elder-plinius/CL4R1T4S
CL4R1T4S is a project that provides transparency by collecting and sharing the hidden system prompts and guidelines used by major AI models and agents to promote trust and understanding of AI behavior.
Awesome AI Agents › LLM Research Repositories
leoncuhk/awesome-quant-ai
Awesome Quant AI is a curated reading list covering the intersection of artificial intelligence, machine learning and quantitative investing. It opens with an overview of the field, framing it around the tension between market efficiency and exploitable inefficiencies, factor decay, the limits of statistical arbitrage and realistic cost modelling, then maps machine learning techniques onto those problems: supervised learning for return and volatility forecasting, unsupervised methods for regime detection and factor discovery, reinforcement learning for execution and position policies, generative models for synthetic market scenarios, and language and multimodal models for reasoning over filings, news and earnings calls. A design section lays out a research process from objective setting and alpha research through model calibration, backtesting with out-of-sample validation, risk management and live deployment. For readers of an agent list the relevant material is the AI-agent trading track, which compares rule-based quantitative trading, algorithmic trading and agent-based trading, and then indexes concrete projects: TradingAgents, which simulates a trading firm with analyst, trader and risk-manager roles on LangGraph; FinRobot; FinRL and its agent-era extensions; Vibe-Trading with its swarm presets and MCP server; an autonomous prediction-market agent for Kalshi and Polymarket; and Microsoft RD-Agent in its quantitative finance mode. A research papers section collects the corresponding literature, including TradingAgents, FinAgent, FinMem and FinCon, and the repository also carries the author's own survey notes on LLM multi-agent trading with a comparison table and open problems. Remaining sections list strategy types, data and backtesting platforms, courses, books and communities. The list suits practitioners and researchers who want an organised entry point to financial agent systems alongside the classical quantitative methods they must still work with.
https://github.com/leoncuhk/awesome-quant-ai
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