Welcome to LLMQuant, an open-source community focusing on AI, LLM (large language model) and Quantitative research. We aim to leverage AI for quantitative research with feasible collection of techniques and solutions.
We are LLMQuant, an open-source community focusing on AI, LLM (large language model) and Quantitative research. We aim to leverage AI to quantitative research with feasible collection of techniques and solutions.
Magents is an open-source Python framework for a multi-strategy hedge fund backtesting and simulation system. The platform is designed as a multi-agent system in which independent strategy "pods" operate concurrently within a shared simulation environment. The goal is to enable realistic backtesting of multiple trading strategies under one umbrella, with unified data feeds and rigorous risk controls. At LLMQuant, we offer a range of solutions to help you apply LLMs effectively in your financial workflows
Quant Wiki is an open-source Chinese quantitative finance encyclopedia dedicated to creating a free, open, and continuously updated knowledge-sharing platform for quantitative finance. Here, users can learn about the core principles of quantitative trading, commonly used models, algorithm design, and practical trading strategies. We provide a wealth of resources, including factor models, event-driven strategies, execution cost optimization, and more, to help users quickly master essential skills in quantitative investing and advance toward professional expertise.
- 📈 Sentiment Agent: Utilize LLMs to gauge market sentiment from news, social media, and other sources to inform your investment decisions.
- 📝 Quant Copilot: Apply LLMs for various NLP tasks such as text generation, summarization, and translation within the financial domain.
- 📚 Join: Join the community and explore the latest AI use case in quantitative research with us, you will receive the event updates and access our best AI4Quant solutions.
- 🛠️ Contribute: You can contribute to our community by sharing your use case of AI in quantitative finance. We strongly appreciate the code contribution to our github project repositories.
- 🧠 Apply: Apply our best AI4Quant solutions in production environment, which are verified by experienced quantiative researchers and AI experts.
- 🤝 Community Support: Join our community of like-minded professionals to share knowledge, ask questions, and collaborate on projects.
To get started with LLMQuant, visit our website and explore the following sections:
- 📘 Tutorials: Detailed guides to help you get started with LLMs in finance.
- 💾 Resources: Downloadable code, datasets, and models.
- 📰 Update: Stay updated with the latest news and expert opinions.
- 🌐 Community: Connect with other professionals in the field.
We welcome contributions from the community! If you have tutorials, tools, or insights to share, please check our contribution guidelines.
For any questions or inquiries, feel free to reach out to us at [email protected].
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