from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt  
import pandas as pd

# Google stock
url = 'http://apmonitor.com/che263/uploads/Main/goog.csv'

# import data with pandas
data = pd.read_csv(url)
print(data['Close'][0:5])
print('min: '+str(min(data['Close'][0:20])))
print('max: '+str(max(data['Close'][0:20])))

# GEKKO model
m = GEKKO()

# input data
x = m.Param(value=np.array(data['Open']))

# parameters to optimize
a = m.FV()
a.STATUS=1
b = m.FV()
b.STATUS=1
c = m.FV()
c.STATUS=1

# variables
y = m.CV(value=np.array(data['Close']))
y.FSTATUS=1

# regression equation
m.Equation(y==b*m.exp(a*x)+c)

# regression mode
m.options.IMODE = 2

# optimize
m.options.solver = 1
m.solve(disp=True)

# print parameters
print('Optimized, a = ' + str(a.value[0]))
print('Optimized, b = ' + str(b.value[0]))
print('Optimized, c = ' + str(c.value[0]))

# plot data
plt.figure()
plt.plot(data['Open'],data['Close'],'ro',label='Stock Data')
plt.plot(x.value,y.value,'bx',label='Predicted')
plt.xlabel('Open Price')
plt.ylabel('Close Price')
plt.legend()
plt.show()