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Review RSI function - Python

does this RSI code looks correct for you? RSI I'm getting with this code are very often touching oscillator peaks (0 or 100) when comparing to RSIs on different market data apps (TradingView etc.) they hardly ever do that.

Does this formula looks correct? I found it in quite old book Technical Traders Guide to Computer Analysis of the Futures Market. Also it returns almost identical results as RSI=100-(100-RS) where RS=AVG GAIN/AVG LOSS. But still I'm a bit mad that results are different comparing to RSIs available on the web...

# 
# Formula used:
#
# RSI(n) = 100 * (avg_up(n) / (avg_up(n)+avg_down(n)))
#
# Where:
#
# avg_up = average percentage gain from n-periods
# avg_down = average percentage loss from n-periods
# n = number of periods to calculate averages
#

def calculate_percentage_gains_and_losses(prices):
    
    percentage = {"gains":[0.0],
                  "losses":[0.0]}

    for i in range(len(prices)-1):
        
        diff=((float(prices[i + 1]) / float(prices[i]))*100)-100
        
        if diff>=0:
            percentage["gains"].append(diff)
            percentage["losses"].append(0.0)
        else:
            percentage["losses"].append(abs(diff))
            percentage["gains"].append(0.0)

    return percentage

def calculate_avg_percentage_gains_and_losses(prices, gains, losses, periods):

    avg_percentage = {"gains":[ 0.0 for x in range(periods - 1)], 
                      "losses":[ 0.0 for x in range(periods - 1)]}

    for i in range(periods,len(prices)+1):
            avg_percentage["gains"].append(sum(gains[i - periods:i]) / periods)
            avg_percentage["losses"].append(sum(losses[i - periods:i]) / periods)

    return avg_percentage

def calculate_relative_strength_index(prices, periods):

    percentage = calculate_percentage_gains_and_losses(prices)
    avg_percentage = calculate_avg_percentage_gains_and_losses(prices, percentage["gains"], percentage["losses"], periods)
    rsi_list=[0.0 for x in range(periods - 1)]

    for i in range(periods - 1, len(prices)):
        rsi = 100 * round((avg_percentage["gains"][i] / (avg_percentage["gains"][i] + avg_percentage["losses"][i])), 2)
        rsi_list.append(rsi)

    return rsi_list

EDIT Here is the code after adjustment

def calculate_percentage_gains_and_losses(prices):
    
    percentage = {"gains":[0.0],
                  "losses":[0.0]}

    for i in range(len(prices)-1):
        
        diff=((float(prices[i + 1]) / float(prices[i]))*100)-100
        
        if diff>=0:
            percentage["gains"].append(diff)
            percentage["losses"].append(0.0)
        else:
            percentage["losses"].append(abs(diff))
            percentage["gains"].append(0.0)

    return percentage

def calculate_smoothed_avg_percentage_gains_and_losses(prices, gains, losses, periods):

    avg_percentage = {"gains":[ 0.0 if i<(periods-1) else sum(gains[:periods]) / periods for i in range(periods)], 
                      "losses":[ 0.0 if i<(periods-1) else sum(losses[:periods]) / periods for i in range(periods)]}

    for i in range(periods, len(prices)):
        avg_percentage["gains"].append((gains[i] + (avg_percentage["gains"][i-1]* (periods-1))) / periods)
        avg_percentage["losses"].append((losses[i] + (avg_percentage["losses"][i-1]* (periods-1))) / periods)

    return avg_percentage

def calculate_relative_strength_index(prices, periods):

    percentage = calculate_percentage_gains_and_losses(prices)
    avg_percentage = calculate_smoothed_avg_percentage_gains_and_losses(prices, percentage["gains"], percentage["losses"], periods)
    rsi=[ 0.0 if i < (periods-1) else round((100 * (avg_percentage["gains"][i] / (avg_percentage["gains"][i] + avg_percentage["losses"][i]))),2) for i in range(len(prices))]

    return rsi


source https://stackoverflow.com/questions/74791649/review-rsi-function-python
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