April 2019
Intermediate to advanced
426 pages
11h 13m
English
Content preview from Mastering Python for Finance - Second Edition
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Plotting correlations
Visualizing correlations can also be achieved with the rolling() command. We will use the Last prices of ABN and SANTA on a daily basis from 2016 to 2017, from Quandl. The two datasets are downloaded to the DataFrame df, and its rolling correlations plotted as follows:
In [ ]: %matplotlib inline import quandl quandl.ApiConfig.api_key = QUANDL_API_KEY df = quandl.get(['EURONEXT/ABN.4', 'EURONEXT/SANTA.4'], start_date='2016-01-01', end_date='2017-12-31') df_filled = df.asfreq('D', method='ffill') daily_changes= df_filled.pct_change() abn_returns = daily_changes['EURONEXT/ABN - Last'] santa_returns = daily_changes['EURONEXT/SANTA - Last'] window = int(len(df_filled.index)/2) df_corrs = abn_returns\ .rolling(window=window, ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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