Materialise statistic functions tool provides the execution environment for running the Standard Deviation function and other technical functions against Materialise. Materialise value trend is the prevailing direction of the price over some defined period of time. The concept of trend is an important idea in technical analysis, including the analysis of statistic functions indicators. As with most other technical indicators, the Standard Deviation function function is designed to identify and follow existing trends. Materialise statistical functions help analysts to determine different price movement patterns based on how price series statistical indicators change over time. Please specify Time Period and Deviations to execute this module.
The output start index for this execution was four with a total number of output elements of fifty-seven. Materialise NV Standard Deviation measures the spread of Materialise time series from expected value (the mean).
Materialise Technical Analysis Modules
Most technical analysis of Materialise help investors determine whether a current trend will continue and, if not, when it will shift. We provide a combination of tools to recognize potential entry and exit points for Materialise from various momentum indicators to cycle indicators. When you analyze Materialise charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.
Predictive technical analysis modules help investors to analyze different prices and returns patterns as well as diagnose historical swings to determine the real value of Materialise NV. We use our internally-developed statistical techniques to arrive at the intrinsic value of Materialise NV based on widely used predictive technical indicators. In general, we focus on analyzing Materialise Stock price patterns and their correlations with different microeconomic environment and drivers. We also apply predictive analytics to build Materialise's daily price indicators and compare them against related drivers, such as statistic functions and various other types of predictive indicators. Using this methodology combined with a more conventi