matrix - Rolling Mean of Rolling Correlation dataframe in Python? -


python beginner here.

what i've done far:

  • imported price data yahoo finance list of stocks.

  • between stocks (every combination), computed 20 day rolling correlation dataframe.

i to:

1) calculate 200 day simple moving average each of 20 day rolling correlations.

2) report 200 day moving average results in matrix.

how in python/pandas? thanks, me out ton!

here have far...

import pandas pd pandas import dataframe import datetime import pandas.io.data web pandas.io.data import datareader  stocks = ['spy', 'gld', 'uso'] start = datetime.datetime(2014,1,1) end = datetime.datetime(2015,1,1)  f = web.datareader(stocks, 'yahoo', start, end) adj_close_df = f['adj close']  correls = pd.rolling_corr(adj_close_df, 20)  means = pd.rolling_mean(correls, 200) #<---- error message here!

this start answers questions 1-3 (you should have 1 question per post).

import pandas.io.data web import datetime dt import pandas pd  end_date = dt.datetime.now().date() start_date = end_date - pd.dateoffset(years=5)  symbols = ['aapl', 'ibm', 'gm'] prices = web.get_data_yahoo(symbols=symbols, start=start_date, end=end_date)['adj close'] returns = prices.pct_change() rolling_corr = pd.rolling_corr_pairwise(returns, window=20)

getting rolling mean of rolling correlation relatively simple single stock against others. example:

pd.rolling_mean(rolling_corr.major_xs('aapl').t, 200).tail() out[34]:              aapl        gm       ibm date                                 2015-05-08     1  0.313391  0.324728 2015-05-11     1  0.315561  0.327537 2015-05-12     1  0.317844  0.330375 2015-05-13     1  0.320137  0.333189 2015-05-14     1  0.322119  0.335659

to view correlation matrix recent 200 day window:

>>> rolling_corr.iloc[-200:].mean(axis=0)           aapl        gm       ibm aapl  1.000000  0.322119  0.335659 gm    0.322119  1.000000  0.383672 ibm   0.335659  0.383672  1.000000

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