import os,sys
 import pandas as pd
 import numpy as np
 import datetime
 import pandas_datareader.data as web
 import math
 import matplotlib.pyplot as plt
 from matplotlib import style
 from sklearn.model_selection import cross_val_score
 from sklearn import preprocessing, model_selection, svm
 from sklearn.linear_model import LinearRegression
 start = datetime.datetime(2019, 2, 11)
 end = datetime.datetime(2019, 11, 6)
 #从互联网获取数据
 #df = web.DataReader("XOM", "yahoo", start, end)
 df = web.DataReader("600519.SS", "yahoo", start, end)
 #print(df.head())
 df = df[['Open',  'High',  'Low',  'Close', 'Volume']]
 df['HL_PCT'] = (df['High'] - df['Low']) / df['Close'] * 100.0
 df['PCT_change'] = (df['Close'] - df['Open']) / df['Open'] * 100.0
 df = df[['Close', 'HL_PCT', 'PCT_change', 'Volume']]
 #print(df.head())
 print(len(df))
 forecast_col = 'Close'
 df.fillna(value=-99999, inplace=True)
 forecast_out = int(math.ceil(0.01 * len(df)))
 #预测forecast_out天后的
 print(forecast_out)
 #sys.exit(1)df['label'] = df[forecast_col].shift(-forecast_out)
print(df.shape)
 print(df.tail())
 X = np.array(df.drop(['label'], 1)) X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
 X = X[:-forecast_out]
 df.dropna(inplace=True)
 print(X)
 print(X_lately)
 y = np.array(df['label'])
 #print(y)
 print(X.shape)
 print(y.shape)
 X_train, X_test, y_train ,y_test = model_selection.train_test_split(X,y,test_size=0.2)clf = LinearRegression()
 clf.fit(X_train,y_train)
 accuracy = clf.score(X_test,y_test)print(accuracy)
forecast_set = clf.predict(X_lately)
print(forecast_set,accuracy,forecast_out)
style.use('ggplot')
df['Forecast']=np.nan
last_date = df.iloc[-1].name
 last_unix = last_date.timestamp()
 print(last_date,last_unix)
 one_day = 86400
 next_unix = last_unix + one_dayfor i in forecast_set:
     next_date = datetime.datetime.fromtimestamp(next_unix)
     next_unix += 86400
     df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i] print(df.tail())
df['Close'].plot()
 df['Forecast'].plot()
 plt.show()"""
 svm
 for k in ['linear','poly','rbf','sigmoid']:
     clf2 = svm.SVR(k)
     clf2.fit(X_train,y_train)
     accuracy2 = clf2.score(X_test,y_test)    
     print(accuracy2)
 """
 """
 clf3 = svm.SVC(kernel='linear',C=1)
 scores = cross_val_score(clf3,X,y,cv=5,scoring='f1_macro')
 print(scores)
 """