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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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run_inference.py
+8 -17
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@@ -8,9 +8,8 @@ from config.presets import get_dev_config, get_default_ensemble_config, get_ligh
from labeling.process import label_data
import pandas as pd
from training.directional_training import train_directional_models
from training.bet_sizing import bet_sizing_with_meta_models
from training.ensemble import ensemble_weights
from training.directional_training import train_directional_model
from training.bet_sizing import bet_sizing_with_meta_model
from training.types import PipelineOutcome
def run_inference(preload_models:bool, fallback_raw_config: RawConfig):
@@ -26,7 +25,7 @@ def run_inference(preload_models:bool, fallback_raw_config: RawConfig):
def __inference(config: Config, pipeline_outcome: PipelineOutcome):
# 1. Load data, check for validity and process data
X, returns, forward_returns = load_data(
X, returns = load_data(
assets = config.assets,
other_assets = config.other_assets,
exogenous_data = config.exogenous_data,
@@ -38,26 +37,18 @@ def __inference(config: Config, pipeline_outcome: PipelineOutcome):
)
assert check_data(X, config) == True, "Data is not valid. Cancelling Inference."
events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns, forward_returns)
# 2. Filter for significant events when we want to trade, and label data
events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns)
inference_from: pd.Timestamp = X.index[len(X.index) - 1]
# 2. Filter for significant events when we want to trade, and label data
events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns, forward_returns)
# 3. Train directional models
directional_training_outcome = train_directional_models(X, y, forward_returns, config, config.directional_models, from_index = inference_from, preloaded_training_step = pipeline_outcome.directional_training)
directional_training_outcome = train_directional_model(X, y, forward_returns, config, config.directional_model, from_index = inference_from, preloaded_training_step = pipeline_outcome.directional_training)
# 4. Run bet sizing on primary model's output
bet_sizing_outcomes = [bet_sizing_with_meta_models(X, training_outcome.predictions, y, forward_returns, config.meta_models, config, 'meta', from_index = inference_from, transformations_over_time = preloaded_outcome.meta_transformations, preloaded_models = [b.model_over_time for b in preloaded_outcome.meta_training]) for training_outcome, preloaded_outcome in zip(directional_training_outcome.training, pipeline_outcome.bet_sizing)]
bet_sizing_outcome = bet_sizing_with_meta_model(X, directional_training_outcome.training.predictions, y, forward_returns, config.meta_model, config, 'meta', from_index = inference_from, transformations_over_time = pipeline_outcome.bet_sizing.meta_transformations, preloaded_models = pipeline_outcome.bet_sizing.meta_training.model_over_time)
# 4. Ensemble weights
ensemble_outcome = ensemble_weights([o.weights for o in bet_sizing_outcomes], forward_returns, y, config.no_of_classes, config.mode == 'training')
# 5. (Optional) Additional bet sizing on top of the ensembled weights
ensemble_bet_sizing_outcome = bet_sizing_with_meta_models(X, ensemble_outcome.weights, y, forward_returns, config.meta_models, config, 'ensemble', from_index = inference_from, transformations_over_time = pipeline_outcome.secondary_bet_sizing.meta_transformations, preloaded_models= [b.model_over_time for b in pipeline_outcome.secondary_bet_sizing.meta_training]) if len(config.meta_models) > 0 else None
return PipelineOutcome(directional_training_outcome, bet_sizing_outcomes, ensemble_outcome, ensemble_bet_sizing_outcome)
return PipelineOutcome(directional_training_outcome, bet_sizing_outcome)
if __name__ == '__main__':