Introduction to the ZVT Project
ZVT is a comprehensive framework designed to facilitate quantitative trading and analysis. It provides an all-encompassing solution for data retrieval, storage, backtesting, and real-time trading operations. Let's dive into the key aspects of the ZVT project.
Key Features
Installation
Installing ZVT is straightforward. Users can easily set it up using Python and pip, a popular package manager for Python packages:
python3 -m pip install -U zvt
User Interface
ZVT offers two main types of user interfaces to meet diverse needs:
-
Dash & Plotly UI: This interface is optimal for conducting backtests and research. It requires minimal setup, with just a command-line entry
zvtto start, and then users can access it via a local web address. However, it is not suited for real-time market data or interactive user functions. -
REST API and Standalone UI: More suitable for real-time market data and interactive operations, this setup allows for greater flexibility and scalability. By leveraging the dynamic tagging system integrated with AI capabilities, users can manage trading scenarios with a combination of automation and manual intervention.
Data Capabilities
ZVT provides robust capabilities for accessing and manipulating a wide array of financial data:
- China Stock Data: Users can easily record and query stock data for companies listed in China.
- USA Stock Data: Comprehensive data coverage for US stocks is available with simple commands.
- Hong Kong Stock Data: ZVT also supports extensive data retrieval for stocks listed in Hong Kong.
- Additional data types are supported, such as indices, ETFs, and more, making ZVT a versatile tool for different markets and financial instruments.
Machine Learning Integration
ZVT features an integration with machine learning, allowing users to train predictive models on stock data. The provided example code showcases how data capture, storage, incremental updates, ML training, predictions, and result visualization can all be performed succinctly within the framework.
Trading Entities and Events
ZVT manages tradable entities and their events effectively. Users can define rules for market quote schemas and easily record or query market data at various levels such as daily or historical adjustments. This flexibility empowers users to tailor their trading strategies to specific market conditions.
Extensibility and Community
ZVT is designed to be extensible, with consistent interfaces and a supportive tag system that empowers users to adapt and expand the framework according to their trading strategies and market activities. The project's source code and documentation are available on platforms like GitHub, making it accessible for contributions and collaborative improvements.
In summary, ZVT is a powerful framework that significantly streamlines quantitative trading and analysis. It is equipped with critical tools for data management, machine learning, and flexible interfacing, ready to support both research and real-time trading tasks.