tushare财经接口

import tushare as ts
df=ts.get_hist_data('002714')
filename='D:shuju'
df.to_csv('D:\\tushare\\002714.csv')

输出的结果：可以用作机器学习预测股票的收盘价

import numpy as np

import datetime as dt

import matplotlib.pyplot as mp

import matplotlib.dates as md

import pandas as pd

def dmy2ymd(dmy):

dmy=str(dmy,encoding='utf-8')

date=dt.datetime.strptime(dmy,'%d-%m-%Y').date()

ymd=date.strftime('%Y-%m-%d')

return ymd

dates,closing_prices=np.loadtxt('../data/aapl.csv',delimiter=',',usecols=(1,6),

unpack=True,

dtype=np.dtype('M8[D],f8'),

converters={1:dmy2ymd})

N=5

#预测点的个数

pred_prices=np.zeros(closing_prices.size-N*2+1)

for i in range(pred_prices.size):

a=np.zeros((N,N))

for j in range(N):

a[j,]=closing_prices[i+j:i+j+N]

b=closing_prices[i+N:i+2*N]

x,_,_,_=np.linalg.lstsq(a,b)

#x=np.linalg.lstsq(a,b)

print(a.dot(x),b)

pred_prices[i]=b.dot(x)

mp.figure('stock prices prediction',facecolor='lightgray')

mp.title('stock prices prediction',fontsize=20)

mp.xlabel('date',fontsize=14)

mp.ylabel('prices',fontsize=14)

ax=mp.gca()

ax.xaxis.set_major_locator(md.WeekdayLocator(byweekday=md.MO))

ax.xaxis.set_minor_locator(md.DayLocator())

ax.xaxis.set_major_formatter(md.DateFormatter('%d %b %Y'))

mp.tick_params(labelsize=10)

mp.grid(linestyle=":")

dates=dates.astype(md.datetime.datetime)

mp.plot(dates,closing_prices,'o-',c='gray',label='closing prics')

dates=np.append(dates,dates[-1]+pd.tseries.offsets.BDay())

#预测一个点需要使用10个点

mp.plot(dates[N*2:],pred_prices,'o-',c='orangered',linewidth=3,label='predice')

mp.legend()

mp.gcf().autofmt_xdate()

mp.show()