I'm trying to figure out a list of integer solutions for the problem. I'm trying to maximize the function get_sharpe_ratio. When I run the program, the result is always zero.
Optimization function is inside the function calc_sharp_ratio
Is there any where I did wrong? How can I get the solution I want?
import numpy as np
from gekko import GEKKO
class WeightOptimalSolver:
def __init__(self, stocks, total_money, rf):
self._stocks = stocks
self._rf = rf/252
self._total_money = total_money
def get_sharpe_ratio(self, hands):
shares = []
# Get shares
for h in hands:
shares.append(h * 100)
# Get weigths
required_money = 0
for s in range(0,len(shares)):
required_money += shares[s] * float(self._stocks[s].get_price())
weights = []
for s in range(0,len(shares)):
weight = shares[s] * float(self._stocks[s].get_price()) / required_money
weights.append(weight)
Er = 0
sd = 0
for s in range(0,len(self._stocks)):
Er += float(self._stocks[s].get_Er()) * weights[s]
sd += float(self._stocks[s].get_sd()) * weights[s]
inverse_sharpe_ratio = -1 * ((Er - self._rf)/sd)
return inverse_sharpe_ratio
def get_required_money(self, hands):
required_money = 0
for h in range(0,len(hands)):
required_money += hands[h] * 100 * float(self._stocks[h].get_price())
return required_money
def calc_sharp_ratio(self):
m = GEKKO(remote = True)
hands = [m.Var(integer = True, ub = 0) for h in range(len(self._stocks))]
m.Equation(self.get_required_money(hands) >= -1 * self._total_money)
m.Obj(self.get_sharpe_ratio(hands))
m.options.SOLVER = 1
m.solve()
return hands
if __name__ == '__main__':
import Stocks
import datetime
import WeightOptimalSolver
Today = datetime.datetime.today()
dateToday = Today.strftime('%Y-%m-%d')
stocks = Stocks.Stocks('Data/' + dateToday + 'hedgeA' + '.csv').get_stocks()
wos = WeightOptimalSolver.WeightOptimalSolver(stocks[0:2], 15000, 0.03)
hands = wos.calc_sharp_ratio()
print(hands)
The result is:
apm 70.191.46.236_gk_model0 <br><pre> ----------------------------------------------------------------
APMonitor, Version 1.0.1
APMonitor Optimization Suite
----------------------------------------------------------------
Warning: there is insufficient data in CSV file 70.191.46.236_gk_model0.csv
--------- APM Model Size ------------
Each time step contains
Objects : 0
Constants : 0
Variables : 3
Intermediates: 0
Connections : 0
Equations : 2
Residuals : 2
Number of state variables: 3
Number of total equations: - 1
Number of slack variables: - 1
---------------------------------------
Degrees of freedom : 1
----------------------------------------------
Steady State Optimization with APOPT Solver
----------------------------------------------
Iter: 1 I: 0 Tm: 0.00 NLPi: 2 Dpth: 0 Lvs: 0 Obj: NaN Gap: 0.00E+00
Successful solution
---------------------------------------------------
Solver : APOPT (v1.0)
Solution time : 1.279999999678694E-002 sec
Objective : NaN
Successful solution
---------------------------------------------------
[[0.0], [0.0]]
All gekko problems as defined as static equations and then compiled and sent to a solver. It appears that the definition of the equations changes each time the function is called. The solution is to either use a solver such as Scipy.optimize.minimize as shown here or else use Gekko functions to build the model. Scipy.optimize.minimize is a nonlinear programming solver so the solution may include fractional hands. Here is an example of a mixed integer solution with Gekko.
from gekko import GEKKO
m = GEKKO() # create GEKKO model
# create integer variables
x1 = m.Var(integer=True,lb=-5,ub=10)
x2 = m.Var(integer=True,lb=-1,ub=2)
m.Minimize(4*x1**2-4*x2*x1**2+x2**2+x1**2-x1+1)
m.options.SOLVER = 1 # APOPT solver
m.solve()
print('x1: ' + str(x1.value[0]))
print('x2: ' + str(x2.value[0]))
The problem you posted can't be tested because of missing packages Stocks and WeightOptimalSolver as well as the data file 'Data/' + dateToday + 'hedgeA' + '.csv'. If possible, try to share a problem that is reproducible for future questions.