Pandas' EMA not matching the stock's EMA?

Sort the DataFrame so that the dates are in increasing order. Since your data is in decreasing order by date, if you don't sort the dates first, your ewm calculation exponentially weights the earliest dates the most, rather than the latest date (as it should be).

import pandas as pddf = pd.read_csv('intc_data.txt', parse_dates=['Date'], index_col=['Date'])df['backward_ewm'] = df['Close'].ewm(span=20,min_periods=0,adjust=False,ignore_na=False).mean()df = df.sort_index()df['ewm'] = df['Close'].ewm(span=20,min_periods=0,adjust=False,ignore_na=False).mean()print(df[['ewm', 'backward_ewm']].tail())

yields

                  ewm  backward_ewmDate                               2018-01-26  45.370936     48.2056382018-01-29  45.809895     48.0083372018-01-30  46.093714     47.8007942018-01-31  46.288599     47.6966672018-02-01  46.418256     47.650000

This agrees with Marketwatch which says the EWMA(20) on 2018-02-01 was 46.42.

I suggest using Pandas TA to calculate technical indicators in python. I find it more accurate and has many more indicators than the ones that come with pandas.

Using Pandas TA, the 20 period exponential moving average is calculated like:

import pandas_ta as tadata["EMA20"] = ta.ema(df2["Close"], length=20)