1) Configure a Logistic Regression Model and Define Triangular Distribution.

This Blog entry is from the Monte Carlo Model section in Learn Palisade.

In this guide we have implemented a Linear Regression Model in an Excel Spreadsheet across four strongly correlating variables from the training dataset.  We are using intraday GBPUSD with 5M candlesticks whereby we have a chart configured with 700 of the most recent candlesticks. 

The model is comprised of the following independent variables, coefficient and intercept:

Name

Value

Intercept

-0.000810676

_4_Mode

-0.000200686

_4_Median

0.03422778

_4_Skew

-0.000148064

_4_HighPrice

-0.070443585

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The triangular distributions are determined as follows and as based on the data:

Field

Max

Min

Most Likely

_4_Mode

0.00191

-0.03038

-0.010

_4_Median

0.023948

-0.01913

-0.00078

_4_Skew

1.542245

-26.2921

-0.10329

_4_HighPrice

0.00191

-0.03038

-0.01086

Although we are using an explainable model, the function could just as easily be a call to a Neural Network DLL (although in example of Jube Capital optimisation software this is performed automatically upon Neural Network retraining as shown in the conclusion):

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Launch @Risk and click on the value cells:

3.png

Click on Define Assumption, then click Triang when the dialog opens:

4.png

Click Select Distribution to be presented with the configuration.  Using the values as described in the above table, enter the Min, Max and Most Likely for each variable:

5.png

Click ok to place the distribution assumption and then repeat for each variable:

6.png

Click on the formula output cell in yellow and click on the Add Output button:

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Specify an appropriate name or retain the default, then click ok.