QUANTAXIS/QUANTAXIS
QUANTAXIS
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 covers several high-level capability areas, including financial market data management for price ticks and order books, quantitative factor research, and strategy backtesting. It also includes tools for custom indicator calculation and a pub-sub messaging system for real-time data routing.
The project is implemented in Python.
Features
- Algorithmic Trading Frameworks - Provides a modular software platform for building, simulating, and executing automated investment strategies.
- Trading Strategy Backtesters - Offers a high-performance engine for simulating trading strategies by validating logic against historical market data.
- Technical Indicator Calculators - Includes an expression builder for creating and batch-processing custom technical indicators across entire markets.
- Automated Trading Execution - Executes buy and sell orders for stocks and futures across multiple accounts through a unified interface.
- Multi-Account Trading Protocols - Standardizes the tracking of holdings and orders across different exchanges through a common data representation layer.
- Position Tracking - Standardizes the monitoring of holdings and trades across various financial markets and brokerage accounts.
- Quantitative Trading Platforms - Offers a comprehensive local environment for backtesting strategies and executing trades across multiple financial markets.
- Market Data - Implements a high-performance database system for storing and updating tick-level financial market data.
- Time Series Databases - Provides a high-performance time-series store optimized for rapid retrieval of historical market ticks and order books.
- Quantitative Financial Modeling - Provides tools for developing and testing mathematical indicators to identify predictive patterns in financial time-series data.
- Technical Indicator Engines - Provides an expression-based builder for batch processing custom mathematical technical indicators across large datasets.
- Unified Trading Interfaces - Provides a unified tool for monitoring positions and executing orders across various financial assets and brokerage accounts.
- Shared Memory Data Exchange - Utilizes shared-memory regions to move large datasets between processes without copying overhead.
- Distributed Computing Frameworks - Distributes asynchronous computational workloads across a local network of remote agents.
- Quantitative Computing Distribution - Spreads heavy mathematical workloads and strategy simulations across remote agents to increase processing speed.
- Distributed Task Schedulers - Coordinates recurring jobs and spreads asynchronous computations across a network of remote agents.
- Distributed Task Workers - Implements a system for spreading heavy quantitative computations across a network of remote worker nodes.
- Message Bus Architectures - Uses a pub-sub message bus to coordinate real-time data flows and task distribution between system components.
- Trading Frameworks - Local quantitative solution for data, backtesting, and multi-account trading.
Star history
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