In finance, many issues depend on optimization, such as choosing an optimal portfolio with an objective function and with a set of constraints. For those cases, we could use a SciPy
optimization module called scipy.optimize
. Assume that we want to estimate the x value that minimizes the value of y, where y =3 + x2. Obviously, the minimum value of y is achieved when x takes a value of 0.
>>>import scipy.optimize as optimize >>>def my_f(x): Return 3 + x**2 >>>optimize.fmin(my_f,5) # 5 is initial value Optimization terminated successfully Current function values: 3:000000 Iterations: 20 Function evaluations: 40 Array([ 0. ])
To find a list of all input variables to this fmin()
function and their meanings, issue help(optimize.fmin)
. To list all the functions included in scipy.optimize
, issue dir(optimize)
.