quantitative-finance

FinRL is an open-source framework for financial reinforcement learning, with about 16,000 stars on GitHub, supporting training and backtesting for trading in stocks, cryptocurrencies, etc., and pointing to the next-generation version FinRL-X.

The tweet shares free code from the book 'Machine Learning in Finance: From Theory to Practice', which integrates machine learning with quantitative finance for data modeling and financial decision-making.

Horizon is an AI-powered tool that allows traders to describe trading strategies in plain language, automatically generating backtests with detailed metrics for iterative testing and improvement.

The article discusses the impact of Stephen Boyd's 2006 mathematics textbook on convex optimization, used by Google, Renaissance Technologies, and others, and promotes his free Stanford course EE364A available on YouTube.

The paper explains how removing market beta and factor exposure reveals true trading signals, emphasizing rigorous stress testing to avoid overfitting and false confidence in backtests.

The paper reports an improvement in Information Coefficient from 0.0613 to 0.0843 using an agent-guided research process, compared to a GRU baseline, with simulated results on held-out data from 2021 to 2025.

Man Group has open-sourced ArcticDB, a high-performance, serverless DataFrame database designed for Python data science and quantitative finance scenarios, supporting billions of rows of time-series data, S3/LMDB/Azure storage, time travel and snapshots, and C++ engine acceleration.

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.