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Published
20 hours ago •
rainx
rainx
api.GetSecurityQuotes 中返回的几个保留字段reversed_bytes中能不能提取出日期信息呀
数据没有日期，但有时间 reversed_bytes0 请参考
OrderedDict([('market', 0), ('code', '000001'), ('active1', 4322), ('price', 10.32), ('last_close', 10.55), ('open', 10.53), ('high', 10.58), ('low', 10.31), ('reversed_bytes0 ', 14999269), ('reversed_bytes1', -1032), ('vol', 700358), ('cur_vol', 11212), ('amount', 730667648.0), ('s_vol', 395083), ('b_vol', 305275), ('reversed_bytes2', -1), ('revers ed_bytes3', 7961), ('bid1', 10.32), ('ask1', 10.33), ('bid_vol1', 3143), ('ask_vol1', 4526), ('bid2', 10.31), ('ask2', 10.34), ('bid_vol2', 7046), ('ask_vol2', 2052), ('bid3', 10.3), ('ask3', 10.35), ('bid_vol3', 14425), ('ask_vol3', 2227), ('bid4', 10.29), ('ask4', 10.36), ('bid_vol4', 1237), ('ask_vol4', 2586), ('bid5', 10.28), ('ask5', 10.37), ( 'bid_vol5', 2470), ('ask_vol5', 1223), ('reversed_bytes4', (1018,)), ('reversed_bytes5', 1), ('reversed_bytes6', -29), ('reversed_bytes7', -19), ('reversed_bytes8', 18), ('rev ersed_bytes9', 0.0), ('active2', 4322)])
--
reversed_bytes0 ', 14999269 TIME：14:59:57.368
def format_time(time_stamp):
time = time_stamp[:-6] + ':'
if int(time_stamp[-6:-4]) < 60:
time += '%s:' % time_stamp[-6:-4]
time += '%06.3f' % (
int(time_stamp[-4:]) * 60 / 10000.0
)
else:
time += '%02d:' % (
int(time_stamp[-6:]) * 60 / 1000000
)
time += '%06.3f' % (
(int(time_stamp[-6:]) * 60 % 1000000) * 60 / 1000000.0
)
return time
format_time('%' % reversed_bytes0)
创建了相关的分支 https://github.com/rainx/pytdx/tree/feautre/add-time-column-to-get-security-quotes 如果测试没有问题就合并到主干
我测了下 这个time的时间戳 相较于本地时间差了20秒左右 @rainx @liuyug
In [19]: QA.QA_fetch_get_stock_realtime('tdx','000004')
Out[19]:
servertime active1 active2 last_close open high low price cur_vol s_vol b_vol vol ... ask3 ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:03:01.644348 000004 10:02:48.024 281 281 19.95 19.49 20.26 19.3 19.97 34 931 1866 2797 ... 20.12 43 19.93 2 20.13 33 19.88 5 20.14 2 19.87
2
[1 rows x 32 columns]
In [20]: QA.QA_fetch_get_stock_realtime('tdx','000002')
Out[20]:
servertime active1 active2 last_close open high low price cur_vol s_vol b_vol vol ... ask3 ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:03:09.164440 000002 10:02:47.814 663 663 26.96 26.46 26.56 26.2 26.32 100 85183 79588 164771 ... 26.34 112 26.29 468 26.35 944 26.28 206 26.36 34 26.27
146
[1 rows x 32 columns]
In [21]: QA.QA_fetch_get_stock_realtime('tdx','600010')
Out[21]:
servertime active1 active2 last_close open high low price cur_vol s_vol b_vol vol ... ask3 ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:03:15.266577 600010 10:03:06.306 536 536 1.5 1.49 1.5 1.48 1.5 619 174595 125949 300543 ... 1.52 171529 1.47 144620 1.53 111224 1.46 94821 1.54 51345 1.45 60267
[1 rows x 32 columns]
应该是解析方法的问题 @liuyug @rainx
In [1]: import QUANTAXIS as QA
In [2]: QA.QA_fetch_get_stock_realtime('tdx','600010')
QUANTAXIS>> Selecting the Best Server IP of TDX
USING DEFAULT STOCK IP
USING DEFAULT FUTURE IP
QUANTAXIS>> === The BEST SERVER ===
stock_ip 180.153.18.170 future_ip 47.107.75.159
Out[2]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol b_vol ... ask3 ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:05:40.518 600010 10:05:20.712 10053452 568 568 1.5 1.49 1.5 1.48 1.49 1 177039 127252 ... 1.52 171840 1.47 144961 1.53 111396 1.46 94841 1.54 51601 1.45 64545
[1 rows x 33 columns]
In [3]: QA.QA_fetch_get_stock_realtime('tdx','000002')
Out[3]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:05:45.864312 000002 10:05:09.378 10051563 715 715 26.96 26.46 26.56 26.2 26.38 7 87222 ... 274 26.34 544 26.4 572 26.33 81 26.41 88 26.32 41
[1 rows x 33 columns]
In [4]: QA.QA_fetch_get_stock_realtime('tdx','000004')
Out[4]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5 datetime code ...
2019-08-26 10:05:49.411807 000004 10:05:16.800 10052800 297 297 19.95 19.49 20.26 19.3 20.01 9 960 ... 7 19.96 17 20.12 43 19.95 14 20.13 34 19.93 2
[1 rows x 33 columns]
In [5]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[5]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5 datetime code ...
2019-08-26 10:05:53.146422 000001 10:05:22.068 10053678 718 718 14.65 14.42 14.5 14.17 14.23 90 308615 ... 1329 14.21 4306 14.27 227 14.2 7107 14.28 1185 14.19 4695
[1 rows x 33 columns]
多次获取同一个股票的实时 reverse_byte0 只出现了非常微小的变化
In [8]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[8]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:07:50.171591 000001 10:07:20.292 10073382 757 757 14.65 14.42 14.5 14.17 14.19 385 329319 ... 1177 14.17 2984 14.23 762 14.16 3944 14.24 235 14.15 7473
[1 rows x 33 columns]
In [9]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[9]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:07:50.484442 000001 10:07:20.292 10073382 757 757 14.65 14.42 14.5 14.17 14.19 385 329319 ... 1177 14.17 2984 14.23 762 14.16 3944 14.24 235 14.15 7473
[1 rows x 33 columns]
In [10]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[10]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:07:50.717429 000001 10:07:20.292 10073382 757 757 14.65 14.42 14.5 14.17 14.19 385 329319 ... 1177 14.17 2984 14.23 762 14.16 3944 14.24 235 14.15 7473
[1 rows x 33 columns]
In [11]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[11]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:07:51.123309 000001 10:07:20.292 10073382 757 757 14.65 14.42 14.5 14.17 14.19 385 329319 ... 1177 14.17 2984 14.23 762 14.16 3944 14.24 235 14.15 7473
[1 rows x 33 columns]
In [12]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[12]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:07:56.646339 000001 10:07:23.898 10073983 759 759 14.65 14.42 14.5 14.17 14.18 1391 335775 ... 2024 14.16 3924 14.22 1161 14.15 7480 14.23 757 14.14 2492
[1 rows x 33 columns]
In [13]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[13]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:07:59.992633 000001 10:07:25.698 10074283 760 760 14.65 14.42 14.5 14.17 14.18 108 335883 ... 2024 14.16 3924 14.22 1161 14.15 7479 14.23 757 14.14 2492
[1 rows x 33 columns]
并且可以看出 reversed_bytes0 active1 active2 这三个字段关系非常密切
怀疑active 是今天的交易的tick数?
In [23]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[23]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:10:24.763325 000001 10:10:04.092 10100682 808 808 14.65 14.42 14.5 14.15 14.19 266 354675 ... 286 14.17 1478 14.24 12 14.16 3574 14.25 475 14.15 6484
[1 rows x 33 columns]
In [24]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[24]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:10:25.715401 000001 10:10:05.886 10100981 809 809 14.65 14.42 14.5 14.15 14.2 7 354679 ... 286 14.18 1677 14.24 12 14.17 1492 14.25 475 14.16 3619
[1 rows x 33 columns]
In [25]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[25]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:10:28.359810 000001 10:10:07.680 10101280 810 810 14.65 14.42 14.5 14.15 14.21 1905 354679 ... 649 14.17 1505 14.23 286 14.16 3649 14.24 12 14.15 6581
[1 rows x 33 columns]
In [26]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[26]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:10:29.809632 000001 10:10:07.680 10101280 810 810 14.65 14.42 14.5 14.15 14.21 1905 354679 ... 649 14.17 1505 14.23 286 14.16 3649 14.24 12 14.15 6581
[1 rows x 33 columns]
In [27]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[27]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:10:30.736140 000001 10:10:07.680 10101280 810 810 14.65 14.42 14.5 14.15 14.21 1905 354679 ... 649 14.17 1505 14.23 286 14.16 3649 14.24 12 14.15 6581
[1 rows x 33 columns]
In [28]: QA.QA_fetch_get_stock_realtime('tdx','000001')
Out[28]:
servertime reversed_bytes0 active1 active2 last_close open high low price cur_vol s_vol ... ask_vol3 bid3 bid_vol3 ask4 ask_vol4 bid4 bid_vol4 ask5 ask_vol5 bid5 bid_vol5
datetime code ...
2019-08-26 10:10:32.136020 000001 10:10:09.480 10101580 811 811 14.65 14.42 14.5 14.15 14.21 271 354679 ... 286 14.17 1504 14.24 12 14.16 3649 14.25 487 14.15 6585
[1 rows x 33 columns]
刚和天总沟通了一下，发现暂时还没有数据的“数据产生时间”字段。希望有这个字段提供出来。
目前增加的这个servertime字段(06b17ccf4c452)是服务器的当前时间（大致等于接口调用的时间），并不是数据产生时间。没有这个时间值，我们就要靠别的手段来判断行情的实际产生时间了。
刚和天总沟通了一下，发现暂时还没有数据的“数据产生时间”字段。希望有这个字段提供出来。
目前增加的这个
servertime字段(06b17cc)是服务器的当前时间（大致等于接口调用的时间），并不是数据产生时间。没有这个时间值，我们就要靠别的手段来判断行情的实际产生时间了。
新增的这个servertime就代表了数据产生的时间时间，比如连续调用get_security_quotes，第一次返回的servertime=1，第二次返回的值servertime的值仍然=1，就说明你调用的频次太快了（实际行情盘口数据可能3秒才刷新一次，而你通过代码调用可能两次间隔不到1秒钟），两次返回的数据是一样的。
刚和天总沟通了一下，发现暂时还没有数据的“数据产生时间”字段。希望有这个字段提供出来。 目前增加的这个
servertime字段(06b17cc)是服务器的当前时间（大致等于接口调用的时间），并不是数据产生时间。没有这个时间值，我们就要靠别的手段来判断行情的实际产生时间了。新增的这个servertime就代表了数据产生的时间时间，比如连续调用get_security_quotes，第一次返回的servertime=1，第二次返回的值servertime的值仍然=1，就说明你调用的频次太快了（实际行情盘口数据可能3秒才刷新一次，而你通过代码调用可能两次间隔不到1秒钟），两次返回的数据是一样的。 哦，这样啊，明白了。多谢多谢。