Comments (12)
rainx
commented on July 23, 2024
1
更新了一下字段的标题，并且增加了category字段，区分类型， 至于 “送转股 分红 配股 配股价 “ 这些，应该是根据后面的字段和类别可以算出来了，由于我这边金融的公式了解的还不算多，所以还没有加，等后续理解了每个公式的含义后再追加一下
year month day category panqianliutong panhouliutong qianzongguben \
0 2001 1 4 5 4200.000000 9200.000000 33000.0
1 2001 3 30 5 9200.000000 10000.000000 33000.0
2 2001 6 8 1 4098.000000 0.000000 0.0
3 2002 5 20 1 2434.006250 0.000000 0.0
4 2003 5 29 1 1218.003125 0.000000 0.0
5 2004 6 1 1 2.000000 0.000000 0.0
6 2005 7 11 1 2050.000000 520.000000 0.0
7 2005 7 12 2 10000.000000 20000.000000 33000.0
8 2006 1 16 1 0.000000 4098.000000 0.0
9 2006 1 16 5 20000.000000 26000.000000 66000.0
10 2006 7 21 1 19456.503125 0.000000 0.0
11 2007 1 16 5 26000.000000 26100.800781 66000.0
12 2007 5 25 1 6400.502344 0.000000 0.0
13 2008 3 26 1 0.400000 0.000000 0.0
14 2008 5 22 5 26100.800781 26100.800781 66000.0
15 2009 1 16 5 26100.800781 66000.000000 76000.0
16 2009 5 11 1 0.300000 1032.000000 0.0
17 2009 5 12 9 66000.000000 145200.000000 76000.0
18 2009 5 22 5 145200.000000 160600.000000 167200.0
19 2010 6 2 1 0.500000 0.000000 0.0
20 2011 5 16 1 6400.502344 0.000000 0.0
21 2011 5 23 5 160600.000000 167200.000000 167200.0
22 2012 6 12 1 0.500000 0.000000 0.0
23 2013 6 13 1 6400.502344 0.000000 0.0
24 2014 6 18 1 0.500000 0.000000 0.0
25 2015 6 12 1 77824.125781 0.000000 0.0
26 2016 5 18 1 0.300000 0.000000 0.0
27 2017 6 16 1 77824.125781 0.000000 0.0
houzongguben
0 33000.0
1 33000.0
2 0.0
3 0.0
4 0.0
5 0.0
6 0.0
7 66000.0
8 0.0
9 66000.0
10 0.0
11 66000.0
12 0.0
13 0.0
14 76000.0
15 76000.0
16 0.0
17 167200.0
18 167200.0
19 0.0
20 0.0
21 167200.0
22 0.0
23 0.0
24 0.0
25 0.0
26 0.0
27 0.0
--------------------
from pytdx.
zfsamzfsam
commented on July 23, 2024
1
能否提供通过除息除权信息去算出前复权价格的API?
from pytdx.
yutiansut
commented on July 23, 2024
事实上可能是有些丢失字段? 总之 应该标题不是那个songguprice这些 而是前后流通盘大小 @rainx
from pytdx.
rainx
commented on July 23, 2024
@yutiansut 嗯嗯，这些字段之前主要参照tradex里面相对应的字段取值，的确具体的含义没有深究.. 而且有写字节的确跳过解析了
pos += 1 #skip a byte
year, month, day, hour, minite, pos = get_datetime(9, body_buf, pos)
pos += 1 #skip a byte主要是在日期解析前后分别跳过了一个字节，不过还无法确认具体的含义..
之前一直没有安装通达信的软件，我今天争取结合你给的信息再检查一下，然后把名称这边也纠正一下
from pytdx.
yutiansut
commented on July 23, 2024
@rainx 大大效率好高~~~ 赞~~(≧▽≦)/~
from pytdx.
yutiansut
commented on July 23, 2024
因为目前pytdx没法直接拿到前复权的价格,所以只能通过这些信息去算出复权价格
from pytdx.
yutiansut
commented on July 23, 2024
继续更新:
和通达信数据对比后 category应该代表这几个
"""
category:
1 : 除息除权
2 : 送配股上市
3 : 非流通股上市
5 : 股本变化
9 : 转配股上市
"""
from pytdx.
codingoc
commented on July 23, 2024
请问如何通过除权除息信息去计算前复权的价格呢
from pytdx.
rainx
commented on July 23, 2024
@yutiansut 修复了 category == 1 和 category == 11 时出错的字段..
增加了：
fenhong peigujia songzhuangu peigu suogu
/usr/local/Cellar/python3/3.5.2_3/Frameworks/Python.framework/Versions/3.5/bin/python3.5 /Users/rainx/dev/pytdx/pytdx/parser/get_xdxr_info.py
year month day category fenhong peigujia songzhuangu peigu suogu \
0 2001 1 4 5 NaN NaN NaN NaN None
1 2001 3 30 5 NaN NaN NaN NaN None
2 2001 6 8 1 3.0 0.0 0.0 0.0 None
3 2002 5 20 1 2.6 0.0 0.0 0.0 None
4 2003 5 29 1 2.3 0.0 0.0 0.0 None
5 2004 6 1 1 2.0 0.0 0.0 0.0 None
6 2005 7 11 1 2.5 0.0 10.0 0.0 None
7 2005 7 12 2 NaN NaN NaN NaN None
8 2006 1 16 1 0.0 0.0 3.0 0.0 None
9 2006 1 16 5 NaN NaN NaN NaN None
10 2006 7 21 1 0.8 0.0 0.0 0.0 None
11 2007 1 16 5 NaN NaN NaN NaN None
12 2007 5 25 1 0.6 0.0 0.0 0.0 None
13 2008 3 26 1 0.4 0.0 0.0 0.0 None
14 2008 5 22 5 NaN NaN NaN NaN None
15 2009 1 16 5 NaN NaN NaN NaN None
16 2009 5 11 1 0.3 0.0 12.0 0.0 None
17 2009 5 12 9 NaN NaN NaN NaN None
18 2009 5 22 5 NaN NaN NaN NaN None
19 2010 6 2 1 0.5 0.0 0.0 0.0 None
20 2011 5 16 1 0.6 0.0 0.0 0.0 None
21 2011 5 23 5 NaN NaN NaN NaN None
22 2012 6 12 1 0.5 0.0 0.0 0.0 None
23 2013 6 13 1 0.6 0.0 0.0 0.0 None
24 2014 6 18 1 0.5 0.0 0.0 0.0 None
25 2015 6 12 1 0.2 0.0 0.0 0.0 None
26 2016 5 18 1 0.3 0.0 0.0 0.0 None
27 2017 6 16 1 0.2 0.0 0.0 0.0 None
panqianliutong panhouliutong qianzongguben houzongguben
0 4200.000000 9200.000000 33000.0 33000.0
1 9200.000000 10000.000000 33000.0 33000.0
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 NaN NaN NaN NaN
7 10000.000000 20000.000000 33000.0 66000.0
8 NaN NaN NaN NaN
9 20000.000000 26000.000000 66000.0 66000.0
10 NaN NaN NaN NaN
11 26000.000000 26100.800781 66000.0 66000.0
12 NaN NaN NaN NaN
13 NaN NaN NaN NaN
14 26100.800781 26100.800781 66000.0 76000.0
15 26100.800781 66000.000000 76000.0 76000.0
16 NaN NaN NaN NaN
17 66000.000000 145200.000000 76000.0 167200.0
18 145200.000000 160600.000000 167200.0 167200.0
19 NaN NaN NaN NaN
20 NaN NaN NaN NaN
21 160600.000000 167200.000000 167200.0 167200.0
22 NaN NaN NaN NaN
23 NaN NaN NaN NaN
24 NaN NaN NaN NaN
25 NaN NaN NaN NaN
26 NaN NaN NaN NaN
27 NaN NaN NaN NaN
from pytdx.
yutiansut
commented on July 23, 2024
@rainx 🥇 赞赞赞 💯 👍
from pytdx.
zzeric
commented on July 23, 2024
有了权息数据，就可以自行计算除权因子了，不需要用爬虫去同花顺抓取
from pytdx.
yutiansut
commented on July 23, 2024
是的
from pytdx.
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