QLIB

Introducing RD_Agent: LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D We are excited to announce the release of RD-Agent📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D. RD-Agent is now available on GitHub, and we welcome your star🌟! To learn more, please visit the RD-Agent repository. github.com/microsoft/qlib

Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large language models and multi-agent systems, current quantitative research pipelines suffer from limited automation, weak interpretability, and fragmented coordination across key components such as factor mining and model innovation. In this paper, we propose R&D-Agent for Quantitative Finance, in short RD-Agent(Q), the first data-centric multi-agent framework designed to automate the full-stack research and development of quantitative strategies via coordinated factor-model co-optimization. RD-Agent(Q) decomposes the quant process into two iterative stages: a Research stage that dynamically sets goal-aligned prompts, formulates hypotheses based on domain priors, and maps them to concrete tasks, and a Development stage that employs a code-generation agent, Co-STEER, to implement task-specific code, which is then executed in real-market backtests. The two stages are connected through a feedback stage that thoroughly evaluates experimental outcomes and informs subsequent iterations, with a multi-armed bandit scheduler for adaptive direction selection. Empirically, RD-Agent(Q) achieves up to 2X higher annualized returns than classical factor libraries using 70% fewer factors, and outperforms state-of-the-art deep time-series models on real markets. Its joint factor-model optimization delivers a strong balance between predictive accuracy and strategy robustness.

• Iterative Improvement Both components improve through an iterative feedback loop, ensuring the system becomes increasingly effective over time through accumulated domain knowledge.

• Development Component Implements the ideas generated by research. The Co-STEER code-generation agent writes and executes task-specific backtesting code in real-market environments.

What is RD-Agents? R&D is crucial for industrial productivity enhancement, especially in the AI era where the core aspects focus on data and models. Microsoft is committed to automating these high-value generic R&D processes.

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Anthropic pays $500,000+ a year for engineers who build what this person just explained for free. 110 million monthly downloads. React took double the time to reach the same number. One protocol, every AI platform, zero re-integration. Claude, ChatGPT, VS Code, Cursor - it just works. Most people will scroll past this and keep converting REST APIs to MCP servers one to one - and wonder why their agents keep breaking. bookmark & watch today. free. straight from the person who built the protocol.

drowning in low-quality PRs? we're giving maintainers the power to set contribution limits, starting with a PR cap for outside contributors and an allowlist for your trusted ones. sneak peek below 👇

New: LongCat just dropped an excellent open-source talking-avatar model (probably SOTA) + MIT licensed 🔥 Made a Hugging Face Space for it and it's very impressive. So many cool products to build with it: AI tutors with a face, dubbing pipelines, talking-head coding agents (imagine Claude Code with a face), NPC dialogue, etc... Sharing the Hugging Face (free) demo below 👇

I lost my job yesterday. Rent was due. No backup plan. So I opened Claude. Told it: “Analyze top Polymarket wallets (last 90 days) and build me something profitable” $25 → $4,237 overnight. It scanned 10,000 wallets, filtered by win rate, sizing, behavior. Found 7 traders with real edge. Then built an autonomous agent: reads live news maps events to markets detects mispricing finds cross-market arbitrage sizes using Kelly Deployed at 11:47PM. Closed the laptop. Woke up to: $25 → $4,237 94 trades 0 input No emotions. No hesitation. Just information asymmetry at machine speed. Wall Street pays millions for this. I pay $20/month. Copy it: t.me/KreoPolyBot?st…

OpenAI is offering $2M in tokens to every YC company in the spring and summer batches. We extended the summer deadline to May 25 so more founders can get in on it. ycombinator.com/apply

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I did the math, and $2M in OpenAI tokens is equivalent to a 450 pre-AI human engineering team for 2 years, in terms of raw LOC output.

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