trade bot.\n- [Chartscout](https:\u002F\u002Fchartscout.io) - Real-time cryptocurrency chart pattern detection with automated alerts using pattern recognition algorithms\n* [MarginSafe.ai](https:\u002F\u002Fmarginsafe.ai) - AI stock analysis platform specialized in intrinsic value and Wyckoff timing.\n\n### Lottery & Gamble\n\n- [LotteryPredict](https:\u002F\u002Fgithub.com\u002Fchengstone\u002FLotteryPredict) - Use LSTM to predict lottery.\n\n### Arbitrage\n\n- [ArbitrageBot](https:\u002F\u002Fgithub.com\u002FBatuhanUsluel\u002FArbitrageBot) - Arbitrage bot that currently works on bittrex & poloniex.\n- [r2](https:\u002F\u002Fgithub.com\u002Fbitrinjani\u002Fr2) - Automatic arbitrage trading system powered by Node.js + TypeScript.\n- [cryptocurrency-arbitrage](https:\u002F\u002Fgithub.com\u002Fmanu354\u002Fcryptocurrency-arbitrage) - A crypto currency arbitrage opportunity calculator. Over 800 currencies and 50 markets.\n- [bitcoin-arbitrage](https:\u002F\u002Fgithub.com\u002Fmaxme\u002Fbitcoin-arbitrage) - Bitcoin arbitrage opportunity detector.\n- [blackbird](https:\u002F\u002Fgithub.com\u002Fbutor\u002Fblackbird) - Long \u002F short market-neutral strategy.\n\n## Data Sources\n\n#### Traditional Markets\n\n- 🌟 [Quandl](https:\u002F\u002Fwww.quandl.com\u002Ftools\u002Fapi) - Get millions of financial and economic dataset from hundreds of publishers via a single free API.\n- [yahoo-finance](https:\u002F\u002Fgithub.com\u002Flukaszbanasiak\u002Fyahoo-finance) - Python module to get stock data from Yahoo! Finance.\n- [Tushare](https:\u002F\u002Fgithub.com\u002Fwaditu\u002Ftushare) - TuShare is a utility for crawling historical data of China stocks.\n- [Congressional Stock Brain](https:\u002F\u002Fcongressionalstockbrain.com) - Free AI-powered tool that scores U.S. STOCK Act congressional trade disclosures by significance. Committee weighting, timing analysis, 537 members tracked.\n- [Financial Data](https:\u002F\u002Ffinancialdata.net\u002F) - Stock Market and Financial Data API.\n- [StockAInsights](https:\u002F\u002Fstockainsights.com) - Institutional-grade financial statements API with AI extraction from SEC filings — not XBRL. Covers domestic and foreign filers (20-F, 6-K, 40-F), normalized quarterly and annual data.\n- [ValueRay](https:\u002F\u002Fwww.valueray.com\u002Fapi) - Technical, quantitative and sentiment data for stocks and ETFs with risk metrics, peer percentiles and market regime signals. Optimized for AI\u002FLLM agents.\n\n#### Crypto Currencies\n\n- [CryptoInscriber](https:\u002F\u002Fgithub.com\u002FOptixal\u002FCryptoInscriber) - A live crypto currency historical trade data blotter. Download live historical trade data from any crypto exchange.\n- [CoinPulse](https:\u002F\u002Fgithub.com\u002Fsoutone\u002Fcoinpulse-python) - Python SDK for cryptocurrency portfolio tracking with real-time prices, P\u002FL calculations, backtesting, and price alerts. Free tier: 25 req\u002Fhr.\n- [Gekko-Datasets](https:\u002F\u002Fgithub.com\u002FxFFFFF\u002FGekko-Datasets) - Gekko trading bot dataset dumps. Download and use history files in SQLite format.\n- [Frostbyte Crypto API](https:\u002F\u002Fagent-gateway-kappa.vercel.app) - Free real-time cryptocurrency price data API. Supports BTC, ETH, SOL, and 20+ tokens. No signup or API key required for basic endpoints. JSON responses with price, 24h change, market cap, and volume.\n- [CoinPaprika API](https:\u002F\u002Fapi.coinpaprika.com) - Free cryptocurrency market data API with prices, volume, market cap, and OHLCV for 7,000+ coins. No API key required. Includes MCP server for AI agent integration.\n- [DexPaprika API](https:\u002F\u002Fapi.dexpaprika.com) - Free DEX and DeFi data API — real-time pool data, token prices, OHLCV, and trade history across all chains. No API key, no rate limits. Includes MCP server for AI agents.\n- [Philidor](https:\u002F\u002Fdocs.philidor.io\u002Fdocs) - Institutional-grade DeFi risk scoring for 700+ vaults across 9 protocols and 6 chains. REST API and MCP server (Claude, Cursor, Windsurf). Deterministic 0–10 risk scores, tiers (Prime\u002FCore\u002FEdge), portfolio analysis, oracle monitoring. No API key required.\n- [PreReason](https:\u002F\u002Fwww.prereason.com) - Pre-analyzed financial market briefings optimized for AI agent consumption. 17 briefings covering BTC on-chain, macro (Fed balance sheet, M2, Treasury yields), and cross-asset correlations. Returns regime classification, trend signals, and confidence scores in markdown.\n- [Satoshi API](https:\u002F\u002Fgithub.com\u002FBortlesboat\u002Fbitcoin-api) - Bitcoin fee intelligence API with 108 endpoints for fee estimates, mempool analysis, block data, and mining stats. Self-hostable, Apache 2.0.\n- [TBD Predict](https:\u002F\u002Fgithub.com\u002Fego-protocol\u002Ftbd-vote-cli) - Solana-based prediction market for human opinions with an agent CLI and AGENTS.md spec for AI agents to authenticate, list opinion campaigns, and place bets via JSON-friendly commands.\n\n#### News Data\n\n- [WorldMonitor](https:\u002F\u002Fgithub.com\u002Fkoala73\u002Fworldmonitor) - AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface.\n\n#### Alternative Data\n\n- [Pizzint](https:\u002F\u002Fwww.pizzint.watch\u002F) - Pentagon Pizza Index (PizzINT) is a real-time Pentagon pizza tracker that visualizes unusual activity at Pentagon-area pizzerias. It highlights a signal that has historically aligned with late-night, high-tempo operations and breaking news.\n\n#### Prediction Markets\n\n- [Parsec API](https:\u002F\u002Fdocs.parsecapi.com) - Unified prediction market infrastructure for normalized data, execution, and live streams across Polymarket, Kalshi, Opinion, Limitless, and PredictFun. MCP server for AI agent trading. Generous free tier.\n- [PolyMind](https:\u002F\u002Fpolyminds.netlify.app\u002F) - Real-time Polymarket trading alerts with multi-AI analysis (Groq, Claude, Gemini). Track whale bets, volume spikes, coordinated wallets, and 12 signal types. Free tier available.\n\n## Research Tools\n\n- [Synthical](https:\u002F\u002Fsynthical.com) - AI-powered collaborative environment for Research.\n- 🌟🌟 [TensorTrade](https:\u002F\u002Fgithub.com\u002Ftensortrade-org\u002Ftensortrade) - Trade efficiently with reinforcement learning.\n- [ML-Quant](https:\u002F\u002Fwww.ml-quant.com\u002F) - Quant resources from ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs.\n- [JAQS](https:\u002F\u002Fgithub.com\u002FquantOS-org\u002FJAQS) - An open source quant strategies research platform.\n- [pyfolio](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fpyfolio) - Portfolio and risk analytics in Python.\n- [alphalens](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Falphalens) - Performance analysis of predictive (alpha) stock factors.\n- [empyrical](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fempyrical) - Common financial risk and performance metrics. Used by Zipline and pyfolio.\n- [zvt](https:\u002F\u002Fgithub.com\u002Fzvtvz\u002Fzvt) - Zero vector trader.\n- [CongressionalStockBrain](https:\u002F\u002Fcongressionalstockbrain.com) - AI-powered tool that ingests U.S. STOCK Act congressional trade disclosures and converts them into machine-scored signals for retail investors.\n- [WFGY](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY) – Open source framework for debugging and stress testing LLM agents and RAG pipelines. Includes a 16 mode failure map and long-horizon stress tests that are useful for financial research agents.\n- [ChainPulse](https:\u002F\u002Fgithub.com\u002FBortlesboat\u002Fchainpulse) - AI-powered Bitcoin network intelligence CLI for natural language queries on mempool, fees, blocks, and mining analysis.\n- [CRNG](https:\u002F\u002Fgithub.com\u002Fbrotto\u002Fcrng) - Contingency RNG, generates random numbers with real market fat tails (K=5-220) and volatility clustering. Matches 86% of real market metrics vs 14% for NumPy. Includes regime detector.\n- [Chart Library](https:\u002F\u002Fchartlibrary.io) - Visual chart pattern search engine. Upload a screenshot or type a ticker+date to find the 10 most similar historical chart patterns and see what happened next. 24M+ embeddings, 19K symbols, REST API + MCP server.\n- [Coinugget](https:\u002F\u002Fcoinugget.com) - Real-time RSI signals, price action & volume spikes dashboard for crypto traders. Free, no sign-up required.\n\n## Trading System\n\nFor Back Test & Live trading\n\n### Traditional Market\n\n**System**\n\n- [the0](https:\u002F\u002Fgithub.com\u002Falexanderwanyoike\u002Fthe0) - Self-hosted execution engine for algorithmic trading bots. Supports Python, TypeScript, Rust, C++, C#, Scala, and Haskell. Each bot runs in an isolated container with scheduled or streaming execution.\n- 🌟🌟🌟 [OpenBB](https:\u002F\u002Fgithub.com\u002FOpenBB-finance\u002FOpenBB) - AI-powered opensource research and analytics workspace.\n- 🌟🌟 [zipline](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fzipline) - A python algorithmic trading library.\n- 🌟 [TradingView](http:\u002F\u002Ftradingview.com\u002F) - Get real-time information and market insights.\n- [rqalpha](https:\u002F\u002Fgithub.com\u002Fricequant\u002Frqalpha) - A extendable, replaceable Python algorithmic backtest & trading framework.\n- [backtrader](https:\u002F\u002Fgithub.com\u002Fbacktrader\u002Fbacktrader) - Python backtesting library for trading strategies.\n- [kungfu](https:\u002F\u002Fgithub.com\u002Ftaurusai\u002Fkungfu) - Kungfu Master trading system.\n- [finclaw](https:\u002F\u002Fgithub.com\u002FNeuZhou\u002Ffinclaw) - AI-native quantitative trading engine with 484 alpha factors, genetic algorithm strategy evolution, walk-forward backtesting and paper trading. Supports A-shares, crypto, and MCP server for AI agent integration.\n- [lean](https:\u002F\u002Fgithub.com\u002FQuantConnect\u002FLean) - Algorithmic trading engine built for easy strategy research, backtesting and live trading.\n\n**Combine & Rebuild**\n\n- [pylivetrader](https:\u002F\u002Fgithub.com\u002Falpacahq\u002Fpylivetrader) - Python live trade execution library with zipline interface.\n- [CoinMarketCapBacktesting](https:\u002F\u002Fgithub.com\u002FJimmyWuMadchester\u002FCoinMarketCapBacktesting) - As backtest frameworks for coin trading strategy.\n\n### Crypto Currencies\n\n- [zenbot](https:\u002F\u002Fgithub.com\u002FDeviaVir\u002Fzenbot) - Command-line crypto currency trading bot using Node.js and MongoDB.\n- [bot18](https:\u002F\u002Fgithub.com\u002Fcarlos8f\u002Fbot18) - High-frequency crypto currency trading bot developed by Zenbot.\n- [magic8bot](https:\u002F\u002Fgithub.com\u002Fmagic8bot\u002Fmagic8bot) - Crypto currency trading bot using Node.js and MongoDB.\n- [catalyst](https:\u002F\u002Fgithub.com\u002Fenigmampc\u002Fcatalyst) - An algorithmic trading library for Crypto-Assets in python.\n- [QuantResearchDev](https:\u002F\u002Fgithub.com\u002Fmounirlabaied\u002FQuantResearchDev) - Quant Research dev & Traders open source project.\n- [MACD](https:\u002F\u002Fgithub.com\u002Fsudoscripter\u002FMACD) - Zenbot MACD Auto-Trader.\n- [abu](https:\u002F\u002Fgithub.com\u002Fbbfamily\u002Fabu) - A quant trading system base on python.\n\n#### Plugins\n\n- [CoinMarketCapBacktesting](https:\u002F\u002Fgithub.com\u002FJimmyWuMadchester\u002FCoinMarketCapBacktesting) - Tests bt and Quantopian Zipline as backtesting frameworks for coin trading strategy.\n- [Gekko-BacktestTool](https:\u002F\u002Fgithub.com\u002FxFFFFF\u002FGekko-BacktestTool) - Batch backtest, import and strategy params optimalization for Gekko Trading Bot.\n\n## TA Lib\n\n- [pandas_talib](https:\u002F\u002Fgithub.com\u002Ffemtotrader\u002Fpandas_talib) - A Python Pandas implementation of technical analysis indicators.\n- [finta](https:\u002F\u002Fgithub.com\u002Fpeerchemist\u002Ffinta) - Common financial technical indicators implemented in Python-Pandas (70+ indicators).\n- [tulipnode](https:\u002F\u002Fgithub.com\u002FTulipCharts\u002Ftulipnode) - Official Node.js wrapper for Tulip Indicators. Provides over 100 technical analysis overlay and indicator functions.\n- [techan.js](https:\u002F\u002Fgithub.com\u002Fandredumas\u002Ftechan.js) - A visual, technical analysis and charting (Candlestick, OHLC, indicators) library built on D3.\n\n## Exchange API\n\nDo it in real world!\n\n- [Trade It](https:\u002F\u002Fdocs.tradeit.app\u002Fmcp) - MCP for trading on common brokerages (Robinhood, ETrade, Schwab, Webull, Public, tastytrade, Coinbase, Kraken so far)\n- [IbPy](https:\u002F\u002Fgithub.com\u002Fblampe\u002FIbPy) - Python API for the Interactive Brokers on-line trading system.\n- [HuobiFeeder](https:\u002F\u002Fgithub.com\u002Fmmmaaaggg\u002FHuobiFeeder) - Connect HUOBIPRO exchange, get market\u002Fhistorical data for ABAT trading platform backtest analysis and live trading.\n- [ctpwrapper](https:\u002F\u002Fgithub.com\u002Fnooperpudd\u002Fctpwrapper) - Shanghai future exchange CTP api.\n- [PENDAX](https:\u002F\u002Fgithub.com\u002FCompendiumFi\u002FPENDAX-SDK) - Javascript SDK for Trading\u002FData API and Websockets for cryptocurrency exchanges like FTX, FTXUS, OKX, Bybit, & More\n\n### Framework\n\n- [tf-quant-finance](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Ftf-quant-finance) - High-performance TensorFlow library for quantitative finance.\n\n### Visualizing\n\n- [playground](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fplayground) - Play with neural networks.\n- [netron](https:\u002F\u002Fgithub.com\u002Flutzroeder\u002Fnetron) - Visualizer for deep learning and machine learning models.\n- [KLineChart](https:\u002F\u002Fgithub.com\u002Fliihuu\u002FKLineChart) - Highly customizable professional lightweight financial charts\n\n### GYM Environment\n\n- 🌟 [TradingGym](https:\u002F\u002Fgithub.com\u002FYvictor\u002FTradingGym) - Trading and Backtesting environment for training reinforcement learning agent.\n- [TradzQAI](https:\u002F\u002Fgithub.com\u002Fkkuette\u002FTradzQAI) - Trading environment for RL agents, backtesting and training.\n- [btgym](https:\u002F\u002Fgithub.com\u002FKismuz\u002Fbtgym) - Scalable, event-driven, deep-learning-friendly backtesting library.\n\n## Articles\n\n- [The-Economist](https:\u002F\u002Fgithub.com\u002Fnailperry-zd\u002FThe-Economist) - The Economist.\n- [nyu-mlif-notes](https:\u002F\u002Fgithub.com\u002Fwizardforcel\u002Fnyu-mlif-notes) - NYU machine learning in finance notes.\n- [Using LSTMs to Turn Feelings Into Trades](https:\u002F\u002Fwww.quantopian.com\u002Fposts\u002Fwatch-our-webinar-buying-happiness-using-lstms-to-turn-feelings-into-trades-now?utm_source=forum&utm_medium=twitter&utm_campaign=sentiment-analysis)\n\n## Others\n\n- [zipline-tensorboard](https:\u002F\u002Fgithub.com\u002Fjimgoo\u002Fzipline-tensorboard) - TensorBoard as a Zipline dashboard.\n- [gekko-quasar-ui](https:\u002F\u002Fgithub.com\u002FH256\u002Fgekko-quasar-ui) - An UI port for gekko trading bot using Quasar framework.\n- [Floom](https:\u002F\u002Fgithub.com\u002FFloomAI\u002FFloom) AI gateway and marketplace for developers, enables streamlined integration and least volatile approach of AI features into products\n- [LendTrain](https:\u002F\u002Fwww.lendtrain.com) - AI-native mortgage refinance plugin for Claude Code with real-time institutional pricing, state-specific closing costs, FHA Streamline\u002FVA IRRRL detection, and regulatory compliance. Uses MCP (Model Context Protocol) to connect LLMs to live mortgage pricing.\n- [Registry Broker](https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fhashnet-mcp-js) - Universal AI agent index for discovering trading agents across Virtuals Protocol, NANDA, MCP, and other registries.\n- [KeepRule](https:\u002F\u002Fkeeprule.com) - AI-powered investment discipline tracking platform with curated principles from 26 legendary investors including Buffett, Munger, and Dalio. Helps traders maintain rational decision-making.\n- [Philidor](https:\u002F\u002Fdocs.philidor.io\u002Fdocs) - DeFi risk infrastructure for AI agents: MCP server and REST API for vault risk scores, portfolio analysis, and due diligence. No API key. 700+ vaults, 9 protocols, 6 chains.\n- [Hindsight](https:\u002F\u002Fhindsight.vectorize.io) - State-of-the-art long-term memory for AI agents by Vectorize. Open source, self-hostable, with integrations for LangChain, CrewAI, MCP, and more. Gives financial trading agents persistent memory across sessions.\n\n#### Other Resource\n\n- 🌟🌟🌟 [Stock-Prediction-Models](https:\u002F\u002Fgithub.com\u002Fhuseinzol05\u002FStock-Prediction-Models) - Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations.\n- 🌟🌟 [Financial Machine Learning](https:\u002F\u002Fgithub.com\u002Ffirmai\u002Ffinancial-machine-learning) - A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.\n- 🌟 [Awesome-Quant-Machine-Learning-Trading](https:\u002F\u002Fgithub.com\u002Fgrananqvist\u002FAwesome-Quant-Machine-Learning-Trading) - Quant \u002F Algorithm trading resources with an emphasis on Machine Learning.\n- [awesome-quant](https:\u002F\u002Fgithub.com\u002Fwilsonfreitas\u002Fawesome-quant) - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).\n- [FinancePy](https:\u002F\u002Fgithub.com\u002Fdomokane\u002FFinancePy) - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.\n- [Explore Finance Service Libraries & Projects](https:\u002F\u002Fkandi.openweaver.com\u002Fexplore\u002Ffinancial-services#Top-Authors) - Explore a curated list of Fintech popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources on kandi.\n- [AgentMarket](https:\u002F\u002Fagentmarket.cloud) - B2A marketplace for AI agents. 189 listings, 28M+ real energy data records, LangChain\u002FMCP integration.\n\n","# 金融领域的超酷AI [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome) [![社区](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F733027681184251937.svg?style=flat&label=加入社区&color=7289DA)](https:\u002F\u002Fdiscord.gg\u002FcqaUf47)\n\n全球金融市场每天都有数以百万计的交易发生。数据增长迅速，但人类难以完全理解。借助最新的人工智能研究成果，人们可以自动且智能地进行分析与交易。本列表汇集了用于战胜市场的研究、工具和代码。\n\n[[中文资源](.\u002Fchinese.md)]\n\n## 目录\n\n- [代理](#agents)\n- [大语言模型](#llms)\n- [论文](#papers)\n- [课程与书籍](#courses--books)\n- [策略与研究](#strategies--research)\n  - [时间序列数据](#time-series-data)\n  - [投资组合管理](#portfolio-management)\n  - [高频交易](#high-frequency-trading)\n  - [事件驱动](#event-drive)\n  - [加密货币策略](#crypto-currencies-strategies)\n  - [技术分析](#technical-analysis)\n  - [彩票与赌博](#lottery--gamble)\n  - [套利](#arbitrage)\n- [数据源](#data-sources)\n- [研究工具](#research-tools)\n- [交易系统](#trading-system)\n- [TA Lib](#ta-lib)\n- [交易所API](#exchange-api)\n- [文章](#articles)\n- [其他](#others)\n\n## 代理\n\n- 🌟🌟 [nofx](https:\u002F\u002Fgithub.com\u002FNoFxAiOS\u002Fnofx) - 多交易所人工智能交易平台，具备多AI竞争自进化机制及实时仪表盘。\n- [TradingAgents](https:\u002F\u002Fgithub.com\u002FTauricResearch\u002FTradingAgents) - 基于大语言模型的多代理金融交易框架。\n- 🌟 [FinRobot](https:\u002F\u002Fgithub.com\u002FAI4Finance-Foundation\u002FFinRobot) - 开源AI代理平台，利用大语言模型进行金融分析。\n- [AgentFund](https:\u002F\u002Fgithub.com\u002FRioBot-Grind\u002Fagentfund) - 去中心化AI代理众筹平台，在Base区块链上采用基于里程碑的托管机制。\n- 🌟 [ATLAS](https:\u002F\u002Fgithub.com\u002Fchrisworsey55\u002Fatlas-gic) - 自我改进型AI交易系统，包含25个代理、卡帕西风格的自动研究、达尔文式选择、自主代理生成以及多队列元加权。\n- [InvicTrade](https:\u002F\u002Finvictrade.com) - 基于AI的交易信号，历史胜率高达74%，结合多位传奇投资者的策略，运用多模型AI智能。\n- [OpenFinClaw](https:\u002F\u002Fgithub.com\u002FcryptoSUN2049\u002FopenFinclaw) - 原生AI的一人对冲基金平台。专家代理团队可在60秒内将自然语言转化为量化策略。支持多市场（美股、港股、A股、加密货币），具备自进化策略流水线及社区排行榜。\n- [ProfitPlay Agent Arena](https:\u002F\u002Fgithub.com\u002Fjarvismaximum-hue\u002Fprofitplay-starter) - 开放式预测市场竞技场，AI代理在其中实时参与BTC\u002FETH\u002FSOL预测游戏。提供Python和Node.js SDK，拥有9个实时市场，并支持REST + WebSocket API。\n\n## 大语言模型\n\n- 🌟🌟🌟 [Nof1](https:\u002F\u002Fthenof1.com\u002F) - 用于衡量AI投资能力的基准测试。每个模型均获得1万美元真实资金，在真实市场中使用相同提示和输入数据进行操作。\n- 🌟 [AI对冲基金](https:\u002F\u002Fgithub.com\u002Fvirattt\u002Fai-hedge-fund) - 探索如何利用AI做出交易决策。\n- 🌟🌟 [MarS](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FMarS) - 基于生成式基础模型的金融市场模拟引擎。\n- 🌟🌟 [大型语言模型在财务报表分析中的应用](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4835311) - GPT-4在预测未来盈利变化、生成有用叙事性见解以及制定更高夏普比率和阿尔法值的交易策略方面，表现优于专业财务分析师，这表明大语言模型有望在金融决策中发挥核心作用。\n- [FinRpt](https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.07322) - 用于股票研究报告生成的数据集、评估体系及基于大语言模型的多代理框架。\n- [PIXIU](https:\u002F\u002Fgithub.com\u002Fchancefocus\u002FPIXIU) - 开源资源，提供金融领域大型语言模型、包含13.6万条指令样本的数据集以及全面的评估基准。\n- [FinGPT](https:\u002F\u002Fgithub.com\u002FAI4Finance-Foundation\u002FFinGPT) - 为所有对金融领域的大语言模型和自然语言处理感兴趣的人士提供实践平台。\n- [MACD + RSI + ADX策略（由ChatGPT驱动）由TradeSmart提供](https:\u002F\u002Fwww.tradingview.com\u002Fscript\u002FGxkUyJKW-MACD-RSI-ADX-Strategy-ChatGPT-powered-by-TradeSmart\u002F ) - 向ChatGPT询问了最流行的交易指标。我们采纳了其所有建议。\n- [一款由ChatGPT开发的交易算法在股市中实现了500%的回报。我对这对对冲基金和散户投资者意味着什么的解读](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FChatGPT\u002Fcomments\u002F13duech\u002Fa_chatgpt_trading_algorithm_delivered_500_returns\u002F)\n- [使用ChatGPT调整策略参数](https:\u002F\u002Ftwitter.com\u002F0xUnicorn\u002Fstatus\u002F1663413848593031170)\n- [动手实践大语言模型：训练并部署实时金融顾问](https:\u002F\u002Fgithub.com\u002Fiusztinpaul\u002Fhands-on-llms) - 使用Falcon 7B和CometLLM训练并部署实时金融顾问聊天机器人。\n- [OctoBot的ChatGPT策略](https:\u002F\u002Fblog.octobot.online\u002Ftrading-using-chat-gpt) - 利用ChatGPT根据技术指标决定交易哪种加密货币。\n- [大语言模型与金融](https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.13125) - 一种三阶段微调流程（SFT → DPO → 合成数据强化学习），将Qwen2.5和DeepSeek-R1适配到Open FinLLM排行榜上的金融任务中，同时探讨了金融领域的跨任务迁移和数据规模法则。\n\n## 技能\n\n- [XVARY股票研究](https:\u002F\u002Fgithub.com\u002Fxvary-research\u002Fclaude-code-stock-analysis-skill) — Claude Code技能，适用于公开的SEC EDGAR数据及市场数据：`\u002Fanalyze`、`\u002Fscore`、`\u002Fcompare`。MIT。\n\n## 论文\n\n- [投机理论 L. 巴舍利耶，1900年](http:\u002F\u002Fwww.radio.goldseek.com\u002Fbachelier-thesis-theory-of-speculation-en.pdf) - 决定证券交易所波动的因素是。\n- [股市中的布朗运动 奥斯本，1959年](http:\u002F\u002Fm.e-m-h.org\u002FOsbo59.pdf) - 普通股价格可以被视为处于统计平衡状态的一组决策。\n- [算法交易领域中强化学习技术应用研究，2015年](http:\u002F\u002Fwww.doc.ic.ac.uk\u002Fteaching\u002Fdistinguished-projects\u002F2015\u002Fj.cumming.pdf)\n- [用于金融投资组合管理问题的深度强化学习框架](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1706.10059.pdf)\n- [1994年：强化学习在交易中的应用](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F1551-reinforcement-learning-for-trading.pdf)\n- [龙王、黑天鹅与危机预测 迪迪埃·索内特](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0907.4290.pdf) - 在各种系统中广泛存在的条件下，事件规模分布中的幂律现象。\n- [金融交易作为博弈：一种深度强化学习方法](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.02787.pdf) - 深度强化学习为这种交易智能体的端到端训练提供了一个框架。\n- [机器学习在交易中的应用](https:\u002F\u002Fcims.nyu.edu\u002F~ritter\u002Fritter2017machine.pdf) - 通过合理选择奖励函数，强化学习技术可以成功应对风险规避的情况。\n- [2018年：机器学习的十大金融应用](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=3197726) - 幻灯片回顾了几种重要的金融机器学习应用。\n- [FinRL：量化金融中用于自动化股票交易的深度强化学习库，2020年](https:\u002F\u002Farxiv.org\u002Fabs\u002F2011.09607) - 介绍一个名为FinRL的深度强化学习库，帮助初学者接触量化金融并开发自己的股票交易策略。\n- [2020年：基于深度强化学习的自动化股票交易——集成策略](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=3690996) - 提出一种集成策略，利用深度强化学习方案来学习最大化投资回报的股票交易策略。\n\n## 课程、书籍和博客\n\n- 🌟 [QuantResearch](https:\u002F\u002Fgithub.com\u002Fletianzj\u002FQuantResearch) - 定量分析、策略与回测 https:\u002F\u002Fletianzj.github.io\u002F\n- [纽约大学：金融领域强化学习高级方法概述](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fadvanced-methods-reinforcement-learning-finance\u002Fhome\u002Fwelcome)\n- [优达学城：用于交易的人工智能](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fai-for-trading--nd880)\n- [金融人工智能](https:\u002F\u002Fcfte.education\u002F) - 在线学习金融科技。\n- [Advanced-Deep-Trading](https:\u002F\u002Fgithub.com\u002FRachnog\u002FAdvanced-Deep-Trading) - 基于《金融机器学习进展》一书的实验。\n- [金融机器学习进展](https:\u002F\u002Fwww.amazon.com\u002FAdvances-Financial-Machine-Learning-Marcos-ebook\u002Fdp\u002FB079KLDW21\u002Fref=sr_1_1?s=books&ie=UTF8&qid=1541717436&sr=1-1) - 利用先进的机器学习解决方案来克服现实世界的投资难题。\n- [使用生成式AI构建金融软件](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fbuild-financial-software-with-generative-ai?ar=false&lpse=B&) - 一本关于如何使用ChatGPT和Copilot等生成式AI工具动手构建金融软件的书。\n- [金融AI实战](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Ffinancial-ai-in-practice) - 一本关于创建盈利且符合监管要求的金融应用程序的书。\n- [程序员的投资之道](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Finvesting-for-programmers) - 一本关于如何利用Python和生成式AI来最大化投资组合、分析市场并做出数据驱动型投资决策的书。\n- [精通Python金融编程](https:\u002F\u002Fgithub.com\u002Fjamesmawm\u002Fmastering-python-for-finance-second-edition) - 《精通Python金融编程（第二版）》的源代码。\n- [MLSys-NYU-2022](https:\u002F\u002Fgithub.com\u002Fjacopotagliabue\u002FMLSys-NYU-2022\u002Ftree\u002Fmain) - 纽约大学坦登工程学院2022年“金融中的机器学习”课程的幻灯片、脚本和相关资料。\n- [训练并部署无服务器API以预测加密货币价格](https:\u002F\u002Fgithub.com\u002FPaulescu\u002Fhands-on-train-and-deploy-ml) - 在本教程中，你不会构建一个让你一夜暴富的机器学习系统。但你将掌握所需的MLOps框架和工具，这些工具结合大量的实验，最终可能带你走向成功。\n- [KeepRule](https:\u002F\u002Fkeeprule.com) - 一款由AI驱动的投资纪律平台，其原则源自包括巴菲特、芒格和达利欧在内的26位传奇投资者。\n\n## 策略与研究\n\n### 时间序列数据\n\n带有技术分析指标的价格与成交量数据处理\n\n- 🌟🌟 [stockpredictionai](https:\u002F\u002Fgithub.com\u002Fborisbanushev\u002Fstockpredictionai) - 一个完整的股票价格走势预测流程。\n- 🌟 [Personae](https:\u002F\u002Fgithub.com\u002FCeruleanacg\u002FPersonae) - 实现了用于量化交易的深度强化学习与监督学习环境。\n- 🌟 [Ensemble-Strategy](https:\u002F\u002Fgithub.com\u002FAI4Finance-LLC\u002FDeep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020) - 面向自动化股票交易的深度强化学习。\n- [FinRL](https:\u002F\u002Fgithub.com\u002FAI4Finance-LLC\u002FFinRL-Library) - 用于量化金融中自动化股票交易的深度强化学习库。\n- [AutomatedStockTrading-DeepQ-Learning](https:\u002F\u002Fgithub.com\u002Fsachink2010\u002FAutomatedStockTrading-DeepQ-Learning) - 构建深度Q学习强化代理模型作为自动化交易机器人。\n- [tf_deep_rl_trader](https:\u002F\u002Fgithub.com\u002Fmiroblog\u002Ftf_deep_rl_trader) - 交易环境（OpenAI Gym）+ PPO（TensorForce）。\n- [trading-gym](https:\u002F\u002Fgithub.com\u002F6-Billionaires\u002Ftrading-gym) - 用于训练短期交易智能体的交易环境。\n- [trading-rl](https:\u002F\u002Fgithub.com\u002FKostis-S-Z\u002Ftrading-rl) - 基于价格追踪的金融交易深度强化学习。\n- [deep_rl_trader](https:\u002F\u002Fgithub.com\u002Fmiroblog\u002Fdeep_rl_trader) - 交易环境（OpenAI Gym）+ DDQN（Keras-RL）。\n- [Quantitative-Trading](https:\u002F\u002Fgithub.com\u002FCeruleanacg\u002FQuantitative-Trading) - 实现量化交易的相关论文与代码。\n- [gym-trading](https:\u002F\u002Fgithub.com\u002Fhackthemarket\u002Fgym-trading) - 用于强化学习算法交易模型的环境。\n- [zenbrain](https:\u002F\u002Fgithub.com\u002Fcarlos8f\u002Fzenbrain) - 机器学习机器人框架。\n- [DeepLearningNotes](https:\u002F\u002Fgithub.com\u002FAlphaSmartDog\u002FDeepLearningNotes) - 量化分析中的机器学习应用。\n- [stock_market_reinforcement_learning](https:\u002F\u002Fgithub.com\u002Fkh-kim\u002Fstock_market_reinforcement_learning) - 使用Keras实现的深度强化学习，结合OpenAI Gym环境进行股市交易。\n- [Chaos Genius](https:\u002F\u002Fgithub.com\u002Fchaos-genius\u002Fchaos_genius) - 基于机器学习的异常检测与根本原因分析引擎。\n- [mlforecast](https:\u002F\u002Fgithub.com\u002FNixtla\u002Fmlforecast) - 可扩展的基于机器学习的时间序列预测工具。\n- [patternity](https:\u002F\u002Fgithub.com\u002Fquantium-ai\u002Fpatternity) - 一种确定性算法，专注于历史数据中的模式识别，用于股票价格预测。\n- [Quantium Research](https:\u002F\u002Fgithub.com\u002Fquantium-ai\u002Fresearch) - 探索非常规量化技术的研究实验。\n\n### 投资组合管理\n\n- [Deep-Reinforcement-Stock-Trading](https:\u002F\u002Fgithub.com\u002FAlbert-Z-Guo\u002FDeep-Reinforcement-Stock-Trading) - 一个轻量级的深度强化学习框架，用于投资组合管理。\n- [qtrader](https:\u002F\u002Fgithub.com\u002Ffilangel\u002Fqtrader) - 用于投资组合管理的强化学习方法。\n- [PGPortfolio](https:\u002F\u002Fgithub.com\u002FZhengyaoJiang\u002FPGPortfolio) - 针对金融投资组合管理问题的深度强化学习框架。\n- [DeepDow](https:\u002F\u002Fgithub.com\u002Fjankrepl\u002Fdeepdow) - 利用深度学习进行投资组合优化。\n- [skfolio](https:\u002F\u002Fgithub.com\u002Fskfolio\u002Fskfolio) - 基于scikit-learn构建的投资组合优化Python库。\n\n### 高频交易\n\n- [High-Frequency-Trading-Model-with-IB](https:\u002F\u002Fgithub.com\u002Fjamesmawm\u002FHigh-Frequency-Trading-Model-with-IB) - 使用Interactive Brokers API，结合配对交易和均值回归策略的高频交易模型。\n- 🌟 [SGX-Full-OrderBook-Tick-Data-Trading-Strategy](https:\u002F\u002Fgithub.com\u002Frorysroes\u002FSGX-Full-OrderBook-Tick-Data-Trading-Strategy) - 基于全订单簿逐笔数据，运用数据科学方法（机器学习）开发的高频交易（HFT）策略解决方案。\n- [HFT_Bitcoin](https:\u002F\u002Fgithub.com\u002Fghgr\u002FHFT_Bitcoin) - 对比特币交易所高频交易的分析。\n\n### 事件驱动\n\n- 🌟🌟 [stockpredictionai](https:\u002F\u002Fgithub.com\u002Fborisbanushev\u002Fstockpredictionai) - 完整的股票价格走势预测流程。\n- 🌟 [trump2cash](https:\u002F\u002Fgithub.com\u002Fmaxbbraun\u002Ftrump2cash) - 一款由特朗普推文驱动的股票交易机器人。\n\n### 加密货币策略\n\n- [LSTM-Crypto-Price-Prediction](https:\u002F\u002Fgithub.com\u002FSC4RECOIN\u002FLSTM-Crypto-Price-Prediction) - 使用LSTM-RNN预测加密货币市场的价格趋势，以指导交易。\n- [tforce_btc_trader](https:\u002F\u002Fgithub.com\u002Flefnire\u002Ftforce_btc_trader) - TensorForce比特币交易机器人。\n- [Tensorflow-NeuroEvolution-Trading-Bot](https:\u002F\u002Fgithub.com\u002FSC4RECOIN\u002FTensorflow-NeuroEvolution-Trading-Bot) - 一种种群模型，可迭代地繁殖和变异，用于加密货币交易。\n- [gekkoga](https:\u002F\u002Fgithub.com\u002Fgekkowarez\u002Fgekkoga) - 使用Gekko求解交易策略优化问题的遗传算法。\n- [Gekko_ANN_Strategies](https:\u002F\u002Fgithub.com\u002Fmarkchen8717\u002FGekko_ANN_Strategies) - 针对Gekko交易机器人设计的ANN交易策略。\n- [gekko-neuralnet](https:\u002F\u002Fgithub.com\u002Fzschro\u002Fgekko-neuralnet) - Gekko的神经网络交易策略。\n- [bitcoin_prediction](https:\u002F\u002Fgithub.com\u002FllSourcell\u002Fbitcoin_prediction) - Siraj Raval在YouTube上发布的“比特币预测”相关代码。\n\n### 技术分析\n\n- [QTradeX](https:\u002F\u002Fgithub.com\u002FsquidKid-deluxe\u002FQTradeX-Algo-Trading-SDK) - 一个功能强大且灵活的 Python 框架，用于设计、回测、优化和部署算法交易机器人。\n- [quant-trading](https:\u002F\u002Fgithub.com\u002Fje-suis-tm\u002Fquant-trading) - Python 量化交易策略。\n- [Gekko-Bot-Resources](https:\u002F\u002Fgithub.com\u002Fcloggy45\u002FGekko-Bot-Resources) - Gekko 机器人资源。\n- [gekko_tools](https:\u002F\u002Fgithub.com\u002Ftommiehansen\u002Fgekko_tools) - Gekko 策略、工具等。\n- [gekko RSI_WR](https:\u002F\u002Fgithub.com\u002Fzzmike76\u002Fgekko) - Gekko RSI_WR 策略。\n- [gekko HL](https:\u002F\u002Fgithub.com\u002Fmounirlabaied\u002Fgekko-strat-hl) - 计算下行峰值并据此交易。\n- [EthTradingAlgorithm](https:\u002F\u002Fgithub.com\u002FPhilipid3s\u002FEthTradingAlgorithm) - 使用 Python 3.5 和 ZipLine 库的以太坊交易算法。\n- [gekko_trading_stuff](https:\u002F\u002Fgithub.com\u002Fthegamecat\u002Fgekko-trading-stuff) - 强大的加密货币交易平台。\n- [forex.analytics](https:\u002F\u002Fgithub.com\u002Fmkmarek\u002Fforex.analytics) - 基于 OHLC 数据集，利用遗传算法进行技术分析的 Node.js 原生库。\n- [Bitcoin_MACD_Strategy](https:\u002F\u002Fgithub.com\u002FVermeirJellen\u002FBitcoin_MACD_Strategy) - 比特币 MACD 交叉交易策略回测。\n- [crypto-signal](https:\u002F\u002Fgithub.com\u002FCryptoSignal\u002Fcrypto-signal) - 针对 Bittrex、Binance、GDAX 等平台的自动化加密货币交易及技术分析（TA）机器人。\n- [Gekko-Strategies](https:\u002F\u002Fgithub.com\u002FxFFFFF\u002FGekko-Strategies) - Gekko 交易机器人的策略，附带回测结果和一些实用工具。\n- [gekko-gannswing](https:\u002F\u002Fgithub.com\u002Fjohndoe75\u002Fgekko-gannswing) - Gekko 交易机器人使用的江恩摆动交易策略。\n- [Chartscout](https:\u002F\u002Fchartscout.io) - 基于模式识别算法，实时检测加密货币图表形态并发出自动预警。\n* [MarginSafe.ai](https:\u002F\u002Fmarginsafe.ai) - 专注于内在价值和威科夫时机判断的人工智能股票分析平台。\n\n### 彩票与博彩\n\n- [LotteryPredict](https:\u002F\u002Fgithub.com\u002Fchengstone\u002FLotteryPredict) - 使用 LSTM 预测彩票号码。\n\n### 套利\n\n- [ArbitrageBot](https:\u002F\u002Fgithub.com\u002FBatuhanUsluel\u002FArbitrageBot) - 目前可在 Bittrex 和 Poloniex 上运行的套利机器人。\n- [r2](https:\u002F\u002Fgithub.com\u002Fbitrinjani\u002Fr2) - 由 Node.js + TypeScript 驱动的自动化套利交易系统。\n- [cryptocurrency-arbitrage](https:\u002F\u002Fgithub.com\u002Fmanu354\u002Fcryptocurrency-arbitrage) - 一款加密货币套利机会计算器，覆盖超过 800 种币种和 50 个市场。\n- [bitcoin-arbitrage](https:\u002F\u002Fgithub.com\u002Fmaxme\u002Fbitcoin-arbitrage) - 比特币套利机会检测器。\n- [blackbird](https:\u002F\u002Fgithub.com\u002Fbutor\u002Fblackbird) - 多空市场中性策略。\n\n## 数据源\n\n#### 传统市场\n\n- 🌟 [Quandl](https:\u002F\u002Fwww.quandl.com\u002Ftools\u002Fapi) - 通过一个免费的API，从数百家数据提供商获取数百万个金融和经济数据集。\n- [yahoo-finance](https:\u002F\u002Fgithub.com\u002Flukaszbanasiak\u002Fyahoo-finance) - 用于从Yahoo! Finance获取股票数据的Python模块。\n- [Tushare](https:\u002F\u002Fgithub.com\u002Fwaditu\u002Ftushare) - TuShare是一个用于抓取中国股票历史数据的工具。\n- [Congressional Stock Brain](https:\u002F\u002Fcongressionalstockbrain.com) - 免费的AI驱动工具，根据重要性对美国《STOCK法案》中的议员交易披露进行评分。考虑委员会权重、时机分析，跟踪537名议员。\n- [Financial Data](https:\u002F\u002Ffinancialdata.net\u002F) - 股票市场和金融数据API。\n- [StockAInsights](https:\u002F\u002Fstockainsights.com) - 提供机构级财务报表API，利用AI从SEC文件中提取数据——而非XBRL格式。覆盖国内外申报者（20-F、6-K、40-F），提供标准化的季度和年度数据。\n- [ValueRay](https:\u002F\u002Fwww.valueray.com\u002Fapi) - 提供股票和ETF的技术、量化及情绪数据，并附带风险指标、同行百分位排名和市场状态信号。专为AI\u002FLLM代理优化。\n\n#### 加密货币\n\n- [CryptoInscriber](https:\u002F\u002Fgithub.com\u002FOptixal\u002FCryptoInscriber) - 实时加密货币历史交易记录簿。可从任何加密交易所下载实时历史交易数据。\n- [CoinPulse](https:\u002F\u002Fgithub.com\u002Fsoutone\u002Fcoinpulse-python) - 用于加密货币投资组合追踪的Python SDK，提供实时价格、盈亏计算、回测和价格提醒功能。免费版：每小时25次请求。\n- [Gekko-Datasets](https:\u002F\u002Fgithub.com\u002FxFFFFF\u002FGekko-Datasets) - Gekko交易机器人数据转储。可下载并使用SQLite格式的历史数据文件。\n- [Frostbyte Crypto API](https:\u002F\u002Fagent-gateway-kappa.vercel.app) - 免费的实时加密货币价格数据API。支持BTC、ETH、SOL等20多种代币。基础端点无需注册或API密钥。返回JSON格式响应，包含价格、24小时变化、市值和交易量。\n- [CoinPaprika API](https:\u002F\u002Fapi.coinpaprika.com) - 免费的加密货币市场数据API，提供7,000多种币种的价格、成交量、市值和OHLCV数据。无需API密钥。包含MCP服务器，便于AI代理集成。\n- [DexPaprika API](https:\u002F\u002Fapi.dexpaprika.com) - 免费的DEX和DeFi数据API——实时流动性池数据、代币价格、OHLCV以及跨链交易历史。无需API密钥，无速率限制。包含MCP服务器，供AI代理使用。\n- [Philidor](https:\u002F\u002Fdocs.philidor.io\u002Fdocs) - 针对9个协议、6条链上700多个金库的机构级DeFi风险评分。提供REST API和MCP服务器（Claude、Cursor、Windsurf）。确定性的0–10风险评分、等级划分（Prime\u002FCore\u002FEdge）、投资组合分析、预言机监控。无需API密钥。\n- [PreReason](https:\u002F\u002Fwww.prereason.com) - 预先分析好的金融市场简报，专为AI代理消费优化。共17份简报，涵盖BTC链上数据、宏观因素（美联储资产负债表、M2、国债收益率）以及跨资产相关性。以Markdown格式返回市场状态分类、趋势信号和置信度评分。\n- [Satoshi API](https:\u002F\u002Fgithub.com\u002FBortlesboat\u002Fbitcoin-api) - 比特币费用情报API，提供108个端点，用于费用估算、内存池分析、区块数据和挖矿统计。可自行托管，采用Apache 2.0许可。\n- [TBD Predict](https:\u002F\u002Fgithub.com\u002Fego-protocol\u002Ftbd-vote-cli) - 基于Solana的预测市场，用于收集人类意见。提供代理CLI和AGENTS.md规范，允许AI代理通过JSON友好的命令进行身份验证、列出意见活动并下注。\n\n#### 新闻数据\n\n- [WorldMonitor](https:\u002F\u002Fgithub.com\u002Fkoala73\u002Fworldmonitor) - AI驱动的新闻聚合、地缘政治监测和基础设施跟踪，整合在一个统一的情势感知界面中。\n\n#### 替代数据\n\n- [Pizzint](https:\u002F\u002Fwww.pizzint.watch\u002F) - 五角大楼披萨指数（PizzINT）是一个实时的五角大楼周边披萨店活动追踪器，能够可视化异常活动。该信号通常与深夜高节奏行动和突发新闻相关联。\n\n#### 预测市场\n\n- [Parsec API](https:\u002F\u002Fdocs.parsecapi.com) - 统一的预测市场基础设施，适用于Polymarket、Kalshi、Opinion、Limitless和PredictFun等平台，提供标准化数据、执行和实时流。配备MCP服务器，支持AI代理交易。提供慷慨的免费层级。\n- [PolyMind](https:\u002F\u002Fpolyminds.netlify.app\u002F) - 实时Polymarket交易提醒，结合多AI分析（Groq、Claude、Gemini）。可追踪鲸鱼投注、成交量激增、协同钱包操作以及12种信号类型。提供免费层级。\n\n## 研究工具\n\n- [Synthical](https:\u002F\u002Fsynthical.com) - AI驱动的协作式研究环境。\n- 🌟🌟 [TensorTrade](https:\u002F\u002Fgithub.com\u002Ftensortrade-org\u002Ftensortrade) - 利用强化学习高效交易。\n- [ML-Quant](https:\u002F\u002Fwww.ml-quant.com\u002F) - 来自ArXiv（sanity）、SSRN、RePec、期刊、播客、视频和博客的量化资源。\n- [JAQS](https:\u002F\u002Fgithub.com\u002FquantOS-org\u002FJAQS) - 开源量化策略研究平台。\n- [pyfolio](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fpyfolio) - Python中的投资组合和风险分析工具。\n- [alphalens](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Falphalens) - 用于分析预测性（alpha）股票因子表现的工具。\n- [empyrical](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fempyrical) - 常用的金融风险和绩效指标。被Zipline和pyfolio使用。\n- [zvt](https:\u002F\u002Fgithub.com\u002Fzvtvz\u002Fzvt) - 零向量交易者。\n- [CongressionalStockBrain](https:\u002F\u002Fcongressionalstockbrain.com) - AI驱动的工具，用于解析美国《STOCK法案》中的议员交易披露，并将其转化为面向散户投资者的机器评分信号。\n- [WFGY](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY) - 开源框架，用于调试和压力测试LLM代理及RAG管道。包含16种故障模式图和长周期压力测试，对金融研究代理非常有用。\n- [ChainPulse](https:\u002F\u002Fgithub.com\u002FBortlesboat\u002Fchainpulse) - AI驱动的比特币网络情报CLI，可通过自然语言查询内存池、手续费、区块和挖矿分析。\n- [CRNG](https:\u002F\u002Fgithub.com\u002Fbrotto\u002Fcrng) - 应急随机数生成器，能生成具有真实市场厚尾分布（K=5-220）和波动率聚类的随机数。其结果与真实市场的匹配度达86%，而NumPy仅为14%。内置市场状态检测器。\n- [Chart Library](https:\u002F\u002Fchartlibrary.io) - 可视化图表形态搜索引擎。上传截图或输入股票代码+日期，即可找到最相似的10个历史图表形态，并查看后续走势。拥有2400万条嵌入数据、19,000个标的，提供REST API和MCP服务器。\n- [Coinugget](https:\u002F\u002Fcoinugget.com) - 面向加密货币交易者的实时RSI信号、价格行为及成交量激增仪表盘。免费，无需注册。\n\n## 交易系统\n\n用于回测与实盘交易\n\n### 传统市场\n\n**系统**\n\n- [the0](https:\u002F\u002Fgithub.com\u002Falexanderwanyoike\u002Fthe0) - 自托管的算法交易机器人执行引擎。支持 Python、TypeScript、Rust、C++、C#、Scala 和 Haskell。每个机器人运行在隔离的容器中，可按计划或流式执行。\n- 🌟🌟🌟 [OpenBB](https:\u002F\u002Fgithub.com\u002FOpenBB-finance\u002FOpenBB) - 基于 AI 的开源研究与分析工作区。\n- 🌟🌟 [zipline](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fzipline) - 一个 Python 算法交易库。\n- 🌟 [TradingView](http:\u002F\u002Ftradingview.com\u002F) - 获取实时信息和市场洞察。\n- [rqalpha](https:\u002F\u002Fgithub.com\u002Fricequant\u002Frqalpha) - 一个可扩展、可替换的 Python 算法回测与交易框架。\n- [backtrader](https:\u002F\u002Fgithub.com\u002Fbacktrader\u002Fbacktrader) - 用于交易策略的 Python 回测库。\n- [kungfu](https:\u002F\u002Fgithub.com\u002Ftaurusai\u002Fkungfu) - 功夫大师交易系统。\n- [finclaw](https:\u002F\u002Fgithub.com\u002FNeuZhou\u002Ffinclaw) - 原生 AI 的量化交易引擎，内置 484 种阿尔法因子、遗传算法策略进化、向前递推回测及模拟交易功能。支持 A 股、加密货币，并提供 MCP 服务器以集成 AI 代理。\n- [lean](https:\u002F\u002Fgithub.com\u002FQuantConnect\u002FLean) - 专为便捷策略研究、回测和实盘交易而构建的算法交易引擎。\n\n**组合与重建**\n\n- [pylivetrader](https:\u002F\u002Fgithub.com\u002Falpacahq\u002Fpylivetrader) - 具有 zipline 接口的 Python 实时交易执行库。\n- [CoinMarketCapBacktesting](https:\u002F\u002Fgithub.com\u002FJimmyWuMadchester\u002FCoinMarketCapBacktesting) - 作为币种交易策略的回测框架。\n\n### 加密货币\n\n- [zenbot](https:\u002F\u002Fgithub.com\u002FDeviaVir\u002Fzenbot) - 使用 Node.js 和 MongoDB 的命令行加密货币交易机器人。\n- [bot18](https:\u002F\u002Fgithub.com\u002Fcarlos8f\u002Fbot18) - 由 Zenbot 开发的高频加密货币交易机器人。\n- [magic8bot](https:\u002F\u002Fgithub.com\u002Fmagic8bot\u002Fmagic8bot) - 使用 Node.js 和 MongoDB 的加密货币交易机器人。\n- [catalyst](https:\u002F\u002Fgithub.com\u002Fenigmampc\u002Fcatalyst) - 用于加密资产的 Python 算法交易库。\n- [QuantResearchDev](https:\u002F\u002Fgithub.com\u002Fmounirlabaied\u002FQuantResearchDev) - 量化研究开发者与交易员的开源项目。\n- [MACD](https:\u002F\u002Fgithub.com\u002Fsudoscripter\u002FMACD) - Zenbot MACD 自动交易器。\n- [abu](https:\u002F\u002Fgithub.com\u002Fbbfamily\u002Fabu) - 基于 Python 的量化交易系统。\n\n#### 插件\n\n- [CoinMarketCapBacktesting](https:\u002F\u002Fgithub.com\u002FJimmyWuMadchester\u002FCoinMarketCapBacktesting) - 测试 bt 和 Quantopian Zipline 作为币种交易策略的回测框架。\n- [Gekko-BacktestTool](https:\u002F\u002Fgithub.com\u002FxFFFFF\u002FGekko-BacktestTool) - 用于 Gekko 交易机器人的批量回测、导入及策略参数优化工具。\n\n## 技术分析库\n\n- [pandas_talib](https:\u002F\u002Fgithub.com\u002Ffemtotrader\u002Fpandas_talib) - Python Pandas 实现的技术分析指标。\n- [finta](https:\u002F\u002Fgithub.com\u002Fpeerchemist\u002Ffinta) - 在 Python-Pandas 中实现的常用金融技术指标（70 多种）。\n- [tulipnode](https:\u002F\u002Fgithub.com\u002FTulipCharts\u002Ftulipnode) - Tulip 指标的官方 Node.js 封装。提供超过 100 种技术分析叠加和指标函数。\n- [techan.js](https:\u002F\u002Fgithub.com\u002Fandredumas\u002Ftechan.js) - 基于 D3 构建的可视化技术分析与图表库（蜡烛图、OHLC、指标等）。\n\n## 交易所 API\n\n在真实世界中操作！\n\n- [Trade It](https:\u002F\u002Fdocs.tradeit.app\u002Fmcp) - 用于在常见券商（Robinhood、ETrade、Schwab、Webull、Public、tastytrade、Coinbase、Kraken 等）上进行交易的 MCP。\n- [IbPy](https:\u002F\u002Fgithub.com\u002Fblampe\u002FIbPy) - 交互式经纪商在线交易系统的 Python API。\n- [HuobiFeeder](https:\u002F\u002Fgithub.com\u002Fmmmaaaggg\u002FHuobiFeeder) - 连接 HUOBIPRO 交易所，获取市场\u002F历史数据，用于 ABAT 交易平台的回测分析和实盘交易。\n- [ctpwrapper](https:\u002F\u002Fgithub.com\u002Fnooperpudd\u002Fctpwrapper) - 上海期货交易所 CTP API。\n- [PENDAX](https:\u002F\u002Fgithub.com\u002FCompendiumFi\u002FPENDAX-SDK) - 用于 FTX、FTXUS、OKX、Bybit 等加密货币交易所的交易\u002F数据 API 和 WebSockets 的 JavaScript SDK。\n\n### 框架\n\n- [tf-quant-finance](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Ftf-quant-finance) - 高性能的 TensorFlow 量化金融库。\n\n### 可视化\n\n- [playground](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fplayground) - 与神经网络互动。\n- [netron](https:\u002F\u002Fgithub.com\u002Flutzroeder\u002Fnetron) - 深度学习和机器学习模型的可视化工具。\n- [KLineChart](https:\u002F\u002Fgithub.com\u002Fliihuu\u002FKLineChart) - 高度可定制的专业轻量级金融图表。\n\n### GYM 环境\n\n- 🌟 [TradingGym](https:\u002F\u002Fgithub.com\u002FYvictor\u002FTradingGym) - 用于训练强化学习智能体的交易与回测环境。\n- [TradzQAI](https:\u002F\u002Fgithub.com\u002Fkkuette\u002FTradzQAI) - 用于 RL 智能体训练、回测的交易环境。\n- [btgym](https:\u002F\u002Fgithub.com\u002FKismuz\u002Fbtgym) - 可扩展、事件驱动、适合深度学习的回测库。\n\n## 文章\n\n- [The-Economist](https:\u002F\u002Fgithub.com\u002Fnailperry-zd\u002FThe-Economist) - 《经济学人》。\n- [nyu-mlif-notes](https:\u002F\u002Fgithub.com\u002Fwizardforcel\u002Fnyu-mlif-notes) - 纽约大学金融领域的机器学习笔记。\n- [使用 LSTM 将情绪转化为交易](https:\u002F\u002Fwww.quantopian.com\u002Fposts\u002Fwatch-our-webinar-buying-happiness-using-lstms-to-turn-feelings-into-trades-now?utm_source=forum&utm_medium=twitter&utm_campaign=sentiment-analysis)\n\n## 其他\n\n- [zipline-tensorboard](https:\u002F\u002Fgithub.com\u002Fjimgoo\u002Fzipline-tensorboard) - 将 TensorBoard 用作 Zipline 的仪表板。\n- [gekko-quasar-ui](https:\u002F\u002Fgithub.com\u002FH256\u002Fgekko-quasar-ui) - 使用 Quasar 框架为 Gekko 交易机器人开发的 UI 端口。\n- [Floom](https:\u002F\u002Fgithub.com\u002FFloomAI\u002FFloom) 专为开发者打造的 AI 网关与市场，支持将 AI 功能以最简化且波动最小的方式无缝集成到产品中。\n- [LendTrain](https:\u002F\u002Fwww.lendtrain.com) - 面向 Claude Code 的原生 AI 抵押贷款再融资插件，提供实时机构级定价、各州特定的结案成本、FHA Streamline 和 VA IRRRL 自动检测以及合规性保障。采用 MCP（模型上下文协议）将大语言模型与实时抵押贷款定价系统连接。\n- [Registry Broker](https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fhashnet-mcp-js) - 通用 AI 代理索引，用于跨 Virtuals 协议、NANDA、MCP 及其他注册中心发现交易代理。\n- [KeepRule](https:\u002F\u002Fkeeprule.com) - 基于 AI 的投资纪律跟踪平台，汇集了包括巴菲特、芒格和达利欧在内的 26 位传奇投资者的精选原则，帮助交易者保持理性决策。\n- [Philidor](https:\u002F\u002Fdocs.philidor.io\u002Fdocs) - 专为 AI 代理设计的 DeFi 风险基础设施：提供用于金库风险评分、投资组合分析及尽职调查的 MCP 服务器和 REST API。无需 API 密钥，覆盖 700 多个金库、9 个协议和 6 条链。\n- [Hindsight](https:\u002F\u002Fhindsight.vectorize.io) - Vectorize 推出的面向 AI 代理的先进长期记忆解决方案。开源且可自行部署，支持与 LangChain、CrewAI、MCP 等集成，为金融交易代理提供跨会话的持久化记忆。\n\n#### 其他资源\n\n- 🌟🌟🌟 [Stock-Prediction-Models](https:\u002F\u002Fgithub.com\u002Fhuseinzol05\u002FStock-Prediction-Models) - 股票预测模型合集，汇集了用于股票预测的机器学习和深度学习模型，包含交易机器人和模拟工具。\n- 🌟🌟 [Financial Machine Learning](https:\u002F\u002Fgithub.com\u002Ffirmai\u002Ffinancial-machine-learning) - 精选的实用金融机器学习（FinML）工具与应用列表，主要以 Python 为主。\n- 🌟 [Awesome-Quant-Machine-Learning-Trading](https:\u002F\u002Fgithub.com\u002Fgrananqvist\u002FAwesome-Quant-Machine-Learning-Trading) - 专注于机器学习的量化\u002F算法交易资源。\n- [awesome-quant](https:\u002F\u002Fgithub.com\u002Fwilsonfreitas\u002Fawesome-quant) - 为量化金融从业者精心整理的超赞库、包和资源清单。\n- [FinancePy](https:\u002F\u002Fgithub.com\u002Fdomokane\u002FFinancePy) - 一个专注于金融衍生品定价与风险管理的 Python 库，涵盖固定收益、权益、外汇及信用衍生品。\n- [探索金融服务类库与项目](https:\u002F\u002Fkandi.openweaver.com\u002Fexplore\u002Ffinancial-services#Top-Authors) - 在 Kandi 上浏览精选的热门与新兴金融科技库、顶尖作者、趋势项目套件、讨论区、教程及学习资源。\n- [AgentMarket](https:\u002F\u002Fagentmarket.cloud) - 面向 AI 代理的 B2A 市场。现有 189 个代理列表，超过 2800 万条真实能源数据记录，并支持 LangChain\u002FMCP 集成。","# awesome-ai-in-finance 快速上手指南\n\n`awesome-ai-in-finance` 并非单一的可安装软件包，而是一个精选的开源项目、研究论文、数据集和工具框架的资源列表。本指南将指导你如何基于该列表中的核心资源（以热门的 **FinRL** 和 **TradingAgents** 为例）搭建本地开发环境并运行第一个 AI 交易策略。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下要求：\n\n*   **操作系统**: Linux (推荐 Ubuntu 20.04+), macOS, 或 Windows (建议使用 WSL2)。\n*   **Python 版本**: 3.8 - 3.10 (部分金融库对高版本 Python 支持尚不完善)。\n*   **前置依赖**:\n    *   `git`: 用于克隆仓库。\n    *   `pip` 或 `conda`: 包管理工具。\n    *   `CCXT`: 如果需要连接加密货币交易所。\n    *   `TA-Lib`: 技术分析库（系统级依赖，需单独安装）。\n\n> **国内加速建议**：\n> 推荐使用清华源或阿里源加速 Python 包下载：\n> ```bash\n> export PIP_INDEX_URL=https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n> ```\n\n### 安装系统级依赖 (TA-Lib)\n许多量化策略依赖 `TA-Lib`，请先在系统层面安装：\n\n**Ubuntu\u002FDebian:**\n```bash\nsudo apt-get update\nsudo apt-get install -y build-essential libta-lib-dev\n```\n\n**macOS (使用 Homebrew):**\n```bash\nbrew install ta-lib\n```\n\n**Windows:**\n建议下载预编译的 `.whl` 文件安装，或使用 WSL2 环境。\n\n## 安装步骤\n\n由于该列表包含多个独立项目，以下以列表中标志性的深度强化学习库 **FinRL** 和多智能体框架 **TradingAgents** 为例进行安装。\n\n### 方案 A：安装 FinRL (适合初学者与研究者)\n\n1.  **克隆仓库**:\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002FAI4Finance-Foundation\u002FFinRL.git\n    cd FinRL\n    ```\n\n2.  **创建虚拟环境并安装依赖**:\n    ```bash\n    python -m venv finrl_env\n    source finrl_env\u002Fbin\u002Factivate  # Windows: finrl_env\\Scripts\\activate\n    \n    pip install -r requirements.txt\n    # 如果 requirements.txt 缺失，手动安装核心包：\n    pip install gymnasium pandas numpy torch stable-baselines3 ccxt yfinance\n    ```\n\n### 方案 B：安装 TradingAgents (多智能体 LLM 框架)\n\n1.  **克隆仓库**:\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002FTauricResearch\u002FTradingAgents.git\n    cd TradingAgents\n    ```\n\n2.  **安装依赖**:\n    ```bash\n    pip install -e .\n    # 需要配置 LLM API Key (如 OpenAI, Anthropic 等)\n    export OPENAI_API_KEY=\"your-api-key-here\"\n    ```\n\n## 基本使用\n\n以下示例展示如何使用 **FinRL** 运行一个基于深度强化学习的股票交易演示。\n\n### 1. 导入库与环境初始化\n在你的 Python 脚本或 Jupyter Notebook 中：\n\n```python\nimport sys\nsys.path.append(\".\u002FFinRL\")\n\nfrom finrl import config\nfrom finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv\nfrom finrl.agents.stablebaselines3.models import DRLAgent\nimport pandas as pd\nimport yfinance as yf\n\n# 下载数据 (以苹果股票为例)\nticker = \"AAPL\"\ndata = yf.download(ticker, start=\"2020-01-01\", end=\"2023-01-01\")\ndata.reset_index(inplace=True)\ndata.columns = [col.lower() for col in data.columns]\ndata.rename(columns={\"adj close\": \"adjcp\"}, inplace=True)\n\n# 简单的特征工程\ndata['tech_indicator'] = data['close'].rolling(window=5).mean()\ndata.dropna(inplace=True)\n\n# 定义状态空间列\nstate_space = ['open', 'high', 'low', 'close', 'volume', 'tech_indicator']\n```\n\n### 2. 构建交易环境\n```python\n# 初始化环境\nenv = StockTradingEnv(\n    df=data,\n    state_space=state_space,\n    initial_amount=100000,\n    transaction_cost_pct=0.001,\n    reward_scaling=1e-4\n)\n```\n\n### 3. 训练模型 (使用 PPO 算法)\n```python\n# 实例化 DRL 代理\nagent = DRLAgent(env=env)\n\n# 训练 PPO 模型\nmodel = agent.get_model(\"ppo\")\ntrained_model = agent.train_model(model=model, tb_log_name='first_run', total_timesteps=5000)\n```\n\n### 4. 执行预测与评估\n```python\n# 使用训练好的模型进行预测\ndf_account_value = DRLAgent.DRL_prediction(\n    model=trained_model,\n    environment=env\n)\n\nprint(\"账户价值变化预览:\")\nprint(df_account_value.tail())\n```\n\n### 进阶：使用 LLM 生成策略 (基于 TradingAgents)\n如果你已配置好 API Key，可以直接运行命令行工具让 AI 智能体分析市场：\n\n```bash\npython main.py --tickers AAPL,GOOG --mode trade --start_date 2023-01-01 --end_date 2023-12-31\n```\n\n> **注意**: 实际生产环境中，请务必替换为真实的市场数据源（如聚宽、米筐或交易所直连接口），并进行严格的历史回测和风险控制。本指南仅用于技术验证。","某量化初创团队试图构建一个基于大语言模型的多策略自动交易系统，以捕捉全球金融市场的瞬时机会。\n\n### 没有 awesome-ai-in-finance 时\n- **资源分散难整合**：团队成员需花费数周在 GitHub、arXiv 和各类论坛中盲目搜索，难以区分哪些是过时的教程，哪些是真正可用的 SOTA（最先进）模型。\n- **架构重复造轮子**：缺乏现成的多智能体协作框架参考，开发人员不得不从零编写基础的交易代理逻辑，导致核心策略研发进度严重滞后。\n- **数据与接口混乱**：面对海量的时间序列数据和复杂的交易所 API，团队难以快速找到经过验证的数据源和适配工具，回测结果因数据清洗不当而失真。\n- **策略验证成本高**：缺少权威的基准测试（如 Nof1）和模拟引擎，团队无法在实盘前有效评估 AI 策略的真实盈利能力，面临巨大的资金风险。\n\n### 使用 awesome-ai-in-finance 后\n- **一站式技术选型**：直接利用列表中精选的 FinRobot 和 TradingAgents 框架，团队在两天内便搭建起具备自我进化能力的多智能体交易雏形。\n- **前沿策略快速落地**：通过引用列表中的高频交易和事件驱动策略论文及代码实现，开发人员将原本需要一个月的策略研发周期缩短至一周。\n- **基础设施即插即用**：依据推荐的数据源和 TA-Lib 工具链，迅速完成了高质量的数据清洗与特征工程，显著提升了回测的准确度。\n- **科学评估体系**：引入 MarS 市场模拟引擎和 Nof1 基准测试，团队在零资金风险下完成了多轮压力测试，确信策略夏普比率达标后才敢实盘部署。\n\nawesome-ai-in-finance 将原本数月的基础设施搭建与调研工作压缩至数天，让团队能专注于核心阿尔法策略的创新而非重复造轮子。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fgeorgezouq_awesome-ai-in-finance_d47bf4fb.png","georgezouq","GeorgeZou","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fgeorgezouq_79122081.jpg","Founder of StaringOS and FlorativaBio, AIGC | Quant Finance | SynBio. What a wonderful world!","StaringOS","Beijing China","zousongqi@foxmail.com",null,"https:\u002F\u002Fgithub.com\u002Fgeorgezouq",5678,653,"2026-04-14T11:52:44","CC0-1.0",5,"","未说明",{"notes":89,"python":87,"dependencies":90},"该仓库是一个资源列表（Awesome List），汇集了金融领域 AI 相关的研究论文、工具代码、课程书籍和数据源，本身不是一个可直接运行的单一软件工具。因此，README 中未提供具体的操作系统、硬件配置或依赖库要求。用户需根据列表中引用的具体子项目（如 FinRL, FinGPT, TradingAgents 等）分别查看其各自的环境需求。部分提到的模型（如 Falcon 7B, GPT-4, Qwen2.5）通常需要较高的 GPU 显存和特定的深度学习框架支持。",[],[35,14],[93,94,95,96,97,98,99,100,101,102,103,104],"reinforcement-learning","deep-learning","neural-network","financial","quantitative-finance","analysis","technology-analysis","quant","cryptocurrency","stock-market","awesome","awesome-list","2026-03-27T02:49:30.150509","2026-04-15T06:55:38.977924",[],[]]