DUCKQUANT / THE QUANT & AI DIRECTORY
2,336 resources, ranked by in-directory PageRank
AI-powered US-equity research terminal with deep-research agents grounded in SEC filings, a fundamental and technical stock screener with backtesting…
module to get stock data from Yahoo! Finance
A book about creating profitable, regulation-compliant financial applications
Book about how to build financial software hands-on using generative AI tools like ChatGPT and Copilot
Hosted MetaTrader 5 & MT4 broker API (REST + WebSocket) to connect broker accounts, stream live ticks, fetch historical candles, and place or manage…
Prediction-market data service with free market metadata and tag datasets without an account, while tick-level data requires paid access after five trial file…
Portfolio analysis and optimization API with anonymous free access to selected endpoints for up to 20 assets, subject to a shared one-request-per-second limit
HaasOnline offers quite advanced tools to automate TA strategy at a cost which does seem more like an enterprise solution
Point-in-time US equity fundamentals from SEC EDGAR with filing dates and restatement flags; free API access includes 250 requests per day without a credit…
Evidence-driven market recap CLI for AI agents, producing Markdown reports and JSON Evidence Packs for A/HK/US stocks, funds, and portfolios
Community repository of ready-to-use and example trading strategies for the Freqtrade bot.
Contamination-resistant A-share backtesting environment for LLM trading agents with point-in-time masking, entity/date anonymization, fingerprinted replay, and…
Technical, quantitative and sentiment data for stocks and ETFs with risk metrics, peer percentiles and market regime signals. Optimized for AI/LLM agents
Self-hosted A-share research workbench built on TradingAgents, with a web interface and no brokerage connection by default
AI alpha engine: feature engineering, scoring, Kelly sizing
Build, test, and automate trading strategies without coding. Profectus AI is a no-code algo builder designed for MetaTrader traders.
A opensource AI trading platform in real market,
Use ChatGPT to answer questions, write, create images, complete work, and code—all in one place. Get started for free or download the app.
Qwen is an official platform from Qwen that empowers both everyday users and developers with unified access to Qwen’s series of open-source and proprietary…
A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python
Actively maintained, high-performance backtesting and live trading framework with AI-assisted strategy tooling (MCP server, skills, agent, web platform). Fork…
Library of 501 agentskills.io-format skills giving AI coding agents trading-infrastructure playbooks for order idempotency, look-ahead-bias elimination, kill sw
Python - Multi-agent LLM trading analysis where 12 AI agents (analysts, debaters, risk manager) debate stock picks in real-time, supporting US equities and
AI trading operating system with 9 ML models (RF, XGBoost, EMAformer, MOIRAI-2, Chronos-2, DDPM, PPO RL, GNN, LR) ranked by Sharpe ratio, Claude AI…
TypeScript DeepSeek Harness - Agent-native quantitative research toolkit for DeepSeek Harness: 46 tools across data, alpha, ML, risk, execution and ecosystem
Yu, Y., et al. (2023). FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design. arXiv preprint arXiv:2311.13743
Peng, Khushi, Poon — CAST: A Cross-Asset State-Space Trading System for Drawdown Control in Stock Markets, ICDM 2026. , . Source of the objective…
investpy - Python - Financial Data Extraction from Investing.com with Python! https://investpy.readthedocs.io/ .
akshare - Python - AkShare is an elegant and simple financial data interface library for Python, built for human beings! https://akshare.readthedocs.io .
A three-stage fine-tuning pipeline (SFT DPO synthetic-data RL) that adapts Qwen2.5 and DeepSeek-R1 to financial tasks on the Open FinLLM Leaderboard, with…
for Equity Research Report Generation — Authors: Song Jin, Shuqi Li, Shukun Zhang, Rui Yan. While LLMs have shown great success in financial tasks…
Notes and experiments on machine learning applied to quantitative analysis.
Sven Buechel, Simon Junker, Thore Schlaak, Claus Michelsen and Udo Hahn. . In EMNLP-ECONLP, 2019
Tuan Lai, Trung Bui, Sheng Li and Nedim Lipka. . In ACL-ECONLP, 2018
Explore this resource at its source website for documentation and current information.
Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz and Noam Slonim. . In EMNLP-ECONLP, 2019
