Time Series APIProfessional time series modelling and simulation library for C++ | |
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Time Series API Ranking & Summary
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- License:
- Free to try
- Price:
- USD $334
- Publisher Name:
- PERITECH
- Operating Systems:
- Windows
- File Size:
- 3.4 MB
Time Series API Tags
- Library time series simulate Time series prediction API c++ library C++ Google API C++ component time series database time series compressor API Viewer view time series time series visualization Time Library C++ class library time evolution simulation net framework v2.0.50727 mobilebrothers apps for free family guy overnet download vazu torrent webnms snmp api htc touch sql vb.net calculator control betfair horse data api 521 matlab synchronize time
Time Series API Description
Size: 26.10 MB License: Commercial OS: Windows Price: $334 Buy full version Publisher: PERITECH Updated: 7 Jul 2013 Downloads: 44 (1 last week) 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
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