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fix bug

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This commit is contained in:
bxdd
2021-01-26 07:32:06 +00:00
parent 06dbd02b99
commit 6a145df87c
11 changed files with 118 additions and 71 deletions
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8
examples/high_freq/highfreq_handler.py
View File

@@ -29,8 +29,8 @@ class HighFreqHandler(DataHandlerLP):
new_l.append(p)
return new_l
infer_processors = []
learn_processors = []
infer_processors = check_transform_proc(infer_processors)
learn_processors = check_transform_proc(learn_processors)
data_loader = {
"class": "QlibDataLoader",
@@ -179,8 +179,6 @@ class HighFreqBacktestHandler(DataHandler):
end_time=None,
freq="1min",
):
infer_processors = check_transform_proc(infer_processors)
learn_processors = check_transform_proc(learn_processors)
data_loader = {
"class": "QlibDataLoader",
"kwargs": {
@@ -207,7 +205,7 @@ class HighFreqBacktestHandler(DataHandler):
fields += [
template_fillnan.format(template_paused.format("$close")),
]
names += ["$close0"]
names += ["$vwap0"]
fields += [
"If(Eq({1}, np.nan), 0, If(Or(Gt({2}, Mul(1.001, {4})), Lt({2}, Mul(0.999, {3}))), 0, {1}))".format(
template_fillnan.format(template_paused.format("$close")),

6
examples/high_freq/highfreq_ops.py
View File

@@ -11,7 +11,7 @@ class DayFirst(ElemOperator):
super(DayFirst, self).__init__(feature, "day_first")
def _load_internal(self, instrument, start_index, end_index, freq):
_calendar = Cal.get_calender_day(freq=freq)[0]
_calendar = Cal.get_calendar_day(freq=freq)[0]
series = self.feature.load(instrument, start_index, end_index, freq)
return series.groupby(_calendar[series.index]).transform("first")
@@ -21,7 +21,7 @@ class DayLast(ElemOperator):
super(DayLast, self).__init__(feature, "day_last")
def _load_internal(self, instrument, start_index, end_index, freq):
_calendar = Cal.get_calender_day(freq=freq)[0]
_calendar = Cal.get_calendar_day(freq=freq)[0]
series = self.feature.load(instrument, start_index, end_index, freq)
return series.groupby(_calendar[series.index]).transform("last")
@@ -40,7 +40,7 @@ class Date(ElemOperator):
super(Date, self).__init__(feature, "date")
def _load_internal(self, instrument, start_index, end_index, freq):
_calendar = Cal.get_calender_day(freq=freq)[0]
_calendar = Cal.get_calendar_day(freq=freq)[0]
series = self.feature.load(instrument, start_index, end_index, freq)
return pd.Series(_calendar[series.index], index=series.index)

18
examples/high_freq/highfreq_processor.py
View File

@@ -1,7 +1,6 @@
import numpy as np
import pandas as pd
from qlib.data.dataset.processor import Processor
from qlib.log import TimeInspector
from qlib.data.dataset.utils import fetch_df_by_index
@@ -11,8 +10,9 @@ class HighFreqNorm(Processor):
self.fit_end_time = fit_end_time
def fit(self, df_features):
fetch_df = fetch_df_by_index(df, slice(self.fit_start_time, self.fit_end_time), level="datetime")
del df
print("==============fit==============")
fetch_df = fetch_df_by_index(df_features, slice(self.fit_start_time, self.fit_end_time), level="datetime")
del df_features
df_values = fetch_df.values
names = {
"price": slice(0, 10),
@@ -23,17 +23,18 @@ class HighFreqNorm(Processor):
self.feature_vmax = {}
self.feature_vmin = {}
for name, name_val in names.items():
part_values = df_values[:, name_val]
part_values = df_values[:, name_val].astype(np.float32)
if name == "volume":
df_features.loc(axis=1)[name_val] = np.log1p(part_values)
part_values = np.log1p(part_values)
self.feature_med[name] = np.nanmedian(part_values)
part_values = part_values - self.feature_med # mean, copy
part_values = part_values - self.feature_med[name] # mean, copy
self.feature_std[name] = np.nanmedian(np.absolute(part_values)) * 1.4826 + 1e-12
part_values = part_values / self.feature_std
part_values = part_values / self.feature_std[name]
self.feature_vmax[name] = np.nanmax(part_values)
self.feature_vmin[name] = np.nanmin(part_values)
def __call__(self, df_features):
print("==============call==============")
df_features.set_index("date", append=True, drop=True, inplace=True)
df_values = df_features.values
names = {
@@ -58,13 +59,12 @@ class HighFreqNorm(Processor):
part_values[slice3] = -3.5
# print("start_call_feature_reshape")
idx = df_features.index.droplevel("datetime").drop_duplicates()
idx.set_names(['instrument', 'datetime'], inplace=True)
feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240)
feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240)
df_new_features = pd.DataFrame(
data=np.concatenate((feat, feat_1), axis=1),
index=idx,
columns=["FEATURE_%d" % i for i in range(12 * 240)],
).sort_index()
return df_new_features

94
examples/high_freq/workflow.py
View File

@@ -73,31 +73,36 @@ if __name__ == "__main__":
qlib.init(
provider_uri=provider_uri,
custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull],
redis_port=233,
redis_port=-1,
region=REG_CN,
auto_mount=False,
)
MARKET = "csi300"
MARKET = "test_10"
BENCHMARK = "SH000300"
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"
###################################
# train model
###################################
DATA_HANDLER_CONFIG0 = {
"start_time": "2017-01-01 00:00:00",
"end_time": "2020-11-30 15:00:00",
"start_time": start_time,
"end_time": end_time,
"freq": "1min",
"fit_start_time": "2017-01-01 00:00:00",
"fit_end_time": "2020-08-31 15:00:00",
"instruments": "all",
"fit_start_time": start_time,
"fit_end_time": train_end_time,
"instruments": MARKET,
"infer_processors": [{"class": "HighFreqNorm", "module_path": "highfreq_processor", "kwargs": {}}],
}
DATA_HANDLER_CONFIG1 = {
"start_time": "2017-01-01 00:00:00",
"end_time": "2020-11-30 15:00:00",
"start_time": start_time,
"end_time": end_time,
"freq": "1min",
"instruments": "all",
"instruments": MARKET,
}
task = {
@@ -111,10 +116,10 @@ if __name__ == "__main__":
"kwargs": DATA_HANDLER_CONFIG0,
},
"segments": {
"train": ("2017-01-01 00:00:00", "2020-08-31 15:00:00"),
"train": (start_time, train_end_time),
"test": (
"2020-09-01 00:00:00",
"2020-11-30 15:00:00",
test_start_time,
end_time,
),
},
},
@@ -127,19 +132,72 @@ if __name__ == "__main__":
"kwargs": {
"handler": {
"class": "HighFreqBacktestHandler",
"module_path": "highfreq_hander",
"module_path": "highfreq_handler",
"kwargs": DATA_HANDLER_CONFIG1,
},
"segments": {
"train": ("2017-01-01 00:00:00", "2020-08-31 15:00:00"),
"train": (start_time, train_end_time),
"test": (
"2020-09-01 00:00:00",
"2020-11-30 15:00:00",
test_start_time,
end_time,
),
},
},
},
}
Cal.get_calender_day(freq="1min") # TO FIX: load the calendar day for cache
##=============load the calendar for cache=============
Cal.calendar(freq="1min")
Cal.get_calendar_day(freq="1min")
##=============get data=============
dataset = init_instance_by_config(task["dataset"])
dataset_backtest = init_instance_by_config(task["dataset_backtest"])
xtrain, xtest = dataset.prepare(['train', 'test'])
backtest_train, backtest_test = dataset_backtest.prepare(['train', 'test'])
print(xtrain, xtest)
print(backtest_train, backtest_test)
del xtrain, xtest
del backtest_train, backtest_test
##=============dump dataset=============
dataset.to_pickle(path="dataset.pkl")
dataset_backtest.to_pickle(path="dataset_backtest.pkl")
del dataset, dataset_backtest
##=============reload dataset=============
file_dataset = open("dataset.pkl", "rb")
dataset = pickle.load(file_dataset)
file_dataset.close()
file_dataset_backtest = open("dataset_backtest.pkl", "rb")
dataset_backtest = pickle.load(file_dataset_backtest)
file_dataset_backtest.close()
##=============reload_dataset=============
dataset.init(init_type=DataHandlerLP.IT_LS)
dataset_backtest.init(init_type=DataHandlerLP.IT_LS)
##=============reinit qlib=============
qlib.init(
provider_uri=provider_uri,
custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull],
redis_port=-1,
region=REG_CN,
auto_mount=False,
)
Cal.calendar(freq="1min") #load the calendar for cache
Cal.get_calendar_day(freq="1min") #load the calendar for cache
##=============test dataset
xtrain, xtest = dataset.prepare(['train', 'test'])
backtest_train, backtest_test = dataset_backtest.prepare(['train', 'test'])
print(xtrain, xtest)
print(backtest_train, backtest_test)
del xtrain, xtest
del backtest_train, backtest_test

2
examples/workflow_by_code.py
View File

@@ -30,7 +30,7 @@ if __name__ == "__main__":
GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
qlib.init(provider_uri=provider_uri, region=REG_CN)
qlib.init(provider_uri=provider_uri, region=REG_CN, redis_port=233)
market = "csi300"
benchmark = "SH000300"

4
qlib/config.py
View File

@@ -291,12 +291,12 @@ class QlibConfig(Config):
def register(self):
from .utils import init_instance_by_config
from .data.ops import register_custom_ops
from .data.ops import register_all_ops
from .data.data import register_all_wrappers
from .workflow import R, QlibRecorder
from .workflow.utils import experiment_exit_handler
register_custom_ops(self)
register_all_ops(self)
register_all_wrappers(self)
# set up QlibRecorder
exp_manager = init_instance_by_config(self["exp_manager"])

2
qlib/data/data.py
View File

@@ -123,7 +123,7 @@ class CalendarProvider(abc.ABC):
H["c"][flag] = _calendar, _calendar_index
return _calendar, _calendar_index
def get_calender_day(self, freq="day", future=False):
def get_calendar_day(self, freq="day", future=False):
flag = f"{freq}_future_{future}_day"
if flag in H["c"]:
_calendar, _calendar_index = H["c"][flag]

36
qlib/data/dataset/__init__.py
View File

@@ -87,34 +87,16 @@ class DatasetH(Dataset):
"""
super().__init__(handler, segments)
def init(self, init_type: str = DataHandlerLP.IT_FIT_SEQ, enable_cache: bool = False):
"""
Initialize the data of Qlib
def init(self, **kwargs):
Parameters
----------
init_type : str
- if `init_type` == DataHandlerLP.IT_FIT_SEQ:
the input of `DataHandlerLP.fit` will be the output of the previous processor
- if `init_type` == DataHandlerLP.IT_FIT_IND:
the input of `DataHandlerLP.fit` will be the original df
- if `init_type` == DataHandlerLP.IT_LS:
The state of the object has been load by pickle
enable_cache : bool
default value is false:
- if `enable_cache` == True:
the processed data will be saved on disk, and handler will load the cached data from the disk directly
when we call `init` next time
"""
self.handler.init(init_type=init_type, enable_cache=enable_cache)
logger = get_module_logger("DatasetH")
handler_init_kwargs = {}
for arg_key, arg_value in kwargs.items():
if arg_key in getfullargspec(self.handler.init).args:
handler_init_kwargs[arg_key] = arg_value
else:
logger.info(f"init arguments[{arg_key}] is ignored.")
self.handler.init(**handler_init_kwargs)
def setup_data(self, handler: Union[dict, DataHandler], segments: list):
"""

2
qlib/data/dataset/handler.py
View File

@@ -433,6 +433,8 @@ class DataHandlerLP(DataHandler):
except AttributeError:
print("please set drop_raw = False if you want to use raw data")
raise
except:
raise
return df
def fetch(

1
qlib/data/dataset/loader.py
View File

@@ -147,7 +147,6 @@ class QlibDataLoader(DLWParser):
"""
self.filter_pipe = filter_pipe
self.swap_level = swap_level
print("swap level", swap_level)
super().__init__(config)
def load_group_df(

16
qlib/data/ops.py
View File

@@ -17,11 +17,13 @@ from ..log import get_module_logger
try:
from ._libs.rolling import rolling_slope, rolling_rsquare, rolling_resi
from ._libs.expanding import expanding_slope, expanding_rsquare, expanding_resi
except ImportError as err:
except ImportError:
print(
"#### Do not import qlib package in the repository directory in case of importing qlib from . without compiling #####"
)
raise
except:
raise
np.seterr(invalid="ignore")
@@ -1451,6 +1453,9 @@ class OpsWrapper(object):
def __init__(self):
self._ops = {}
def reset(self):
self._ops = {}
def register(self, ops_list):
for operator in ops_list:
if not issubclass(operator, ExpressionOps):
@@ -1469,12 +1474,15 @@ class OpsWrapper(object):
Operators = OpsWrapper()
Operators.register(OpsList)
def register_custom_ops(C):
"""register custom operator"""
def register_all_ops(C):
"""register all operator"""
logger = get_module_logger("ops")
Operators.reset()
Operators.register(OpsList)
if getattr(C, "custom_ops", None) is not None:
Operators.register(C.custom_ops)
logger.debug("register custom operator {}".format(C.custom_ops))