Optimal investment – single asset

In a financial market context, both outcomes and alternatives are more complex, but the Kelly rule logic does still apply. It was made popular by Ed Thorp, who first applied it profitably to gambling (described in Beat the Dealer) and later started the successful hedge fund Princeton/Newport Partners.

 With continuous outcomes, the growth rate of capital is defined by an integrate over the probability distribution of the different returns that can be optimized numerically:

We can solve this expression for the optimal f* using the scipy.optimize module:

def norm_integral(f, m, st):
val, er = quad(lambda s: np.log(1+f*s)*norm.pdf(s, m, st), m-3*st,
m+3*st)
return -val


def norm_dev_integral(f, m, st):
val, er = quad(lambda s: (s/(1+f*s))*norm.pdf(s, m, st), m-3*st,
m+3*st)
return val

m = .058
s = .216
# Option 1: minimize the expectation integral
sol = minimize_scalar(norm_integral, args=(
m, s), bounds=[0., 2.], method='bounded')
print('Optimal Kelly fraction: {:.4f}'.format(sol.x))
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