quantitative-finance

Introduces tvscreener, a Python library that can directly fetch TradingView stock screener data, supports multiple markets and 13,000+ technical indicators, returns Pandas DataFrame, and is convenient for quantitative strategy use.

Promotional post about Horizon, an AI-driven trading strategy platform that lets users backtest and deploy strategies in plain English, citing a professor managing $200M with AI agents and returning 56% last year.

Kirk Borne highlights the 3rd edition of 'Machine Learning for Trading' by Stefan Jansen, a comprehensive 826-page book on building AI-driven trading systems, and announces an accompanying live workshop on August 15.

This preprint introduces AlphaSchema, a framework that constructs and explores a structured space of trading semantics for LLM-based alpha mining, decoupling semantic exploration from factor implementation. Experiments on the Chinese stock market show that it discovers factor pools with strong predictive and portfolio performance.

This paper studies LLMs for parent-order execution in algorithmic trading, introducing PACE, a hierarchical framework that outperforms traditional baselines on Shenzhen Stock Exchange data and suggests LLMs can complement human traders.

Awesome AI in Finance is an open-source curated resource list covering papers, tools, datasets, and trading frameworks for AI and quantitative finance, organized into categories like agents, research, strategies, data, and trading stacks.

A Columbia University paper introduces a strictly causal Hidden Markov Model that adapts to changing market regimes, achieving a 2.18 Sharpe ratio vs 1.18 for SPX buy & hold and reducing max drawdown from -14.62% to -5.43% during the 2025 selloff.

Former Citadel fund manager Brett Caughran summarizes ten key points on how to apply AI agents in investing, with the author noting that points 5 and 6 are especially fair.

A quantitative finance expert systematically explained the practical use of Python in quantitative finance at PyData, covering tools such as NumPy, SciPy, Pandas, Numba, and recommended the open-source library DX Analytics and the resource list awesome-quant.

Discusses the trade-offs of using Kullback-Leibler divergence in quantitative analysis, framing it as a Hamlet-like dilemma for quants.

Goldman Sachs open-sourced its internal quantitative trading toolkit gs-quant, providing institutional-grade derivatives pricing, risk management, and strategy development tools. It has received 11.3K GitHub stars.

A curated list of 10 free GitHub repositories for algorithmic trading and quantitative finance, covering tools for backtesting, data analysis, portfolio optimization, and AI-driven trading strategies.

This thread explores how math/programming Olympiad backgrounds mass-produce tech founders, pointing out that the internalized systematic problem-solving ability, belief, and peer effect from the competition paradigm are the core engine, with quantitative finance as a transfer station, but also reminds that entrepreneurship requires skills beyond problem-solving.

A GitHub repo aggregates free, open-source quantitative finance tools—pricing engines, backtesters, order books, and HFT simulators—typically guarded by firms, including Microsoft's AI quant platform.

XAlpha introduces a memory-driven AI quant researcher that integrates financial knowledge and discovery feedback to automate the full hypothesis-to-code alpha discovery loop, achieving stronger performance on CSI300.

AI Trader is a fully autonomous trading agent now available for free.

The article summarizes the core learning methodology of world-class geometer and quantitative king James Simons: incubation by the subconscious, stripping away noise, and an extreme aversion to rote memorization, emphasizing deep thinking, leaving mental space, and cross-disciplinary learning through leveraging networks of genius.

A tweet highlights Stanford professor Stephen Boyd's free convex optimization course and textbook, noting that Citadel pays $400K for this skill. The course teaches optimal portfolio allocation, but emphasizes that the optimizer only works with a genuine edge in signals.

An educational thread explaining the mathematical foundations used by quantitative trading firms like Renaissance Technologies, covering concepts from Bernoulli to Brownian motion.

This paper introduces OpenFinGym, a unified multi-task gym environment for evaluating large language model agents in quantitative finance, covering forecasting, market generation, real-time trading, and fraud detection with verifiable execution and automated task construction.