Calculating returns from a dataframe with financial data

The easiest way to do this is to use the DataFrame.pct_change() method.

Here is a quick example

In[1]: aapl = get_data_yahoo('aapl', start='11/1/2012', end='11/13/2012')

In[2]: appl
Out[2]: 
          Open    High     Low   Close    Volume  Adj Close
Date                                                           
2012-11-01  598.22  603.00  594.17  596.54  12903500     593.83
2012-11-02  595.89  596.95  574.75  576.80  21406200     574.18
2012-11-05  583.52  587.77  577.60  584.62  18897700     581.96
2012-11-06  590.23  590.74  580.09  582.85  13389900     580.20
2012-11-07  573.84  574.54  555.75  558.00  28344600     558.00
2012-11-08  560.63  562.23  535.29  537.75  37719500     537.75
2012-11-09  540.42  554.88  533.72  547.06  33211200     547.06
2012-11-12  554.15  554.50  538.65  542.83  18421500     542.83
2012-11-13  538.91  550.48  536.36  542.90  19033900     542.90

In[3]: aapl.pct_change()
Out[3]:
                Open      High       Low     Close    Volume  Adj Close
Date                                                                   
2012-11-01       NaN       NaN       NaN       NaN       NaN        NaN
2012-11-02 -0.003895 -0.010033 -0.032684 -0.033091  0.658945  -0.033090
2012-11-05 -0.020759 -0.015378  0.004959  0.013558 -0.117186   0.013550
2012-11-06  0.011499  0.005053  0.004311 -0.003028 -0.291453  -0.003024
2012-11-07 -0.027769 -0.027423 -0.041959 -0.042635  1.116864  -0.038263
2012-11-08 -0.023020 -0.021426 -0.036815 -0.036290  0.330747  -0.036290
2012-11-09 -0.036049 -0.013073 -0.002933  0.017313 -0.119522   0.017313
2012-11-12  0.025406 -0.000685  0.009237 -0.007732 -0.445323  -0.007732
2012-11-13 -0.027502 -0.007250 -0.004251  0.000129  0.033244   0.000129

The best way to calculate forward looking returns without any chance of bias is to use the built in function pd.DataFrame.pct_change(). In your case all you need to use is this function since you have monthly data, and you are looking for the monthly return.

If, for example, you wanted to look at the 6 month return, you would just set the param df.pct_change(periods = 6) and that will give you the 6 month percent return.

Because you have a relatively small data set, the easiest way is to resample on the parameters that you need to calculate the data on then use the pct_change() function again.

However because of the nice properties of log it is common to use the formula for calculating returns (if you plan on computing statistics on the return series):

Which you would implement as such:

log_return = np.log(vfiax_monthly.open / vfiax_monthly.open.shift())

Instead of slicing, use .shift to move the index position of values in a DataFrame/Series. For example:

returns = (vfiax_monthly.open - vfiax_monthly.open.shift(1))/vfiax_monthly.open.shift(1)

This is what pct_change is doing under the bonnet. You can also use it for other functions e.g.:

(3*vfiax_monthly.open + 2*vfiax_monthly.open.shift(1))/5

You might also want to looking into the rolling and window functions for other types of analysis of financial data.