Parsing QMT local data with python
Get local market data
get_local_data(field_list=[], stock_code=[], period='1d', start_time='', end_time='', count=-1,dividend_type='none', fill_data=True, data_dir=data_dir)paraphrase
Get the market data from the local data file, which is used to quickly obtain the market data of the historical part in batches
parameter
field_list - list data field list, if empty, all fields
stock_list - list contract code list
period - string period
start_time - string start time
end_time - string end time
count - the number of int data
dividend_type - string Dividend method
fill_data - bool Whether to fill in the missing data backwards
data_dir - string Userdata_mini path of MiniQmt companion path, used to read data files directly. By default, xtdata will directly obtain this path from MiniQmt through the connection, no additional settings are required. If you need to adjust, you can pass the data path as data_dir, or you can directly modify xtdata.data_dir to change the default value
return
When the period is 1m 5m 1dK line period
return dict { field1 : value1, field2 : value2, ... }
field1, field2, ... : data fields
value1, value2, ... : pd.DataFrame dataset, index is stock_list, columns is time_list
The DataFrame corresponding to each field has the same dimension and the same index
When period is the tick period
return dict { stock1 : value1, stock2 : value2, ... }
stock1, stock2, ... : contract code
value1, value2, ... : np.ndarray data set, arranged in ascending order of data timestamp time
Remark
Only used to get level1 data
call demo
from xtquant import xtdata
import pandas as pd
import datetime,time
df=xtdata.get_local_data(field_list=['time','open','high','low','close','volume','amount'],stock_code=['600050.SH'],count=10)
df2=pd.concat([df[i].loc['600050.SH'].T for i in['time','open','high','low','close','volume','amount']],axis=1)
df2.columns=['time','open','high','low','close','volume','amount']
df2['time2']=df2.index
df2['time']=df2['time2'].apply(lambda x: datetime.datetime.fromtimestamp(int(x/1000.0)))
df2.reset_index(drop=True,inplace=True)
print(df2)output
time open high low close volume amount time2
0 2023-01-09 4.98 5.01 4.88 4.98 3246943 1.608308e+09 1673193600000
1 2023-01-10 4.95 5.02 4.90 4.98 2571948 1.277204e+09 1673280000000
2 2023-01-11 4.97 5.12 4.93 4.96 3127401 1.572082e+09 1673366400000
3 2023-01-12 4.94 5.14 4.92 5.11 3870406 1.952001e+09 1673452800000
4 2023-01-13 5.07 5.14 4.99 5.06 3227926 1.631200e+09 1673539200000
5 2023-01-16 5.06 5.10 4.98 5.02 2944490 1.483375e+09 1673798400000
6 2023-01-17 4.99 5.04 4.94 4.95 2094947 1.043011e+09 1673884800000
7 2023-01-18 4.95 5.12 4.91 5.07 3392366 1.713247e+09 1673971200000
8 2023-01-19 5.04 5.18 5.02 5.17 4324306 2.205093e+09 1674057600000
9 2023-01-20 5.19 5.62 5.17 5.53 7470132 4.101551e+09 1674144000000