It overhauls the traditional OOP approach that represents strategies as classes and other data structures, which are easier to write and extend compared to vectors, but harder to analyze and also require additional effort to do it quickly. It builds upon the idea that each instance of a trading strategy can be represented in a vectorized form, so multiple strategy instances can be packed into a single multi-dimensional array, processed in a highly efficient manner, and compared easily. Vectorbt was implemented to address common performance shortcomings of backtesting libraries. Accessing and analyzing this information for yourself could give you an information advantage in your own trading. With it, you can traverse a huge number of strategy configurations, time periods, and instruments in little time, to explore where your strategy performs best and to uncover hidden patterns in data. While there are many great backtesting packages for Python, vectorbt combines an extremely fast backtester and a data science tool: it excels at processing performance and offers interactive tools to explore complex phenomena in trading. Support us to get access to parallelization, portfolio optimization, pattern recognition, event projections, limit order support, and 100+ other hot features! Visualize strategy performance using interactive charts and dashboards (both in Jupyter and browser)įetch and process data periodically, send Telegram notifications, and more
Supercharge pandas and your favorite tools to run much faster
#BACKTESTING SOFTWARE FOR MAC SERIES#
Uncover hidden patterns in financial marketsĪnalyze time series and engineer new features for ML models Optimize your trading strategy against many parameters, assets, and periods in one go
#BACKTESTING SOFTWARE FOR MAC FULL#
Retain full control over execution and your data (as opposed to web-based services such as TradingView) Backtest strategies in a couple of lines of Python codeĮnjoy the best of both worlds: the ecosystem of Python and the speed of C
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