Python tdx数据接口
感谢大家对 pytdx 项目的关注
pytdx 为个人学习目的开发的项目2 年前(2018年)我加入新的公司之后，该项目一直处于没有维护的状态，目前也没有更多精力维护此项目，项目代码已经老旧过时。TypeScript, Swift, Kotlin, 和一些系统级开发语言 Rust, Golang)。Python 的新版本，新特性，已经很久没有跟进学习了，后续会补上。希望后续与大家更多的交流。经过慎重的考虑，决定在Github上Archive本项目。希望可以得到大家的理解。再次感谢。
Peace & Love
这个是api拿到的
In [11]: with api.connect('119.147.212.81', 7709):
...: print(api.to_df(api.get_xdxr_info(1, '600300')))
...:
...:
...:
year month day cash peigu_price songgu_num peigu_percent
0 2001 1 4 4200.000000 33000.0 9200.000000 33000.0
1 2001 3 30 9200.000000 33000.0 10000.000000 33000.0
2 2001 6 8 4098.000000 0.0 0.000000 0.0
3 2002 5 20 2434.006250 0.0 0.000000 0.0
4 2003 5 29 1218.003125 0.0 0.000000 0.0
5 2004 6 1 2.000000 0.0 0.000000 0.0
6 2005 7 11 2050.000000 0.0 520.000000 0.0
7 2005 7 12 10000.000000 33000.0 20000.000000 66000.0
8 2006 1 16 0.000000 0.0 4098.000000 0.0
9 2006 1 16 20000.000000 66000.0 26000.000000 66000.0
10 2006 7 21 19456.503125 0.0 0.000000 0.0
11 2007 1 16 26000.000000 66000.0 26100.800781 66000.0
12 2007 5 25 6400.502344 0.0 0.000000 0.0
13 2008 3 26 0.400000 0.0 0.000000 0.0
14 2008 5 22 26100.800781 66000.0 26100.800781 76000.0
15 2009 1 16 26100.800781 76000.0 66000.000000 76000.0
16 2009 5 11 0.300000 0.0 1032.000000 0.0
17 2009 5 12 66000.000000 76000.0 145200.000000 167200.0
18 2009 5 22 145200.000000 167200.0 160600.000000 167200.0
19 2010 6 2 0.500000 0.0 0.000000 0.0
20 2011 5 16 6400.502344 0.0 0.000000 0.0
21 2011 5 23 160600.000000 167200.0 167200.000000 167200.0
22 2012 6 12 0.500000 0.0 0.000000 0.0
23 2013 6 13 6400.502344 0.0 0.000000 0.0
24 2014 6 18 0.500000 0.0 0.000000 0.0
25 2015 6 12 77824.125781 0.0 0.000000 0.0
26 2016 5 18 0.300000 0.0 0.000000 0.0
27 2017 6 16 77824.125781 0.0 0.000000 0.0这个是tdx的软件里查到的结果
维维股份(600300)权息资料
────────────
权息日 类别 送转股 分红 配股 配股价 前流通盘 后流通盘 前总股本 后总股本
────────────────────────────────────────────
20170616 除权除息 0.200
20160518 除权除息 0.300
20150612 除权除息 0.200
20140618 除权除息 0.500
20130613 除权除息 0.600
20120612 除权除息 0.500
20110523 股本变化 160600.0 167200.0 167200.0 167200.0
20110516 除权除息 0.600
20100602 除权除息 0.500
20090522 股本变化 145200.0 160600.0 167200.0 167200.0
20090512 转配股上市 66000.0 145200.0 76000.0 167200.0
20090511 除权除息 12.00 0.300
20090116 股本变化 26100.8 66000.0 76000.0 76000.0
20080522 股本变化 26100.8 26100.8 66000.0 76000.0
20080326 除权除息 0.400
20070525 除权除息 0.600
20070116 股本变化 26000.0 26100.8 66000.0 66000.0
20060721 除权除息 0.800
20060116 股本变化 20000.0 26000.0 66000.0 66000.0
20060116 除权除息 3.00
20050712 送配股上市 10000.0 20000.0 33000.0 66000.0
20050711 除权除息 10.00 2.500
20040601 除权除息 2.000
20030529 除权除息 2.300
20020520 除权除息 2.600
20010608 除权除息 3.000
20010330 股本变化 9200.0 10000.0 33000.0 33000.0
20010104 股本变化 4200.0 9200.0 33000.0 33000.0
最近使用pytdx下美股数据，确实很爽。有个问题请教下：
现在下载美股的股票数据是未复权数据吗？如果是的话，如何进行复权啊？这个对回测影响非常大啊。
另外关于美股数据的准确性，请问是否有过研究？
谢谢
查询深市股票正常，当查询沪市股票时会出错，api.get_k_data('600031', '2017-09-14', '2017-09-15')
Traceback (most recent call last):
File "E:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 2442, in get_loc
return self._engine.get_loc(key)
File "pandas_libs\index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc (pandas_libs\index.c:5280)
File "pandas_libs\index.pyx", line 154, in pandas._libs.index.IndexEngine.get_loc (pandas_libs\index.c:5126)
File "pandas_libs\hashtable_class_helper.pxi", line 1210, in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas_libs\hashtable.c:20523)
File "pandas_libs\hashtable_class_helper.pxi", line 1218, in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas_libs\hashtable.c:20477)
KeyError: 'datetime'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:/Quant/WebGrab/QueryTDX.py", line 114, in
get_tdx_history_day()
File "E:/Quant/WebGrab/QueryTDX.py", line 71, in get_tdx_history_day
data = api.get_k_data('600031', '2017-09-14', '2017-09-15')
File "E:\Anaconda3\lib\site-packages\pytdx\hq.py", line 167, in get_k_data
data['date'] = data['datetime'].apply(lambda x: x[0:10])
File "E:\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1964, in getitem
return self._getitem_column(key)
File "E:\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1971, in _getitem_column
return self._get_item_cache(key)
File "E:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1645, in _get_item_cache
values = self._data.get(item)
File "E:\Anaconda3\lib\site-packages\pandas\core\internals.py", line 3590, in get
loc = self.items.get_loc(item)
File "E:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 2444, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas_libs\index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc (pandas_libs\index.c:5280)
File "pandas_libs\index.pyx", line 154, in pandas._libs.index.IndexEngine.get_loc (pandas_libs\index.c:5126)
File "pandas_libs\hashtable_class_helper.pxi", line 1210, in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas_libs\hashtable.c:20523)
File "pandas_libs\hashtable_class_helper.pxi", line 1218, in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas_libs\hashtable.c:20477)
KeyError: 'datetime'
get_minute_time_data，get_transaction_data，get_security_quotes这些函数如果传入基金代码，返回的价格都大了10倍。
这个是通达信的行情数据本身就给乘了10倍吗？
import requests
import pandas as pd
import numpy as np
# 00 不复权 01前复权 02后复权
def get_k_data_year(code,year,if_fq):
data_=[]
url='http://d.10jqka.com.cn/v2/line/hs_%s/%s/%s.js'%(str(code),str(if_fq),str(year))
for item in requests.get(url).text.split('\"')[3].split(';'):
data_.append(item.split(','))
return pd.DataFrame(data_,index=list(np.asarray(data_).T[0]),columns=['date','open','high','low','close','volume','amount','factor'])
if __name__=='__main__':
print(get_k_data_year('000001','2016','01'))
print(get_k_data_year(600010,2016,'01'))将
pkg.extend(struct.pack('<B9sHHHI', market, code, category, count, start, last_value))
改为
pkg.extend(struct.pack('<B9sHHiH', market, code, category,0, start, count))
可以解决 start，count不起作用的问题
建议将默认值start=0, count=700，这也是通达信的默认值
def get_instrument_bars(self, category, market, code, start=0, count=700):
cmd = GetInstrumentBars(self.client)
cmd.setParams(category, market, code, start, count)
return cmd.call_api()
感谢楼主分享，非常有用的代码，请问是否打算提供下单接口？如果可以的话那就太有用了。
如题
GitBook中提到获取股票列表还有第3个参数“数量”，但实际代码（get_security_list.py）中并没有这个部分
pre_close数值有很大比例有误，例如000001的20170909~20170911的pre_close (即20170908的close) 应为11.44，但目前API返回888.0024995803833
看到有issue提出不知道如果下载完整的数据,关于数据位置指针的使用,给一个示例代码:
from pytdx.hq import TdxHq_API
api=TdxHq_API()
with api.connect():
data=[]
for i in range(10):
data+=api.get_security_bars(9,0,'000001',(9-i)*800,800)
print(api.to_df(data))open | close | high | low | vol | amount | year | month | day | hour | minute | datetime
-- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | --
27.70 | 27.80 | 27.90 | 27.60 | 1270.0 | 3.530600e+06 | 1991 | 12 | 23 | 15 | 0 | 1991-12-23 15:00
27.90 | 29.05 | 29.30 | 27.00 | 1050.0 | 3.050250e+06 | 1991 | 12 | 24 | 15 | 0 | 1991-12-24 15:00
29.15 | 29.30 | 30.00 | 29.10 | 2269.0 | 6.648170e+06 | 1991 | 12 | 25 | 15 | 0 | 1991-12-25 15:00
29.30 | 28.00 | 29.30 | 28.00 | 1918.0 | 5.370400e+06 | 1991 | 12 | 26 | 15 | 0 | 1991-12-26 15:00
28.00 | 28.45 | 28.50 | 28.00 | 2105.0 | 5.988725e+06 | 1991 | 12 | 27 | 15 | 0 | 1991-12-27 15:00
28.40 | 29.25 | 29.30 | 28.40 | 1116.0 | 3.264300e+06 | 1991 | 12 | 28 | 15 | 0 | 1991-12-28 15:00
29.30 | 28.80 | 29.40 | 28.80 | 1059.0 | 3.049920e+06 | 1991 | 12 | 30 | 15 | 0 | 1991-12-30 15:00
29.15 | 29.15 | 29.40 | 29.00 | 1886.0 | 5.497690e+06 | 1992 | 1 | 2 | 15 | 0 | 1992-01-02 15:00
29.20 | 29.10 | 29.30 | 29.00 | 2212.0 | 6.436920e+06 | 1992 | 1 | 3 | 15 | 0 | 1992-01-03 15:00
29.10 | 29.70 | 29.80 | 29.05 | 2634.0 | 7.822980e+06 | 1992 | 1 | 6 | 15 | 0 | 1992-01-06 15:00
29.70 | 29.90 | 30.20 | 29.55 | 2931.0 | 8.763690e+06 | 1992 | 1 | 7 | 15 | 0 | 1992-01-07 15:00
29.80 | 29.65 | 30.20 | 29.55 | 1928.0 | 5.716520e+06 | 1992 | 1 | 8 | 15 | 0 | 1992-01-08 15:00
29.70 | 29.70 | 29.80 | 29.30 | 1535.0 | 4.558950e+06 | 1992 | 1 | 9 | 15 | 0 | 1992-01-09 15:00
29.65 | 29.80 | 29.80 | 29.50 | 1525.0 | 4.544500e+06 | 1992 | 1 | 10 | 15 | 0 | 1992-01-10 15:00
29.80 | 29.65 | 29.80 | 29.60 | 1353.0 | 4.011645e+06 | 1992 | 1 | 13 | 15 | 0 | 1992-01-13 15:00
29.65 | 29.20 | 29.65 | 29.00 | 1711.0 | 4.996120e+06 | 1992 | 1 | 14 | 15 | 0 | 1992-01-14 15:00
29.20 | 29.10 | 29.40 | 29.00 | 2205.0 | 6.416550e+06 | 1992 | 1 | 15 | 15 | 0 | 1992-01-15 15:00
29.00 | 28.90 | 29.00 | 28.55 | 1268.0 | 3.664520e+06 | 1992 | 1 | 16 | 15 | 0 | 1992-01-16 15:00
28.90 | 28.80 | 28.90 | 28.35 | 1143.0 | 3.291840e+06 | 1992 | 1 | 17 | 15 | 0 | 1992-01-17 15:00
28.80 | 29.45 | 29.50 | 28.80 | 1085.0 | 3.195325e+06 | 1992 | 1 | 20 | 15 | 0 | 1992-01-20 15:00
29.50 | 29.30 | 29.80 | 29.20 | 1576.0 | 4.617680e+06 | 1992 | 1 | 21 | 15 | 0 | 1992-01-21 15:00
29.25 | 28.95 | 29.30 | 28.80 | 1592.0 | 4.608840e+06 | 1992 | 1 | 22 | 15 | 0 | 1992-01-22 15:00
28.95 | 29.20 | 29.30 | 28.95 | 1477.0 | 4.312840e+06 | 1992 | 1 | 23 | 15 | 0 | 1992-01-23 15:00
29.25 | 29.05 | 29.70 | 29.05 | 2327.0 | 6.759935e+06 | 1992 | 1 | 24 | 15 | 0 | 1992-01-24 15:00
29.55 | 29.40 | 29.60 | 29.30 | 2571.0 | 7.558740e+06 | 1992 | 1 | 27 | 15 | 0 | 1992-01-27 15:00
29.40 | 29.50 | 29.50 | 29.35 | 2336.0 | 6.891200e+06 | 1992 | 1 | 28 | 15 | 0 | 1992-01-28 15:00
29.50 | 30.00 | 30.05 | 29.50 | 2127.0 | 6.381000e+06 | 1992 | 1 | 29 | 15 | 0 | 1992-01-29 15:00
29.95 | 30.20 | 30.35 | 29.95 | 1735.0 | 5.239700e+06 | 1992 | 1 | 30 | 15 | 0 | 1992-01-30 15:00
31.15 | 32.00 | 32.00 | 31.15 | 2228.0 | 7.129600e+06 | 1992 | 2 | 1 | 15 | 0 | 1992-02-01 15:00
32.00 | 32.32 | 32.70 | 31.90 | 1735.0 | 5.607520e+06 | 1992 | 2 | 2 | 15 | 0 | 1992-02-02 15:00
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ...
9.30 | 9.36 | 9.39 | 9.27 | 546016.0 | 5.091620e+08 | 2017 | 6 | 27 | 15 | 0 | 2017-06-27 15:00
9.35 | 9.43 | 9.49 | 9.33 | 1168796.0 | 1.102438e+09 | 2017 | 6 | 28 | 15 | 0 | 2017-06-28 15:00
9.43 | 9.43 | 9.45 | 9.37 | 488804.0 | 4.598104e+08 | 2017 | 6 | 29 | 15 | 0 | 2017-06-29 15:00
9.40 | 9.39 | 9.43 | 9.31 | 499633.0 | 4.680035e+08 | 2017 | 6 | 30 | 15 | 0 | 2017-06-30 15:00
9.40 | 9.40 | 9.43 | 9.34 | 388349.0 | 3.644659e+08 | 2017 | 7 | 3 | 15 | 0 | 2017-07-03 15:00
9.40 | 9.34 | 9.41 | 9.30 | 488362.0 | 4.565770e+08 | 2017 | 7 | 4 | 15 | 0 | 2017-07-04 15:00
9.29 | 9.37 | 9.38 | 9.27 | 567720.0 | 5.292941e+08 | 2017 | 7 | 5 | 15 | 0 | 2017-07-05 15:00
9.36 | 9.40 | 9.41 | 9.31 | 738911.0 | 6.913872e+08 | 2017 | 7 | 6 | 15 | 0 | 2017-07-06 15:00
9.37 | 9.47 | 9.48 | 9.34 | 760369.0 | 7.170844e+08 | 2017 | 7 | 7 | 15 | 0 | 2017-07-07 15:00
9.45 | 9.59 | 9.66 | 9.44 | 1360815.0 | 1.303090e+09 | 2017 | 7 | 10 | 15 | 0 | 2017-07-10 15:00
9.61 | 10.25 | 10.46 | 9.61 | 3812086.0 | 3.842010e+09 | 2017 | 7 | 11 | 15 | 0 | 2017-07-11 15:00
10.27 | 10.34 | 10.58 | 10.20 | 2998844.0 | 3.113681e+09 | 2017 | 7 | 12 | 15 | 0 | 2017-07-12 15:00
10.30 | 10.90 | 10.90 | 10.24 | 2994534.0 | 3.180145e+09 | 2017 | 7 | 13 | 15 | 0 | 2017-07-13 15:00
10.81 | 10.90 | 10.94 | 10.66 | 1722570.0 | 1.864449e+09 | 2017 | 7 | 14 | 15 | 0 | 2017-07-14 15:00
10.95 | 10.81 | 11.33 | 10.72 | 3273123.0 | 3.608692e+09 | 2017 | 7 | 17 | 15 | 0 | 2017-07-17 15:00
10.75 | 11.05 | 11.14 | 10.62 | 2349431.0 | 2.558434e+09 | 2017 | 7 | 18 | 15 | 0 | 2017-07-18 15:00
10.99 | 11.09 | 11.19 | 10.88 | 1933075.0 | 2.131336e+09 | 2017 | 7 | 19 | 15 | 0 | 2017-07-19 15:00
11.08 | 10.97 | 11.22 | 10.91 | 1537338.0 | 1.695061e+09 | 2017 | 7 | 20 | 15 | 0 | 2017-07-20 15:00
10.83 | 10.89 | 10.95 | 10.69 | 1501020.0 | 1.625416e+09 | 2017 | 7 | 21 | 15 | 0 | 2017-07-21 15:00
10.82 | 10.95 | 11.06 | 10.73 | 1692664.0 | 1.846887e+09 | 2017 | 7 | 24 | 15 | 0 | 2017-07-24 15:00
10.98 | 11.00 | 11.27 | 10.95 | 1954768.0 | 2.172115e+09 | 2017 | 7 | 25 | 15 | 0 | 2017-07-25 15:00
10.92 | 10.74 | 11.18 | 10.66 | 1697412.0 | 1.846282e+09 | 2017 | 7 | 26 | 15 | 0 | 2017-07-26 15:00
10.72 | 10.59 | 10.77 | 10.53 | 1194490.0 | 1.273889e+09 | 2017 | 7 | 27 | 15 | 0 | 2017-07-27 15:00
10.61 | 10.74 | 10.81 | 10.58 | 819195.0 | 8.777693e+08 | 2017 | 7 | 28 | 15 | 0 | 2017-07-28 15:00
10.80 | 10.67 | 10.82 | 10.45 | 1575864.0 | 1.671814e+09 | 2017 | 7 | 31 | 15 | 0 | 2017-07-31 15:00
10.64 | 11.04 | 11.08 | 10.60 | 2035709.0 | 2.222888e+09 | 2017 | 8 | 1 | 15 | 0 | 2017-08-01 15:00
11.05 | 11.15 | 11.34 | 10.96 | 2062069.0 | 2.307727e+09 | 2017 | 8 | 2 | 15 | 0 | 2017-08-02 15:00
11.14 | 11.01 | 11.22 | 10.97 | 984219.0 | 1.090954e+09 | 2017 | 8 | 3 | 15 | 0 | 2017-08-03 15:00
11.00 | 11.17 | 11.29 | 10.93 | 1353951.0 | 1.511390e+09 | 2017 | 8 | 4 | 15 | 0 | 2017-08-04 15:00
11.06 | 11.00 | 11.17 | 10.90 | 860644.0 | 9.469757e+08 | 2017 | 8 | 7 | 15 | 0 | 2017-08-07 15:00
检对数据后，发现扩展行情和普通行情的价格顺序不一样
open,close,high,low应该改为open,high,low,close
查看增仓数据是unpack类型的问题
在ex_get_transaction_data.py里
(raw_time, price, volume, zengcang, nature) = struct.unpack("<HIIIH", body_buf[pos: pos + 16])
应为
(raw_time, price, volume, zengcang, nature) = struct.unpack("<HIIiH", body_buf[pos: pos + 16])
但nature数据目前看不出跟开/平/换等有什么联系
from pytdx.reader import TdxMinBarReader,TdxFileNotFoundException
import sys,os
import pymongo
import json
import time,datetime
def QA_save_tdx_to_mongo(file_dir):
reader = TdxMinBarReader()
for a,v,files in os.walk(file_dir):
for file in files:
fname=file_dir+'\\'+file
df=reader.get_df(fname)
df['datetime'] = list(df.index)[j]
df['code'] = str(file)[2:8]
df['market']=str(file)[0:2]
data_json=json.loads(df.to_json(orient='records'))
print(data_json)
input()
if __name__=='__main__':
file_dir='C:\\users\\yutiansut\\desktop\\sh5fz'
QA_save_tdx_to_mongo(file_dir)
一贱你就笑 09:52:25
pytdx 连接到服务器后好像过一段时间后就会自动断开连接，有没有api可以检测连接状态呀？
产品研发-徐景RainX 10:16:56
连接这个阶段有没有持续有api请求？
产品研发-徐景RainX 10:17:34
这个我后面研究一下，争取可以做一个断线重连的方案...
一贱你就笑 10:21:14
没有持续api请求，就是过一阵再请求就发现disconnect，恩，或者提供一个连接状态查询也蛮好的[表情]
产品研发-徐景RainX 10:26:07
嗯，好，暂时来说，可以考虑如果长期没有连接的话，发送一个查询市场股票数量这样的小一些的请求来维持一下
产品研发-徐景RainX 10:26:24
我后面看一下，把请求状态加一下
增加扩展行情里面所有市场类别的定义，方便程序调用是使用，如
In [7]: api.to_df(api.get_markets())
Out[7]:
market category name short_name
0 1 1 临时股 TP
1 4 12 郑州商品期权 OZ
2 5 12 大连商品期权 OD
3 6 12 上海商品期权 OS
4 8 12 上海个股期权 QQ
5 27 5 香港指数 FH
6 28 3 郑州商品 QZ
7 29 3 大连商品 QD
8 30 3 上海期货 QS
9 31 2 香港主板 KH
10 32 2 香港权证 KR
11 33 8 开放式基金 FU
12 34 9 货币型基金 FB
13 35 8 招商理财产品 LC
14 36 9 招商货币产品 LB
15 37 11 国际指数 FW
16 38 10 国内宏观指标 HG
17 40 11 **概念股 CH
18 41 11 美股知名公司 MG
19 43 1 B股转H股 HB
20 44 1 股份转让 SB
21 47 3 股指期货 CZ
22 48 2 香港创业板 KG
23 49 2 香港信托基金 KT
24 54 6 国债预发行 GY
25 60 3 主力期货合约 MA
26 62 5 中证指数 ZZ
27 71 2 港股通 GH
这里，我们可以定义一组常量
在 params
TDXParams.EX_MARKET_HK_ MAINBOARD = 31
并且，我们定义一组映射
KH -> 31
香港主板 -> 31
在任何需要调用到category的函数里，用
api.get_transaction_data(31,'00005')
可以使用
api.get_transaction_data('KH','00005')
api.get_transaction_data('香港主板','00005')
来调用
或者使用常亮
api.get_transaction_data(TDXParams.EX_MARKET_HK_ MAINBOARD, '00005')
建议在行情类返回的数据增加股票代码字段，获取行情输入的都是代码，增加这一字段也不难，代码作为股票的ID，在后期批量处理时性能提升方面将会非常明显。
在通达信行情主页，可以通过点击涨幅，涨速，换手等字段来获取按照相应字段排序的股票行情列表，所以是否可以提供相应功能的api
代码示例如下：
# return 1 if sh, 0 if sz
def get_stock_type(stock, index = False):
if stock[:4] == "1318" return 0
if stock[:2] in ['01', '51'] or stock[:3] in ["110", "131", "132", "133", "500", "502", "505", "601", "603", "900]:
return 1
if stock[:2] in ["10", "15", "16", "18", "39"] or stock[:3] in ["000", "002", "200", "300", "115"]
return 0
return 0
1 除权除息 002175 2008-05-29
2 送配股上市 000656 2015-04-29
3 非流通股上市 000656 2010-02-10
4 未知股本变动 600642 1993-07-19
5 股本变化 000656 2017-06-30
6 增发新股 600887 2002-08-20
7 股份回购 600619 2000-09-08
8 增发新股上市 600186 2001-02-14
9 转配股上市 600811 2017-07-25
10 可转债上市 600418 2006-07-07
11 扩缩股 600381 2014-06-27
12 非流通股缩股 600339 2006-04-10
13 送认购权证 600008 2006-04-19
14 送认沽权证 000932 2006-03-01
QQ留言
@翻开魔法书 09:27:02
@产品研发-徐景RainX data = api.get_security_quotes([(0, '000005'), (0, '002893')]) 因为002893 没有数据，这条就返回None了
单独收取 0,002893 获取的数据
body_buf=b'\x01\x17\x01\x00\x00002893\x00\x00\x00\x00\x00\x00\x00\xa4\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00
...: \x00\x00\x00\x00\x00\x00\x00\x00'尝试解析
In [4]: from pytdx.helper import get_datetime, get_volume, get_price
...: from collections import OrderedDict
...: import struct
...: import six
error Traceback (most recent call last)
<ipython-input-5-c8fcf5eb19bb> in <module>()
69 reversed_bytes7, pos = get_price(body_buf, pos)
70 reversed_bytes8, pos = get_price(body_buf, pos)
---> 71 (reversed_bytes9, active2) = struct.unpack("<HH", body_buf[pos: pos + 4])
72 pos += 4
73
error: unpack requires a bytes object of length 4有基础的有L2的
[HQHOST]
HostNum=6
PrimaryHost=0
HostName01=长城国瑞行情DNS
IPAddress01=tdx.xmzq.com.cn
Port01=7709
HostName02=长城国瑞电信1
IPAddress02=218.85.139.19
Port02=7709
HostName03=长城国瑞电信2
IPAddress03=218.85.139.20
Port03=7709
HostName04=长城国瑞网通
IPAddress04=58.23.131.163
Port04=7709
HostName05=上证云成都电信一
IPAddress05=218.6.170.47
Port05=7709
HostName06=上证云北京联通一
IPAddress06=123.125.108.14
Port06=7709
[INFOHOST]
INFO=1
HostNum=6
PrimaryHost=0
HostName01=长城国瑞资讯DNS
IPAddress01=tdx.xmzq.com.cn
Port01=7711
HostName02=长城国瑞资讯1
IPAddress02=218.85.139.19
Port02=7711
HostName03=长城国瑞资讯2
IPAddress03=218.85.139.20
Port03=7711
HostName04=长城国瑞网通
IPAddress04=58.23.131.163
Port04=7711
HostName05=上证云成都电信一
IPAddress05=218.6.170.47
Port05=7711
HostName06=上证云北京联通一
IPAddress06=123.125.108.14
Port06=7711
HostName01=高级行情_深圳双线1
IPAddress01=112.74.31.192
Port01=443
HostName02=高级行情_武汉电信2
IPAddress02=119.97.185.16
Port02=7709
Areas02=1
HostName03=高级行情_北京双线2
IPAddress03=59.110.61.176
Port03=7709
[L2Host]
HostNum=21
HostName01=高级行情_上海双线1
IPAddress01=106.14.76.29
Port01=7709
HostName02=高级行情_上海双线2
IPAddress02=139.196.174.113
Port02=7709
HostName03=高级行情_上海双线3
IPAddress03=139.196.175.118
Port03=7709
HostName04=高级行情_上海电信1
IPAddress04=61.152.107.173
Port04=7707
Areas04=1
HostName05=高级行情_上海电信2
IPAddress05=61.152.168.232
Port05=7715
Areas05=1
HostName06=高级行情_深圳电信1
IPAddress06=113.105.73.81
Port06=7721
Areas06=1
HostName07=高级行情_深圳电信2
IPAddress07=183.3.223.36
Port07=7709
Areas07=1
HostName08=高级行情_深圳电信3
IPAddress08=119.147.86.172
Port08=443
Areas08=1
HostName09=高级行情_深圳双线1
IPAddress09=112.74.31.192
Port09=443
HostName10=高级行情_深圳双线2
IPAddress10=120.77.76.11
Port10=7709
HostName11=高级行情_深圳双线3
IPAddress11=120.77.76.34
Port11=7709
HostName12=高级行情_东莞电信1
IPAddress12=113.105.142.138
Port12=7709
Areas12=1
HostName13=高级行情_东莞电信2
IPAddress13=113.105.142.139
Port13=7709
Areas13=1
HostName14=高级行情_武汉电信1
IPAddress14=119.97.185.4
Port14=7709
Areas14=1
HostName15=高级行情_武汉电信2
IPAddress15=119.97.185.16
Port15=7709
Areas15=1
HostName16=高级行情_北京联通
IPAddress16=61.135.142.90
Port16=443
Areas16=2
HostName17=高级行情_北京双线1
IPAddress17=101.200.127.219
Port17=443
HostName18=高级行情_北京双线2
IPAddress18=59.110.61.176
Port18=7709
HostName19=高级行情_济南联通
IPAddress19=123.129.245.202
Port19=80
Areas19=2
HostName20=高级行情_深圳双线4
IPAddress20=119.23.145.96
Port20=7709
HostName21=高级行情_上海双线4
IPAddress21=106.14.38.17
Port21=7709
[DSHOST]
HostNum=2
HostName01=实时全球主站1
IPAddress01=119.97.185.11
Port01=443
HostName02=实时全球主站2
IPAddress02=116.228.6.74
Port02=443
作者是直接解析的行情dll的那几个函数么，我通过wireshark抓包发现通达信PC端的连接流程要多了好多个信令，而pytdx只用了0c0218930001030003000d0001 0c0218940001030003000d0002 0c031899000120002000db0fd5d0c9ccd6a4a8af0000008fc22540130000d500c9ccbdf0d7ea00000002三个初始化语句，是咋发现只要这三个就可以了。
现在调用接口设定是首先从pytdx.hq中引入TdxHq_API这个类
from pytdx.hq import TdxHq_API
api = TdxHq_API()需要用户自己选ip地址去连接:
api.connect(ip='119.147.212.81',port=7709)并且没有热更换ip的方法
考虑一种用装饰器@重载的方法去更换和自动选择ip
维护一个可知的公共ip池,好像l1行情都是7709端口,然后通过ping的方法找到延迟最小的那个ip,写成一个函数(eg: select_ip(func)),然后在每次解析行情前都去重载这个方法:
问题在于:没有找到一个很好的适用于所有系统的ping方法:
# windows下的cmd方法,缺点是耗时比较长
def QA_web_ping(url):
ms_list = []
p = Popen(["ping", url],
stdin=PIPE, stdout=PIPE, stderr=PIPE,
shell=True)
out = p.stdout.read()
list = str(out).split('=')
for item in list:
if 'ms' in item:
ms_list.append(int(item.split('ms')[0]))
return ms_list[-1]我目前就想出来这几个解决办法,不知道 @rainx 大大有没有什么其他的解决办法?
刚才忽然想到如果下载的数据能存成zipline支持的格式，就能做成一个完整的回测系统，如果加上交易模块，自动交易就可以完美实现了，这就是中文版的zipline-live啊。请问有没有此计划？
已经开放的这些接口好像获取不到市盈率，流通股数等信息，有的话麻烦告知，谢谢！
下面是部分券商目前的TDX普通行情服务器IP，端口都是7709。目前我的网络多数都能PING通，可以参考使用于测试。
安信 | 112.95.140.74
安信 | 112.95.140.92
安信 | 112.95.140.93
安信 | 114.80.149.19
安信 | 114.80.149.21
安信 | 114.80.149.22
安信 | 114.80.149.91
安信 | 114.80.149.92
安信 | 121.14.104.60
安信 | 121.14.104.66
安信 | 123.126.133.13
安信 | 123.126.133.14
安信 | 123.126.133.21
安信 | 211.139.150.61
安信 | 59.36.5.11
广发 | 119.29.19.242
广发 | 123.138.29.107
广发 | 123.138.29.108
广发 | 124.232.142.29
广发 | 183.57.72.11
广发 | 183.57.72.12
广发 | 183.57.72.13
广发 | 183.57.72.15
广发 | 183.57.72.21
广发 | 183.57.72.22
广发 | 183.57.72.23
广发 | 183.57.72.24
广发 | 183.60.224.177
广发 | 183.60.224.178
国泰君安 | 113.105.92.100
国泰君安 | 113.105.92.101
国泰君安 | 113.105.92.102
国泰君安 | 113.105.92.103
国泰君安 | 113.105.92.104
国泰君安 | 113.105.92.99
国泰君安 | 117.34.114.13
国泰君安 | 117.34.114.14
国泰君安 | 117.34.114.15
国泰君安 | 117.34.114.16
国泰君安 | 117.34.114.17
国泰君安 | 117.34.114.18
国泰君安 | 117.34.114.20
国泰君安 | 117.34.114.27
国泰君安 | 117.34.114.30
国泰君安 | 117.34.114.31
国信 | 182.131.3.252
国信 | 183.60.224.11
国信 | 58.210.106.91
国信 | 58.63.254.216
国信 | 58.63.254.219
国信 | 58.63.254.247
海通 | 123.125.108.90
海通 | 175.6.5.153
海通 | 182.118.47.151
海通 | 182.131.3.245
海通 | 202.100.166.27
海通 | 222.161.249.156
海通 | 42.123.69.62
海通 | 58.63.254.191
海通 | 58.63.254.217
华林 | 120.55.172.97
华林 | 139.217.20.27
华林 | 202.100.166.21
华林 | 202.96.138.90
华林 | 218.106.92.182
华林 | 218.106.92.183
华林 | 220.178.55.71
华林 | 220.178.55.86
实现和wind一样的收益率前/后复权算法:
具体代码见
https://github.com/yutiansut/QUANTAXIS/blob/0.4.1/QUANTAXIS/QAFetch/QATdx.py
封装好的接口:
import QUANTAXIS as QA
QA.QA_fetch_get_stock_day('tdx',code,start,end,if_fq)
#'tdx'是通达信引擎
#if_fq 是复权字段 00 是不复权 01是前复权02 是后复权get_security_bars等方法内的start,count参数，用数据测试了下，初步理解应该是：
start：表示从现在时点反向移动几个BAR，比如今天是9-6，start=1表示获取数据最后一天是9-5
count：表示获取BAR数量，从start确定的日期时间反向计算
对分钟线也成立。
总体来说，可以这么理解：
BAR获取数据，是从当前时间对应BAR向前推（start+count）个BAR开始，count个数量的BAR。
start参数跟大家一般理解不同，翻开get_security_bars.py看了下，是不是根据通达信发包定义的？
def setParams(self, category, market, code, start, count):
if type(code) is six.text_type:
code = code.encode("utf-8")
self.category = category
values = (
0x10c,
0x01016408,
0x1c,
0x1c,
0x052d,
market,
code,
category,
1,
start,
count,
0, 0, 0 # I + I + H total 10 zero
)
pkg = struct.pack("<HIHHHH6sHHHHIIH", *values)
self.send_pkg = pkg
不知道这样理解是否正确？
建议在后续版本的文档说明下。
4 : 获取股票列表
参数：市场代码, 起始位置 如： 0,0 或 1,100
api.get_security_list(1, 0)
测试上市和深市，只能取到1000个，这个函数怎么才能取到全部的股票代码列表呢？
谢谢！
感谢您的贡献，能否提供返回字段的含义，比如行情返回的reversed_bytes，active2，查看了代码，也没找到注释。另外，是否可解析出换手率数据。
row = OrderedDict(
[
('year', year),
('month', month),
('day', day),
('datetime',datetime(year,month,day，15)),
.....
]
方便直接用于计算换手率，而不用通过year,month,day再处理。我自己添加，但是每次更新库之后，得自己改。
File "<string>", line 1, in <module>
File "/private/var/folders/5s/l2bgtyj542q0s473sr72_jc00000gp/T/pip-build-CdNGhd/pytdx/setup.py", line 44
**cythonkw,
^
SyntaxError: invalid syntax
def ping(ip):
__time1 = datetime.datetime.now()
api = TdxHq_API()
try:
with api.connect(ip, 7709):
api.get_security_bars(9, 0, '000001', 0, 1)
return float(datetime.datetime.now() - x1)
except:
return datetime.timedelta(9, 9, 0)
def select_best_ip():
listx = ['180.153.18.170', '180.153.18.171', '202.108.253.130', '202.108.253.131', '60.191.117.167', '115.238.56.198', '218.75.126.9', '115.238.90.165',
'124.160.88.183', '60.12.136.250', '218.108.98.244', '218.108.47.69', '14.17.75.71', '180.153.39.51']
data = [ping(x) for x in listx]
return listx[data.index(min(data))]请问是否支持数据推送的方式？我看到提供的接口都是请求响应的方式。
def _cal_price1000(self, base_p, diff):
return (base_p + diff)/1000
在2.7中为非真除，小数点后会被丢掉
建议1：
def _cal_price1000(self, base_p, diff):
return (float)(base_p + diff)/1000
建议2：
from future import division
tdx的k线是走bar和count的模式,不是很好用,改进了下
trade_date_sse=['1990-12-19', '1990-12-20', '1990-12-21', '1990-12-24', '1990-12-25', '1990-12-26', '1990-12-27', '1990-12-28', '1990-12-31', '1991-01-02', '1991-01-03', '1991-01-04', '1991-01-07', '1991-01-08', '1991-01-09', '1991-01-10', '1991-01-11', '1991-01-14', '1991-01-15', '1991-01-16', '1991-01-17', '1991-01-18', '1991-01-21', '1991-01-22', '1991-01-23', '1991-01-24', '1991-01-25', '1991-01-28', '1991-01-29', '1991-01-30', '1991-01-31', '1991-02-01', '1991-02-04', '1991-02-05', '1991-02-06', '1991-02-07', '1991-02-08', '1991-02-11', '1991-02-12', '1991-02-13', '1991-02-14', '1991-02-19', '1991-02-20', '1991-02-21', '1991-02-22', '1991-02-25', '1991-02-26', '1991-02-27', '1991-02-28', '1991-03-01', '1991-03-04', '1991-03-05', '1991-03-06', '1991-03-07', '1991-03-08', '1991-03-11', '1991-03-12', '1991-03-13', '1991-03-14', '1991-03-15', '1991-03-18', '1991-03-19', '1991-03-20', '1991-03-21', '1991-03-22', '1991-03-25', '1991-03-26', '1991-03-27', '1991-03-28', '1991-03-29', '1991-04-01', '1991-04-02', '1991-04-03', '1991-04-04', '1991-04-05', '1991-04-08', '1991-04-09', '1991-04-10', '1991-04-11', '1991-04-12', '1991-04-15', '1991-04-16', '1991-04-17', '1991-04-18', '1991-04-19', '1991-04-22', '1991-04-23', '1991-04-24', '1991-04-25', '1991-04-26', '1991-04-29', '1991-04-30', '1991-05-02', '1991-05-03', '1991-05-06', '1991-05-07', '1991-05-08', '1991-05-09', '1991-05-10', '1991-05-13', '1991-05-14', '1991-05-15', '1991-05-16', '1991-05-17', '1991-05-20', '1991-05-21', '1991-05-22', '1991-05-23', '1991-05-24', '1991-05-27', '1991-05-28', '1991-05-29', '1991-05-30', '1991-05-31', '1991-06-03', '1991-06-04', '1991-06-05', '1991-06-06', '1991-06-07', '1991-06-10', '1991-06-11', '1991-06-12', '1991-06-13', '1991-06-14', '1991-06-17', '1991-06-18', '1991-06-19', '1991-06-20', '1991-06-21', '1991-06-24', '1991-06-25', '1991-06-26', '1991-06-27', '1991-06-28', '1991-07-01', '1991-07-02', '1991-07-03', '1991-07-04', '1991-07-05', '1991-07-08', '1991-07-09', '1991-07-10', '1991-07-11', '1991-07-12', '1991-07-15', '1991-07-16', '1991-07-17', '1991-07-18', '1991-07-19', '1991-07-22', '1991-07-23', '1991-07-24', '1991-07-25', '1991-07-26', '1991-07-29', '1991-07-30', '1991-07-31', '1991-08-01', '1991-08-02', '1991-08-05', '1991-08-06', '1991-08-07', '1991-08-08', '1991-08-09', '1991-08-12', '1991-08-13', '1991-08-14', '1991-08-15', '1991-08-16', '1991-08-19', '1991-08-20', '1991-08-21', '1991-08-22', '1991-08-23', '1991-08-26', '1991-08-27', '1991-08-28', '1991-08-29', '1991-08-30', '1991-09-02', '1991-09-03', '1991-09-04', '1991-09-05', '1991-09-06', '1991-09-09', '1991-09-10', '1991-09-11', '1991-09-12', '1991-09-13', '1991-09-16', '1991-09-17', '1991-09-18', '1991-09-19', '1991-09-20', '1991-09-23', '1991-09-24', '1991-09-25', '1991-09-26', '1991-09-27', '1991-09-30', '1991-10-03', '1991-10-04', '1991-10-07', '1991-10-08', '1991-10-09', '1991-10-10', '1991-10-11', '1991-10-14', '1991-10-15', '1991-10-16', '1991-10-17', '1991-10-18', '1991-10-21', '1991-10-22', '1991-10-23', '1991-10-24', '1991-10-25', '1991-10-28', '1991-10-29', '1991-10-30', '1991-10-31', '1991-11-01', '1991-11-04', '1991-11-05', '1991-11-06', '1991-11-07', '1991-11-08', '1991-11-11', '1991-11-12', '1991-11-13', '1991-11-14', '1991-11-15', '1991-11-18', '1991-11-19', '1991-11-20', '1991-11-21', '1991-11-22', '1991-11-25', '1991-11-26', '1991-11-27', '1991-11-28', '1991-11-29', '1991-12-02', '1991-12-03', '1991-12-04', '1991-12-05', '1991-12-06', '1991-12-09', '1991-12-10', '1991-12-11', '1991-12-12', '1991-12-13', '1991-12-16', '1991-12-17', '1991-12-18', '1991-12-19', '1991-12-20', '1991-12-23', '1991-12-24', '1991-12-25', '1991-12-26', '1991-12-27', '1991-12-30', '1991-12-31', '1992-01-02', '1992-01-03', '1992-01-06', '1992-01-07', '1992-01-08', '1992-01-09', '1992-01-10', '1992-01-13', '1992-01-14', '1992-01-15', '1992-01-16', '1992-01-17', '1992-01-20', '1992-01-21', '1992-01-22', '1992-01-23', '1992-01-24', '1992-01-27', '1992-01-28', '1992-01-29', '1992-01-30', '1992-01-31', '1992-02-03', '1992-02-07', '1992-02-10', '1992-02-11', '1992-02-12', '1992-02-13', '1992-02-14', '1992-02-17', '1992-02-18', '1992-02-19', '1992-02-20', '1992-02-21', '1992-02-24', '1992-02-25', '1992-02-26', '1992-02-27', '1992-02-28', '1992-03-02', '1992-03-03', '1992-03-04', '1992-03-05', '1992-03-06', '1992-03-09', '1992-03-10', '1992-03-11', '1992-03-12', '1992-03-13', '1992-03-16', '1992-03-17', '1992-03-18', '1992-03-19', '1992-03-20', '1992-03-23', '1992-03-24', '1992-03-25', '1992-03-26', '1992-03-27', '1992-03-30', '1992-03-31', '1992-04-01', '1992-04-02', '1992-04-03', '1992-04-06', '1992-04-07', '1992-04-08', '1992-04-09', '1992-04-10', '1992-04-13', '1992-04-14', '1992-04-15', '1992-04-16', '1992-04-17', '1992-04-20', '1992-04-21', '1992-04-22', '1992-04-23', '1992-04-24', '1992-04-27', '1992-04-28', '1992-04-29', '1992-04-30', '1992-05-04', '1992-05-05', '1992-05-06', '1992-05-07', '1992-05-08', '1992-05-11', '1992-05-12', 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'2016-10-21', '2016-10-24', '2016-10-25', '2016-10-26', '2016-10-27', '2016-10-28', '2016-10-31', '2016-11-01', '2016-11-02', '2016-11-03', '2016-11-04', '2016-11-07', '2016-11-08', '2016-11-09', '2016-11-10', '2016-11-11', '2016-11-14', '2016-11-15', '2016-11-16', '2016-11-17', '2016-11-18', '2016-11-21', '2016-11-22', '2016-11-23', '2016-11-24', '2016-11-25', '2016-11-28', '2016-11-29', '2016-11-30', '2016-12-01', '2016-12-02', '2016-12-05', '2016-12-06', '2016-12-07', '2016-12-08', '2016-12-09', '2016-12-12', '2016-12-13', '2016-12-14', '2016-12-15', '2016-12-16', '2016-12-19', '2016-12-20', '2016-12-21', '2016-12-22', '2016-12-23', '2016-12-26', '2016-12-27', '2016-12-28', '2016-12-29', '2016-12-30', '2017-01-03', '2017-01-04', '2017-01-05', '2017-01-06', '2017-01-09', '2017-01-10', '2017-01-11', '2017-01-12', '2017-01-13', '2017-01-16', '2017-01-17', '2017-01-18', '2017-01-19', '2017-01-20', '2017-01-23', '2017-01-24', '2017-01-25', '2017-01-26', '2017-02-03', '2017-02-06', '2017-02-07', '2017-02-08', '2017-02-09', '2017-02-10', '2017-02-13', '2017-02-14', '2017-02-15', '2017-02-16', '2017-02-17', '2017-02-20', '2017-02-21', '2017-02-22', '2017-02-23', '2017-02-24', '2017-02-27', '2017-02-28', '2017-03-01', '2017-03-02', '2017-03-03', '2017-03-06', '2017-03-07', '2017-03-08', '2017-03-09', '2017-03-10', '2017-03-13', '2017-03-14', '2017-03-15', '2017-03-16', '2017-03-17', '2017-03-20', '2017-03-21', '2017-03-22', '2017-03-23', '2017-03-24', '2017-03-27', '2017-03-28', '2017-03-29', '2017-03-30', '2017-03-31', '2017-04-05', '2017-04-06', '2017-04-07', '2017-04-10', '2017-04-11', '2017-04-12', '2017-04-13', '2017-04-14', '2017-04-17', '2017-04-18', '2017-04-19', '2017-04-20', '2017-04-21', '2017-04-24', '2017-04-25', '2017-04-26', '2017-04-27', '2017-04-28', '2017-05-02', '2017-05-03', '2017-05-04', '2017-05-05', '2017-05-08', '2017-05-09', '2017-05-10', '2017-05-11', '2017-05-12', '2017-05-15', '2017-05-16', '2017-05-17', '2017-05-18', '2017-05-19', '2017-05-22', '2017-05-23', '2017-05-24', '2017-05-25', '2017-05-26', '2017-05-31', '2017-06-01', '2017-06-02', '2017-06-05', '2017-06-06', '2017-06-07', '2017-06-08', '2017-06-09', '2017-06-12', '2017-06-13', '2017-06-14', '2017-06-15', '2017-06-16', '2017-06-19', '2017-06-20', '2017-06-21', '2017-06-22', '2017-06-23', '2017-06-26', '2017-06-27', '2017-06-28', '2017-06-29', '2017-06-30', '2017-07-03', '2017-07-04', '2017-07-05', '2017-07-06', '2017-07-07', '2017-07-10', '2017-07-11', '2017-07-12', '2017-07-13', '2017-07-14', '2017-07-17', '2017-07-18', '2017-07-19', '2017-07-20', '2017-07-21', '2017-07-24', '2017-07-25', '2017-07-26', '2017-07-27', '2017-07-28', '2017-07-31', '2017-08-01', '2017-08-02', '2017-08-03', '2017-08-04', '2017-08-07', '2017-08-08', '2017-08-09', '2017-08-10', '2017-08-11', '2017-08-14', '2017-08-15', '2017-08-16', '2017-08-17', '2017-08-18', '2017-08-21', '2017-08-22', '2017-08-23', '2017-08-24', '2017-08-25', '2017-08-28', '2017-08-29', '2017-08-30', '2017-08-31', '2017-09-01', '2017-09-04', '2017-09-05', '2017-09-06', '2017-09-07', '2017-09-08', '2017-09-11', '2017-09-12', '2017-09-13', '2017-09-14', '2017-09-15', '2017-09-18', '2017-09-19', '2017-09-20', '2017-09-21', '2017-09-22', '2017-09-25', '2017-09-26', '2017-09-27', '2017-09-28', '2017-09-29', '2017-10-09', '2017-10-10', '2017-10-11', '2017-10-12', '2017-10-13', '2017-10-16', '2017-10-17', '2017-10-18', '2017-10-19', '2017-10-20', '2017-10-23', '2017-10-24', '2017-10-25', '2017-10-26', '2017-10-27', '2017-10-30', '2017-10-31', '2017-11-01', '2017-11-02', '2017-11-03', '2017-11-06', '2017-11-07', '2017-11-08', '2017-11-09', '2017-11-10', '2017-11-13', '2017-11-14', '2017-11-15', '2017-11-16', '2017-11-17', '2017-11-20', '2017-11-21', '2017-11-22', '2017-11-23', '2017-11-24', '2017-11-27', '2017-11-28', '2017-11-29', '2017-11-30', '2017-12-01', '2017-12-04', '2017-12-05', '2017-12-06', '2017-12-07', '2017-12-08', '2017-12-11', '2017-12-12', '2017-12-13', '2017-12-14', '2017-12-15', '2017-12-18', '2017-12-19', '2017-12-20', '2017-12-21', '2017-12-22', '2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28']import numpy as np
import pandas as pd
from pytdx.hq import TdxHq_API
from QUANTAXIS.QAUtil import (QA_util_date_valid, QA_util_log_info,
QA_util_web_ping, trade_date_sse)
import datetime
def QA_fetch_get_stock_day(code, start_date,end_date,ip='119.147.212.81',port=7709):
with api.connect(ip, port):
# 判断end_date在哪个位置
index_0=str(datetime.date.today())
index_of_index_0=trade_date_sse.index(index_0)
index_of_index_end=trade_date_sse.index(end_date)
index_of_index_start=trade_date_sse.index(start_date)
index_of_end=index_of_index_0-index_of_index_end
index_length=index_of_index_end+1-index_of_index_start
data = api.get_security_bars(9, 0, code,index_of_end, index_length) # 返回普通list
if __name__=='__main__':
print(QA_fetch_get_stock_day('000001','2017-07-03','2017-07-10'))PS C:\Users\yutiansut\quantaxis> & python c:/Users/yutiansut/quantaxis/QUANTAXIS/QAFetch/QATdx.py
QUANTAXIS>> start QUANTAXIS
QUANTAXIS>> ip:127.0.0.1 port:27017
QUANTAXIS>> Server initialized for gevent.
QUANTAXIS>> Welcome to QUANTAXIS, the Version is 0.4.0-alpha-dev08
[OrderedDict([('open', 9.4), ('close', 9.4), ('high', 9.43), ('low', 9.34), ('vol', 388349.0), ('amount', 364465856.0), ('year', 2017), ('month', 7), ('day', 3), ('hour', 15), ('minute', 0),
('datetime', '2017-07-03 15:00')]), OrderedDict([('open', 9.4), ('close', 9.34), ('high', 9.41), ('low', 9.3), ('vol', 488362.0), ('amount', 456577024.0), ('year', 2017), ('month', 7), ('day'
, 4), ('hour', 15), ('minute', 0), ('datetime', '2017-07-04 15:00')]), OrderedDict([('open', 9.29), ('close', 9.37), ('high', 9.38), ('low', 9.27), ('vol', 567720.0), ('amount', 529294112.0),
('year', 2017), ('month', 7), ('day', 5), ('hour', 15), ('minute', 0), ('datetime', '2017-07-05 15:00')]), OrderedDict([('open', 9.36), ('close', 9.4), ('high', 9.41), ('low', 9.31), ('vol',
738911.0), ('amount', 691387200.0), ('year', 2017), ('month', 7), ('day', 6), ('hour', 15), ('minute', 0), ('datetime', '2017-07-06 15:00')]), OrderedDict([('open', 9.37), ('close', 9.47), (
'high', 9.48), ('low', 9.34), ('vol', 760369.0), ('amount', 717084352.0), ('year', 2017), ('month', 7), ('day', 7), ('hour', 15), ('minute', 0), ('datetime', '2017-07-07 15:00')]), OrderedDic
t([('open', 9.45), ('close', 9.59), ('high', 9.66), ('low', 9.44), ('vol', 1360815.0), ('amount', 1303090048.0), ('year', 2017), ('month', 7), ('day', 10), ('hour', 15), ('minute', 0), ('date
time', '2017-07-10 15:00')])]
PS C:\Users\yutiansut\quantaxis>感谢提供该项目，相当好。只是我在下载试用的时候，发现无法获取到5档行情，我对tdx协议不了解，无法解决，请帮忙
由于夜盘算一个新的交易日的开始，今天是9月11日，通达信返回的是2017-09-12 21:00:00，对于股票软件这样处理没有问题，但程序量化时通常需要resample成30分钟线或者其他周期数据，或者set_index，这样数据会被resort，夜盘数据会放到2017-09-12 15:00:00的数据后面。
涉及各种数据接口，比如get_minute_time_data，get_history_minute_time_data，get_transaction_data，get_history_transaction_data
建议对于夜盘数据，把返回的时间减去一天，周五的夜盘因为会按下周一的交易日，所以需要减3天
既然可以读行情，应该也可以用于交易吧？
get_history_transaction_data（）只能获取最多2000行分笔数据，9:30后的数据基本没有办法获取到。
rt
能否提供类似 通达信->系统->盘后数据下载->日线/分钟线下载 功能的API,
而无需开启通达信软件
试了大豆和铁矿的数据，好像不同品种数据的转换公式还不一样！
market_code = 1 if str(code)[0] == '6' else 0;
start_date = get_real_trade_date(start_date, 1)
end_date = get_real_trade_date(end_date, -1)
data = []
for i in range(10):
data += self.get_security_bars(9, market_code, code, (9 - i) * 800, 800)
我推荐如下一些时序数据库:
tstables(HDF5)
https://github.com/mysl/tstables
/https://github.com/afiedler/tstables
A Python package to store time series data in HDF5 files using PyTables.
It stores time series data into daily partitions and provides functions to query for subsets of data across partitions.
HDF5做的time series data,以Day为分区(分块)存储数据,并能够跨区(分块)访问数据
尤其适合于存储:TICK数据/1MIN数据/...
InfluxDB
http://www.algotrader.com/algotrader-3-1-integrates-influxdb/
We’re pleased to announce that AlgoTrader release 3.1 will use InfluxDB for storage of live and historical market data.
InfluxDB is an open source database specialized in handling time series data with high availability and high performance requirements. It’s the world’s top ranked time series database, according to the DB-Engines Ranking of Time Series DBMS.
Benefits of InfluxDB include:
Easy installation with zero dependencies, yet highly extensible
Store up to one million values per second
Time-centric functions with a simple SQL-like query language
Supports data tagging for flexible queries
Highly efficient compression to reduce storage footprint
Supports a range of high-availability and clustering schemes
Market data is the foundation upon which quantitative trading strategies are built. It’s often necessary to store and process vast amounts of historical data to back test and run strategies. InfluxDB simplifies this process and enables AlgoTrader to deliver the following new features:
Record live tick data (one million ticks per second)
Storage of downloaded historical data
Automatically store end-of-day market data
Speed up back tests by up to 200%
Store fundamental data
Real-time aggregation of tick data into bar data
Replay historical data for back testing
During our benchmark testing, InfluxDB easily handled the storage of 100 billion ticks on a simple laptop.
MongoDB
这个就不多说了,比较常用
时间 开盘价 最高价 最低价 收盘价 持仓 成交 结算
20160804 3166.199951 3178.000000 3145.000000 3177.199951 30489 10262 3171.600098
20160805 3181.000000 3209.800049 3166.199951 3193.000000 30331 10937 3191.399902
20160808 3190.000000 3224.399902 3168.199951 3223.000000 30029 11751 3216.600098
20160809 3221.000000 3247.000000 3215.000000 3244.000000 29372 10838 3239.000000
20160810 3244.199951 3252.199951 3231.000000 3234.199951 28266 8585 3235.600098
20160811 3231.600098 3269.600098 3226.000000 3226.199951 27821 12119 3237.600098
请求格式如下
[01 01 08 6A 01 01 16 00 16 00 FF 23 4A 4E 56 44 41 00 00 00 00 13 09 00 01 00 00 00 00 00 F0 00]
感觉回复格式经过了压缩
能否加微信讨论 mi-fox
解析/存储数据到数据库(HDF5/InfluxDB/MongoDB)中
1.指定板块代码,生成一个相对应股票集合的API
2.指定股票,列出对应板块的API
参考文档:
通达信行业、板块与自定义指数
http://blog.sina.com.cn/s/blog_623d2d280102vt8y.html
React
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Typescript
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Django
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Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
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Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
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D3
Data-Driven Documents codes.
Tencent
China tencent open source team.