TimeScape and MATLAB
This paper describes how Xenomorph's TimeScape analytics and data management solution can be accessed and utilised within MathWorks' MATLAB computational and visualization environment.
It is intended for MATLAB users who wish to combine data and analytics hosted in TimeScape with MATLAB tools in order to produce best-of-breed analytical solutions based on cleansed and validated data.
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Introduction
MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation. MATLAB code is widely used for financial modelling and analysis and can be integrated with other languages and applications, and distributed as algorithms and applications.
Xenomorph TimeScape provides a complete analytics and data management suite that encompasses the capture, cleansing, validation, storage and analysis of complex data types. Its normalised data model enables data consistency, quality and auditability despite disparate and distributed sources, and presents a single common view for all downstream users and systems.
TimeScape's interface and query language enable the fast, easy analysis of historic data of any type and frequency, alongside hundreds of ready-to-run functions and an extensible framework for the easy integration of proprietary calculations.
This paper describes how TimeScape can be easily embedded within MATLAB using the TimeScape COM Interface as a source of data and analytics. It is intended for MATLAB users who wish to combine TimeScape analytics and data with MATLAB computational and visualization tools, to produce best-of-breed analytical solutions.
White paper contents
- 1 Introduction
- 2 Objectives
- 3 TimeScape and MATLAB Integration
- 4 Basket Optimisation Example
- 4.1 Accessing TimeScape from MATLAB
- 4.2 Load FTSE 100 Index basket definition from TimeScape
- 4.3 Load closing prices for each index constituent and index closing price
- 4.4 Utilise MATLAB to calculate weights for each index constituent.
- 4.5 Create a MATLAB timeseries from the calculated data
- 4.6 Use MATLAB to plot the calculated and actual closing prices.0
- 4.7 Save weighted basket definition to TimeScape
- 5 Principal Component Analysis Example
- 5.1 Create a matrix of the weighted closing prices for each component and calculate their covariance
- 5.2 Calculate eigenvalues and eigenvectors
- 5.3 Plot Principal Components
- 5.4 Create TimeScape properties to save eigenvalues and eigenvectors
- 5.5 Save eigenvalues and eigenvectors to TimeScape
- 6 Alternative approaches and future direction
- 7 Conclusion
- 8 Technical Appendix
- 8.1 TimeScape COM server/data access
- 8.2 Optional Arguments
- 8.3 Handling TimeScape Errors
- 8.4 TimeSeries
- 8.5 Mutliple Time Series
- 8.6 Reference Properties
- 8.7 Matrix Properties
- 8.8 List Properties
- 8.9 Queries
- 8.10 Saving Data to TimeScape
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View complete TimeScape and MATLAB white paper PDF. |