Forecasting: residuals from seasonal decomposition appears to be highly auto-corelated, why? I am using a publicly available data Kaggle: Rossmann Store Sales and trying to forecast sales. I am using Python.
My timeseries is stationary, confirmed via the Dickey-Fuller test. However, I wanted to perform seasonal decomposition.
I performed seasonal decompositions using statsmodels.tsa.seasonal.seasonal_decompose. And my seasonal decomposition looks like this:

When I plot ACF of residuals there appears to be too much autocorelation!

Am I doing something wrong? or looking at it the wrong way?
 My understanding is residuals should show no autocorelation because trend and seasonal have been taken out or adjusted for. 
Update 1: Using freq=13 I perform seasonal decomposition and ACF of residuals is given below:

Update 2: As requested by @IrishStat, I am posting the original data
Head(10):
Date
2013-01-01       0
2013-01-02    5737
2013-01-03    5292
2013-01-04    5623
2013-01-05    5018
2013-01-06       0
2013-01-07    9277
2013-01-08    7479
2013-01-09    6681
2013-01-10    6680
Name: Sales, dtype: int64

This is the plot of original data:

 A: You disaggregate a time series into three components -- trend, seasonal and residual. 


*

*The trend component is supposed to capture the slowly-moving overall level of the series. 

*The seasonal component captures patterns that repeat every season. 

*The residual is what is left. It may or may not be autocorrelated. For example, there can be some autocorrelated pattern evolving quickly around the slowly moving trend plus the seasonal fluctuations. This kind of pattern cannot be ascribed to the trend component (the former moves too fast) or the seasonal component (the former does not obey seasonal timing). So it is left in the remainder.


See also section "Time series decomposition" from Hyndman & Athanasopoulos "Forecasting: principles and practice".
A: Correct decomposition requires a good model which might have either fixed seasonal effects or seasonal auto-regressive effects and one or more trends ( or level shifts ) and constant error variance and constant parameters over time. Perhaps your data set ( which you should post) doesn't conform to the requiremnents/assumptions and thus needs "special handling" or empirically-based model formulation.
