DUCKQUANT / THE QUANT & AI DIRECTORY
14,444 resources, ranked by in-directory PageRank
Showing 2,485–2,520 of 14,444 resources
Real-time SEC Form 4 insider-trading alerts pushed to Discord, filtered to large buys, multi-insider cluster buys, first-time insider buyers, and notable…
Prediction market data infrastructure for Polymarket and Kalshi, with REST API, ClickHouse SQL access, 200–500M orderbook snapshots/day at 1ms resolution,…
Independent manual code audits of trading bots and their backtests: catches unmodeled commission/slippage, signal-vs-fill price drift, and other gaps…
Explore this resource at its source website for documentation and current information.
Jane Street Blog Homepage
Authors: Yimin Du. Rolling-window factor pipelines for Chinese A-share markets contain a subtle but costly flaw: daily price-move limits (+/-10% main-board…
Authors: Yu Shi, Zongliang Fu, Shuo Chen, Bohan Zhao, Wei Xu, Changshui Zhang, Jian Li. The success of large-scale pre-training paradigm, exemplified by Large…
Authors: Alex Kim, Maximilian Muhn, Valeri Nikolaev. We investigate whether large language models (LLMs) can successfully perform financial statement…
Open-book financial QA benchmark of 10,231 questions grounded in public filings; reveals high hallucination rates in frontier LLMs answering from 10-Ks
Indian market data API and MCP server for NSE/BSE stocks, financials, screeners, and institutional flows
High-performance datastore for time series and tick data built on MongoDB, used widely in quant research.
GPU-accelerated limit-order-book simulator enabling large-scale reinforcement learning for trading.
Proposes a six-axis evaluation framework for reinforcement-learning agents in financial markets.
Combines LLM-generated signals with risk-sensitive reinforcement learning for trading agents.
Variance-bounded REINFORCE variant for mining stable formulaic alpha factors.
Reinforcement learning generates diverse, synergistic formulaic alpha factor sets.
Benchmarks deep-learning models for limit-order-book forecasting across multiple assets.
Systematisation of knowledge on robustness and security failures in LLM financial trading schemes.
Benchmark testing whether LLM agents can trade stocks profitably in real-world markets.
Benchmark for evaluating LLM-based agents on realistic financial decision-making tasks.
Releases a financial LLM, instruction dataset and evaluation benchmark for finance tasks.
Multi-agent reinforcement-learning environment for optimal execution with endogenous liquidity.
Supporting data package for the Portfolio Optimization Book R · 0
godzilla.dev is an open-source C++/Python infrastructure for self-hosted crypto funding rate arbitrage and market making, with ultra low-latency and enterprise…
This program trains an agent: StarTrader to trade like a human using a deep reinforcement learning algorithm: deep deterministic policy gradient (DDPG)…
Applying Reinforcement Learning in Quantitative Trading
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results.…
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management.
Github -Deep Reinforcement Learning based Trading Agent for Bitcoin
Playing idealized trading games with deep reinforcement learning
Algorithmic trading with deep learning experiments
very good curated list of notebooks showing deep learning reinforcement learning models. Also contain topics on outlier detections/overbought oversold…
Public repository by valeman.
Companion webpage to the book "Mathematics For Machine Learning"
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
