一.TuShare简介和环境安装
TuShare是一个著名的免费、开源的python财经数据接口包。其官网主页为：TuShare -财经数据接口包。该接口包如今提供了大量的金融数据，涵盖了股票、基本面、宏观、新闻的等诸多类别数据（具体请自行查看官网），并还在不断更新中。TuShare可以基本满足量化初学者的回测需求 环境安装：pip install tushare。如果是老版本升级，可以用升级命令pip install tushare --upgrade3，在python中导入包：import tushare as ts
二.Tushare的应用
我们主要还是应该掌握如何用tushare获取股票行情数据，使用的是ts.get_hist_data()函数或者ts.get_k_data（）函数。
输入参数为： code：股票代码，即6位数字代码，或者指数代码（sh=上证指数 sz=深圳成指 hs300=沪深300指数 sz50=上证50 zxb=中小板 cyb=创业板） start：开始日期，格式YYYY-MM-DD end：结束日期，格式YYYY-MM-DD ktype：数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为D retry_count：当网络异常后重试次数，默认为3 pause:重试时停顿秒数，默认为0 返回值说明： 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 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 turnover:换手率[注：指数无此项]
1：使用tushare包获取某股票的历史行情数据。
import tushare as ts# 使用tushare包获取某股票的历史行情数据。df = ts.get_k_data(code='600519',start='2000-01-01')# 将从Tushare中获取的数据存储至本地df.to_csv("600519.csv")# 将原数据中的时间作为行索引，并将字符串类型的时间序列化成时间对象类型# 将date这一列作为源数据的行索引且将数据类型转成时间类型df = pd.read_csv('./600519.csv',index_col='date',parse_dates=['date'])df.drop(labels='Unnamed: 0',axis=1,inplace=True)# 多出来一行 Unnamed: 0 ，需要去掉它# inplace默认值为false 将删除的操作映射到原数据 2：输出该股票所有收盘比开盘上涨3%以上的日期。
#指定条件#输出该股票所有收盘比开盘上涨3%以上的日期。#（收盘-开盘）/开盘 >= 0.03df['close'] - df['open'] / df['open'] >= 0.03# 打印结果：date2001-08-27 True2001-08-28 True2001-08-29 True2001-08-30 True2001-10-12 True ... 2019-08-02 True2019-08-05 True2019-08-06 True2019-08-07 True2019-08-08 True2019-08-09 True
#将上述表达式返回的布尔值作为df的行索引：取出了所有符合需求的行数据df.loc[(df['close']-df['open']) / df['open'] >= 0.03] # 打印结果：open close high low volume codedate 2001-08-27 5.392 5.554 5.902 5.132 406318.00 6005192001-08-28 5.467 5.759 5.781 5.407 129647.79 6005192001-09-10 5.531 5.734 5.757 5.470 18878.89 600519... ... ... ... ... ... ...2004-11-25 9.251 9.561 9.676 9.251 5924.14 600519... ... ... ... ... ... ...2017-11-16 676.406 709.043 709.881 676.406 60716.00 600519... ... ... ... ... ... ...2019-04-10 903.000 947.990 951.900 900.000 67814.00 6005192019-04-16 904.900 939.900 939.900 901.220 46423.00 6005192019-05-10 875.660 907.120 910.780 868.190 79907.00 6005192019-05-15 890.240 927.000 933.000 890.240 63124.00 6005192019-06-11 876.000 910.890 915.610 875.000 80106.00 6005192019-06-20 932.500 975.000 975.500 932.200 67271.00 600519
df.loc[(df['close'] - df['open']) / df['open'] >= 0.03].index# index 取到行索引df.loc[(df['close'] - df['open']) / df['open'] >= 0.03].index# 打印结果：DatetimeIndex(['2001-08-27', '2001-08-28', '2001-09-10', '2001-12-21', '2002-01-18', '2002-01-31', '2003-01-14', '2003-10-29', '2004-01-05', '2004-01-14', ... '2019-01-15', '2019-02-11', '2019-03-01', '2019-03-18', '2019-04-10', '2019-04-16', '2019-05-10', '2019-05-15', '2019-06-11', '2019-06-20'], dtype='datetime64[ns]', name='date', length=301, freq=None)
3：输出该股票所有开盘比前日收盘跌幅超过2%的日期。
#输出该股票所有开盘比前日收盘跌幅超过2%的日期。#（开盘 - 前日收盘） / 前日收盘 < -0.02# df['close'].shift(1)) 收盘数据往下移一位(df['open'] - df['close'].shift(1)) / df['close'].shift(1) < -0.02# 打印结果DatetimeIndex(['2001-09-12', '2002-06-26', '2002-12-13', '2004-07-01', '2004-10-29', '2006-08-21', '2006-08-23', '2007-01-25', '2007-02-01', '2007-02-06', '2007-03-19', '2007-05-21', '2007-05-30', '2007-06-05', '2007-07-27', '2007-09-05', '2007-09-10', '2008-03-13', '2008-03-17', '2008-03-25', '2008-03-27', '2008-04-22', '2008-04-23', '2008-04-29', '2008-05-13', '2008-06-10', '2008-06-13', '2008-06-24', '2008-06-27', '2008-08-11', '2008-08-19', '2008-09-23', '2008-10-10', '2008-10-15', '2008-10-16', '2008-10-20', '2008-10-23', '2008-10-27', '2008-11-06', '2008-11-12', '2008-11-20', '2008-11-21', '2008-12-02', '2009-02-27', '2009-03-25', '2009-08-13', '2010-04-26', '2010-04-30', '2011-08-05', '2012-03-27', '2012-08-10', '2012-11-22', '2012-12-04', '2012-12-24', '2013-01-16', '2013-01-25', '2013-09-02', '2014-04-25', '2015-01-19', '2015-05-25', '2015-07-03', '2015-07-08', '2015-07-13', '2015-08-24', '2015-09-02', '2015-09-15', '2017-11-17', '2018-02-06', '2018-02-09', '2018-03-23', '2018-03-28', '2018-07-11', '2018-10-11', '2018-10-24', '2018-10-25', '2018-10-29', '2018-10-30', '2019-05-06', '2019-05-08'], dtype='datetime64[ns]', name='date', freq=None)
# 取出符合要求的行数据df.loc[(df['open'] - df['close'].shift(1)) / df['close'].shift(1) < -0.02]df.loc[(df['open'] - df['close'].shift(1)) / df['close'].shift(1) < -0.02].index# 执行结果为：DatetimeIndex(['2001-09-12', '2002-06-26', '2002-12-13', '2004-07-01', '2004-10-29', '2006-08-21', '2006-08-23', '2007-01-25', '2007-02-01', '2007-02-06', '2007-03-19', '2007-05-21', '2007-05-30', '2007-06-05', '2007-07-27', '2007-09-05', '2007-09-10', '2008-03-13', '2008-03-17', '2008-03-25', '2008-03-27', '2008-04-22', '2008-04-23', '2008-04-29', '2008-05-13', '2008-06-10', '2008-06-13', '2008-06-24', '2008-06-27', '2008-08-11', '2008-08-19', '2008-09-23', '2008-10-10', '2008-10-15', '2008-10-16', '2008-10-20', '2008-10-23', '2008-10-27', '2008-11-06', '2008-11-12', '2008-11-20', '2008-11-21', '2008-12-02', '2009-02-27', '2009-03-25', '2009-08-13', '2010-04-26', '2010-04-30', '2011-08-05', '2012-03-27', '2012-08-10', '2012-11-22', '2012-12-04', '2012-12-24', '2013-01-16', '2013-01-25', '2013-09-02', '2014-04-25', '2015-01-19', '2015-05-25', '2015-07-03', '2015-07-08', '2015-07-13', '2015-08-24', '2015-09-02', '2015-09-15', '2017-11-17', '2018-02-06', '2018-02-09', '2018-03-23', '2018-03-28', '2018-07-11', '2018-10-11', '2018-10-24', '2018-10-25', '2018-10-29', '2018-10-30', '2019-05-06', '2019-05-08'], dtype='datetime64[ns]', name='date', freq=None)
4：假如我从2010年1月1日开始，每月第一个交易日买入1手股票，每年最后一个交易日卖出所有股票，到今天为止，我的收益如何？
price_last = df['open'][-1]df = df['2010-01':'2019-01'] #剔除首尾无用的数据#Pandas提供了resample函数用便捷的方式对时间序列进行重采样，根据时间粒度的变大或者变小分为降采样和升采样：df_monthly = df.resample("M").first()df_yearly = df.resample("A").last()[:-1] #去除最后一年# [:-1] 把19年去掉,还没到19年底,19年只买了，还没卖ost_moneycost_money = df_monthly['open'].sum()*100# cost_money 3339687.1df_yearly['open'].sum()*1200# 12个月 一个月买100支 2948584.7999999993recv_monry = df['open'][-1] * 800 + df_yearly['open'].sum()*1200# df['open'][-1] * 800 为19年还剩的钱,今天是8月份 800支recv_monry - cost_money# 391697.69999999925 循环的方式实现
price_last = df['open'][-1]df = df['2010-01':'2019-01'] #剔除首尾无用的数据#Pandas提供了resample函数用便捷的方式对时间序列进行重采样，根据时间粒度的变大或者变小分为降采样和升采样：df_monthly = df.resample("M").first()df_yearly = df.resample("A").last()[:-1] #去除最后一年# [:-1] 把19年去掉,还没到19年底,19年只买了，还没卖cost_money = 0hold = 0 #每年持有的股票for year in range(2010, 2019): cost_money -= df_monthly.loc[str(year)]['open'].sum()*100 hold += len(df_monthly[str(year)]['open']) * 100 if year != 2019: cost_money += df_yearly[str(year)]['open'][0] * hold hold = 0 #每年持有的股票cost_money += hold * price_lastprint(cost_money)