Projects sharing features with Strategies

This is a library of cryptocurrency trading algorithms and technical analysis strategies designed for use with the Freqtrade trading bot. The project provides a collection of pre-defined rules and mathematical indicators used to automate the buying and selling of digital assets. The repository focuses on algorithmic trading strategies and bot-driven asset management to remove manual execution from cryptocurrency trades. It enables quantitative trading analysis by allowing the development and testing of rule-based logic against historical market data. The system utilizes class-based strategy

Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati

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

Easyquant is a quantitative trading framework and event-driven engine designed for executing automated trading strategies and managing real-time market data across multiple accounts. It includes an algorithmic strategy engine and a market data integration layer to process stock quotes and order book data from external providers. The system features a trading backtesting simulator that uses market time simulation to verify strategy behavior under specific timestamps. It supports dynamic strategy deployment via a hot-reloading module system, allowing trading logic to be updated and injected int

czsc is a technical analysis library and quantitative research environment focused on Chan theory. It functions as a multi-timeframe fractal analyzer and backtesting framework used to identify market tops, bottoms, and trend structures. The system distinguishes itself through the use of bi-segment topological linking to analyze the directional flow of price. It utilizes a boolean signal composition engine to combine technical indicators with logical operators, creating complex executable rules for automated trading. The platform covers quantitative strategy research via a notebook-style loop

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

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

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

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

Qbot is a multi-purpose platform designed to support automated recruitment, quantitative trading, and distributed service orchestration. It functions as a comprehensive framework that integrates artificial intelligence into specialized workflows, enabling users to build and deploy systems for candidate screening, financial strategy execution, and context-aware knowledge retrieval. The platform distinguishes itself through a modular architecture that combines high-performance distributed communication with domain-specific automation. It provides a robust foundation for managing microservices t

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

This project is a quantitative trading platform and algorithmic trading bot designed for market data aggregation, strategy backtesting, and trade execution. It functions as a comprehensive system for collecting financial data via APIs and web sources, simulating investment strategies against historical records, and programmatically managing investment positions through brokerage interfaces. The platform distinguishes itself through institutional sentiment analysis and market intelligence tools. It monitors institutional fund activity, tracks corporate actions like equity pledges, and crawls f

Panda Factor is a quantitative trading infrastructure and alpha factor framework. It serves as a backend system for building, calculating, and managing mathematical signals designed to predict the price movements of financial assets. The project functions as a technical indicator engine that generates quantitative metrics from price and volume data. It utilizes a financial data pipeline to automate the synchronization of market data from multiple providers on a nightly schedule. The system provides capabilities for quantitative alpha generation and the construction of financial indicators us

quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering, and algorithmic trading. It serves as a centralized library of documentation covering mathematical models, financial instruments, and systematic trading strategies. The project integrates AI-driven capabilities through a modular retrieval-augmented generation framework that extracts structured data from research papers and news. It features a multi-agent workflow engine designed to discover and validate predictive alpha factors, alongside tools for local large language model

This project is a suite of machine learning and statistical tools designed for stock price prediction, financial time series forecasting, and the execution of algorithmic trading strategies. It provides a collection of deep learning and statistical models used to forecast asset prices and market trends. The system includes a market scenario simulator that uses Monte Carlo sampling to generate potential price paths and estimate financial risk. It further features a portfolio optimization tool for calculating asset distributions to maximize returns based on historical volatility, as well as a m

Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading strategies. It functions as a high-speed system that combines a financial time-series library, a multi-factor analysis tool, and a quantitative backtesting engine to support comprehensive trading research. The framework is distinguished by its high-speed computing core, which utilizes multi-threaded execution to process large volumes of market data for technical indicator generation. It supports a modular strategy composition model where signal, risk, and fund management component

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

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

Abu is an algorithmic trading framework designed for the development, backtesting, and optimization of automated trading strategies. It functions as a quantitative financial analysis library that processes time-series data to identify market trends, volatility patterns, and key price levels. The platform distinguishes itself through a modular architecture that integrates diverse financial data sources and a rule-based engine for automated risk management. It enables users to construct complex trading signals by layering technical indicators and machine learning models, while simultaneously en

This project is a Python wrapper for the TA-Lib C library, serving as a financial technical analysis library and quantitative trading tool. It provides a collection of mathematical functions designed to analyze market price movements, identify trading signals, and recognize candlestick patterns within financial data. The library focuses on the computation of trend, momentum, and volume metrics. It includes specialized tools for candlestick pattern recognition to detect recurring price action shapes in both historical and real-time data. The system integrates with NumPy arrays to process cont

Superalgos is a cryptocurrency algorithmic trading platform used for designing, backtesting, and deploying automated trading bots. It centers on a visual strategy designer that allows users to create indicators and trading logic through a graphical interface instead of writing manual code. The platform features a token-gated signal network that enables a decentralized marketplace for broadcasting and monetizing trading intelligence. Access to these signals and predictions is managed via digital tokens and reputation scores, while a distributed trading infrastructure allows users to coordinate

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

This project is an algorithmic trading engine designed for the automated execution of cryptocurrency strategies. It provides a modular execution core that connects to multiple centralized and decentralized exchanges, allowing users to deploy rule-based trading logic across various spot and futures markets. The platform serves as a comprehensive environment for the entire trading lifecycle, from initial strategy development to live market operations. What distinguishes this platform is its integrated suite for quantitative analysis and predictive modeling. It features a robust backtesting engi

ElegantRL is a deep reinforcement learning framework and quantitative trading platform designed for automating financial decision making. It provides a system for designing and training agents using massively parallel GPU execution and includes a coordination layer for multi-agent reinforcement learning. Additionally, it features a GPU-based solver for NP-complete and nonconvex mathematical optimization problems. The platform distinguishes itself through GPU-accelerated environments that simulate thousands of parallel market interactions on a single device to accelerate data collection. It in

Kronos is a financial time-series forecasting framework and quantitative trading strategy simulator. It functions as a research environment designed to analyze historical market data, train predictive models, and evaluate the performance of automated trading signals. The platform distinguishes itself through its deep learning sequence predictors and probabilistic market modeling tools. By utilizing sequence-based architectures and statistical sampling, the system generates multiple potential price trajectories and volatility estimates to quantify uncertainty. It also supports transfer learnin

finmarketpy is a quantitative trading framework and financial market analysis tool. It provides a Python-based library for simulating trading strategies against historical market data, computing the value of options contracts, and extracting trends from financial datasets. The system includes specialized engines for financial options pricing using numerical calculations and a backtesting library to assess risk and performance before live deployment. It further enables the detection of market seasonality and the execution of event studies to measure asset price behavior around specific time wi

QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti