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Daily GitHub Project Recommendation: AI Hedge Fund - Simulating Investment Masters, Exploring New Possibilities in Intelligent Finance!

Today, we bring you the project virattt/ai-hedge-fund, an exciting AI-driven hedge fund proof-of-concept project. It aims to explore how artificial intelligence can assist or even make trading decisions. If you are interested in AI applications in finance, multi-agent systems, or quantitative investment strategies, then this project is definitely not to be missed! With over 36,000 stars, it has become a popular choice in this field.

Project Highlights

The core appeal of AI Hedge Fund lies in its unique multi-agent collaborative system. It is not a single AI model, but rather simulates an “AI investment team” composed of 17 agents. Among them, the most striking feature is that it “invites” the investment philosophies of 11 financial legends (such as Warren Buffett, Charlie Munger, Michael Burry, Cathie Wood, etc.), transforming their strategies into independent AI agents. In addition, the project also includes professional agents for valuation, sentiment analysis, fundamentals, technical analysis, risk management, and portfolio management, all collaborating to form a comprehensive investment decision chain.

From a technical perspective, this project is an excellent case study for how multi-agent systems can collaborate in complex decision-making scenarios. It adopts a modular approach, allowing each agent to focus on a specific investment philosophy or analysis task. From an application perspective, although the author explicitly states that this is for educational and research purposes only, not real trading, it provides a sandbox environment for learners to personally experience and understand how modern quantitative investment strategies are constructed and how AI integrates various information to make judgments.

Technical Details and Applicable Scenarios

AI Hedge Fund is developed in Python, supporting deployment via Poetry or Docker, which significantly lowers the barrier to entry. It utilizes mainstream Large Language Model (LLM) APIs (such as OpenAI, Groq, etc.) for intelligent decision-making and text analysis, and also supports local LLMs (Ollama), enhancing flexibility. The project provides the ability to run a simulated hedge fund and backtest historical data, allowing you to observe AI performance under different market conditions.

This project is particularly suitable for:

  • Developers and researchers interested in AI applications in finance.
  • Learners who wish to understand the design and implementation of multi-agent systems.
  • Quantitative investment enthusiasts, for learning and testing investment strategies.
  • Any curious minds hoping to explore the frontier of financial AI.

How to Get Started

Want to dive deeper into or run this “AI Investment Dream Team”?

  1. Visit the GitHub repository: virattt/ai-hedge-fund
  2. Clone the repository and follow the instructions in the README to set up the environment using Poetry or Docker.
  3. Configure your LLM and financial data API keys (some financial data is free).
  4. Run src/main.py or src/backtester.py to start your AI investment simulation journey!

Call to Action

AI Hedge Fund is a project full of innovative spirit. Whether you want to learn about the intersection of AI and finance, or contribute your code and ideas to this project, we encourage you to visit the GitHub repository to explore. Give it a star, or contribute your strength, and together advance education and research in AI in the financial sector!

Daily GitHub Project Recommendation: niri - A Scrollable-Tiling Wayland Compositor That Pushes Boundaries!

Today, we want to recommend an exciting open-source project — niri, a Wayland compositor that challenges your perception of traditional window managers. If you are tired of windows frequently resizing automatically, or are looking for a more efficient and personalized desktop experience, then niri is definitely worth your in-depth exploration.

Project Highlights

The core philosophy of niri is “scrollable-tiling,” which is fundamentally different from traditional tiling managers. It arranges windows in columns that extend infinitely to the right, and the best part is that when you open a new window, existing windows will never change size because of it. This means your workspace will remain stable, allowing you to focus more on your tasks.

  • Unique Workflow: Each monitor has independent window strips and dynamic workspaces, eliminating the issue of windows “overflowing” onto adjacent monitors. Even if a monitor is disconnected, its workspace can intelligently move and retain its layout, greatly enhancing the experience for multi-monitor users.
  • Rich and Thoughtful Features:
    • Dynamic Workspaces: Drawing inspiration from GNOME’s design, it provides flexible workspace management.
    • Overview Mode: A unique overview feature that scales and displays all workspaces and windows, giving you a complete overview.
    • Privacy and Screen Sharing: Built-in screenshot UI, and supports screen sharing via xdg-desktop-portal-gnome, even allowing you to obscure sensitive windows to protect privacy.
    • Intuitive Interaction: Supports touchpad and mouse gestures, making desktop operations more natural and smooth.
    • Efficient Organization: Allows grouping multiple windows into tabs, helping you better manage complex task flows.
    • Extreme Personalization: From window spacing, borders, size to gradient borders supporting Oklab/Oklch, and even animated effects with custom shaders, niri provides extensive configuration options, allowing you to fully customize your desktop.

Technical Details and Applicable Scenarios

niri is built from scratch using the Rust language, which not only ensures its excellent performance, stability, and security but also reflects its modern design philosophy.

As a Wayland compositor, niri is ideal for Linux users who pursue extreme efficiency and high customizability. Especially for users with multi-monitor setups who wish to escape the frequent window changes common in traditional tiling managers, it offers a brand new solution. Although it does not have built-in Xwayland support, with a simple xwayland-satellite configuration, it can perfectly compatible with various X11 applications, including Steam games, JetBrains IDEs, and Electron apps, ensuring a seamless transition.

How to Get Started / Links

niri is already quite stable, and many users are using it daily. If you want to experience this unique window management approach, you can visit the Getting Started Wiki page in its GitHub repository, where detailed installation and configuration guides are available.

Call to Action

niri has nearly 9,000 Stars, indicating its active community and the excellent quality of the project. If you are a fan of Wayland desktop environments, or are looking for a unique window management experience, niri is highly recommended. Feel free to explore, share your experience, and discuss and contribute with the community to make this project even better!

Daily GitHub Project Recommendation: DeepEval - Your LLM Application Quality Guardian!

When building and deploying Large Language Model (LLM) applications, ensuring the accuracy, reliability, and security of their output is a core challenge faced by every developer. Today, we bring you a powerful tool that can completely revolutionize your LLM application testing process — DeepEval! This Python open-source framework, with over 8,200 stars and more than 700 forks, was created precisely to address LLM evaluation pain points, allowing your AI applications to undergo rigorous unit testing just like traditional software.

Project Highlights

DeepEval is like Pytest for the LLM world; it’s not just an evaluation framework, but also a guardian of your LLM application’s quality. It can treat LLM output like code, enabling automated, repeatable quality validation.

  • Powerful Local Evaluation Capabilities: One of DeepEval’s most striking features is its ability to run various complex LLM and NLP models on your local machine for evaluation, greatly ensuring data privacy and evaluation efficiency. Whether it’s G-Eval, hallucination detection, answer relevance, RAGAS, etc., it can provide professional evaluation metrics, covering various LLM application scenarios such as RAG pipelines, AI agents, and chatbots.
  • Comprehensive Quality Assurance:
    • Professional Evaluation Metric Library: Includes a rich library of built-in evaluation metrics, such as RAG-specific context recall, faithfulness, agent task completion, tool correctness, and even safety metrics like bias and toxicity detection, helping you precisely pinpoint issues.
    • Powerful Red Teaming: With just a few lines of code, you can perform red teaming on LLM applications for over 40 types of security vulnerabilities, including SQL injection, prompt injection, etc., effectively preventing potential risks.
    • Effortless Benchmarking: Supports benchmarking mainstream LLM models, such as MMLU, HellaSwag, etc., helping you quickly select and optimize models.
  • Seamless Integration and Extension: DeepEval seamlessly integrates with mainstream LLM frameworks like LangChain, LlamaIndex, and Hugging Face, and supports CI/CD environments, deeply embedding LLM evaluation into your development workflow. You can even create custom evaluation metrics based on your needs!

Applicable Scenarios and Technical Details

DeepEval is primarily written in Python, making it an ideal choice for all LLM developers, AI engineers, and data scientists. It allows you to “unit test” LLM inputs and outputs just like writing traditional software test cases, thereby determining the optimal model, prompt, and architecture. Whether it’s optimizing RAG pipelines, improving agent workflows, or preventing prompt drift, DeepEval provides powerful support. Furthermore, when combined with its official platform Confident AI, it enables a complete LLM evaluation lifecycle including dataset management, A/B testing, result debugging, and product monitoring, making your iteration process more efficient and controllable.

How to Get Started

Eager to experience DeepEval’s powerful features? Getting started is very simple:

  1. Install via pip: pip install -U deepeval
  2. Refer to the official documentation to quickly write your first LLM test case!

GitHub Repository Address: https://github.com/confident-ai/deepeval

Call to Action

The success of LLM applications is inseparable from rigorous evaluation and testing. DeepEval provides us with such a powerful set of tools, ensuring that AI quality is no longer a black box. If you are also troubled by LLM evaluation, we highly recommend you explore this project! Give it a Star, or even contribute your code, and together drive the development of the LLM evaluation ecosystem!