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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/labellers/fixed_time_three_class_balanced.py
+9 -5
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@@ -1,16 +1,20 @@
from data_loader.types import ForwardReturnSeries
from data_loader.types import ReturnSeries, ForwardReturnSeries
from ..types import EventLabeller, EventsDataFrame
import pandas as pd
from .utils import create_forward_returns
class FixedTimeHorionThreeClassBalancedEventLabeller(EventLabeller):
time_horizon: int
def __init__(self, time_horizon: int = 1):
def __init__(self, time_horizon: int):
self.time_horizon = time_horizon
def label_events(self, event_start_times: pd.DatetimeIndex, forward_returns: ForwardReturnSeries) -> EventsDataFrame:
def label_events(self, event_start_times: pd.DatetimeIndex, returns: ReturnSeries) -> tuple[EventsDataFrame, ForwardReturnSeries]:
forward_returns = create_forward_returns(returns, self.time_horizon)
cutoff_point = returns.index[-self.time_horizon]
event_start_times[event_start_times < cutoff_point]
event_candidates = forward_returns[event_start_times]
def get_bins_threeway(x):
@@ -35,10 +39,10 @@ class FixedTimeHorionThreeClassBalancedEventLabeller(EventLabeller):
return 1
labels = event_candidates.map(map_class_threeway)
return pd.DataFrame({
return (pd.DataFrame({
'start': event_start_times,
'end': event_start_times + pd.Timedelta(days=self.time_horizon),
'label': labels,
'returns': forward_returns[event_start_times]
})
}), forward_returns[event_start_times])