Correcting RSI Calculation Errors in a Wilder-Style Implementation

# How to go about computing RSI?


# How to go about computing RSI?












I've written python code that I believe computes RSI. I wrote the code based on what I saw in stockcharts.com found here

Here is the code:

```
def getRSI(close, n=14):
    """
    Computes the Relative Strength Index of a trend
    :param close: closing prices
    :param n: lookback period
    :return: a np array containing the RSI for all periods spanning 
closing price data
    """

# compute a vector for change between daily closing prices
change = np.zeros(len(close))
for i in range(1, len(close)):
    change[i] = close[i-1] - close[i]

# compute a vector of gains and losses
gain = np.zeros(len(close))
loss = np.zeros(len(close))

for i in range(1, len(close)):
    if change[i] >= 0:
        gain[i] = change[i]
    else:
        loss[i] = math.fabs(change[i])

avg_gain = np.zeros(len(close))
avg_loss = np.zeros(len(close))

avg_gain[n] = np.average(gain[0:n])
avg_loss[n] = np.average(loss[0:n])

for i in range(n, len(close)):
    avg_gain[i] = (avg_gain[i-1]*(n-1) + gain[i])/n
    avg_loss[i] = (avg_loss[i-1]*(n-1) + loss[i])/n

RS = np.zeros(len(close))
for i in range(n, len(close)):
    RS[i] = avg_gain[i]/avg_loss[i]

RSI = np.zeros(len(close))
for i in range(n, len(close)):
    RSI[i] = 100 - (100/(1+RS[i]))

return RSI
```

I tried to compute the RSI for COP for prices starting from 26th November 2006 to 26th November 2018. But the curve I get is completely different from what I see on Yahoo Finance or even StockCharts for that matter. What am I doing wrong?

## Answer by amdopt (score 5)

https://quant.stackexchange.com/a/42846

I have corrected your code so that it works as it should. This code could be cleaned up a lot. I just adjusted your existing code so that you would be able to understand it. I added some comments preceded by ## on the lines that were changed. See below.

```
import numpy as np
from math import fabs

def getRSI(close, n=14):

    # compute a vector for change between daily closing prices
    change = np.zeros(len(close))
    for i in range(1, len(close)):        
        change[i] = close[i] - close[i-1] ##Reversed the order of these.

    # compute a vector of gains and losses
    gain = np.zeros(len(close))
    loss = np.zeros(len(close))

    for i in range(1, len(close)):
        if change[i] >= 0:
            gain[i] = change[i]
            loss[i] = 0.00 ##Each array element needs a value.
        else:
            gain[i] = 0.00 ##Each array element needs a value.
            loss[i] = fabs(change[i])

    avg_gain = np.zeros(len(close) - n) ##Array length was wrong.
    avg_loss = np.zeros(len(close) - n) ##Array length was wrong.

    avg_gain[0] = np.average(gain[1:n+1]) ##First array element has a different calculation.
    avg_loss[0] = np.average(loss[1:n+1]) ##First array element has a different calculation.

    for i in range(1, len(close) - n): ##Loop counter was wrong.
        avg_gain[i] = (avg_gain[i-1]*(n-1) + gain[i+n])/n ##Indexes were wrong.
        avg_loss[i] = (avg_loss[i-1]*(n-1) + loss[i+n])/n ##Indexes were wrong.

    RS = np.zeros(len(close) - n) ##Array length was wrong.
    for i in range(0, len(close) - n): ##Loop counter was wrong.
        RS[i] = avg_gain[i]/avg_loss[i]

    RSI = np.zeros(len(close) - n) ##Array length was wrong.
    for i in range(0, len(close) - n): ##Loop counter was wrong.
        if avg_loss[i] == 0: ##This was missing. Could throw an error without it.
            RSI[i] = 100
        else:
            RSI[i] = 100 - (100/(1+RS[i]))

    return RSI
```