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qmt更加详细的教程方法#xff0c;会持续慢慢梳理。
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获取日线行情数…qmt编程之获取期货行情数据
qmt更加详细的教程方法会持续慢慢梳理。
也可找寻博主的历史文章搜索关键词查看解决方案
感谢关注咨询免费开通量化回测与获取实盘权限欢迎和博主联系
获取日线行情数据
示例
from xtquant import xtdata
xtdata.get_market_data_ex([],[rb2401.SF],period1d)返回值
# 返回结果
{rb2401.SF: time open high low close volume \20230117 1673884800000 4001.0 4027.0 3973.0 4011.0 1038 20230118 1673971200000 4027.0 4051.0 4014.0 4037.0 314 20230119 1674057600000 4043.0 4085.0 4043.0 4080.0 352 20230120 1674144000000 4075.0 4076.0 4050.0 4070.0 502 20230130 1675008000000 4127.0 4157.0 4080.0 4084.0 992 ... ... ... ... ... ... ... 20231017 1697472000000 3658.0 3672.0 3637.0 3647.0 1068036 20231018 1697558400000 3652.0 3660.0 3605.0 3615.0 1361935 20231019 1697644800000 3615.0 3650.0 3595.0 3644.0 1313338 20231020 1697731200000 3650.0 3659.0 3601.0 3610.0 1418587 20231023 1697990400000 3600.0 3616.0 3558.0 3573.0 1513440 amount settelementPrice openInterest preClose suspendFlag 20230117 4.148817e07 3996.0 573 4061.0 0 20230118 1.267393e07 4036.0 713 4011.0 0 20230119 1.431537e07 4066.0 821 4037.0 0 20230120 2.040859e07 4065.0 944 4080.0 0 20230130 4.090941e07 4123.0 1201 4070.0 0 ... ... ... ... ... ... 20231017 3.900789e10 3652.0 1870289 3657.0 0 20231018 4.950385e10 3634.0 1951142 3647.0 0 20231019 4.759753e10 3624.0 1886883 3615.0 0 20231020 5.149242e10 3629.0 1880167 3644.0 0 20231023 5.423026e10 0.0 1961524 3610.0 0 [183 rows x 11 columns]}
获取tick行情
示例
from xtquant import xtdata
xtdata.get_market_data_ex([],[rb2401.SF],periodtick)
返回值
time lastPrice open high low lastClose amount volume pvolume stockStatus openInt lastSettlementPrice askPrice bidPrice askVol bidVol settlementPrice transactionNum
20230925085900 1695603540500 3778.0 3786.0 3787.0 3766.0 3779.0 1.291532e10 341961 0 0 1651554 3773.0 [3779.0, 3780.0, 3781.0, 3782.0, 3783.0] [3777.0, 3776.0, 3775.0, 3774.0, 3773.0] [635, 0, 0, 0, 0] [138, 0, 0, 0, 0] 0.0 0
20230925090000 1695603600500 3779.0 3786.0 3787.0 3766.0 3779.0 1.296989e10 343405 0 0 1652373 3773.0 [3780.0, 3781.0, 3782.0, 3783.0, 3784.0] [3778.0, 3777.0, 3776.0, 3775.0, 3774.0] [916, 0, 0, 0, 0] [168, 0, 0, 0, 0] 0.0 0
20230925090001 1695603601000 3780.0 3786.0 3787.0 3766.0 3779.0 1.307600e10 346211 0 0 1651646 3773.0 [3787.0, 3788.0, 3789.0, 3790.0, 3791.0] [3779.0, 3778.0, 3777.0, 3776.0, 3775.0] [420, 0, 0, 0, 0] [20, 0, 0, 0, 0] 0.0 0
20230925090001 1695603601500 3783.0 3786.0 3787.0 3766.0 3779.0 1.309460e10 346703 0 0 1651496 3773.0 [3784.0, 3785.0, 3786.0, 3787.0, 3788.0] [3776.0, 3775.0, 3774.0, 3773.0, 3772.0] [46, 0, 0, 0, 0] [89, 0, 0, 0, 0] 0.0 0
20230925090002 1695603602000 3783.0 3786.0 3787.0 3766.0 3779.0 1.312842e10 347597 0 0 1651258 3773.0 [3784.0, 3785.0, 3786.0, 3787.0, 3788.0] [3782.0, 3781.0, 3780.0, 3779.0, 3778.0] [41, 0, 0, 0, 0] [7, 0, 0, 0, 0] 0.0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
20230928145958 1695884398500 3690.0 3690.0 3717.0 3684.0 3682.0 3.781059e10 1021634 0 0 1697198 3684.0 [3690.0, 0.0, 0.0, 0.0, 0.0] [3690.0, 0.0, 0.0, 0.0, 0.0] [54, 0, 0, 0, 0] [126, 0, 0, 0, 0] 0.0 0
20230928145959 1695884399000 3690.0 3690.0 3717.0 3684.0 3682.0 3.781148e10 1021658 0 0 1697179 3684.0 [3690.0, 0.0, 0.0, 0.0, 0.0] [3690.0, 0.0, 0.0, 0.0, 0.0] [20, 0, 0, 0, 0] [112, 0, 0, 0, 0] 0.0 0
20230928145959 1695884399500 3690.0 3690.0 3717.0 3684.0 3682.0 3.781395e10 1021725 0 0 1697158 3684.0 [3690.0, 0.0, 0.0, 0.0, 0.0] [3690.0, 0.0, 0.0, 0.0, 0.0] [20, 0, 0, 0, 0] [46, 0, 0, 0, 0] 0.0 0
20230928150000 1695884400000 3690.0 3690.0 3717.0 3684.0 3682.0 3.781502e10 1021754 0 0 1697143 3684.0 [3690.0, 0.0, 0.0, 0.0, 0.0] [3690.0, 0.0, 0.0, 0.0, 0.0] [10, 0, 0, 0, 0] [63, 0, 0, 0, 0] 0.0 0
20230928150000 1695884400500 3690.0 3690.0 3717.0 3684.0 3682.0 3.781502e10 1021754 0 0 1697143 3684.0 [3690.0, 0.0, 0.0, 0.0, 0.0] [3690.0, 0.0, 0.0, 0.0, 0.0] [10, 0, 0, 0, 0] [63, 0, 0, 0, 0] 3700.0 0
149943 rows × 18 columns
