Files
drift/data_loader/process.py
T
Mark Aron Szulyovszky 3eb3ea94e3 Refactor(Training): new outcome types, representative pipeline steps, bet-sizing (#187)
* 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
2022-01-29 06:41:40 +01:00

19 lines
728 B
Python

import pandas as pd
from config.types import Config
import warnings
from utils.helpers import get_first_valid_return_index
from data_loader.types import XDataFrame
def check_data(X: XDataFrame, config: Config) -> bool:
""" Returns True if data is valid, else returns False."""
if has_enough_samples_to_train(X, config) == False:
warnings.warn("Not enough samples to train")
return False
return True
def has_enough_samples_to_train(X: XDataFrame, config: Config) -> bool:
first_valid_index = get_first_valid_return_index(X.iloc[:,0])
samples_to_train = len(X) - first_valid_index
return samples_to_train > config.sliding_window_size_base + config.sliding_window_size_meta + 100