qmt编程之获取股票订单流数据
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获取股票订单流数据
获取股票在某个价位的订单数量
提示
1.该数据通过get_market_data和get_market_data_ex接口获取，period参数选择orderflow1m 或者 orderflow1d
2.获取历史数据前需要先用download_history_data下载历史数据，订单流数据仅提供orderflow1m周期数据下载，其他周期的订单流数据都是通过1m周期合成的
3.订单流版 权限数据
原生pytrhon
python
from xtquant import xtdata
# 订单流数据仅提供1m周期数据下载，其他周期的订单流数据都是通过1m周期合成的
period = "orderflow1m"
# 下载000001.SZ的1m订单流数据
xtdata.download_history_data("000001.SZ",period=period)
# 获取000001.SZ的1m订单流数据
xtdata.get_market_data_ex([],["000001.SZ"],period=period)["000001.SZ"]
参数
| 名称 | 类型 | 描述 |
|---|---|---|
field | list | 数据字段，详情见下方field字段表 |
stock_list | list | 合约代码列表 |
period | str | 订单流数据周期——orderflow1m, orderflow5m, orderflow15m, orderflow30m, orderflow1h, orderflow1d |
start_time | str | 数据起始时间，格式为 %Y%m%d 或 %Y%m%d%H%M%S，填""为获取历史最早一天 |
end_time | str | 数据结束时间，格式为 %Y%m%d 或 %Y%m%d%H%M%S ，填""为截止到最新一天 |
count | int | 数据个数 |
dividend_type | str | 除权方式 |
fill_data | bool | 是否填充数据 |
field字段可选：
| field | 数据类型 | 含义 |
|---|---|---|
time | str | 时间 |
price | str | 价格段 |
buyNum | str | 各价格对应的买方订单量 |
sellNum | str | 各价格对应的卖方订单量 |
period字段可选:
| period | 数据类型 | 含义 |
|---|---|---|
orderflow1m | str | 1m周期订单流数据 |
orderflow5m | str | 5m周期订单流数据 |
orderflow15m | str | 15m周期订单流数据 |
orderflow30m | str | 30m周期订单流数据 |
orderflow1h | str | 1h周期订单流数据 |
orderflow1d | str | 1d周期订单流数据 |
返回值 返回一个 {stock_code:pd.DataFrame} 结构的dict对象，默认的列索引为取得的全部字段. 如果给定了 fields 参数, 则列索引与给定的 fields 对应.
示例
示例
# 下载000001.SZ的orderflow1m，以获取历史数据
# orderflow仅提供1m周期进行下载，其他周期皆在系统底层通过1m订单流数据进行合成给出
xtdata.download_history_data("000001.SZ",period="orderflow1m")
# 获取000001.SZ，1m订单流数据
period = "orderflow1m"
data1 = xtdata.get_market_data_ex([],["000001.SZ"],period=period)["000001.SZ"]
# 获取000001.SZ, 5m订单流数据
period = "orderflow5m"
data2 = xtdata.get_market_data_ex([],["000001.SZ"],period=period)["000001.SZ"]
# 获取000001.SZ 1d订单流数据
period = "orderflow1d"
data3 = xtdata.get_market_data_ex([],["000001.SZ"],period=period)["000001.SZ"]
# 订阅实时000001.SZ 1m订单流数据
period = "orderflow1m"
# 进行数据订阅
xtdata.subscribe_quote("000001.SZ", period = period)
# 获取订阅后的实时数据
data4 = xtdata.get_market_data_ex([],["000001.SZ"],period=period)["000001.SZ"]
print(data1)
print(data2)
print(data3)
print(data4)data1返回值
time price buyNum sellNum
20230324093000 1679621400000 [12.85] [4230] [0]
20230324093100 1679621460000 [12.790000000000001, 12.8, 12.81, 12.82, 12.83... [888, 453, 769, 2536, 0, 1854, 1722] [837, 3372, 1525, 6121, 575, 3324, 0]
20230324093200 1679621520000 [12.77, 12.780000000000001, 12.790000000000001... [0, 3267, 5211, 318] [1843, 1505, 3051, 197]
20230324093300 1679621580000 [12.780000000000001, 12.790000000000001, 12.8] [0, 5552, 107] [3990, 1539, 0]
20230324093400 1679621640000 [12.8, 12.81] [889, 1728] [852, 1611]
... ... ... ... ...
20231026134900 1698299340000 [10.36, 10.370000000000001, 10.38] [0, 255, 353] [15, 140, 0]
20231026135000 1698299400000 [10.370000000000001, 10.38] [0, 596] [3106, 0]
20231026135100 1698299460000 [10.370000000000001, 10.38] [0, 608] [175, 0]
20231026135200 1698299520000 [10.370000000000001, 10.38] [0, 944] [667, 0]
20231026135300 1698299580000 [10.370000000000001, 10.38] [0, 160] [106, 0]
34396 rows × 4 columnsdata2返回值
time price buyNum sellNum
20230324093500 1679621700000 [12.77, 12.780000000000001, 12.790000000000001... [0, 3267, 11651, 1767, 4135, 3092, 0, 1854, 5952] [1843, 5495, 5427, 4580, 4744, 6121, 575, 3324...
20230324094000 1679622000000 [12.81, 12.82, 12.83, 12.84, 12.85, 12.86] [3515, 603, 4610, 5587, 3346, 158] [3358, 2884, 4953, 1099, 61, 0]
20230324094500 1679622300000 [12.790000000000001, 12.8, 12.81, 12.82, 12.83... [0, 322, 3573, 526, 604, 935, 1270] [964, 11150, 2242, 4940, 1407, 517, 0]
20230324095000 1679622600000 [12.77, 12.780000000000001, 12.790000000000001... [935, 11904, 119, 754, 2892] [6065, 6067, 4771, 5898, 0]
20230324095500 1679622900000 [12.780000000000001, 12.790000000000001, 12.8,... [300, 1229, 6217, 197] [739, 4098, 858, 0]
... ... ... ... ...
20231026110500 1698289500000 [10.32, 10.33, 10.34] [0, 1318, 264] [3, 9260, 0]
20231026111000 1698289800000 [10.33, 10.34] [0, 1880] [4062, 0]
20231026111500 1698290100000 [10.33, 10.34] [0, 1965] [1729, 0]
20231026112000 1698290400000 [10.33, 10.34, 10.35, 10.36] [0, 1414, 5373, 257] [1309, 2367, 775, 0]
20231026112500 1698290700000 [10.33, 10.34, 10.35] [0, 1077, 258] [487, 499, 0]
6839 rows × 4 columnsdata3返回值
time price buyNum sellNum
20230324000000 1679587200000 [12.77, 12.780000000000001, 12.790000000000001... [935, 17170, 22882, 27895, 62600, 53273, 39324... [8938, 27896, 31737, 80764, 68784, 68695, 2731...
20230327000000 1679846400000 [12.47, 12.48, 12.49, 12.5, 12.51, 12.52, 12.5... [0, 8792, 4885, 4997, 50228, 57248, 31828, 348... [915, 24135, 25945, 30326, 82575, 40025, 32308...
20230328000000 1679932800000 [12.55, 12.56, 12.57, 12.58, 12.59, 12.6, 12.6... [0, 2411, 2096, 8403, 17269, 13652, 30554, 201... [2002, 5320, 11049, 10937, 16325, 26177, 26658...
20230329000000 1680019200000 [12.52, 12.530000000000001, 12.540000000000001... [0, 5689, 49134, 29969, 16598, 15290, 23969, 1... [16122, 54360, 33434, 13624, 30877, 22648, 264...
20230330000000 1680105600000 [12.41, 12.42, 12.43, 12.44, 12.45000000000000... [0, 19093, 24669, 16814, 9488, 7165, 9891, 109... [7093, 37216, 34430, 13969, 12035, 11947, 1369...
... ... ... ... ...
20231020000000 1697731200000 [10.52, 10.53, 10.540000000000001, 10.55, 10.5... [419, 13251, 17713, 12059, 6547, 14152, 17650,... [5527, 2180, 5684, 4222, 8746, 20424, 22532, 4...
20231023000000 1697990400000 [10.43, 10.44, 10.450000000000001, 10.46, 10.4... [0, 11496, 18358, 23063, 24492, 14307, 7609, 2... [11067, 15592, 21853, 16322, 26661, 14717, 256...
20231024000000 1698076800000 [10.44, 10.450000000000001, 10.46, 10.47, 10.4... [0, 7838, 11767, 11598, 10783, 8160, 7532, 223... [6030, 15551, 17457, 7944, 12948, 3154, 17360,...
20231025000000 1698163200000 [10.36, 10.370000000000001, 10.38, 10.39, 10.4... [0, 30043, 48101, 93420, 77355, 58783, 34336, ... [15876, 59255, 135796, 82676, 96175, 51600, 32...
20231026000000 1698249600000 [10.31, 10.32, 10.33, 10.34, 10.35, 10.36, 10.... [2314, 3430, 13070, 30194, 45518, 29091, 40124... [16564, 3579, 42438, 42624, 26508, 26492, 1297...
143 rows × 4 columnsdata4返回值
time price buyNum sellNum
20230324093000 1679621400000 [12.85] [4230] [0]
20230324093100 1679621460000 [12.790000000000001, 12.8, 12.81, 12.82, 12.83... [888, 453, 769, 2536, 0, 1854, 1722] [837, 3372, 1525, 6121, 575, 3324, 0]
20230324093200 1679621520000 [12.77, 12.780000000000001, 12.790000000000001... [0, 3267, 5211, 318] [1843, 1505, 3051, 197]
20230324093300 1679621580000 [12.780000000000001, 12.790000000000001, 12.8] [0, 5552, 107] [3990, 1539, 0]
20230324093400 1679621640000 [12.8, 12.81] [889, 1728] [852, 1611]
... ... ... ... ...
20231026134100 1698298860000 [10.36, 10.370000000000001] [0, 11] [44, 0]
20231026134200 1698298920000 [10.36, 10.370000000000001] [0, 206] [86, 0]
20231026134300 1698298980000 [10.36, 10.370000000000001] [0, 0] [78, 0]
20231026134400 1698299040000 [10.36, 10.370000000000001] [0, 33] [291, 0]
20231026134500 1698299100000 [10.36] [0] [14]