Salar C1 (Ireland) Technical Analysis Overview

    F000003ZYH -- Ireland Fund  

    GBp 13,913  15.00  0.11%

    Salar C1 GBP Distr has Coefficient Of Variation of 356.78 and Risk Adjusted Performance of 0.2534. Salar C1 technical analysis provides you with the way to harness past market data to determine a pattern that measures the direction of the fund future prices. In other words you can use this information to find out if the fund will indeed mirror its model of past prices and volume data or the prices will eventually revert. We found nineteen technical drivers for Salar C1 GBP Distr which can be compared to its competition. Please validate Salar C1 GBP Variance, Jensen Alpha and the relationship between Standard Deviation and Information Ratio to decide if Salar C1 is priced more or less accurately providing market reflects its prevalent price of 13913.0 per share.
    Horizon     30 Days    Login   to change

    Salar C1 GBP Technical Analysis

    Time Period
      Portfolio Optimization  Portfolio Optimization  
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    Salar C1 GBP Trend Analysis

    Use this graph to draw trend lines for Salar C1 GBP Distr. You can use it to identify possible trend reversals for Salar C1 as well as other signals and approximate when it will take place. Remember, you need at least two touches of the trend line with actual Salar C1 price movement. To start drawing, click on the pencil icon on top-right. To remove the trend, use eraser icon.

    Salar C1 Best Fit Change Line

    The following chart estimates an ordinary least squares regression model for Salar C1 GBP Distr applied against its price change over selected period. The best fit line has a slop of ? % . It has 0 observation points and a regression sum of squares at 0.0, which is the sum of squared deviations for the predicted Salar C1 price change compared to its average price change.

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    Salar C1 Market Strength

    Salar C1 October 20, 2018 Daily Price Condition

    Additionally see Investing Opportunities. Please also try Pattern Recognition module to use different pattern recognition models to time the market across multiple global exchanges.