Projects sharing features with Sequoia X

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

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

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

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

Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research. The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction

Stock is an algorithmic trading framework designed for the development, backtesting, and execution of automated investment strategies. It provides a comprehensive environment for quantitative market analysis, enabling users to build systems that connect to brokerage interfaces for order placement based on predefined technical rules. The platform distinguishes itself through integrated data acquisition and analysis capabilities, including a financial data collection engine that utilizes proxy rotation and session persistence to maintain stable connectivity and bypass rate limits. It supports h

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

This project is a comprehensive framework for engineering financial data pipelines, designed to automate the collection, cleaning, and synchronization of large-scale market datasets. It functions as a quantitative trading data engine, providing the infrastructure necessary to manage historical and real-time asset pricing information for research and machine learning workflows. The system distinguishes itself through a configuration-driven approach to orchestration, allowing users to manage complex data acquisition tasks across multiple financial providers. It features resilient middleware tha

This project is an Android hybrid mobile framework that enables the development of applications running web content inside a native Android container. It functions as a cross-platform build system that packages shared web assets into native binaries, allowing developers to access device hardware and system APIs via JavaScript. The framework utilizes a native device API bridge and a plugin architecture to expose hardware sensors and system-level functionality not available through standard browser interfaces. It employs a manifest-driven orchestration system to manage application metadata, bra

Zenbot is an automated cryptocurrency trading bot designed to execute trades on exchanges based on technical analysis and predefined risk parameters. It functions as a technical analysis engine that processes market data through mathematical indicators to generate actionable trade signals. The system includes a genetic algorithm strategy optimizer to automatically discover the most profitable parameter configurations. It provides multiple simulation environments, including a trading strategy backtester for replaying historical data and a paper trading simulator for testing strategies against

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 project is an automated trading and agentic workflow platform designed to orchestrate complex financial tasks through state-based graphs. It provides a comprehensive framework for building, deploying, and managing autonomous agents that execute multi-step analytical processes, monitor real-time market conditions, and perform high-speed trade execution. The platform distinguishes itself through a robust agentic plugin ecosystem that integrates directly with popular AI-powered development environments and command-line interfaces. It features a specialized financial analysis engine capable

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

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

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

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

This project is a Python-based stock market tracker and financial data dashboard designed to fetch and visualize real-time and historical market data. It functions as a containerized financial application and analysis tool for monitoring price trends and calculating financial indicators. The system includes a metadata-driven reporting engine that transforms database tables into interactive web reports without manual page development. It further extends its analysis capabilities by embedding third-party financial interfaces and indicator calculators directly into the user interface via frames.

This library is a Python-based tool for retrieving historical and real-time financial market data from public sources. It functions as a programmatic interface for downloading stock prices, dividends, financial statements, and corporate calendars, allowing users to perform automated research and analysis on various market assets. The project distinguishes itself by structuring retrieved financial time series directly into tabular data frames, which facilitates mathematical analysis and manipulation of market metrics. It supports efficient data retrieval through multi-threaded batch downloadin

This project is a Python quantitative finance library designed for gathering, manipulating, and analyzing stock market data. It provides a suite of tools for quantitative stock analysis, including an equity screening framework for filtering stocks based on technical and fundamental criteria. The library features a machine learning price predictor for classifying stock movements and forecasting future price directions. It also includes a financial technical analysis tool to calculate indicators such as Bollinger Bands, RSI, and MACD, alongside an algorithmic trading simulator for testing portf

Tushare is a financial data library for the Python programming environment that provides access to historical and real-time market information. It functions as a data interface for retrieving stock trading records, corporate financial statements, and macroeconomic indicators to support quantitative analysis and research. The library distinguishes itself by automatically transforming raw API responses into tabular data structures, allowing for direct integration with data analysis workflows. It manages access to these datasets through token-based authentication and utilizes schema-mapped parsi

This project is a collection of predictive models and quantitative tools for stock price forecasting. It implements a variety of machine learning architectures, including generative adversarial networks, long short-term memory networks, and language models for financial analysis. The system distinguishes itself by combining time-series forecasting with natural language processing to convert financial news into numerical sentiment scores. It also incorporates synthetic market data generation and automated hyperparameter optimization using Bayesian and reinforcement learning methods to reduce p

Ashare is a market data aggregator and financial time-series table generator designed to provide a stable stream of price and volume data for quantitative analysis. It functions as a multi-provider data proxy that converts raw asset price feeds into structured tables for immediate processing. The system ensures high availability for data feeds through a failover mechanism that automatically switches between primary and backup market data sources. This provider-agnostic layer allows the tool to maintain continuous data availability without altering the underlying analysis logic. The project c

This project provides technical documentation and reference guides for spot trading, including specifications for REST, WebSocket, and FIX protocols. It serves as a comprehensive resource for integrating with spot trading endpoints to execute trades, query account data, and fetch market statistics. The project distinguishes itself by supporting institutional-grade connectivity through the Financial Information eXchange standard and simple binary encoding to reduce latency and payload size. It also includes a dedicated sandbox environment for validating trading logic and strategies without fin

The FinanceToolkit is an open-source Python library for quantitative finance that provides a unified framework for financial analysis, asset valuation, and risk management. It serves as a comprehensive platform for computing over 200 financial metrics and ratios, with capabilities spanning financial ratio analysis, fixed income analytics, macroeconomic data aggregation, options pricing, and portfolio risk management. The toolkit distinguishes itself through a modular architecture that separates data retrieval from computation, with stateless engines for financial models like Black-Scholes, GA

AkShare is a Python financial data library and programmatic interface designed for fetching real-time and historical stock, currency, and economic market data. It serves as a quantitative data acquisition tool for gathering the large-scale financial datasets required for economic research and quantitative analysis. The library provides a unified interface to retrieve datasets from various official and commercial providers, removing the need to write custom scrapers for individual financial sources. It maps standardized function calls to diverse third-party sources to normalize varying respons

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

This is an unofficial client library that provides programmatic access to TradingView chart data, technical indicators, and real-time market prices. It is designed to support automated trading workflows by enabling direct interaction with TradingView’s data and analysis capabilities through code. The library offers a set of tools for working with market data and technical analysis. It includes a historical data extractor for querying past price ranges and indicator values, a real-time market data streamer that uses WebSockets to deliver live price updates and indicator outputs, and a strategy