#1
量化研究-券商金工研报复现
5.3K starsMay 8, 2026 pushfinanceJupyter NotebookFinance
$ npx skills add hugo2046/QuantsPlaybookAlternatives
Deep Reinforcement Stock Trading alternatives for AI agents.
Compare similar skills by workflow fit, trust score, quality, GitHub adoption, maintenance, and install readiness.
Current skill
Deep Reinforcement Stock Trading
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
55
Quality
80
Trust
692
Stars
#2
Applying Reinforcement Learning in Quantitative Trading
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$ npx skills add yuriak/RLQuant#3
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$ npx skills add fja05680/sp500#6
FinRobot: An Open-Source AI Agent Platform for Financial Analysis using LLMs 🚀 🚀 🚀
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$ npx skills add AI4Finance-Foundation/FinRobot#7
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]
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$ npx skills add BlackArbsCEO/Adv_Fin_ML_Exercises#8
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
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$ npx skills add ZhengyaoJiang/PGPortfolio#9
四川大学计算机学院本科课程资料（更新中 🔥）如果您觉得有帮助，还请点亮一下 Star 🌟 哦~ 万分感谢！
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$ npx skills add KarryRen/SCU-CS-Class-Materials#10
Democratizing Internet-scale financial data.
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TradingAgents: Multi-Agents LLM Financial Trading Framework
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$ npx skills add TauricResearch/TradingAgents#12
Funcat 将同花顺、通达信、文华财经麦语言等的公式写法移植到了 Python 中。
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$ npx skills add cedricporter/funcat#13
基于Python的开源量化交易平台开发框架
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$ npx skills add vnpy/vnpy#14
A curated list of awesome resources for quantitative investment and trading strategies focusing on artificial intelligence and machine learning applications in finance.
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$ npx skills add leoncuhk/awesome-quant-ai#15
A local-first personal finance app
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$ npx skills add actualbudget/actual#16
Download market data from Yahoo! Finance's API
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$ npx skills add ranaroussi/yfinanceHow to choose
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