Works on: Windows 10 | Windows 8.1 | Windows 8 | Windows 7 | Windows XP | Windows 2000 | Windows 2003 | Windows 2008 | Windows 98 | Windows ME | Windows NT | Windows Vista | Windows 2012 SHA1 Hash: 15b2bc6ab683cfd86311f92326dbb555dbd6bb20 Size: 6.15 MB File Format: exe
Rating: 2.217391304
out of 5
based on 23 user ratings
Publisher Website: External Link Downloads: 158 License: Demo / Trial Version
Time Series API is a demo software by PERITECH and works on Windows 10, Windows 8.1, Windows 8, Windows 7, Windows XP, Windows 2000, Windows 2003, Windows 2008, Windows 98, Windows ME, Windows NT, Windows Vista, Windows 2012.
You can download Time Series API which is 6.15 MB in size and belongs to the software category Components Libraries. Time Series API was released on 2009-08-17 and last updated on our database on 2017-04-22 and is currently at version 2.1.
Thank you for downloading from SoftPaz! Your download should start any moment now. It would be great if you could rate and share:
Rate this software:
Share in your network:
Time Series API Description
Time Series API is a professional C++ class library for simulating (backtesting) and deploying financial trading strategies as well as general purpose time series modelling. The library is a stand-alone time series engine that can be extended via a component object model.Models are defined using formula syntax and semantics made possible by a set of lightweight interface classes that supersede the component framework. The library supports the modelling of even the most complex ideas, is easily extended, and supports deployment in any timeframe (variable or fixed, with intervals as short as one millisecond). The library also benefits from a set of highly optimized database classes for reading and writing millions of records in seconds.
As a general purpose tool for modelling time series, Time Series API has applications in many domains, such as: * Trading and investment strategy simulation and deployment: o Individual market and inter-market models o Iterative evaluations on baskets o Evaluation on aggregates (e.g. custom indices) o Fundamental company data models * Economic modelling * Time series normalization and processing: o Normalizing neural training data o Data transformations o Timeframe conversions * Data monitoring (e.g. financial, scientific): o Event Notification o Pattern recognition o Filtering applications, (e.g. noise reduction) * Computational modelling o Genetic algorithms
Where to buy?
Last updated price and discount information 7 years agoupdate now