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 |
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):
Launch @Risk and click on the value cells:
Click on Define Assumption, then click Triang when the dialog opens:
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:
Click ok to place the distribution assumption and then repeat for each variable:
Click on the formula output cell in yellow and click on the Add Output button:
Specify an appropriate name or retain the default, then click ok.