金融量化分析【day112】:股票数据分析Tushare2

1、使用tushare包获取某股票的历史行情数据
2、使用pandas包计算该股票历史数据的5日局限和60日均线
3、matplotlib包可视化历史数据的收盘价和历史均线
4、分析输出所有金叉日志和死叉日期
5、如果我从2010年1月1日开始，初始资金为100000元，金叉尽量买入，死叉全部卖出，则到今天为止，我的炒股收益如何？

一、使用tushare包获取某股票的历史行情数据

1、代码

import pandas as pd
import numpy as np
import tushare as ts
%matplotlib auto

df = ts.get_k_data("600519", start="1988-01-01")
df.to_csv("600519.csv")


df = pd.read_csv("600519.csv",index_col='date',parse_dates=['date'])[['open','close','high','low']]
df

2、输出截图

..........

二、使用pandas包计算该股票历史数据的5日均线和60日均线

1、方法一

1、5日均线代码

df['ma5']=df['open'].rolling(5).mean()
df

2、5日均线截图

..............................

3、30日均线代码

df['ma30']=df['open'].rolling(30).mean()
df

4、30日均线截图

..................................第30日如下.....................................................

..................................

2、方法二

1、5日均线代码

pd.DataFrame({'c1':df['close'].cumsum(),'c2':df['close'].cumsum().shift(5)})

2、5日均线截图

.........................

1、30日均线代码

pd.DataFrame({'c1':df['close'].cumsum(),'c2':df['close'].cumsum().shift(30)})

2、30日均线截图

..................................................

3、使用知识点

方法一

方法二

三、matplotlib包可视化历史数据的收盘价和历史均线

1、代码（2010-2019）

import matplotlib.pyplot as plt
df[['close','ma5','ma30']].plot()
plt.show()

2、输出截图（数据比较多所以不是很清晰）

3、代码（2012年）

df.loc["2012",['close','ma5','ma30']].plot()
plt.show()

4、输出（线条区分比较明显）

四、分析输出所有金叉日志和死叉日期

1、金叉日期

方法一

1、代码

golden_cross = []
death_cross = []
for i in range(1,len(df)):
    if df['ma5'][i] >= df['ma30'][i] and df['ma5'][i-1] < df['ma30'][i-1]:
        golden_cross.append(df.index[i].to_pydatetime())
    if df['ma5'][i] <= df['ma30'][i] and df['ma5'][i-1] > df['ma30'][i-1]:
        death_cross.append(df.index[i].to_pydatetime())
golden_cross

2、输出日志

[datetime.datetime(2001, 11, 21, 0, 0),
 datetime.datetime(2002, 1, 25, 0, 0),
 datetime.datetime(2002, 2, 5, 0, 0),
 datetime.datetime(2002, 6, 24, 0, 0),
 datetime.datetime(2002, 12, 6, 0, 0),
 datetime.datetime(2003, 1, 17, 0, 0),
 datetime.datetime(2003, 4, 16, 0, 0),
 datetime.datetime(2003, 6, 2, 0, 0),
 datetime.datetime(2003, 6, 25, 0, 0),
 datetime.datetime(2003, 7, 14, 0, 0),
 datetime.datetime(2003, 10, 15, 0, 0),
 datetime.datetime(2004, 3, 3, 0, 0),
 datetime.datetime(2004, 7, 6, 0, 0),
 datetime.datetime(2004, 9, 14, 0, 0),
 datetime.datetime(2005, 1, 5, 0, 0),
 datetime.datetime(2005, 4, 26, 0, 0),
 datetime.datetime(2005, 6, 15, 0, 0),
 datetime.datetime(2005, 6, 17, 0, 0),
 datetime.datetime(2005, 12, 29, 0, 0),
 datetime.datetime(2006, 7, 31, 0, 0),
 datetime.datetime(2006, 8, 30, 0, 0),
 datetime.datetime(2007, 2, 26, 0, 0),
 datetime.datetime(2007, 4, 16, 0, 0),
 datetime.datetime(2007, 4, 27, 0, 0),
 datetime.datetime(2007, 5, 9, 0, 0),
 datetime.datetime(2007, 5, 24, 0, 0),
 datetime.datetime(2007, 7, 25, 0, 0),
 datetime.datetime(2007, 9, 19, 0, 0),
 datetime.datetime(2007, 10, 10, 0, 0),
 datetime.datetime(2007, 11, 19, 0, 0),
 datetime.datetime(2007, 12, 11, 0, 0),
 datetime.datetime(2008, 3, 6, 0, 0),
 datetime.datetime(2008, 5, 8, 0, 0),
 datetime.datetime(2008, 5, 15, 0, 0),
 datetime.datetime(2008, 7, 17, 0, 0),
 datetime.datetime(2008, 11, 20, 0, 0),
 datetime.datetime(2009, 2, 10, 0, 0),
 datetime.datetime(2009, 4, 9, 0, 0),
 datetime.datetime(2009, 6, 5, 0, 0),
 datetime.datetime(2009, 8, 27, 0, 0),
 datetime.datetime(2009, 9, 9, 0, 0),
 datetime.datetime(2009, 11, 13, 0, 0),
 datetime.datetime(2010, 1, 21, 0, 0),
 datetime.datetime(2010, 2, 4, 0, 0),
 datetime.datetime(2010, 2, 8, 0, 0),
 datetime.datetime(2010, 6, 7, 0, 0),
 datetime.datetime(2010, 7, 20, 0, 0),
 datetime.datetime(2010, 10, 26, 0, 0),
 datetime.datetime(2010, 11, 11, 0, 0),
 datetime.datetime(2011, 2, 14, 0, 0),
 datetime.datetime(2011, 3, 15, 0, 0),
 datetime.datetime(2011, 4, 28, 0, 0),
 datetime.datetime(2011, 10, 26, 0, 0),
 datetime.datetime(2012, 2, 13, 0, 0),
 datetime.datetime(2012, 4, 9, 0, 0),
 datetime.datetime(2012, 7, 26, 0, 0),
 datetime.datetime(2012, 8, 10, 0, 0),
 datetime.datetime(2012, 9, 13, 0, 0),
 datetime.datetime(2012, 9, 28, 0, 0),
 datetime.datetime(2012, 12, 24, 0, 0),
 datetime.datetime(2013, 1, 9, 0, 0),
 datetime.datetime(2013, 3, 12, 0, 0),
 datetime.datetime(2013, 4, 18, 0, 0),
 datetime.datetime(2013, 7, 4, 0, 0),
 datetime.datetime(2013, 10, 22, 0, 0),
 datetime.datetime(2013, 11, 12, 0, 0),
 datetime.datetime(2013, 11, 29, 0, 0),
 datetime.datetime(2014, 1, 24, 0, 0),
 datetime.datetime(2014, 4, 3, 0, 0),
 datetime.datetime(2014, 6, 24, 0, 0),
 datetime.datetime(2014, 9, 5, 0, 0),
 datetime.datetime(2014, 9, 30, 0, 0),
 datetime.datetime(2014, 11, 18, 0, 0),
 datetime.datetime(2014, 12, 1, 0, 0),
 datetime.datetime(2015, 2, 16, 0, 0),
 datetime.datetime(2015, 6, 12, 0, 0),
 datetime.datetime(2015, 7, 16, 0, 0),
 datetime.datetime(2015, 9, 18, 0, 0),
 datetime.datetime(2015, 10, 8, 0, 0),
 datetime.datetime(2015, 12, 7, 0, 0),
 datetime.datetime(2015, 12, 21, 0, 0),
 datetime.datetime(2016, 2, 22, 0, 0),
 datetime.datetime(2016, 8, 11, 0, 0),
 datetime.datetime(2016, 10, 14, 0, 0),
 datetime.datetime(2016, 11, 28, 0, 0),
 datetime.datetime(2017, 7, 25, 0, 0),
 datetime.datetime(2017, 9, 19, 0, 0),
 datetime.datetime(2017, 12, 15, 0, 0),
 datetime.datetime(2018, 3, 19, 0, 0),
 datetime.datetime(2018, 5, 10, 0, 0),
 datetime.datetime(2018, 7, 19, 0, 0),
 datetime.datetime(2018, 7, 26, 0, 0),
 datetime.datetime(2018, 9, 21, 0, 0)]

全部输出

方法二

1、代码

sr1 = df['ma5'] < df['ma30']
sr2 = df['ma5'] > df['ma30']
death_cross = df[sr1 & sr2.shift(1)].index
golden_cross = df[~(sr1 & sr2.shift(1))].index
death_cross

2、输出截图

2、死叉日期

方法一

1、实现代码

golden_cross = []
death_cross = []
for i in range(1,len(df)):
    if df['ma5'][i] >= df['ma30'][i] and df['ma5'][i-1] < df['ma30'][i-1]:
        golden_cross.append(df.index[i].to_pydatetime())
    if df['ma5'][i] <= df['ma30'][i] and df['ma5'][i-1] > df['ma30'][i-1]:
        death_cross.append(df.index[i].to_pydatetime())
death_cross

2、输出日期

 1 [datetime.datetime(2001, 10, 15, 0, 0),
 2  datetime.datetime(2002, 1, 17, 0, 0),
 3  datetime.datetime(2002, 1, 31, 0, 0),
 4  datetime.datetime(2002, 4, 1, 0, 0),
 5  datetime.datetime(2002, 7, 30, 0, 0),
 6  datetime.datetime(2002, 12, 30, 0, 0),
 7  datetime.datetime(2003, 3, 18, 0, 0),
 8  datetime.datetime(2003, 4, 23, 0, 0),
 9  datetime.datetime(2003, 6, 23, 0, 0),
10  datetime.datetime(2003, 7, 1, 0, 0),
11  datetime.datetime(2003, 8, 5, 0, 0),
12  datetime.datetime(2004, 3, 1, 0, 0),
13  datetime.datetime(2004, 5, 12, 0, 0),
14  datetime.datetime(2004, 8, 23, 0, 0),
15  datetime.datetime(2004, 11, 24, 0, 0),
16  datetime.datetime(2005, 4, 21, 0, 0),
17  datetime.datetime(2005, 5, 17, 0, 0),
18  datetime.datetime(2005, 6, 16, 0, 0),
19  datetime.datetime(2005, 9, 27, 0, 0),
20  datetime.datetime(2006, 7, 11, 0, 0),
21  datetime.datetime(2006, 8, 1, 0, 0),
22  datetime.datetime(2007, 2, 9, 0, 0),
23  datetime.datetime(2007, 2, 27, 0, 0),
24  datetime.datetime(2007, 4, 24, 0, 0),
25  datetime.datetime(2007, 4, 30, 0, 0),
26  datetime.datetime(2007, 5, 14, 0, 0),
27  datetime.datetime(2007, 7, 12, 0, 0),
28  datetime.datetime(2007, 9, 13, 0, 0),
29  datetime.datetime(2007, 9, 20, 0, 0),
30  datetime.datetime(2007, 11, 14, 0, 0),
31  datetime.datetime(2007, 11, 23, 0, 0),
32  datetime.datetime(2008, 1, 31, 0, 0),
33  datetime.datetime(2008, 3, 17, 0, 0),
34  datetime.datetime(2008, 5, 13, 0, 0),
35  datetime.datetime(2008, 5, 23, 0, 0),
36  datetime.datetime(2008, 8, 13, 0, 0),
37  datetime.datetime(2008, 12, 31, 0, 0),
38  datetime.datetime(2009, 3, 13, 0, 0),
39  datetime.datetime(2009, 4, 30, 0, 0),
40  datetime.datetime(2009, 8, 20, 0, 0),
41  datetime.datetime(2009, 9, 3, 0, 0),
42  datetime.datetime(2009, 10, 21, 0, 0),
43  datetime.datetime(2009, 12, 21, 0, 0),
44  datetime.datetime(2010, 1, 22, 0, 0),
45  datetime.datetime(2010, 2, 5, 0, 0),
46  datetime.datetime(2010, 3, 1, 0, 0),
47  datetime.datetime(2010, 6, 24, 0, 0),
48  datetime.datetime(2010, 10, 18, 0, 0),
49  datetime.datetime(2010, 11, 2, 0, 0),
50  datetime.datetime(2010, 12, 27, 0, 0),
51  datetime.datetime(2011, 3, 3, 0, 0),
52  datetime.datetime(2011, 3, 31, 0, 0),
53  datetime.datetime(2011, 9, 9, 0, 0),
54  datetime.datetime(2011, 12, 12, 0, 0),
55  datetime.datetime(2012, 4, 6, 0, 0),
56  datetime.datetime(2012, 7, 25, 0, 0),
57  datetime.datetime(2012, 8, 3, 0, 0),
58  datetime.datetime(2012, 8, 14, 0, 0),
59  datetime.datetime(2012, 9, 24, 0, 0),
60  datetime.datetime(2012, 11, 8, 0, 0),
61  datetime.datetime(2012, 12, 26, 0, 0),
62  datetime.datetime(2013, 1, 18, 0, 0),
63  datetime.datetime(2013, 3, 19, 0, 0),
64  datetime.datetime(2013, 6, 21, 0, 0),
65  datetime.datetime(2013, 7, 16, 0, 0),
66  datetime.datetime(2013, 10, 28, 0, 0),
67  datetime.datetime(2013, 11, 27, 0, 0),
68  datetime.datetime(2013, 12, 4, 0, 0),
69  datetime.datetime(2014, 4, 1, 0, 0),
70  datetime.datetime(2014, 4, 30, 0, 0),
71  datetime.datetime(2014, 8, 25, 0, 0),
72  datetime.datetime(2014, 9, 17, 0, 0),
73  datetime.datetime(2014, 10, 13, 0, 0),
74  datetime.datetime(2014, 11, 24, 0, 0),
75  datetime.datetime(2015, 1, 20, 0, 0),
76  datetime.datetime(2015, 6, 11, 0, 0),
77  datetime.datetime(2015, 6, 18, 0, 0),
78  datetime.datetime(2015, 7, 20, 0, 0),
79  datetime.datetime(2015, 9, 30, 0, 0),
80  datetime.datetime(2015, 11, 27, 0, 0),
81  datetime.datetime(2015, 12, 11, 0, 0),
82  datetime.datetime(2016, 1, 6, 0, 0),
83  datetime.datetime(2016, 8, 9, 0, 0),
84  datetime.datetime(2016, 8, 19, 0, 0),
85  datetime.datetime(2016, 11, 21, 0, 0),
86  datetime.datetime(2017, 7, 7, 0, 0),
87  datetime.datetime(2017, 9, 11, 0, 0),
88  datetime.datetime(2017, 11, 30, 0, 0),
89  datetime.datetime(2018, 2, 5, 0, 0),
90  datetime.datetime(2018, 3, 28, 0, 0),
91  datetime.datetime(2018, 6, 29, 0, 0),
92  datetime.datetime(2018, 7, 24, 0, 0),
93  datetime.datetime(2018, 8, 2, 0, 0),
94  datetime.datetime(2018, 10, 15, 0, 0)]

全部日期

方法二

1、代码

sr1 = df['ma5'] < df['ma30']
sr2 = df['ma5'] > df['ma30']
death_cross = df[sr1 & sr2.shift(1)].index
golden_cross = df[~(sr1 & sr2.shift(1))].index
golden_cross

2、输出截图

五、如果我从2010年1月1日开始，初始资金为100000元，金叉尽量买入，死叉全部卖出，则到今天为止，我的炒股收益如何？

1、实现代码

first_money = 100000
money = first_money
hold = 0 #持有多少股
sr1 = pd.Series(1, index=golden_cross)
sr2 = pd.Series(1, index=death_cross)
sr = sr1.append(sr2).sort_index()


for i in range(0, len(sr)):
    p = df['open'][sr.index[i]]
    if sr.iloc[i] == 1:
        #金叉
        buy = (money // (100 * p))
        hold += buy*100
        money -= buy*100*p
    else:
        money += hold * p
        hold = 0
        
p = df['open'][-1]
now_money = hold * p + money

print(now_money - first_money)

2、运行结果

10058413.0

3、使用知识点

posted @ 2018-10-17 08:53 活的潇洒80 阅读(706) 评论(0) 编辑收藏举报

刷新页面返回顶部

运维人生

金融量化分析【day112】:股票数据分析Tushare2

目录

一、使用tushare包获取某股票的历史行情数据

1、代码

2、输出截图

二、使用pandas包计算该股票历史数据的5日均线和60日均线

1、方法一

1、5日均线代码

2、5日均线截图

3、30日均线代码

4、30日均线截图

2、方法二

1、5日均线代码

2、5日均线截图

1、30日均线代码

2、30日均线截图

3、使用知识点

方法一

方法二

三、matplotlib包可视化历史数据的收盘价和历史均线

1、代码（2010-2019）

2、输出截图（数据比较多所以不是很清晰）

3、代码（2012年）

4、输出（线条区分比较明显）

四、分析输出所有金叉日志和死叉日期

1、金叉日期

方法一

方法二

2、死叉日期

方法一

方法二

五、如果我从2010年1月1日开始，初始资金为100000元，金叉尽量买入，死叉全部卖出，则到今天为止，我的炒股收益如何？

公告