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Online serving V11

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This commit is contained in:
lzh222333
2021-05-13 09:43:42 +00:00
parent 370b6aad74
commit d71a666904
13 changed files with 130 additions and 100 deletions
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31
qlib/workflow/online/strategy.py
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@@ -44,28 +44,23 @@ class OnlineStrategy:
"""
raise NotImplementedError(f"Please implement the `prepare_tasks` method.")
def prepare_online_models(self, models, cur_time=None, check_func=None) -> List[object]:
def prepare_online_models(self, models, cur_time=None) -> List[object]:
"""
A typically implementation, but maybe you will need old models by online_tool.
Select some models as the online models from the trained models.
Select some models from trained models and set them to online models.
This is a typically implementation to online all trained models, you can override it to implement complex method.
You can find last online models by OnlineTool.online_models if you still need them.
NOTE: This method offline all models and online the online models prepared by this method (if have). So you can find last online models by OnlineTool.online_models if you still need them.
NOTE: Reset all online models to trained model. If there is no trained models, then do nothing.
Args:
tasks (list): a list of tasks.
check_func: the method to judge if a model can be online.
The parameter is the model record and return True for online.
None for online every models.
models (list): a list of models.
cur_time (pd.Dataframe): current time from OnlineManger. None for latest.
Returns:
List[object]: a list of selected models.
List[object]: a list of online models.
"""
if check_func is not None:
online_models = []
for model in models:
if check_func(model, cur_time):
online_models.append(model)
models = online_models
if not models:
return self.tool.online_models()
self.tool.reset_online_tag(models)
return models
@@ -89,10 +84,10 @@ class OnlineStrategy:
raise NotImplementedError(f"Please implement the `get_collector` method.")
class RollingAverageStrategy(OnlineStrategy):
class RollingStrategy(OnlineStrategy):
"""
This example strategy always use latest rolling model as online model and prepare trading signals using the average prediction of online models
This example strategy always use latest rolling model as online model.
"""
def __init__(
@@ -102,7 +97,7 @@ class RollingAverageStrategy(OnlineStrategy):
rolling_gen: RollingGen,
):
"""
Init RollingAverageStrategy.
Init RollingStrategy.
Assumption: the str of name_id, the experiment name and the trainer's experiment name are same one.