## Relative Strength Index in python pandas

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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

dUp= delta[delta > 0] dDown= delta[delta < 0]

also you need something like:

RolUp = RolUp.reindex_like(delta, method='ffill') RolDown = RolDown.reindex_like(delta, method='ffill')

otherwise `RS = RolUp / RolDown`

will not do what you desire

**Edit:** seems this is a more accurate way of RS calculation:

# dUp= delta[delta > 0] # dDown= delta[delta < 0] # dUp = dUp.reindex_like(delta, fill_value=0) # dDown = dDown.reindex_like(delta, fill_value=0) dUp, dDown = delta.copy(), delta.copy() dUp[dUp < 0] = 0 dDown[dDown > 0] = 0 RolUp = pd.rolling_mean(dUp, n) RolDown = pd.rolling_mean(dDown, n).abs() RS = RolUp / RolDown

**Compute RSI for stocks with python (Relative Strength Index) – tcoil ,** RSI indicator (Relative Strength Index) is an indicator that we can use to measure if given Here we will describe how to calculate RSI with Python and Pandas. Technical analysis of trading data RSI Table of Contents 1 Fetch the daily returns for a stock2 Fetch the daily returns from quandle directly3 Import benchmark4 Preliminary plots5 Moving Averages5.1 Exponential Moving Averages6 Relative strength and Relative Strength Index import matplotlib.pyplot as plt import pandas import quandl # https

It is important to note that there are various ways of defining the RSI. It is commonly defined in at least two ways: using a simple moving average (SMA) as above, or using an exponential moving average (EMA). Here's a code snippet that calculates both definitions of RSI and plots them for comparison. I'm discarding the first row after taking the difference, since it is always NaN by definition.

Note that when using EMA one has to be careful: since it includes a memory going back to the beginning of the data, the result depends on where you start! For this reason, typically people will add some data at the beginning, say 100 time steps, and then cut off the first 100 RSI values.

In the plot below, one can see the difference between the RSI calculated using SMA and EMA: the SMA one tends to be more sensitive. Note that the RSI based on EMA has its first finite value at the first time step (which is the second time step of the original period, due to discarding the first row), whereas the RSI based on SMA has its first finite value at the 14th time step. This is because by default rolling_mean() only returns a finite value once there are enough values to fill the window.

import pandas import pandas_datareader.data as web import datetime import matplotlib.pyplot as plt # Window length for moving average window_length = 14 # Dates start = '2010-01-01' end = '2013-01-27' # Get data data = web.DataReader('AAPL', 'yahoo', start, end) # Get just the adjusted close close = data['Adj Close'] # Get the difference in price from previous step delta = close.diff() # Get rid of the first row, which is NaN since it did not have a previous # row to calculate the differences delta = delta[1:] # Make the positive gains (up) and negative gains (down) Series up, down = delta.copy(), delta.copy() up[up < 0] = 0 down[down > 0] = 0 # Calculate the EWMA roll_up1 = up.ewm(span=window_length).mean() roll_down1 = down.abs().ewm(span=window_length).mean() # Calculate the RSI based on EWMA RS1 = roll_up1 / roll_down1 RSI1 = 100.0 - (100.0 / (1.0 + RS1)) # Calculate the SMA roll_up2 = up.rolling(window_length).mean() roll_down2 = down.abs().rolling(window_length).mean() # Calculate the RSI based on SMA RS2 = roll_up2 / roll_down2 RSI2 = 100.0 - (100.0 / (1.0 + RS2)) # Compare graphically plt.figure(figsize=(8, 6)) RSI1.plot() RSI2.plot() plt.legend(['RSI via EWMA', 'RSI via SMA']) plt.show()

**Investing with Python: RSI – @andrewshamlet,** Python streamlines tasks requiring multiple steps in a single block of code. The Relative Strength Index (RSI) is a momentum indicator developed import pandas as pd import numpy as np from pandas_datareader import� The Relative Strength Index (RSI) is calculated as follows: RSI = 100 - 100 / (1 + RS) RS = Average gain of last 14 trading days / Average loss of last 14 trading days RSI values range from 0 to 100. Traditional interpretation and usage of the RSI is that RSI values of 70 or above indicate that a security is becoming overbought or overvalued, and therefore may be primed for a trend reversal or corrective pullback in price.

My answer is tested on StockCharts sample data.

[StockChart RSI info][1]http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:relative_strength_index_rsi

def RSI(series, period): delta = series.diff().dropna() u = delta * 0 d = u.copy() u[delta > 0] = delta[delta > 0] d[delta < 0] = -delta[delta < 0] u[u.index[period-1]] = np.mean( u[:period] ) #first value is sum of avg gains u = u.drop(u.index[:(period-1)]) d[d.index[period-1]] = np.mean( d[:period] ) #first value is sum of avg losses d = d.drop(d.index[:(period-1)]) rs = pd.stats.moments.ewma(u, com=period-1, adjust=False) / \ pd.stats.moments.ewma(d, com=period-1, adjust=False) return 100 - 100 / (1 + rs) #sample data from StockCharts data = pd.Series( [ 44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42, 45.84, 46.08, 45.89, 46.03, 45.61, 46.28, 46.28, 46.00, 46.03, 46.41, 46.22, 45.64 ] ) print RSI( data, 14 ) #output 14 70.464135 15 66.249619 16 66.480942 17 69.346853 18 66.294713 19 57.915021

**Exponential moving average, Relative strength index using python,** 5.1 Exponential Moving Averages. 6 Relative strength and Relative Strength Index. import matplotlib.pyplot as plt import pandas. import quandl� The following are 40 code examples for showing how to use talib.RSI().They are from open source Python projects. You can vote up the examples you like or vote down the ones you don't like.

I too had this question and was working down the rolling_apply path that **Jev** took. However, when I tested my results, they didn't match up against the commercial stock charting programs I use, such as StockCharts.com or thinkorswim. So I did some digging and discovered that when Welles Wilder created the RSI, he used a smoothing technique now referred to as Wilder Smoothing. The commercial services above use Wilder Smoothing rather than a simple moving average to calculate the average gains and losses.

I'm new to Python (and Pandas), so I'm wondering if there's some brilliant way to refactor out the **for** loop below to make it faster. Maybe someone else can comment on that possibility.

I hope you find this useful.

def get_rsi_timeseries(prices, n=14): # RSI = 100 - (100 / (1 + RS)) # where RS = (Wilder-smoothed n-period average of gains / Wilder-smoothed n-period average of -losses) # Note that losses above should be positive values # Wilder-smoothing = ((previous smoothed avg * (n-1)) + current value to average) / n # For the very first "previous smoothed avg" (aka the seed value), we start with a straight average. # Therefore, our first RSI value will be for the n+2nd period: # 0: first delta is nan # 1: # ... # n: lookback period for first Wilder smoothing seed value # n+1: first RSI # First, calculate the gain or loss from one price to the next. The first value is nan so replace with 0. deltas = (prices-prices.shift(1)).fillna(0) # Calculate the straight average seed values. # The first delta is always zero, so we will use a slice of the first n deltas starting at 1, # and filter only deltas > 0 to get gains and deltas < 0 to get losses avg_of_gains = deltas[1:n+1][deltas > 0].sum() / n avg_of_losses = -deltas[1:n+1][deltas < 0].sum() / n # Set up pd.Series container for RSI values rsi_series = pd.Series(0.0, deltas.index) # Now calculate RSI using the Wilder smoothing method, starting with n+1 delta. 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_of_gains = ((avg_of_gains * (n-1)) + up(d)) / n avg_of_losses = ((avg_of_losses * (n-1)) + down(d)) / n if avg_of_losses != 0: rs = avg_of_gains / avg_of_losses rsi_series[i] = 100 - (100 / (1 + rs)) else: rsi_series[i] = 100 i += 1 return rsi_series

**RSI Calculation With Pandas EWM Function,** RSI Calculation With Pandas EWM Function | Python Vlog| 2018 02:05 - 11:10 | RSI Duration: 17:02
Posted: Jul 21, 2018 Technical Analysis Library in Python 3.7. Pandas Technical Analysis (Pandas TA) is an easy to use library that is built upon Python’s Pandas library with more than 100 Indicators and Utility functions. These indicators are commonly used for financial time series datasets with columns or labels similar to: datetime, open, high, low, close

You can use rolling_apply in combination with a subfunction to make a clean function like this:

def rsi(price, n=14): ''' rsi indicator ''' gain = (price-price.shift(1)).fillna(0) # calculate price gain with previous day, first row nan is filled with 0 def rsiCalc(p): # subfunction for calculating rsi for one lookback period avgGain = p[p>0].sum()/n avgLoss = -p[p<0].sum()/n rs = avgGain/avgLoss return 100 - 100/(1+rs) # run for all periods with rolling_apply return pd.rolling_apply(gain,n,rsiCalc)

**Let's Explore the Relative Strength Index (RSI),** Let's walk through the Relative Strength Index using Python. It goes by RSI - and measures Duration: 23:18
Posted: Jun 18, 2019 To calculate RSI, retype the pandas dataframe into a stockstats dataframe and then calculate the 14-day RSI. stock_df = Sdf.retype(data) data['rsi']=stock_df['rsi_14'] With this approach, you end up with some extra columns in your dataframe. These can easily be removed with the ‘del’ command.

**mtamer/python-rsi: RSI (Relative Strength Index) written in ,** RSI (Relative Strength Index) written in Python. Contribute to mtamer/python-rsi development by creating an account on GitHub. Technical Analysis Library in Python. A Technical Analysis library for financial time series datasets, useful for feature engineering. It is built on Pandas (Modin for speedup).

**Calculating Relative Strength Index for Multiple Pandas column ,** Calculating Relative Strength Index for Multiple Pandas column � python pandas. I want calculate RSI indicator value for multiple column in� RSI: relative strength index; KDJ: Stochastic oscillator; Bolling: including upper band and lower band. MACD: moving average convergence divergence. Including signal and histogram. (see note) CR: WR: Williams Overbought/Oversold index; CCI: Commodity Channel Index; TR: true range; ATR: average true range; line cross check, cross up or cross down.

**Stockstats,** My go-to for this type of work is TA-Lib and the python wrapper for TA-Lib but on (no clue what was wrong either) so I decided to write my own indicator. To calculate RSI, retype the pandas dataframe into a stockstats� Also, I am a software freelance focused on Data Science using Python tools such as Pandas, Scikit-Learn, Zipline or Catalyst. Don’t hesitate to contact me if you need something related to this library, Technical Analysis, Algo Trading, Machine Learning, etc.

##### Comments

- Doesn’t yahoo finance do this automatically? Just curious, I am thinking about building one but don’t see an advantage than yahoo
- does it make a big difference if I use close or adjusted close?
- use adjusted close, because that will take care of splits and dividends
- it plots but something is wrong with the calculations I am using closing prices but it does not match the one on RSI on stockcharts or google finance. The problem is it does not rise above the 70 or below 30 when it is suppose to.
- @user3084006 see my edits, but still you cannot match goolge finance, because goolgle finance chooses the periods dynamically based on the zoom level. that is
`n=14`

in your script is 14 days but in goolge finance it can be 14 days, weeks or whatever depending on zoom level - Got an error Strange reindex_like does not take argument fill_value
- On point... You should probably use
`close = data['Adj. Close']`

instead of`close = data.Close`

otherwise you will get wild results if your date range includes a split. - Thanks, good point - I made the change. By the way, in this case it's 'Adj Close' and not 'Adj. Close'.
- @Moot Thank you for sharing. I noticed that using EMA the results are closer to the ones I get from yahoo finance, but still there is some difference. Why is this happening, in your opinion?
`pandas.stats.moments.ewma`

has become depreacted. It should now be simply`roll_up1 = up.ewm(com=window_length)`

(check github.com/pandas-dev/pandas/pull/11603#issue-50728632)