A concern about Trading with machine learning: Regression

AI & Machine learning
1

Hi there:



 In the course: " Trading with machine learning:  Regression"  … there ia an example of creating input and output parameters… in which,   the input parameters… are 3 moving averages (from 10, 15 and 60 days)…of the close,    the correlation between the previous close values and corresponding 3-day moving average values,  Today's open - Previous open, and finally  Previous close - Today's open …



I was wondering… Why are these 6 parameters used as inputs…?   do they work as input parameters if I for example were to predict the high and the los for the next day for a stock ???



Is there a particular reason for choosing these parameters??  or were day just chosen as an example ???

 if the latter is true…  what parameters  should be suitable for  stocks  and why ??

 

2

Hello Ghery,



While predicting the next day's high and low prices we need to have certain input parameters that give the model information about that particular stock's trend, momentum, and volatility (as is the case with predicting the prices for any other stock).


  1. The moving averages of 3 different time frames can provide information about the trend and momentum of the stock price.
  2. The correlation between the previous close and the corresponding 3-day moving average can indicate the strength of the current trend.
  3. The difference between today's and the previous day's open and close can provide insights into the buying and selling pressure of the market.



    However, this was just an example and these 6 parameters may not work that well for all assets. Different stocks/assets may require different input parameters depending on their unique characteristics. Therefore, it is important to experiment with different sets of input parameters and test their effectiveness in predicting prices.



    Hope this helps!



    Thanks,

    Rushda
3

thanks to you… I really appreciate your help…