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update some little code

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bxdd
2021-01-26 17:02:30 +00:00
parent 1b569d371d
commit 3bdd54308b
3 changed files with 25 additions and 22 deletions
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examples/high_freq/workflow.py
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@@ -38,12 +38,12 @@ if __name__ == "__main__":
MARKET = "all"
BENCHMARK = "SH000300"
DROP_LOAD_DATASET = False # flag wether to test [drop and load dataset]
DROP_LOAD_DATASET = False # flag wether to test [drop and load dataset]
#start_time = "2019-01-01 00:00:00"
#end_time = "2019-12-31 15:00:00"
#train_end_time = "2019-05-31 15:00:00"
#test_start_time = "2019-06-01 00:00:00"
# start_time = "2019-01-01 00:00:00"
# end_time = "2019-12-31 15:00:00"
# train_end_time = "2019-05-31 15:00:00"
# test_start_time = "2019-06-01 00:00:00"
start_time = "2020-09-14 00:00:00"
end_time = "2021-01-18 16:00:00"
train_end_time = "2020-11-30 16:00:00"
@@ -108,11 +108,12 @@ if __name__ == "__main__":
},
}
##=============load the calendar for cache=============
Cal.calendar(freq="1min")
Cal.get_calendar_day(freq="1min")
# unnecessary, but may accelerate
Cal.calendar(freq="1min") # load the calendar for cache
Cal.get_calendar_day(freq="1min") # load the calendar for cache
##=============get data=============
dataset = init_instance_by_config(task["dataset"])
xtrain, xtest = dataset.prepare(["train", "test"])
print(xtrain, xtest)
@@ -124,7 +125,7 @@ if __name__ == "__main__":
del xtrain, xtest
del backtest_train, backtest_test
## example to show how to save the dataset and reload it, and how to use different data
if DROP_LOAD_DATASET:
##=============dump dataset=============
@@ -147,6 +148,7 @@ if __name__ == "__main__":
dataset_backtest.init(init_type=DataHandlerLP.IT_LS)
##=============reinit qlib=============
## Unless you want to modify the provider_uri and other configurations, reinit is unnecessary
qlib.init(
provider_uri=provider_uri,
custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull],
@@ -158,7 +160,7 @@ if __name__ == "__main__":
Cal.calendar(freq="1min") # load the calendar for cache
Cal.get_calendar_day(freq="1min") # load the calendar for cache
##=============test dataset
##=============test dataset=============
xtrain, xtest = dataset.prepare(["train", "test"])
backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"])