Relative Strength Index in python pandas

user_3084006 0
Viewed 103798

I am new to pandas. What is the best way to calculate the relative strength part in the RSI indicator in pandas? So far I got the following:

from pylab import *
import pandas as pd
import numpy as np



def Datapull(Stock):
    try:
        df = (pd.io.data.DataReader(Stock,'yahoo',start='01/01/2010'))
        return df
        print 'Retrieved', Stock
        time.sleep(5)
    except Exception, e:
        print 'Main Loop', str(e)


def RSIfun(price, n=14):
    delta = price['Close'].diff()
    #-----------
    dUp=
    dDown=

    RolUp=pd.rolling_mean(dUp, n)
    RolDown=pd.rolling_mean(dDown, n).abs()

    RS = RolUp / RolDown
    rsi= 100.0 - (100.0 / (1.0 + RS))
    return rsi

Stock='AAPL'
df=Datapull(Stock)
RSIfun(df)

Am I doing it correctly so far? I am having trouble with the difference part of the equation where you separate out upward and downward calculations

pandas finance python
11 Answers

You can get a massive speed up of Bill's answer by using numba. 100 loops of 20k row series( regular = 113 seconds, numba = 0.28 seconds ). Numba excels with loops and arithmetic. 

import numpy as np
import numba as nb

@nb.jit(fastmath=True, nopython=True)   
def calc_rsi( array, deltas, avg_gain, avg_loss, n ):

    # Use Wilder smoothing method
    up   = lambda x:  x if x > 0 else 0
    down = lambda x: -x if x < 0 else 0
    i = n+1
    for d in deltas[n+1:]:
        avg_gain = ((avg_gain * (n-1)) + up(d)) / n
        avg_loss = ((avg_loss * (n-1)) + down(d)) / n
        if avg_loss != 0:
            rs = avg_gain / avg_loss
            array[i] = 100 - (100 / (1 + rs))
        else:
            array[i] = 100
        i += 1

    return array

def get_rsi( array, n = 14 ):   

    deltas = np.append([0],np.diff(array))

    avg_gain =  np.sum(deltas[1:n+1].clip(min=0)) / n
    avg_loss = -np.sum(deltas[1:n+1].clip(max=0)) / n

    array = np.empty(deltas.shape[0])
    array.fill(np.nan)

    array = calc_rsi( array, deltas, avg_gain, avg_loss, n )
    return array

rsi = get_rsi( array or series, 14 )
Admin1 
rsi_Indictor(close,n_days):
    rsi_series = pd.DataFrame(close)


    # Change = close[i]-Change[i-1]
    rsi_series["Change"] = (rsi_series["Close"] - rsi_series["Close"].shift(1)).fillna(0)

    # Upword Movement
    rsi_series["Upword Movement"] = (rsi_series["Change"][rsi_series["Change"] >0])
    rsi_series["Upword Movement"] = rsi_series["Upword Movement"].fillna(0)

    # Downword Movement
    rsi_series["Downword Movement"] = (abs(rsi_series["Change"])[rsi_series["Change"] <0]).fillna(0)
    rsi_series["Downword Movement"] = rsi_series["Downword Movement"].fillna(0)

    #Average Upword Movement
    # For first Upword Movement Mean of first n elements.
    rsi_series["Average Upword Movement"] = 0.00
    rsi_series["Average Upword Movement"][n] = rsi_series["Upword Movement"][1:n+1].mean()

    # For Second onwords
    for i in range(n+1,len(rsi_series),1):
        #print(rsi_series["Average Upword Movement"][i-1],rsi_series["Upword Movement"][i])
        rsi_series["Average Upword Movement"][i] = (rsi_series["Average Upword Movement"][i-1]*(n-1)+rsi_series["Upword Movement"][i])/n

    #Average Downword Movement
    # For first Downword Movement Mean of first n elements.
    rsi_series["Average Downword Movement"] = 0.00
    rsi_series["Average Downword Movement"][n] = rsi_series["Downword Movement"][1:n+1].mean()

    # For Second onwords
    for i in range(n+1,len(rsi_series),1):
        #print(rsi_series["Average Downword Movement"][i-1],rsi_series["Downword Movement"][i])
        rsi_series["Average Downword Movement"][i] = (rsi_series["Average Downword Movement"][i-1]*(n-1)+rsi_series["Downword Movement"][i])/n

    #Relative Index
    rsi_series["Relative Strength"] = (rsi_series["Average Upword Movement"]/rsi_series["Average Downword Movement"]).fillna(0)

    #RSI
    rsi_series["RSI"] = 100 - 100/(rsi_series["Relative Strength"]+1)
    return rsi_series.round(2)

For More Information

user_85134540

You do this using finta package as well just to add above

ref: https://github.com/peerchemist/finta/tree/master/examples

import pandas as pd
from finta import TA
import matplotlib.pyplot as plt

ohlc = pd.read_csv("C:\\WorkSpace\\Python\\ta-lib\\intraday_5min_IBM.csv", index_col="timestamp", parse_dates=True)
ohlc['RSI']= TA.RSI(ohlc)
user_29362640

It is not really necessary to calculate the mean, because after they are divided, you only need to calculate the sum, so we can use Series.cumsum ...

def rsi(serie, n):

    diff_serie = close.diff()
    cumsum_incr = diff_serie.where(lambda x: x.gt(0), 0).cumsum()
    cumsum_decr = diff_serie.where(lambda x: x.lt(0), 0).abs().cumsum()
    rs_serie = cumsum_incr.div(cumsum_decr)
    rsi = rs_serie.mul(100).div(rs_serie.add(1)).fillna(0)

    return rsi
user_118842370

Less code here but seems to work for me:

df['Change'] = (df['Close'].shift(-1)-df['Close']).shift(1)
df['ChangeAverage'] = df['Change'].rolling(window=2).mean()
df['ChangeAverage+'] = df.apply(lambda x: x['ChangeAverage'] if x['ChangeAverage'] > 0 else 0,axis=1).rolling(window=14).mean()
df['ChangeAverage-'] = df.apply(lambda x: x['ChangeAverage'] if x['ChangeAverage'] < 0 else 0,axis=1).rolling(window=14).mean()*-1
df['RSI'] = 100-(100/(1+(df['ChangeAverage+']/df['ChangeAverage-'])))
user_154647930
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