Compute a compounded return series in Python

Question

Greetings all, I have two series of data: daily raw stock price returns (positive or negative floats) and trade signals (buy=1, sell=-1, no trade=0).

The raw price retur

Answer 1

imagine I have a DataMatrix with closing prices, some indicator value, and a trade signal like this:

 >>> data_matrix
                        close          dvi            signal
 2008-01-02 00:00:00    144.9          0.6504         -1             
 2008-01-03 00:00:00    144.9          0.6603         -1             
 2008-01-04 00:00:00    141.3          0.7528         -1             
 2008-01-07 00:00:00    141.2          0.8226         -1             
 2008-01-08 00:00:00    138.9          0.8548         -1             
 2008-01-09 00:00:00    140.4          0.8552         -1             
 2008-01-10 00:00:00    141.3          0.846          -1             
 2008-01-11 00:00:00    140.2          0.7988         -1             
 2008-01-14 00:00:00    141.3          0.6151         -1             
 2008-01-15 00:00:00    138.2          0.3714         1   

I use the signal to create a DataMatrix of returns based on the trade signal:

>>> get_indicator_returns()

                   indicator_returns    
2008-01-02 00:00:00    NaN            
2008-01-03 00:00:00    0.000483       
2008-01-04 00:00:00    0.02451        
2008-01-07 00:00:00    0.0008492      
2008-01-08 00:00:00    0.01615        
2008-01-09 00:00:00    -0.01051       
2008-01-10 00:00:00    -0.006554      
2008-01-11 00:00:00    0.008069       
2008-01-14 00:00:00    -0.008063      
2008-01-15 00:00:00    0.02201 

What I ended up doing is this:

def get_compounded_indicator_cumulative(self):

    indicator_dm = self.get_indicator_returns()
    dates = indicator_dm.index

    indicator_returns = indicator_dm['indicator_returns']
    compounded = array(zeros(size(indicator_returns)))

    compounded[1] = indicator_returns[1]

    for i in range(2, len(indicator_returns)):

        compounded[i] = (1 + compounded[i-1]) * (1 + indicator_returns[i]) - 1

    data = {
        'compounded_returns': compounded
    }

    return DataMatrix(data, index=dates)

For some reason I really struggled with this one...

I'm in the process of converting all my price series to PyTables. Looks promising so far.