请　[注册]　或　[登录]　 | 返回主站

量化交易吧 /  源码分享 帖子：3365773 新帖：5

通过1m行情推导5m,15m,30m等行情数据

有事您说话发表于：7 月 10 日 12：00回复(1)

思路：通过1m行情推导5m,15m,30m等行情数据
方法：用python 读取1M到 dataframe 里，然后resample
目的：减少聚宽本地化数据存储

df = get_price('000001.XSHE', start_date='2019-07-09', end_date='2019-07-10', frequency='1m')
df
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
open close high low volume money
2019-07-09 09:31:00 13.63 13.67 13.68 13.63 1143400.0 15596355.0
2019-07-09 09:32:00 13.67 13.69 13.70 13.67 783800.0 10727583.0
2019-07-09 09:33:00 13.69 13.68 13.71 13.68 540900.0 7405717.0
2019-07-09 09:34:00 13.68 13.69 13.70 13.68 411500.0 5632185.0
2019-07-09 09:35:00 13.70 13.68 13.70 13.68 486100.0 6654323.0
2019-07-09 09:36:00 13.68 13.67 13.69 13.67 272800.0 3731108.0
2019-07-09 09:37:00 13.66 13.67 13.69 13.66 171300.0 2342662.0
2019-07-09 09:38:00 13.67 13.68 13.68 13.66 282000.0 3853614.0
2019-07-09 09:39:00 13.68 13.66 13.68 13.66 168600.0 2304859.0
2019-07-09 09:40:00 13.66 13.65 13.67 13.65 203900.0 2785913.0
2019-07-09 09:41:00 13.65 13.63 13.65 13.62 261800.0 3569102.0
2019-07-09 09:42:00 13.63 13.63 13.64 13.61 273500.0 3727157.0
2019-07-09 09:43:00 13.64 13.64 13.65 13.64 170800.0 2331083.0
2019-07-09 09:44:00 13.65 13.64 13.65 13.62 315700.0 4306803.0
2019-07-09 09:45:00 13.63 13.60 13.64 13.60 313100.0 4260555.0
2019-07-09 09:46:00 13.60 13.63 13.63 13.60 134100.0 1825819.0
2019-07-09 09:47:00 13.62 13.59 13.63 13.59 385700.0 5247569.0
2019-07-09 09:48:00 13.59 13.58 13.59 13.58 245100.0 3329665.0
2019-07-09 09:49:00 13.57 13.59 13.60 13.57 226800.0 3081024.0
2019-07-09 09:50:00 13.59 13.60 13.60 13.56 169400.0 2300557.0
2019-07-09 09:51:00 13.59 13.61 13.61 13.59 453900.0 6171954.0
2019-07-09 09:52:00 13.60 13.60 13.60 13.59 364400.0 4955388.0
2019-07-09 09:53:00 13.60 13.60 13.62 13.60 356800.0 4853578.0
2019-07-09 09:54:00 13.60 13.60 13.61 13.59 100500.0 1366567.0
2019-07-09 09:55:00 13.61 13.61 13.62 13.60 121900.0 1659043.0
2019-07-09 09:56:00 13.61 13.62 13.62 13.60 116400.0 1584391.0
2019-07-09 09:57:00 13.62 13.64 13.65 13.62 145100.0 1977713.0
2019-07-09 09:58:00 13.65 13.65 13.65 13.64 133200.0 1817718.0
2019-07-09 09:59:00 13.65 13.64 13.65 13.63 135200.0 1844973.0
2019-07-09 10:00:00 13.65 13.64 13.65 13.63 99500.0 1357421.0
... ... ... ... ... ... ...
2019-07-09 14:31:00 13.58 13.58 13.59 13.58 117200.0 1592258.0
2019-07-09 14:32:00 13.59 13.59 13.59 13.58 113800.0 1545852.0
2019-07-09 14:33:00 13.58 13.59 13.60 13.58 100400.0 1364174.0
2019-07-09 14:34:00 13.58 13.57 13.59 13.57 67700.0 919268.0
2019-07-09 14:35:00 13.58 13.57 13.59 13.57 177800.0 2414611.0
2019-07-09 14:36:00 13.57 13.57 13.58 13.57 99800.0 1354903.0
2019-07-09 14:37:00 13.57 13.57 13.58 13.57 94300.0 1280156.0
2019-07-09 14:38:00 13.57 13.57 13.58 13.57 109200.0 1482393.0
2019-07-09 14:39:00 13.57 13.57 13.58 13.57 64700.0 878053.0
2019-07-09 14:40:00 13.57 13.56 13.58 13.56 75700.0 1026956.0
2019-07-09 14:41:00 13.56 13.56 13.57 13.56 83300.0 1129664.0
2019-07-09 14:42:00 13.56 13.57 13.57 13.56 89300.0 1211393.0
2019-07-09 14:43:00 13.56 13.56 13.56 13.55 221100.0 2998109.0
2019-07-09 14:44:00 13.56 13.58 13.58 13.55 176600.0 2394734.0
2019-07-09 14:45:00 13.58 13.55 13.58 13.55 320100.0 4338364.0
2019-07-09 14:46:00 13.54 13.54 13.55 13.54 202800.0 2746928.0
2019-07-09 14:47:00 13.55 13.55 13.56 13.54 154000.0 2086493.0
2019-07-09 14:48:00 13.55 13.56 13.57 13.55 96500.0 1308327.0
2019-07-09 14:49:00 13.57 13.56 13.58 13.55 175400.0 2378805.0
2019-07-09 14:50:00 13.55 13.55 13.56 13.55 73100.0 991180.0
2019-07-09 14:51:00 13.56 13.57 13.58 13.56 229800.0 3117588.0
2019-07-09 14:52:00 13.56 13.58 13.58 13.56 208000.0 2822832.0
2019-07-09 14:53:00 13.57 13.58 13.58 13.55 83700.0 1135359.0
2019-07-09 14:54:00 13.57 13.56 13.59 13.56 422900.0 5741235.0
2019-07-09 14:55:00 13.56 13.57 13.58 13.55 198400.0 2690498.0
2019-07-09 14:56:00 13.56 13.56 13.57 13.56 158100.0 2143902.0
2019-07-09 14:57:00 13.56 13.56 13.57 13.55 112600.0 1527141.0
2019-07-09 14:58:00 13.56 13.56 13.56 13.56 5000.0 67842.0
2019-07-09 14:59:00 13.56 13.56 13.56 13.56 0.0 0.0
2019-07-09 15:00:00 13.59 13.59 13.59 13.59 1986400.0 26995176.0

240 rows × 6 columns

df.resample('5min',closed='right').sum()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
open close high low volume money
2019-07-09 09:30:00 68.37 68.41 68.49 68.34 3365700.0 46016163.0
2019-07-09 09:35:00 68.35 68.33 68.41 68.30 1098600.0 15018156.0
2019-07-09 09:40:00 68.20 68.14 68.23 68.09 1334900.0 18194700.0
2019-07-09 09:45:00 67.97 67.99 68.05 67.90 1161100.0 15784634.0
2019-07-09 09:50:00 68.00 68.02 68.06 67.97 1397500.0 19006530.0
2019-07-09 09:55:00 68.18 68.19 68.22 68.12 629400.0 8582216.0
2019-07-09 10:00:00 68.08 68.11 68.17 68.05 586200.0 7982295.0
2019-07-09 10:05:00 68.05 68.01 68.09 67.95 788000.0 10721514.0
2019-07-09 10:10:00 67.99 67.98 68.05 67.93 933100.0 12682380.0
2019-07-09 10:15:00 67.98 67.96 68.01 67.94 624300.0 8486493.0
2019-07-09 10:20:00 68.01 68.01 68.05 67.99 710300.0 9661509.0
2019-07-09 10:25:00 67.92 67.91 67.98 67.85 1331100.0 18078771.0
2019-07-09 10:30:00 67.86 67.86 67.92 67.81 876900.0 11896633.0
2019-07-09 10:35:00 67.86 67.81 67.87 67.77 511200.0 6931454.0
2019-07-09 10:40:00 67.83 67.82 67.87 67.75 865000.0 11725203.0
2019-07-09 10:45:00 67.74 67.74 67.77 67.70 618500.0 8376086.0
2019-07-09 10:50:00 67.55 67.54 67.60 67.48 2608200.0 35182527.0
2019-07-09 10:55:00 67.43 67.45 67.49 67.39 1253000.0 16887068.0
2019-07-09 11:00:00 67.44 67.44 67.48 67.37 2468900.0 33246169.0
2019-07-09 11:05:00 67.50 67.53 67.57 67.48 773300.0 10438367.0
2019-07-09 11:10:00 67.54 67.52 67.64 67.46 769800.0 10401557.0
2019-07-09 11:15:00 67.43 67.41 67.47 67.38 545100.0 7349661.0
2019-07-09 11:20:00 67.37 67.37 67.42 67.32 735600.0 9911370.0
2019-07-09 11:25:00 67.41 67.40 67.47 67.37 604700.0 8156699.0
2019-07-09 11:30:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 11:35:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 11:40:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 11:45:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 11:50:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 11:55:00 0.00 0.00 0.00 0.00 0.0 0.0
... ... ... ... ... ... ...
2019-07-09 12:30:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 12:35:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 12:40:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 12:45:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 12:50:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 12:55:00 0.00 0.00 0.00 0.00 0.0 0.0
2019-07-09 13:00:00 67.47 67.48 67.53 67.40 964000.0 13002987.0
2019-07-09 13:05:00 67.71 67.74 67.77 67.67 695700.0 9422106.0
2019-07-09 13:10:00 67.73 67.73 67.77 67.70 422500.0 5723518.0
2019-07-09 13:15:00 67.73 67.70 67.75 67.65 672500.0 9103390.0
2019-07-09 13:20:00 67.58 67.59 67.64 67.54 816100.0 11031583.0
2019-07-09 13:25:00 67.60 67.61 67.64 67.57 433100.0 5854292.0
2019-07-09 13:30:00 67.60 67.62 67.65 67.55 451200.0 6099373.0
2019-07-09 13:35:00 67.67 67.61 67.72 67.57 477500.0 6457281.0
2019-07-09 13:40:00 67.60 67.62 67.67 67.54 1855600.0 25107706.0
2019-07-09 13:45:00 67.59 67.62 67.68 67.50 617700.0 8348572.0
2019-07-09 13:50:00 67.55 67.53 67.60 67.46 751800.0 10149509.0
2019-07-09 13:55:00 67.66 67.69 67.72 67.62 435800.0 5898325.0
2019-07-09 14:00:00 67.78 67.82 67.88 67.76 1122900.0 15234098.0
2019-07-09 14:05:00 67.84 67.83 67.90 67.78 1992400.0 27018603.0
2019-07-09 14:10:00 67.91 67.92 67.97 67.86 1458000.0 19815209.0
2019-07-09 14:15:00 67.96 68.00 68.03 67.93 739000.0 10046584.0
2019-07-09 14:20:00 68.06 68.00 68.08 67.95 1596700.0 21740864.0
2019-07-09 14:25:00 67.95 67.96 67.99 67.90 1319400.0 17935345.0
2019-07-09 14:30:00 67.91 67.90 67.96 67.88 576900.0 7836163.0
2019-07-09 14:35:00 67.85 67.84 67.90 67.84 443700.0 6022461.0
2019-07-09 14:40:00 67.82 67.82 67.86 67.77 890400.0 12072264.0
2019-07-09 14:45:00 67.76 67.76 67.82 67.73 701800.0 9511733.0
2019-07-09 14:50:00 67.82 67.86 67.91 67.78 1142800.0 15507512.0
2019-07-09 14:55:00 67.83 67.83 67.85 67.82 2262100.0 30734061.0

66 rows × 6 columns

       

全部回复

0/140

量化课程

    移动端课程