Tushare test

import tushare

print(tushare.__version__)

import tushare as ts
ts.get_hist_data('600848')  #一次性获取全部日k线数据

import tushare as ts 
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

df = ts.get_hist_data('000001',start='2017-01-01',end='2018-10-10') 
df.head(10)

sz=df.sort_index(axis=0, ascending=True) #对index进行升序排列 
sz_return=sz[['p_change']] #选取涨跌幅数据 
train=sz_return[0:255] #划分训练集 
test=sz_return[255:] #测试集 
#对训练集与测试集分别做趋势图 
plt.figure(figsize=(10,5)) 
train['p_change'].plot() 
plt.legend(loc='best') 
plt.show() 
plt.figure(figsize=(10,5)) 
test['p_change'].plot(c='r') 
plt.legend(loc='best') 
plt.show()

#Simple Average 
from math import sqrt
from sklearn.metrics import mean_squared_error

y_hat_avg = test.copy() #copy test列表 
y_hat_avg['avg_forecast'] = train['p_change'].mean() #求平均值 
plt.figure(figsize=(12,8)) 
plt.plot(train['p_change'], label='Train') 
plt.plot(test['p_change'], label='Test') 
plt.plot(y_hat_avg['avg_forecast'], label='Average Forecast') 
plt.legend(loc='best') 
plt.show() 
rms = sqrt(mean_squared_error(test.p_change, y_hat_avg.avg_forecast)) 
print(rms)

#Moving Average 
from math import sqrt
from sklearn.metrics import mean_squared_error

y_hat_avg = test.copy() 
y_hat_avg['moving_avg_forecast'] = train['p_change'].rolling(30).mean().iloc[-1] 
#30期的移动平均，最后一个数作为test的预测值 
plt.figure(figsize=(12,8)) 
plt.plot(train['p_change'], label='Train') 
plt.plot(test['p_change'], label='Test') 
plt.plot(y_hat_avg['moving_avg_forecast'], label='Moving Average Forecast') 
plt.legend(loc='best') 
plt.show() 
rms = sqrt(mean_squared_error(test.p_change, y_hat_avg.moving_avg_forecast)) 
print(rms)