VectorBT PRO
VectorBT PRO (vectorbtpro) is a next-generation engine for backtesting, algorithmic trading, and research. It's a high-performance, actively-developed, proprietary successor to the vectorbt library, one of the world's most innovative open-source backtesting packages. The PRO version extends the open-source package with new impressive features and useful enhancements.
This tool is a community-led initiative to fix information asymmetry in trading. We bring many SOTA tools and libraries under one umbrella and automate them. Our vision is to make quantitative analysis and algorithmic trading accessible to everyone. Join us!
What is VectorBT PRO?
VectorBT PRO is a Python package that combines a variety of acceleration and data science tools to make trading strategies and markets more transparent to quants.
One of the unique features of VBT is the ability to represent any trading setup as a set of multidimensional arrays, where each column represents a separate configuration of a trading strategy and its environment. This approach allows building and backtesting entire grids of parameters at the speed of C, while the results can be analyzed in detail using the Python's vibrant package ecosystem - the best of both worlds. Learn more.
Why VectorBT PRO?
VectorBT PRO follows an approach totally opposite to that of a conventional backtesting software: instead of running a backtesting job as a single monolithic process, VBT divides the job into dozens of isolated components that can be composed by the user like Lego bricks and run independently of each other; such components include data, indicators, signals, allocations, portfolio, and more. Each component has its own ecosystem of analysis tools, enabling quants to analyze many facets of a trading strategy even before it hits the actual backtesting step. This also means that ML models can be connected at many places and integrated more easily.
Last but not least, VBT was build with parameter optimization in mind: even large spaces of parameters can be tested in the blink of an eye, allowing quants to build dashboards and explore how even a tiny change in parameters impacts the overall performance, in real time!
VectorBT PRO (vectorbtpro) is a next-generation engine for backtesting, algorithmic trading, and research. It's a high-performance, actively-developed, proprietary successor to the vectorbt library, one of the world's most innovative open-source backtesting packages. The PRO version extends the open-source package with new impressive features and useful enhancements.
This tool is a community-led initiative to fix information asymmetry in trading. We bring many SOTA tools and libraries under one umbrella and automate them. Our vision is to make quantitative analysis and algorithmic trading accessible to everyone. Join us!
What is VectorBT PRO?
VectorBT PRO is a Python package that combines a variety of acceleration and data science tools to make trading strategies and markets more transparent to quants.
One of the unique features of VBT is the ability to represent any trading setup as a set of multidimensional arrays, where each column represents a separate configuration of a trading strategy and its environment. This approach allows building and backtesting entire grids of parameters at the speed of C, while the results can be analyzed in detail using the Python's vibrant package ecosystem - the best of both worlds. Learn more.
Why VectorBT PRO?
VectorBT PRO follows an approach totally opposite to that of a conventional backtesting software: instead of running a backtesting job as a single monolithic process, VBT divides the job into dozens of isolated components that can be composed by the user like Lego bricks and run independently of each other; such components include data, indicators, signals, allocations, portfolio, and more. Each component has its own ecosystem of analysis tools, enabling quants to analyze many facets of a trading strategy even before it hits the actual backtesting step. This also means that ML models can be connected at many places and integrated more easily.
Last but not least, VBT was build with parameter optimization in mind: even large spaces of parameters can be tested in the blink of an eye, allowing quants to build dashboards and explore how even a tiny change in parameters impacts the overall performance, in real time!
