Li FT statistic functions tool provides the execution environment for running the Linear Regression function and other technical functions against Li FT. Li FT 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 Linear Regression function function is designed to identify and follow existing trends. Li FT statistical functions help analysts to determine different price movement patterns based on how price series statistical indicators change over time. Please specify Time Period to run this model.
Incorrect Input. Please change your parameters or increase the time horizon required for running this function. The output start index for this execution was zero with a total number of output elements of zero. The Linear Regression model generates relationship between price series of Li FT Power and its peer or benchmark and helps predict Li FT future price from its past values.
Li FT Technical Analysis Modules
Most technical analysis of Li FT 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 WS0 from various momentum indicators to cycle indicators. When you analyze WS0 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.
As an individual investor, you need to find a reliable way to track all your investment portfolios' performance accurately. However, your requirements will often be based on how much of the process you decide to do yourself. In addition to allowing you full analytical transparency into your positions, our tools can tell you how much better you can do without increasing your risk or reducing expected return.
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One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Li FT position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Li FT will appreciate offsetting losses from the drop in the long position's value.
Li FT Pair Trading
Li FT Power Pair Trading Analysis
The ability to find closely correlated positions to Li FT could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Li FT when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Li FT - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Li FT Power to buy it.
The correlation of Li FT is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Li FT moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Li FT Power moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for Li FT can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.