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feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

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* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba

* fix(WalkForward): inference mini-batch parallelization

* fix(WalkForward): don't use the parallel version of any of the functions

* feat(CI): download the data required

* fix(Project): 5min_crypto folder added

* fix(Evaluate): make sure we have numerical stability in returns

* feat(Models): use SKLearn models directly to enable composability

* feat(Inference): batched inference now working, added forecasting_horizon

* fix(Inference): works again

* fix(Inference)

* chore(Models): remove unused Ensemble model

* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then

* Update test.yml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 16:36:35 +01:00
committed by GitHub
parent 5c94af8b01
commit 9d47ee942d
52 changed files with 470 additions and 628 deletions
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labeling/process.py
+14 -12
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@@ -2,16 +2,18 @@ from .types import EventFilter, EventLabeller, EventsDataFrame
from data_loader.types import ForwardReturnSeries, XDataFrame, ReturnSeries, ySeries
def label_data(
event_filter: EventFilter,
event_labeller: EventLabeller,
X: XDataFrame,
returns: ReturnSeries,
forward_returns: ForwardReturnSeries) -> tuple[EventsDataFrame, XDataFrame, ySeries, ForwardReturnSeries]:
event_start_times = event_filter.get_event_start_times(returns)
events = event_labeller.label_events(event_start_times, forward_returns)
X = X.filter(items = events.index, axis = 0)
y = events['label']
forward_returns = events['returns']
event_filter: EventFilter,
event_labeller: EventLabeller,
X: XDataFrame,
returns: ReturnSeries) -> tuple[EventsDataFrame, XDataFrame, ySeries, ForwardReturnSeries]:
return events, X, y, forward_returns
event_start_times = event_filter.get_event_start_times(returns)
print("| Filtered out ", (1 - (len(event_start_times) / len(returns))) * 100, "% of timestamps" )
events, forward_returns = event_labeller.label_events(event_start_times, returns)
X = X.filter(items = events.index, axis = 0)
y = events['label']
forward_returns = events['returns']
return events, X, y, forward_returns