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tushare_test.py
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tushare_test.py
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#coding=utf-8
import tushare as ts
import talib as ta
import numpy as np
import pandas as pd
import os,time,sys,re,datetime
import csv
import scipy
# 两个函数
# 1、get_stock_basics()，这里得到是对应的dataframe数据结构，code,代码 name,名称 industry,所属行业 area,地区 pe,市盈率
# outstanding,流通股本 totals,总股本(万) totalAssets,总资产(万)liquidAssets,流动资产 fixedAssets,固定资产 reserved,公积金
# reservedPerShare,每股公积金 eps,每股收益 bvps,每股净资 pb,市净率 timeToMarket,上市日期
# 2、get_hist_data(),获取每只股票的历史价格和成交量 对应的列有index列,0 - 6列是 date：日期 open：开盘价 high：最高价 close：收盘价
# low：最低价 volume：成交量 price_change：价格变动 p_change：涨跌幅
# 7-12列是 ma5：5日均价 ma10：10日均价 ma20:20日均价 v_ma5:5日均量v_ma10:10日均量 v_ma20:20日均量
# 1 、首先是获取沪深两市的股票列表
def Get_Stock_List():
df = ts.get_stock_basics()
return df
# 然后定义通过MACD判断买入卖出
def Get_MACD(df_Code):
operate_array = []
stcok_code = df_Code.index
for code in df_Code.index:
df = ts.get_hist_data(code, start='2016-01-03')
if df is None: # 如code代表的是一个未上市的新股
stcok_code = stcok_code.drop(code)
continue
dflen = df.shape[0]
#print("11111111111111",df.head(10))
operate = 0
if dflen > 35:
macd, macdsignal, macdhist = ta.MACD(np.array(df['close']), fastperiod=12, slowperiod=26, signalperiod=9)
'''
print("111",macd)
print("222",macdsignal)
print("333",macdhist)'''
SignalMA5 = ta.MA(macdsignal, timeperiod=5, matype=0)
SignalMA10 = ta.MA(macdsignal, timeperiod=10, matype=0)
SignalMA20 = ta.MA(macdsignal, timeperiod=20, matype=0)
# 在后面增加3列，分别是13-15列，对应的是 DIFF DEA DIFF-DEA
df['macd'] = pd.Series(macd, index=df.index) # DIFF
df['macdsignal'] = pd.Series(macdsignal, index=df.index) # DEA
df['macdhist'] = pd.Series(macdhist, index=df.index) # DIFF-DEA
MAlen = len(SignalMA5)
print(df.head(100))
# 2个数组 1.DIFF、DEA均为正，DIFF向上突破DEA，买入信号。 2.DIFF、DEA均为负，DIFF向下跌破DEA，卖出信号。
if df.iat[(dflen - 1), 13] > 0:
if df.iat[(dflen - 1), 14] > 0:
if df.iat[(dflen - 1), 13] > df.iat[(dflen - 1), 14]:
operate = operate + 1 # 买入
else:
if df.iat[(dflen - 1), 14] < 0:
if df.iat[(dflen - 1), 13] < 0:
operate = operate - 1 # 卖出
# 3.DEA线与K线发生背离，行情反转信号。
if df.iat[(dflen - 1), 7] >= df.iat[(dflen - 1), 8] and df.iat[(dflen - 1), 8] >= df.iat[
(dflen - 1), 9]: # K线上涨
if SignalMA5[MAlen - 1] <= SignalMA10[MAlen - 1] and SignalMA10[MAlen - 1] <= SignalMA20[
MAlen - 1]: # DEA下降
operate = operate - 1
elif df.iat[(dflen - 1), 7] <= df.iat[(dflen - 1), 8] and df.iat[(dflen - 1), 8] <= df.iat[
(dflen - 1), 9]: # K线下降
if SignalMA5[MAlen - 1] >= SignalMA10[MAlen - 1] and SignalMA10[MAlen - 1] >= SignalMA20[
MAlen - 1]: # DEA上涨
operate = operate + 1
# 4.分析MACD柱状线，由负变正，买入信号。
if df.iat[(dflen - 1), 15] > 0 and dflen > 30:
for i in range(1, 26):
if df.iat[(dflen - 1 - i), 15] <= 0: #
operate = operate + 1
break
# 由正变负，卖出信号
if df.iat[(dflen - 1), 15] < 0 and dflen > 30:
for i in range(1, 26):
if df.iat[(dflen - 1 - i), 15] >= 0: #
operate = operate - 1
break
operate_array.append(operate)
df_Code['MACD'] = pd.Series(operate_array, index=stcok_code)
return df_Code
# 输出CSV文件
def Output_Csv(df, Dist, industry_name):
TODAY = datetime.date.today()
CURRENTDAY = TODAY.strftime('%Y-%m-%d')
df.to_csv(Dist + industry_name + CURRENTDAY + 'stock.csv', encoding='gbk') # 选择保存
df = Get_Stock_List()
df1=df[df['industry']=='酒店餐饮']
df2=df[df['industry']=='电信运营']
df11 = Get_MACD(df1)
df22 = Get_MACD(df2)
Dist = './data/'
name1 = '酒店餐饮'
name2 = '电信运营'
Output_Csv(df11,Dist,name1)
Output_Csv(df22,Dist,name2)