Projects sharing features with Finmarketpy

VeighNa is an event-driven, modular platform designed for the development, backtesting, and execution of automated financial trading strategies. It provides a comprehensive suite of tools that includes a centralized trading terminal for monitoring portfolios and market conditions, alongside a robust algorithmic trading engine that manages real-time data processing and order execution. The platform distinguishes itself through a highly decoupled architecture that isolates algorithmic logic from market connectivity, allowing for independent strategy development and testing. It utilizes a dynami

bt - flexible backtesting for Python

pyalgotrade is a Python algorithmic trading library designed for developing, backtesting, and executing automated trading strategies. It provides a comprehensive framework for financial strategy backtesting, a technical analysis library for computing mathematical indicators, and connectors for cryptocurrency exchange integration. The project distinguishes itself by supporting sentiment-based trading through the integration of real-time social media feeds and keyword streams. It features a quantitative trading visualization tool for plotting price action and portfolio equity curves, along with

This software is an automated trading tool designed for the Binance cryptocurrency exchange. It functions as a program for executing high-frequency market orders and managing trade exits based on real-time exchange data and price volatility. The bot distinguishes itself through its ability to monitor exchange listings in real time, allowing it to detect and act upon newly released digital assets as they appear. It incorporates stateful trailing stop logic to dynamically adjust exit thresholds, providing automated risk management to protect capital during periods of high market volatility. Th

StockSharp is an algorithmic trading platform and quantitative framework used for developing and deploying trading robots across stock, forex, and cryptocurrency markets. It functions as a multi-asset trading gateway and a dedicated development environment for building, debugging, and scheduling automated strategies. The platform includes a visual strategy workflow editor that maps logic blocks to executable code and a simulation engine that replays historical tick data to validate trading logic. It utilizes a plugin-based broker integration system to normalize diverse exchange protocols into

Lean is an algorithmic trading engine and quantitative finance platform designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive framework for processing time-series market data, managing multi-asset portfolios, and conducting quantitative research across diverse financial markets. The platform distinguishes itself through a modular, event-driven architecture that decouples strategy logic from data ingestion and brokerage connectivity. By utilizing standardized interfaces for data providers and brokerage abstractions, it enable

RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides an event-driven engine for simulating trading strategies against historical market data, with realistic transaction costs, slippage models, and corporate action handling. The platform supports multi-asset class trading including stocks, futures, options, and REITs, with separate sub-accounts for different asset types and configurable margin requirements. The framework distinguishes itself through a plugin-based extensible architecture that allows users to swap out core componen

Zipline is a Python-based algorithmic trading library designed for the development and backtesting of investment strategies. It functions as a quantitative finance engine that processes historical market data to simulate trading interactions and evaluate strategy performance through custom metrics. The platform provides a modular, event-driven framework that manages portfolio state transitions based on time-series data streams. Beyond its core trading capabilities, the system includes a comprehensive financial data analysis toolkit for manipulating large-scale market datasets to support syste

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing

FinRL-Trading is a modular framework designed for the development, training, and deployment of quantitative trading strategies using reinforcement learning and autonomous agent workflows. It provides a comprehensive infrastructure for managing the entire lifecycle of financial models, from data ingestion and strategy generation to live market execution. The platform distinguishes itself through a multi-agent architecture that coordinates specialized tasks such as sentiment analysis, risk assessment, and collaborative research. By utilizing a standardized environment abstraction, it allows rei

efinance is a Python financial data library and programmatic interface designed to automate the acquisition of market data for quantitative trading and analysis. It serves as a toolkit for retrieving real-time and historical information across various asset classes to support the development of backtesting systems and trading strategies. The library provides a multi-asset toolkit for monitoring diverse financial instruments, including stocks, funds, bonds, and futures. It allows for the extraction of specific data points such as shareholder counts, corporate index memberships, and net asset v

aiquanttrade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment of trading strategies powered by machine learning and artificial intelligence. It provides a complete local environment for quantitative research, simulation, and automated live trading through brokerage APIs, supporting both historical backtesting and real-time paper trading without capital risk. The platform distinguishes itself through a modular, event-driven architecture that separates strategy logic from execution, allowing rule-based and machine learning models to be co

This project is a Python quantitative trading framework and library designed for developing, backtesting, and deploying automated financial strategies. It serves as both an algorithmic trading backtester for evaluating historical performance and an event-driven trading engine for executing trades based on quantitative rules. The framework functions as an educational toolkit, providing guided lessons and resources for quantitative finance learning and the application of mathematical models to market data. The system provides capabilities for algorithmic trading automation and financial strate

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and backtest automated financial trading strategies. It functions as a quantitative trading pipeline and financial market simulator, allowing users to build decision policies that optimize asset trading across various financial markets. The framework features a modular integration system for swapping reinforcement learning algorithms through a consistent API. It utilizes a standardized environment wrapper to encapsulate market dynamics into a state-action-reward interface, facilitating

QuantAxis is a quantitative trading platform and algorithmic trading framework. It provides a comprehensive local environment for backtesting strategies, managing financial market data, and executing trades across stocks, futures, and options markets. The system distinguishes itself through a distributed task scheduler that spreads asynchronous computations and heavy mathematical workloads across a network of remote agents. It incorporates a multi-account trading interface to standardize the monitoring of positions and the execution of orders across various brokerage accounts. The platform c

mlfinlab is a Python machine learning library for finance designed for building and validating models used in quantitative trading and portfolio management. It provides a financial data engineering toolkit and a quantitative strategy backtesting framework to transform raw market data into predictive signals and target classes. The library includes a synthetic financial data generator to create artificial datasets that mimic the statistical properties of real assets for stress testing. It also provides specialized tools for financial time series labeling and sampling to prevent data leakage in

pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for developing, testing, and optimizing trading strategies using historical market data. It functions as a trading strategy backtester and a financial performance evaluator, providing a structured environment to simulate trading rules and analyze their statistical reliability. The framework distinguishes itself through a market data integration layer that handles the fetching and caching of historical price data from external providers. It incorporates an event-driven backtesting engine and

Alphalens is a quantitative alpha factor analysis library designed to measure the predictive power of financial factors. It serves as a computational toolset for processing financial time series and calculating performance metrics to evaluate quantitative trading hypotheses. The library distinguishes itself through the use of quantile-based data binning to analyze return distributions across different factor strength levels. It aligns historical alpha signals with forward-looking price changes to isolate predictive effects and transforms these metrics into heatmaps and time-series charts for

Jesse is a Python algorithmic trading framework used for developing, backtesting, and executing quantitative trading strategies. It functions as a trading strategy backtester and a machine learning trading platform, providing an environment to train predictive models on historical market data and deploy them into live strategies. The framework features a standardized crypto exchange connectivity layer that allows for the execution of automated spot and futures trades across multiple cryptocurrency exchanges via an exchange-agnostic interface. It includes a quantitative risk analysis toolset t

QTPyLib, Pythonic Algorithmic Trading

Backtestable AI trading agents and Python algorithmic trading strategies for stocks, options, crypto, futures, forex, SEC filings, FRED macro data, and real brokers.

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

hftbacktest is a high-frequency trading backtesting framework and level 3 market data engine. It serves as a simulation environment for cryptocurrency trading bots and market-making strategies, utilizing a limit order book simulator to model precise market microstructures and liquidity. The system differentiates itself through high-fidelity simulation components, including queue-position modeling to predict fill times and latency-aware execution to simulate network and exchange processing delays. It reconstructs order book states from level 2 and level 3 data and uses raw exchange trade and q

Systematic Trading in python

Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing automated trading rules and simulating investment strategies using historical financial time-series data. The system functions as a quantitative analysis tool, combining a simulation engine for testing trading rules with a financial data visualizer that generates price action charts. It allows for the calculation of technical indicators and the evaluation of portfolio performance through risk-adjusted returns. The platform covers live trading integration via brokerage APIs and

This project is a comprehensive platform for quantitative investment research, machine learning, and algorithmic trading. It provides an end-to-end environment for developing, testing, and executing financial strategies, supporting the entire lifecycle from data ingestion and feature engineering to model training and backtesting. The system is distinguished by its configuration-driven workflow orchestration, which allows researchers to automate complex pipelines and manage experiments through declarative files. It features a high-performance data infrastructure that utilizes custom binary for