Awesome Quant

NumPy is the fundamental package for scientific computing with Python.

SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

Polars is a blazingly fast DataFrame library for manipulating structured data.

Domain specific language for quantitative analytics in finance and trading.

Builtin Python library for all basic statistical calculations.

SymPy is a Python library for symbolic mathematics.

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano.

Python reimagination of spreadsheets as formula-centric objects that are interoperable with pandas.

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.

eXtensible Time Series: Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.

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.

Sparse principal component analysis.

Provides a common interface to time series databases.

Time Series Analysis and Computational Finance.

S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.

Utilities for simple manipulation and quick plotting of time series data.

A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time.

Flexible and efficient time series class & methods.

In-memory tabular data in Julia.

Handle timeseries data on top of the powerful and mature DataFrames.jl.

Time series handling for Julia.

Exact computation and conjecture testing across polynomial maps, linear algebra, and graph algorithms for agent-driven mathematical research.

QuantLib's Python port.

Basic options pricing in Python. *ARCHIVED*.

vollib is a python library for calculating option prices, implied volatility and greeks.

vollib Python implementation.

Fast, vectorised Black-Scholes-Merton and Bachelier pricers and implied volatility fitters, including inverse options for crypto derivatives.

Pricing analytics and Monte Carlo simulation for stochastic volatility models, including the log-normal SV model and the Heston model.

A framework for quantitative finance In python.

Python tools for Finance.

A financial function library for Python.

Lightweight Python library for assembling and analyzing financial data.

Toolkit for integration and analysis.

SABR model Python implementation.

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

Python toolkit for quantitative finance.

Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.

Applications of Monte Carlo methods to financial engineering projects, in Python.

A library for financial options pricing written in Python.

High-performance TensorFlow library for quantitative finance.

A Python library for mathematical finance.

Tools for pricing and plotting of vanilla option prices, greeks and various other analysis around them.

Complete python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options.

PME (Public Market Equivalent) calculation.

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

Cent-accurate mortgage amortization schedules with Decimal arithmetic and published-source validation across six countries.

A Python tool for quick calculations of a stock's fair value using Discounted Cash Flow analysis.

Kelly Criterion implemented in Python to size portfolios based on J. L. Kelly Jr's formula.

A fixed income library for pricing bonds and bond futures, and derivatives such as IRS, cross-currency and FX swaps.

Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.

Toolkit for option pricing, implied volatility surfaces, risk-neutral densities, and pricing kernel surfaces with support for advanced models including Heston, Kou, and Bates.

High-performance pricing engine built on QuantLib. It exposes QuantLib's functionality through gRPC and REST APIs, enabling distributed computations with FlatBuffers serialization.

A Python library for evaluating option trading strategies.

Python client for the FlashAlpha options analytics API.

Free quant finance API with 63 deterministic endpoints + 15 free interactive calculators at [quantoracle.dev](https://quantoracle.dev). Options pricing with full Greeks, Monte Carlo, Kelly, VaR, Sharpe, CAGR, crypto liquidation, impermanent loss, plus live crypto volatility/funding data and 24/7 position monitoring with webhook alerts. 1,000 free calls/day, no API key.

Multi-factor quantitative stock analysis MCP server for US, HK, and CN A-share markets. Transparent 0-100 scoring from 40+ indicators. Listed on Official MCP Registry.

Reverse DCF that solves for the revenue growth, duration, and operating margin a stock price implies, from SEC EDGAR fundamentals.

RQuantLib connects GNU R with QuantLib.

Quantitative Financial Modelling Framework.

The premier open source software solution for teaching and training quantitative finance.

EBM and Asian Option Valuation.

Analysing and Modelling Financial Assets.

Markets and Basic Statistics.

Bonds and Interest Rate Models.

Exotic Option Valuation.

Pricing and Evaluating Basic Options.

Portfolio Selection and Optimization.

Simulation and Inference for Stochastic Differential Equations.

Modelling and estimation of the yield curve.

Constructs a yield curve by the Smith-Wilson method from a table of LIBOR and SWAP rates.

Yield curve or zero-coupon prices interpolation and extrapolation.

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

Pricing a variance swap on an equity index.

Risk Neutral Density Extraction Package.

American options pricing with Least Squares Monte Carlo method.

Estimation of value and hedging strategy of call and put options.

Time Value of Money Functions.

Option Pricing with Efficient Simulation Algorithms.

Credit Default Swap Functions.

Functions and R Code to Accompany Derivatives Markets.

Package for time value of money calculation, time series analysis and computational finance.

R code for quantitative analysis in finance.

options trading studies functions for use with options.data package and shiny.

Financial Market Building Blocks.

Fixed income tools for R.

Quantlib implementation in pure Julia.

A Julia package for quantitative finance.

A financial contract definition, modeling language, and valuation framework.

Modern open-source analytics and market risk library designed and written in Java.

JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java.

Java library with algorithms and methodologies related to mathematical finance.

Free Java components for Quantitative Finance and Algorithmic Trading.

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

A JavaScript library for common financial calculations.

SABR stochastic-volatility model (Hagan 2002 lognormal/normal expansions, Obłój correction, smile calibration); zero dependencies, matches QuantLib's sabrVolatility to 1e-9.

Gatheral SVI volatility surface (raw/natural/jump-wings), butterfly and calendar arbitrage checks, slice calibration; zero dependencies.

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

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

US TIPS inflation math per 31 CFR 356 Appendix B (reference-CPI interpolation, index ratios); reproduces TreasuryDirect's published values.

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

quant finance in pure haskell.

Excel Addin for Haskell.

A financial maths library in Haskell.

Scala Quantitative Finance Library.

Scala library for working with stock data from IFTTT recipes or Google Finance.

Financial maths library for risk-neutral pricing and risk.

Quantitative finance library written in Rust.

Finite difference pricing library written in Rust.

A Python Pandas implementation of technical analysis indicators.

Common financial technical analysis indicators implemented in Pandas.

Financial Technical Analysis Indicator Library (Python bindings for [tulipindicators](https://github.com/TulipCharts/tulipindicators)).

A Python module for fitting the [Log-Periodic Power Law Singularity (LPPLS)](https://en.wikipedia.org/wiki/Didier_Sornette#The_JLS_and_LPPLS_models) model.

Incremental technical analysis library for Python.

A python library for computing technical analysis indicators on streaming data.

Polars-native technical analysis and backtesting with bit-identical batch and streaming parity, plus an agent skill for consistent research-to-live strategy code.

Python wrapper for TA-Lib (<http://ta-lib.org/>).

Technical Analysis Library using Pandas (Python).

Technical Analysis library in pandas for backtesting algotrading and quantitative analysis.

TuneTA optimizes technical indicators using a distance correlation measure to a user defined target feature such as next day return.

Technical Trading Rules.

A Julia wrapper for TA-Lib.

Financial market technical analysis & indicators on top of Temporal.

Visualize OnlineTechnicalIndicators.jl using LightweightCharts.jl.

Technical analysis of financial time series on top of TimeSeries.

Julia Technical Analysis Indicators via online algorithms.

A Java library for technical analysis.

Indicator is a TypeScript module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.

Orderflow trade aggregator for building Footprint Candles from exchange websocket data.

IndicatorGo is a Golang module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.

Aggregate trades into user-defined candles using information driven rules.

Chainable tree-like sliding windows for signal processing and technical analysis.

Financial market primitives in Rust: Price/Quantity/Symbol newtypes, BTreeMap order book, OHLCV aggregation, SMA/EMA/RSI indicators, position ledger with PnL, and composable risk monitor.

Streaming-first technical-analysis library with a Rust core: 514 indicators updating in O(1) per tick, with bit-exact batch-vs-streaming results.

High-performance Rust-powered backtesting engine for quantitative research with parameter sweeps, walk-forward and Monte Carlo.

Self-hosted market-alert daemon: price, RSI/MACD/SMA conditions, and full Pine Script v5 custom indicators evaluated off-TradingView, on crypto (Coinbase) and stocks (Yahoo Finance) with no API key, delivered via ntfy push, email, or Telegram.

Robinhood API client for unattended automation: after the first approved login, sessions renew from a stored refresh token with no password or device approval prompt. Covers stocks, equity and index options with Greeks, futures, IRA accounts, and the official Crypto Trading API.

Scores detected chart patterns against the pattern-free baseline for the same market, timeframe and horizon, reporting lift with cluster-robust confidence intervals instead of a hit rate against 50%.

Linter for mechanical trading-rule conditions: replays every condition over historical bars to catch look-ahead levels, dead branches that can never fire, and regime-drifted absolute thresholds before you trust a backtest.

Fully automated trading bot for the IG Index platform (spread betting and CFDs), supporting demo and live accounts.

Binance-style trading terminal with multi-strategy backtesting and a real-time, signal-only decision desk for crypto, A-shares, and US stocks.

scikit-learn-compatible purged, group-purged, and combinatorial purged (CPCV) cross-validation, walk-forward splitting, and backtest-overfitting statistics (deflated and probabilistic Sharpe ratios, PBO, minimum backtest length) to prevent leakage and overfitting when backtesting trading strategies.

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.

Unofficial CLI and async SDK for the Capital.com broker API: market data, guarded order execution, and real-time streaming.

Conversational multi-agent quant framework where agents rank currently-effective factors for entry timing (time-series rank IC), write complete strategy code that passes sandboxed audit before backtesting, and evolve strategies under multi-objective fitness; every order requires machine approval and the LLM never has a direct order path.

Systematic options trading intelligence for small accounts with desk-based portfolio management, pre-trade validation, and multi-broker consolidation.

AI auto-trading bridge for East Money's miaoxiang (妙想) China A-share simulation platform; BYOK multi-LLM (OpenAI/DeepSeek/Moonshot/GLM/Qwen) decision brain → automated order placement via miaoxiang API, with daily cron review and weekly AI reflection.

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.

Self-hosted AI trading platform with strategy evolution, technical analysis, backtesting, and paper trading via Alpaca.

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 synthesis with dual-tier routing (~$0.003/analysis), real-time Next.js dashboard, Alpaca paper trading, and 277 automated tests.

Natural-language multi-agent finance research agent with 29 swarm presets, 70 skills, and 28 auto-discovered tools; 7 backtest engines covering A-shares/US/Crypto/Futures/Forex/Options plus a cross-market CompositeEngine with shared capital pool; 5-source auto-fallback data layer (tushare/okx/yfinance/akshare/ccxt); 17-tool MCP server; includes trade-journal behavioral diagnostics for 同花顺/东财/富途 exports.

AI crypto trading bot for Bybit with 70.9% walk-forward validated accuracy on out-of-sample data, LightGBM + XGBoost ensemble with 72 ML features.

Self-hosted execution engine for algorithmic trading bots. Write strategies in Python, TypeScript, Rust, C++, C#, Scala, or Haskell and deploy with one command. Each bot runs in an isolated container with scheduled or streaming execution.

Tamper-evident SHA-256 hash-chain audit log and human-readable report for AI trading-agent decisions; read-only, offline, and dependency-free (Python standard library only). [GitHub](https://github.com/Autonomous-Asset-Management-Agents/autonomous_/tree/main/packages/autonomous-audit).

Framework for developing, backtesting, and deploying automated trading algorithms.

Algorithmic trading framework where the same code runs for backtesting and live trading across stocks, options, crypto, futures, and forex with multiple brokers including Alpaca, Interactive Brokers, Tradier, and Schwab.

QSTrader backtesting simulation engine.

Fully integrated backtesting, paper trading, and live deployment.

Pythonic algorithmic trading library.

Zipline, a Pythonic Algorithmic Trading Library.

Python-based open source software framework designed to support portfolio construction and management.

Quantitative finance, and backtesting library.

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

Flexible Backtesting for Python.

Python Backtesting library for trading strategies.

Closed-form expected returns, Sharpe ratios, and skewness of trend-following systems, with complete implementations and multi-decade futures backtests.

Checks whether backtest price data is survivor-only: dead-name detection, measured bias benchmarks, CI integrity gates.

Python library to backtest trading strategies, plot charts, seamlessly download market data, analyze market patterns etc.

Vectorized backtesting framework in Python / pandas, designed to make your backtesting easier.

Python Algorithmic Trading Library.

A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.

This is an execution engine for algo trading.

Python library for backtesting trading strategies and analyzing financial markets.

Computer program to automatically trade binary options martingale style.

the project using big-data technology to provide an uniform way to analyze the whole market.

the project using sql, pandas to provide an uniform and extendable way to record data, computing factors, select securities, backtesting, realtime trading and it could show all of them in clearly charts in realtime.

zipline-compatible live trading library.

zipline's pipeline capability with IEX for live trading.

Zipline extensions and adapters for QuantRocket.

Vectorized backtester and trading engine for QuantRocket based on Pandas.

A fast, extensible, transparent python library for backtesting quantitative strategies.

Python library for backtesting technical/mechanical strategies in the stock and currency markets.

A backtester and spreadsheet library for security analysis.

AI-powered stock analysis system with 13 specialized agents, automated trading via KIS API, supporting Korean & US markets.

AI-powered financial intelligence engine with 8 master strategies across US, CN, and HK markets. Multi-agent architecture with +29.1% annual alpha. 227 tests.

AI-powered Taiwan stock scanner for all 1,900+ TWSE/TPEX listed stocks; chips module (T86 institutional net buy/sell + TDCC 16-tier retail distribution), 13 technical indicators scored 0–100, ATR Chandelier signals with TP1/TP2, dark three.js HUD dashboard. 100% free open data, ~110 unit tests, no API key required.

Async Algorithmic Trading Engine.

Backtest trading strategies in Python.

An Algorithmic Trading Library for Crypto-Assets in Python.

Portfolio analytics for quants, written in Python.

Modern variation of quantstats, with additional features and performance improvements.

QTPyLib, Pythonic Algorithmic Trading <http://qtpylib.io>.

Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:.].

Free, open source crypto trading bot.

Free `pandas` and `scikit-learn` resources for trading simulation, backtesting, and machine learning on financial data.

An AI-oriented Quantitative Investment Platform by Microsoft. Full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.

Python package for Taiwan stock market data, factor research, and vectorized backtesting with pandas-style strategy definitions.

Code and resources for Machine Learning for Algorithmic Trading.

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

An advanced crypto trading bot written in Python.

A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities.

A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020.

Reinforcement learning stock-selection framework for the China A-share market with multi-source factor input (alpha101 + main-force flow + hot-money seats + northbound + institutional + fundamentals), board-aware price limits, and ONNX CPU inference.

Deep Learning based Python Library for Stock Market Prediction and Modelling.

Automated trading system for NOPE strategy over IBKR TWS.

Open source cryptocurrency trading bot for high frequency, arbitrage, TA and social trading with an advanced web interface.

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations.

A Python-based development platform for automated trading systems - from backtesting to optimization to livetrading.

A library built with backtest portability and performance in mind for backtest trading strategies.

QF-Lib is a Python library that provides high quality tools for quantitative finance.

Gather data and trade equities, options, and ETFs via TDAmeritrade.

Find your trading edge, using a powerful toolkit for backtesting, algorithmic trading, and research.

Lean Algorithmic Trading Engine by QuantConnect (Python, C#).

pysystemtrade is the open source version of Robert Carver's backtesting and trading engine that implements systems according to the framework outlined in his book "Systematic Trading", which is further developed on his [blog](https://qoppac.blogspot.com/).

Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.

Fully functioning fast Limit Order Book written in Python.

Algorithmic Trading with Machine Learning.

A quant framework to create cryptocurrencies strategies - from backtesting to optimization to livetrading.

A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.

Realistic limit-order fill simulator for options credit/debit spreads with post-and-wait limits, stale-quote guards, deterministic same-bar tiebreaks, and a patient-then-cross exit; engine-agnostic and zero runtime dependencies.

VeighNa is a Python-based open source quantitative trading system development framework.

Automatically generating signals and trading based on machine learning and feature engineering.

fastquant allows you to easily backtest investment strategies with as few as 3 lines of python code.

A high-performance algorithmic trading platform and event-driven backtester.

Reproducible negative-result benchmark: no model beats a memoryless synthetic binary-options feed (AUC ≈ 0.50), with permutation-null tests and a look-ahead-leak case study.

Yet Another (Python) BackTesting Engine.

TradingStrategy.ai is a market data, backtesting, live trading and investor management framework for decentralised finance.

A base on Python/C++ open source high-performance quant framework for faster analysis and backtesting, contains the complete trading system components for reuse and combination.

A high performance, low-latency backtesting engine for testing quantitative trading strategies on historical and live data in Rust.

Toolkit for quantitative trading analysis. It integrates an advanced market screener, a multi-strategy, multi-asset backtesting engine. Use with built-in GUI or through CLI.

An open‑source, broker‑agnostic Python library that lets you seamlessly deploy strategies from any major backtesting engine to live (or paper) trading with zero code changes and built‑in safety controls.

Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.

Example strategies for the QTradeX platfrom.

AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.

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

AI-powered trading edge discovery platform that validates trading ideas with event studies, statistical tests, and real market data. Web app, MCP server, CLI (`pip install varrd`), and Python SDK.

High-performance analytical core using LLVM JIT (Numba) to process large-scale telemetry for quant diagnostics.

Local-first backtesting engine with built-in overfitting checks (PBO, deflated Sharpe, bootstrap CI, walk-forward) and a native MCP server for AI agents.

Exploring Portfolio-Based Conjectures About Financial Instruments.

Performance Attribution for Equity Portfolios.

Enhanced Quantitative Trading Modelling.

Transaction infrastructure for defining instruments, transactions, portfolios and accounts for trading systems and simulation. Provides portfolio support for multi-asset class and multi-currency portfolios. Actively maintained and developed.

Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and multi-currency portfolios for backtesting and other financial research.

Integrated Quantitative Toolbox with Matlab.

Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader.

Blazing fast Julia backtester.

Modular, asynchronous trading engine in pure Julia.

Quantitative systematic trading strategy development and backtesting.

A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges.

Async Python connector for Binance Spot FIX sessions with Order Entry, Market Data, and Drop Copy support.

Open Source Forex algorithmic trading framework using OANDA REST API.

Open Source composable, real time, market data and trade execution toolkit.

From Idea to Execution - Manage your trading operation across a globally distributed cluster.

An open and opinionated trading platform using productive & familiar open source libraries and tools for strategy research, execution and operation.

Kelp is an open-source Golang algorithmic cryptocurrency trading bot that runs on centralized exchanges and Stellar DEX (command-line usage and desktop GUI).

C++ 17 based framework/library (with sample applications) for testing options based automated trading ideas using DTN IQ real time data feed and Interactive Brokers (TWS API) for trade execution. Comes with built-in [Option Greeks/IV](https://github.com/rburkholder/trade-frame/tree/master/lib/TFOptions) calculation library.

A base on Python/C++ open source high-performance quant framework for faster analysis and backtesting, contains the complete trading system components for reuse and combination. You can use python or c++ freely.

A production-grade, lock-free, high-frequency trading matching engine achieving 150M+ orders/sec.

A C++ CTP trading framework, with very clear logic.

C++23 FIX protocol engine with zero-copy parsing and SIMD acceleration, 3x faster than QuickFIX.

Enterprise-grade deterministic state synchronization engine for MFT gateways and statistical arbitrage. Eliminates microsecond deltas locking order books via topological mathematics. Throughput >41.5M TPS, physical RTT 24.175 ns, atomic validation (0 CPU load). [Website](https://tuhct-sdk.store).

Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage.

Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).

Free, open-source .NET Client for the TD Ameritrade Trading Platform. Helps developers integrate TD Ameritrade API into custom trading solutions.

Open-source Rust framework for building event-driven live-trading & backtesting systems.

Simulated perpetual futures exchange to trade your strategy against.

Local event-driven backtesting engine with no-code strategy builder and FRED vintage, ALFRED, yFinance support.

Open-source self-hosted AI trading signal platform. Generates buy/sell signals using RSI, MACD, EMA, Bollinger Bands for forex, crypto and commodities. Deployable via Docker Compose. ([Demo](https://tradeclaw.win/dashboard)).

Open-source native macOS market cockpit. 12-timeframe consensus scan across 3370 symbols (crypto + equity + ETF + FX + commodity + bond), 23 technical indicators with per-market calibration, real WebSocket streaming. Tauri shell + Python sidecar (FastAPI) + React UI; 110+ exchanges via ccxt.

Crypto perpetual-futures bot with a 7-tab web dashboard and a 15-indicator consensus engine voting across 12 timeframes (1m → 1d). Paper-mode by default. Includes packaged macOS reference build and Windows distribution.

Contamination-resistant A-share backtesting environment for LLM trading agents with point-in-time masking, entity/date anonymization, fingerprinted replay, and trajectory (SFT) export.

A reasoning & decision-trace visible Multi-agent LLM trading-research framework where bull/bear analysts debate each ticker and every decision cites the evidence it rests on.

Portfolio optimization framework combining covariance shrinkage, bootstrap aggregation, and parametric scenario modeling, with reproducible notebooks and an accompanying SSRN paper.

An agentic framework that mimics the real quant trading pipeline to find alpha: economic hypothesis, in-sample iteration, and out-of-sample validation.

Python library for portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models.

Financial portfolio optimization in python, including classical efficient frontier and advanced methods.

Sparse multi-asset factor models with cell-level sign constraints, prior-centred shrinkage, and hierarchical clustering group LASSO (HCGL); scikit-learn compatible.

Optimisation analytics for constructing and backtesting optimal multi-asset portfolios: covariance estimation, rolling optimisation, and performance reporting in one pipeline.

Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, Minimum Variance Portfolios, Maximum Sharpe Ratio Portfolios, and Genetic Algorithms based Portfolios.

fast and scalable design of risk parity portfolios with TensorFlow 2.0.

Implementations regarding "Advances in Financial Machine Learning" by Marcos Lopez de Prado. (Feature Engineering, Financial Data Structures, Meta-Labeling).

Portfolio optimization with deep learning.

Dynamic mean-variance portfolio allocation under regime-switching jump-diffusions with wealth floors, solved analytically via Laplace transforms.

Fast risks with QuantLib.

Automatic Differentation (AAD) Library.

Portfolio and risk analytics in Python.

Terminal-based ETF research and portfolio analytics application for holdings, exposure, concentration, margin, and risk workflows.

Common financial risk and performance metrics.

Computational tools for financial economics include: Gaussian Mixture model of leptokurtotic risk, adaptive Boltzmann portfolios.

Financial Risk Calculations. Optimized for ease of use through class construction and operator overload.

Quantitative Financial Risk Management: awesome OOP tools for measuring, managing and visualizing risk of financial instruments and portfolios. (Last updated: 2015-12-12).

Portfolio construction and quantitative analysis.

This tool is used to visualize the performance of a portfolio.

Collection of algorithms for online portfolio selection.

A program for financial portfolio management, analysis and optimization.

Portfolio's risk and performance analytics and returns predictions.

Risk tools for use within the crude and crude products trading space with partial implementation of R's PerformanceAnalytics.

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python.

Common financial risk and performance metrics. [empyrical](https://github.com/quantopian/empyrical) fork.

Portfolio and risk analytics in Python. [pyfolio](https://github.com/quantopian/pyfolio) fork.

Conditional Value-at-Risk (CVaR) portfolio optimization and Entropy Pooling views / stress-testing in Python.

Library for portfolio optimization (MVO) and rigorous risk metrics (VaR/CVaR).

Interactive risk and payout calculator for Futures and CFD traders; features one-time fee firm comparisons.

Framework-agnostic risk-management toolkit for systematic trading — position sizing, drawdown control, a composable stop engine, correlation limits, and portfolio exposure caps, with adapters for backtesting.py, freqtrade, and vectorbt.

Analysing equity portfolios.

Portfolio design to track an index.

Blazingly fast design of risk parity portfolios.

Portfolio Analysis, Including Numerical Methods for Optimizationof Portfolios.

Econometric tools for performance and risk analysis.

A Julia quantitative portfolio analytics (risk / performance) via online algorithms.

Quantitative risk and performance analysis package for financial time series powered by the Julia language.

PortfolioAllocation is a JavaScript library designed to help constructing financial portfolios made of several assets: bonds, commodities, cryptocurrencies, currencies, exchange traded funds (ETFs), mutual funds, stocks.

Wealth management software to keep track of financial assets like stocks, ETFs or cryptocurrencies and make solid, data-driven investment decisions.

Interactive portfolio rebalancing tool that imports brokerage CSV data, sets target allocations, and generates trade instructions.

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.

Performance analysis of predictive alpha factors.

Performance analysis of predictive (alpha) stock factors.

GPU-accelerated Factors analysis library and Backtester.

PyTorch research stack for multi-factor analysis, bias correction, portfolio optimization, and reproducible backtesting.

Agent-driven A-share factor research engine with 8 MCP tools covering hypothesis design, backtesting, scoring, and anti-overfit detection.

Open-source investment analytics platform bridging academic research and retail finance.

Backtest-overfitting audit for factor research: CSCV probability of backtest overfitting, deflated Sharpe against the declared trial count, White's Reality Check, point-in-time universe membership and disclosure-date alignment. Ships a worked example in which 24 pure-noise series produce a 1.11 Sharpe and the audit says so.

Covariance matrix estimation via factor models.

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.

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.

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

REST API analyzing Reddit and X/Twitter for stock mentions and sentiment, providing buzz scores, trending stocks, and AI-generated trend explanations.

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.

Free AI-powered stock research reports for US, China & HK using Claude Opus and multi-model AI with 20+ technical indicators.

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.

ARCH models in Python.

Python module that allows users to explore data, estimate statistical models, and perform statistical tests.

Python package for timeseries analysis and manipulation.

Python library for timeseries modelling and inference (frequentist and Bayesian) on models.

Automatic extraction of relevant features from time series.

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

A python library for time-series smoothing and outlier detection in a vectorized way.

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

vProbabilistic time series modeling in Python.

Automatic discovery of non-trivial statistical relationships across 500+ time series from FRED, World Bank, EIA, and NOAA using mutual information screening, lagged MI directional testing, and FDR correction.

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

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.

Distributional forecasting of crypto log-returns by tangent-space geodesic extrapolation on the 2-Wasserstein manifold (quantile-function coordinates). Walk-forward CRPS evaluation over 6.75 years across 4 assets × 3 horizons; benchmarked against classical baselines (Static / RW-Drift / HS-Bootstrap / GARCH-N / GARCH-t / GJR-GARCH-t) and a named-econometric panel (HAR-RV, CAViaR-SAV, Markov-switching Normal, FIGARCH, AR(1) Stochastic Volatility, bivariate VAR+GARCH). [Live dashboard](https://accursedgalaxy.github.io/wasserstein-btc/).

Time Series Analysis and Computational Finance.

Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.

Rmetrics - Financial Time Series Objects.

Univariate GARCH Models.

Multivariate GARCH Models.

Run predictions inside the database <https://tidypredict.netlify.com/>.