@author: Administrator
"""
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import talib as ta
import sys
local_path = 'C://Users//Administrator/Desktop/'
if local_path not in sys.path:
sys.path.append(local_path)
from get_data import data_reader as rd
from get_data import tic_toc
rf = RandomForestClassifier()
SS = rd.get_index_day('000905.SH', '2000-01-01', '2018-07-01', freq='1D')
target = []
pct = SS.sclose.pct_change().dropna()
v = pct.values
for i in v:
if i > 0.01:
target.append(3)
elif i < -0.01:
target.append(0)
elif i > 0.5:
import numpy as np
import sys
from sklearn import linear_model
read_path = r'C:\Users\Administrator\Desktop'
if read_path not in sys.path:
sys.path.append(read_path)
from get_data import data_reader as rd
reg = linear_model.LinearRegression()
today = datetime.datetime.today()
today = str(today)[:10]
ss = rd.get_index_day('000001.SH', '2010-01-01', today, '1D')
ss_min = rd.get_index_min('000300.SH', '2010-01-01', today, '5min')
close = ss.sclose
Return = close.pct_change()
Return = pd.DataFrame(Return)
def regression(x):
x = np.array(x)
beta, alpha = np.polyfit(np.arange(len(x)), x, deg=1)
return beta