def evaluateStock(item, steps=6): y = data[item] p1, p2, t, err = selectParameters(y, steps=3, disp=False) pred = sarimaxPrdict(y, p1, p2, t, steps=3, disp=False) return pred
def evaluateStock(y,steps=3): p1,p2,t,err=selectParameters(y,steps=3,disp=False) pred=sarimaxPrdict(y,p1,p2,t,steps=3,disp=False) return pred
from sarimaxModel import predictbyticker import pandas as pd from datetime import timedelta import statsmodels.api as sm import pandas_datareader.data as web import datetime from sarimaxModel import selectParameters from sarimaxModel import sarimaxPrdict #data=pd.read_csv('/Users/pengwang/work/stocks.csv',parse_dates=['Date'],index_col='Date') end = datetime.date.today() months = 18 ticker = 'MFG.AX' day = end.day year = end.year - months // 12 - 1 month = months % 12 + 1 start = datetime.datetime(year, month, day) data = web.DataReader(ticker, "yahoo", start, end)['Adj Close'] y = data.resample('W').mean() y = data['2018-04-01':] #parameters=selectParameters(ticker,y,steps=3,disp=True) parameters = [ 'MFG.AX', 0, 1, 0, 0, 0, 0, 12, 'n', 39.92467376324733, 0.10954492938239571 ] p1, p2, t = parameters[1:4], parameters[4:8], parameters[8] result = sarimaxPrdict(ticker, y, p1, p2, t, steps=3, disp=True)
def evaluateStock(item, steps=3): p1, p2, t, err = optimizeParameter(y, steps=6, disp=False) pred = sarimaxPrdict(y, p1, p2, t, steps=3, disp=False) return pred
import pandas as pd import pandas_datareader.data as web # Package and modules for importing data; this code may change depending on pandas version import datetime import numpy as np import random import statsmodels.api as sm # SARIMAX example from statsmodels.tsa.statespace.sarimax import SARIMAX from sarimaxModel import selectParameters from sarimaxModel import sarimaxPrdict # We will look at stock prices over the past year, starting at January 1, 2016 start = datetime.datetime(2016, 1, 1) end = datetime.date.today() result = pd.DataFrame() name = 'BHP' res = web.DataReader(name, "yahoo", start, end)['Adj Close'] res.name = name y = res.resample('MS').mean() p1, p2, t, err = selectParameters(y, steps=3, disp=True) pred = sarimaxPrdict(y, p1, p2, t, steps=3, disp=True)