How To Calculate Relative Strength Index (RSI) using Fortran?

Member

by dejon , in category: Technology , 9 months ago

How To Calculate Relative Strength Index (RSI) using Fortran?

Facebook Twitter LinkedIn Telegram Whatsapp

1 answer

Member

by adolf , 9 months ago

@dejon 

To calculate the Relative Strength Index (RSI) using Fortran, you can follow these steps:

  1. First, you need to collect historical price data for the asset you want to analyze. You will typically need the closing prices for a given period, such as daily closing prices for the past 14 days.
  2. Next, you need to calculate the daily price changes by subtracting the previous day's closing price from the current day's closing price. This will give you the daily price change.
  3. Next, calculate the average gain and average loss over the specified period. The average gain is the average of all positive price changes, and the average loss is the average of all negative price changes.
  4. Calculate the relative strength (RS) by dividing the average gain by the average loss.
  5. Calculate the RSI using the formula RSI = 100 - (100 / (1 + RS))


Here is a simple Fortran program snippet that demonstrates how to calculate the RSI:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
program rsi_calculator
    implicit none

    integer, parameter :: n = 14
    real :: gains(n), losses(n)
    real :: rs, rsi
    integer :: i

    ! insert code here to read historical price data and calculate daily price changes

    ! calculate average gains and losses
    gains = max(0.0, daily_price_changes)
    losses = abs(min(0.0, daily_price_changes))

    ! calculate average gain and average loss
    average_gain = sum(gains) / n
    average_loss = sum(losses) / n

    ! calculate relative strength
    rs = average_gain / average_loss

    ! calculate RSI
    rsi = 100.0 - (100.0 / (1.0 + rs))

    print *, "Relative Strength Index (RSI):", rsi

end program rsi_calculator


Please note that this is a simple example and may need to be adapted to fit your specific data and requirements. Additionally, make sure to handle cases where the average loss is zero to avoid division by zero.