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Refactor(Training): new outcome types, representative pipeline steps, bet-sizing (#187)

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* refactor(Training): added InferenceResult & TrainedModel types

* refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc.

* fix(Pipeline): getting it to compile

* refactor(WalkForward): separate preprocessing step

* feat(Pipeline): separate out transformations processing step

* refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step

* refactor(WalkForward): moved functions to separate folder

* fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster)

* fix(Tests): and evaluation

* fix(Tests): for realz

* fix(Inference): preloading everything now, renamed primary models to directional models

* fix(BetSizing): was running transformations on the wrong data, oops

* fix(BetSizing): concatenated on the wrong axis accidentally

* fix(Reporting): able to use the new Stats type

* fix(BetSizing): renamed int column names

* fix(Portfolio): name the column properly

* fix(Reporting): rename the correct Series, lol

* fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index

* fix(WalkForward): accidentally using the wrong index

* fix(WalkForward): use the correct indicies to fetch last model/transformations

* fix(CI): changed the name of the results
This commit is contained in:
Mark Aron Szulyovszky
2022-01-29 06:41:40 +01:00
committed by GitHub
parent 42a1bc59cb
commit 3eb3ea94e3
42 changed files with 772 additions and 736 deletions
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reporting/saving.py
+7 -9
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@@ -1,17 +1,15 @@
import pickle
import datetime
from config.types import Config
from typing import Optional, Union
from typing import Optional
import os
import warnings
from reporting.types import Reporting
from training.types import PipelineOutcome
def save_models(all_models: Reporting.Asset, config: Config) -> None:
def save_models(pipeline_outcome: PipelineOutcome, config: Config) -> None:
dict_for_pickle = dict()
dict_for_pickle['config'] = config
dict_for_pickle['all_models'] = all_models
dict_for_pickle['pipeline_outcome'] = pipeline_outcome
date_string = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
@@ -22,7 +20,7 @@ def save_models(all_models: Reporting.Asset, config: Config) -> None:
pickle.dump( dict_for_pickle, open( "output/models/{}.p".format(date_string), "wb" ) )
def load_models(file_name:Union[str, None]) -> tuple[Reporting.Asset, Config]:
def load_models(file_name: Optional[str]) -> tuple[PipelineOutcome, Config]:
if file_name is None:
warnings.warn("No file name provided, will load latest models and configurations.")
@@ -34,7 +32,7 @@ def load_models(file_name:Union[str, None]) -> tuple[Reporting.Asset, Config]:
packacked_dict = pickle.load( open( "output/models/{}".format(file_name), "rb" ) )
config = packacked_dict.pop("config", None)
all_models = packacked_dict.pop("all_models", None)
pipeline_outcome = packacked_dict.pop("pipeline_outcome", None)
return all_models, config
return pipeline_outcome, config