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.

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.

Real-time cryptocurrency chart pattern detection with automated alerts across multiple exchanges.

No-code crypto backtesting platform with 100+ indicators, AI sentiment signals, and 5+ years of historical data across 1,000+ trading pairs.

Live autonomous benchmark that evaluates LLM trading performance on DAX and Nasdaq indices using identical strategies and real-time market data. API access available.

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.

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

Python framework for real-time financial and backtesting trading strategies.

A lightweight, event-loop-style backtest engine that allows a function-driven imperative style using efficient stateful helper functions and data containers.

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.

Credit Default Swap Functions.

Functions and R Code to Accompany Derivatives Markets.

Fixed Income, Asset Allocation, Transaction Cost Analysis, XVA Metrics Libraries.

EBM and Asian Option Valuation.

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.

A python wrapper for Alpha Vantage API for financial data.

Easy to use Bloomberg Desktop API wrapper for Python.

Open dataset of U.S. public-company equity stakes in Anthropic and OpenAI from primary 10-K / 10-Q / 8-K filings, court records, and press releases. Each row tagged with a confidence flag (V verified, P probable, S speculative).

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.

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.

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

High performance datastore for time series and tick data.

Contingency Random Number Generator that produces random numbers with real financial market statistical signatures (fat tails, volatility clustering, kurtosis). Matches 86% of real market metrics vs 14% for NumPy.

In-memory tabular data in Julia.

Extension of data.frame: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.