Time Series API is a C++ library that helps users simulate and deploy financial trading strategies, as well as general-purpose time series modeling. It is a stand-alone, component-based engine for time series processing.
Time Series API is a comprehensive C++ class library suitable for simulating and deploying financial trading strategies, as well as any general purpose time series modelling. The library is built to function as a standalone time series engine that can be extended using the component object model. With a set of lightweight interface classes that supersede the component framework, the library makes it possible to define models using 'formula syntax and semantics.'
Time Series API has been optimized to support even the most complex modelling ideas and is easily extendable for use in any timeframe, fixed or variable, with intervals as short as one millisecond. The library also benefits from a highly optimized set of database classes that can read and write millions of records within seconds.
As a general purpose tool, Time Series API can be applied across a range of domains. It is ideal for trading and investment strategy simulation and deployment, including individual market and inter-market models, iterative evaluations on baskets, evaluation on aggregates such as custom indices, and fundamental company data models.
Other potential applications of Time Series API include economic modelling, time series normalization and processing, data monitoring, event notification, pattern recognition, filtering applications such as noise reduction, and computational modelling using genetic algorithms.
Time Series API has been optimized to support even the most complex modelling ideas and is easily extendable for use in any timeframe, fixed or variable, with intervals as short as one millisecond. The library also benefits from a highly optimized set of database classes that can read and write millions of records within seconds.
As a general purpose tool, Time Series API can be applied across a range of domains. It is ideal for trading and investment strategy simulation and deployment, including individual market and inter-market models, iterative evaluations on baskets, evaluation on aggregates such as custom indices, and fundamental company data models.
Other potential applications of Time Series API include economic modelling, time series normalization and processing, data monitoring, event notification, pattern recognition, filtering applications such as noise reduction, and computational modelling using genetic algorithms.
What's New
Version 2.1.0:
- Support for variable length time intervals (e.g. tick-bars, volume-bars).
- Comprehensive reports and logs allow for in-depth inspection of all aspects of a simulation.
- Advanced memory management allows simulations to operate on decades of tick-by-tick data without straining resources.