Python Machine Learning Blueprints - Second Edition

Evaluating our trades

Let's now construct a function to evaluate our trades. We'll buy similar curves unless they fail to return a positive result. If that happens, we'll eliminate them, as follows:

excluded = {} 
return_list = [] 
def get_returns(r): 
    if excluded.get(r['A']) is None: 
        return_list.append(r['B Ret']) 
        if r['B Ret'] < 0: 
            excluded.update({r['A']:1}) 
 
winf.apply(get_returns, axis=1); 

Now that we have all the returns from our trades stored in return_list, let's evaluate the results:

get_stats(pd.Series(return_list)) 

This generates the following output:

These results are by far the best we've seen. The win/loss ratio and the mean ...