awesome-ai-in-finance

Real-time and historical Hyperliquid and Lighter market data via REST and WebSocket APIs.

AI-powered 13F SEC filing tracker. Monitor hedge fund and institutional investor portfolio changes, with smart money move alerts and historical holding comparisons.

Market sentiment API for AI finance agents covering stocks across Reddit, X/Twitter, news, and Polymarket prediction markets with buzz, sentiment, trending, and comparison signals.

Crypto market data, on-chain analytics, and FX data via REST and MCP, with free endpoints and metered paid access.

AI economy tracking platform. Market cap, funding rounds, AI Bubble Index, company valuations, and compute supply chain data.

AI-powered crypto forecasting and predictions API with machine learning models for 70+ cryptocurrency pairs.

A quant trading system base on python.

Python backtesting library for trading strategies.

High-frequency crypto currency trading bot developed by Zenbot.

An algorithmic trading library for Crypto-Assets in python.

Tests bt and Quantopian Zipline as backtesting frameworks for coin trading strategy.

AI-native quantitative trading engine with 484 alpha factors, genetic algorithm strategy evolution, walk-forward backtesting and paper trading. Supports A-shares, crypto, and MCP server for AI agent integration.

Explore the use of AI to make trading decisions.

Use ChatGPT to determine which cryptocurrency to trade based on technical indicators.

The first benchmark for evaluating LLMs on Environmental, Social, and Governance (ESG) and sustainability knowledge; 1,136 expert-validated multiple-choice questions plus a curated source corpus, evaluating 50 LLMs under zero-shot and RAG (EMNLP 2025).

GPT-4 can outperform professional financial analysts in predicting future earnings changes, generating useful narrative insights, and resulting in superior trading strategies with higher Sharpe ratios and alphas, thereby suggesting a potential central role for LLMs in financial decision-making.

Provides a playground for all people interested in LLMs and NLP in Finance.

The common-stock prices can be regarded as an ensemble of decisions in statistical equilibrium.

Propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return.

The power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems.

Deep reinforcement learning provides a framework toward end-to-end training of such trading agent.

Experiments based on "Advances in financial machine learning" book.

Using advanced ML solutions to overcome real-world investment problems.

Knowledge base and runnable experiments on AI agent engineering, quantitative finance, and LLM-based factor research.

Learn Fintech Online.

Book about how to build financial software hands-on using generative AI tools like ChatGPT and Copilot.

A book about creating profitable, regulation-compliant financial applications.

Decentralized crowdfunding platform for AI agents with milestone-based escrow on Base blockchain.

Self-improving AI trading system with 25 agents, Karpathy-style autoresearch, Darwinian selection, autonomous agent spawning, and multi-cohort meta-weighting.

No-code platform for building multi-agent trading strategies, with chart-drawing agents, event-driven automations, and full execution transparency.

An Open-Source AI Agent Platform for Financial Analysis using LLMs.

AI-powered trading signals with 74% historical win rate, combining strategies from legendary investors using multi-model AI intelligence.

A multi-exchange Al trading platform with multi-Ai competition self-evolution, and real-time dashboard.

B2A marketplace for AI agents. 189 listings, 28M+ real energy data records, LangChain/MCP integration.

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).

Quant / Algorithm trading resources with an emphasis on Machine Learning.

Explore a curated list of Fintech popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources on kandi.

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.

Claude Code skills and agents for investment research, with deterministic position-sizing, portfolio-risk, and walk-forward backtesting tools.

Agent skill for querying U.S. congressional trade disclosures by ticker, member, chamber, transaction type, and date.

Claude skill for bias detection in investment analysis. Apache 2.0.

Claude skills for investment research, screening, compliance, and marketing workflows.

Agent skills for financial reporting, audit, reconciliation, journal entries, and accounting workflows referencing IFRS and GAAP.

Agent skill for source-linked funding and liquidity briefs, preserving observation dates and missing-data states.