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Lumina Vision

Probability-Based Indicator

This indicator analyses price movements of a given period to obtain crucial information for probability distribution analysis such as their mean and standard deviation, Once it has such piece of information it does all the necessary calculations and finally calculates the probability that the current market value will go above or below the given period bars.

Since this indicator effectively leverages the power of probability which doesn't lie, It is a powerful indicator that needs to be on the tool-box of any serious trader.

This indicator can be handy when trying to find the best market entry point for those who trade in the short term as well as those trading in the long term and who are looking for good entry into the market to minimize drawdowns or some tight stop loss getting hit early. This indicator might well be used as the exit signal confirmation.

Reading the Indicator values

This indicator is an easy one to use, On the sub-window left corner the indicator displays the probability of buy and probability of sell, even though the probability of sell is marked in negative values it is not negative, The negative values help in making the histogram appear on the downside during drawing the indicator: For example; When the indicator shows 

Probability indicator 0.83, -0.17.  simply means the probability of buy is 83% and the probability of Sell is 17%

Probability Indicator 0.17, -0.83,  simply means the probability of buy is 17% meanwhile the probability of sell is 83%

This is the early version of the indicator priced at 55$, with each update the price Moves up, Stay tuned


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Matrix is the foundation of complex trading algorithms as it helps you perform complex calculations effortlessly and without the need for too much computation power, It's no doubt that matrix has made possible many of the calculations in modern computers as we all know that bits of information are stored in array forms in our computer memory RAM, Using some of the functions in this library I was able to create machine learning robots that could take on a large number of inputs To use this libra
This is standard library built for flexible neural Networks with performance in mind. Calling this Library is so simple and takes few lines of code:    matrix Matrix = matrix_utils.ReadCsv( "Nasdaq analysis.csv" );       matrix x_train, x_test;    vector y_train, y_test;         matrix_utils.TrainTestSplitMatrices(Matrix,x_train,y_train,x_test,y_test, 0.7 , 42 );    reg_nets = new CRegressorNets(x_train,y_train,AF_RELU_,HL, NORM_MIN_MAX_SCALER); //INitializing network       reg_nets.RegressorN
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