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32 lines
1.5 KiB
32 lines
1.5 KiB
2 years ago
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# Exercise 3 Multi asset returns
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The goal of this exercise is to learn to compute daily returns on a DataFrame that contains many assets (multi-assets).
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```python
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business_dates = pd.bdate_range('2021-01-01', '2021-12-31')
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#generate tickers
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tickers = ['AAPL', 'FB', 'GE', 'AMZN', 'DAI']
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#create indexs
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index = pd.MultiIndex.from_product([business_dates, tickers], names=['Date', 'Ticker'])
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# create DFs
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market_data = pd.DataFrame(index=index,
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data=np.random.randn(len(index), 1),
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columns=['Price'])
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```
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1. **Without using a for loop**, compute the daily returns (return(d) = (price(d)-price(d-1))/price(d-1)) for all the companies and returns a DataFrame as:
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| Date | ('Price', 'AAPL') | ('Price', 'AMZN') | ('Price', 'DAI') | ('Price', 'FB') | ('Price', 'GE') |
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|:--------------------|--------------------:|--------------------:|-------------------:|------------------:|------------------:|
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| 2021-01-01 00:00:00 | nan | nan | nan | nan | nan |
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| 2021-01-04 00:00:00 | 1.01793 | 0.0512955 | 3.84709 | -0.503488 | 0.33529 |
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| 2021-01-05 00:00:00 | -0.222884 | -1.64623 | -0.71817 | -5.5036 | -4.15882 |
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Note: The data is generated randomly, the values you may have a different results. But, this shows the expected DataFrame structure.
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`Hint use groupby`
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