awesome-quant

Implementation of

All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.

Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.

Source code for Algorithmic Trading with Python (2020) by Chris Conlan.

A catalog of algorithmic trading libraries, frameworks, strategies, and educational materials.

Background and resources on Automatic Differentiation (AD) / Adjoint Algorithmic Differentitation (AAD).

Track institutional investor 13F holdings with AI-powered analysis, position change alerts, and filing summaries.

AI stock-research API returning structured analysis (investment thesis, valuation verdict, insider-signal read, earnings, bull-vs-bear, moat, watchlist ranking) for US equities from Polygon/Finnhub/FMP data. Optimized for LLM agents; free tier.

Independent statistical assay office for trading signals and backtests: deflated Sharpe with cumulative trial accounting, probability of backtest overfitting (PBO/CPCV), leakage forensics, placebo tests against matched synthetic null worlds, and pre-registration with Merkle-anchored timestamps — deterministic, Ed25519-signed verdicts anyone can replay. Free demo; hosted API and MCP server. Methodology audit, not investment advice.

Local-first agent-native quant CLI with Optuna TPE optimization, walk-forward testing, anti-overfitting guards, and TradingView Pine v6 code generation. Free trial available.

REST API and MCP server for Brazilian stock market data (B3). Covers 350+ stocks, 400+ FIIs with fundamentals (27+ indicators), dividends, historical prices, financials, and macro indicators sourced from B3, CVM, and BCB.

Brazilian stock market data API for B3/Bovespa quotes, historical OHLCV, dividends, and fundamentals.

US Treasury price quote math: parse and format 32nds quotes (105-16+), ticks, and basis points; zero dependencies.

A Python based library to model cashflow for structured product like Asset-backed securities (ABS) and Mortgage-backed securities (MBS).

This package includes pricing function for selected American call options with underlying assets that generate payouts.

SOFR compounding-in-arrears per ARRC/ISDA conventions (lookback, observation shift, lockout) and the SOFR Index method; reproduces the NY Fed's published averages.

Credit Default Swap Functions.

ISDA 2006 day-count conventions (30/360 family, ACT/360, ACT/365F, ACT/ACT ISDA and ICMA); zero dependencies.

Async Algorithmic Trading Engine.

Multi-agent LLM trading analysis where 12 AI agents (analysts, debaters, risk manager) debate stock picks in real-time, supporting US equities and China A-shares.

This is an execution engine for algo trading.

MCP server returning composite crypto trade verdicts (direction, confidence, regime) across 5 perpetual-futures venues, with cross-venue funding-rate arbitrage and an on-chain Merkle-verified track record. Free tier.

MCP server wrapping the AlphaForge CLI for AI-agent-native backtesting, Optuna TPE optimization, and walk-forward testing of trading strategies from Claude Desktop, Cursor, or Claude Code.

Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost.

Obtain pre market and after hours stock prices for a given symbol.

AkShare is an elegant and simple financial data interface library for Python, built for human beings!

Python interface for retrieving real-time and historical prices from Alpaca API as well as trade execution.

CLI + MCP server for SEC 13F institutional holdings, Form 4 insider trading, and smart money convergence signals (tickers where hedge funds and company insiders are both buying). Free anonymous tier, no signup required.

A python wrapper for Alpha Vantage API for financial data.

REST + MCP API for point-in-time Bitcoin cycle scoring, 22 on-chain series since 2009 (MVRV, NUPL, SOPR, Mayer, Puell), macro-regime composites and look-ahead-aware backtest validation with Deflated-Sharpe-Ratio correction across crypto, stocks, ETFs, commodities and forex. Free tier.

Pre-analyzed financial news via REST API and MCP for AI agents: per-ticker impact and sentiment, a category, and a 1-10 relevance score on every story, plus structured SEC Form 4 insider data. Free tier, no card.

Sentiment analysis and report generation package for financial assets and securities utilizing GPT models.

LLM-powered A-share stock analysis via P2P agent collaboration. Technical analysis (MA60, volume-price patterns, golden eye), deep research reports using proprietary methodology, and market state summaries. Analysis logic stays private via Skill-as-API protocol.

Performance analysis of predictive alpha factors.

Performance analysis of predictive (alpha) stock factors.

AI skills for quantitative factor research: discover, evaluate, mine, backtest, and monitor factors through any AI coding assistant. Supports A-share, HK, and US markets.

Covariance matrix estimation via factor models.

Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.

The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.

ARCH models in Python.

Python package for timeseries analysis and manipulation.

High-performance ETF pattern matching via DTW with cosine pre-filtering. 43× DTW and 58× pattern-match speedup over pure Python using pybind11/C++20. Includes Jupyter notebook with full algorithm walkthrough.

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.