mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
86 lines
2.3 KiB
Python
86 lines
2.3 KiB
Python
import pandas as pd
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import numpy as np
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import os
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import string
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import random
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from itertools import chain, combinations, dropwhile
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def get_files_from_dir(path: str) -> list[str]:
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return [
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f
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for f in os.listdir(path)
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if os.path.isfile(os.path.join(path, f)) and not f.startswith(".")
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]
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def get_first_valid_return_index(series: pd.Series) -> int:
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double_nested_results = np.where(
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np.logical_and(series != 0, np.logical_not(pd.isna(series)))
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)
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if len(double_nested_results) == 0:
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return 0
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nested_result = double_nested_results[0]
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if len(nested_result) == 0:
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return 0
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return nested_result[0]
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def get_last_non_na_index(series: pd.Series, index: int) -> int:
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return next(
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dropwhile(lambda x: pd.isna(x[1]), enumerate(reversed(series[: index + 1])))
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)[0]
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def flatten(list_of_lists: list) -> list:
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return [item for sublist in list_of_lists for item in sublist]
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def weighted_average(df: pd.DataFrame, weights_source: str) -> pd.Series:
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if df.shape[1] == 0:
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return df
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mean_df = df.iloc[:, 0]
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weights = df.loc[weights_source]
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for i, row in df.iterrows():
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if i == weights_source:
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continue
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mean_df.loc[i] = (row * weights).sum() / df.loc[weights_source].sum()
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return mean_df
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def drop_columns_if_exist(df: pd.DataFrame, columns: list) -> pd.DataFrame:
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for column in columns:
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if column in df.columns:
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df = df.drop(column, axis=1)
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return df
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def random_string(n: int) -> str:
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return "".join(random.choices(string.ascii_uppercase + string.digits, k=n))
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def equal_except_nan(row: pd.Series):
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if np.isnan(row.iloc[0]) or np.isnan(row.iloc[1]):
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return np.nan
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if row.iloc[0] == row.iloc[1]:
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return 1.0
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else:
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return 0.0
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def drop_until_first_valid_index(
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df: pd.DataFrame, series: pd.Series
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) -> tuple[pd.DataFrame, pd.Series]:
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first_valid_index = max(
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get_first_valid_return_index(df.iloc[:, 0]),
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get_first_valid_return_index(series),
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)
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return df.iloc[first_valid_index:], series.iloc[first_valid_index:]
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def powerset(input: list) -> list[list]:
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p_set = list(chain.from_iterable(combinations(input, r) for r in range(len(input) + 1))) # type: ignore
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return [list(item) for item in p_set if len(item) > 0]
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