mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
442915f847
* feat(Data): create DataSource, DataCollection, added hourly crypto data * fix(Data): hourly data format, loading & config
219 lines
8.7 KiB
Python
219 lines
8.7 KiB
Python
import pandas as pd
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import numpy as np
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from utils.types import DataSource, FeatureExtractor
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from utils.helpers import deduplicate_indexes
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from data_loader.collections import DataCollection
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from typing import Literal
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import ray
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import os
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def load_data(assets: DataCollection,
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other_assets: DataCollection,
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# exogenous_data: DataCollection,
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target_asset: str,
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load_non_target_asset: bool,
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log_returns: bool,
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forecasting_horizon: int,
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own_features: list[tuple[str, FeatureExtractor, list[int]]],
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other_features: list[tuple[str, FeatureExtractor, list[int]]],
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index_column: Literal['date', 'int'],
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method: Literal['regression', 'classification'],
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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
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narrow_format: bool = False
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) -> tuple[pd.DataFrame, pd.Series, pd.Series]:
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"""
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Loads asset data from the specified path.
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Returns:
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- DataFrame `X` with all the training data
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- Series `y` with the target asset returns shifted by 1 day OR if it's a classification problem, the target class)
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- Series `forward_returns` with the target asset returns shifted by 1 day
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"""
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target_file = [f for f in assets if f[1].startswith(target_asset)]
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other_files = [f for f in assets if load_non_target_asset == True and f[1].startswith(target_asset) == False]
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files = target_file + other_files + other_assets
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def is_target_asset(target_asset: str, file: str): return file.split('.')[0].startswith(target_asset)
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futures = [__load_df.remote(
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data_source=data_source,
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prefix=data_source[1],
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returns='log_returns' if log_returns else 'returns',
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feature_extractors=own_features if is_target_asset(target_asset[1], data_source[1]) else other_features,
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narrow_format=narrow_format,
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) for data_source in files]
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dfs = ray.get(futures)
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dfs = [deduplicate_indexes(df) for df in dfs]
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longest_df = max(dfs, key=lambda df: df.shape[0])
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if narrow_format:
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dfs = pd.concat(dfs, axis=0).fillna(0.)
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else:
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dfs = pd.concat([df.reindex(longest_df.index) for df in dfs], axis=1).fillna(0.)
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dfs.index = pd.DatetimeIndex(dfs.index)
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if index_column == 'int':
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dfs.reset_index(drop=True, inplace=True)
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if narrow_format:
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dfs = dfs.drop(index=dfs.index[0], axis=0)
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## Create target
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target_col = 'target'
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returns_col = target_asset[1] + '_returns'
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forward_returns = __create_target_cum_forward_returns(dfs, returns_col, forecasting_horizon)
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if method == 'regression':
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dfs[target_col] = forward_returns
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elif method == 'classification':
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dfs[target_col] = __create_target_classes(dfs, returns_col, forecasting_horizon, no_of_classes)
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# we need to drop the last row, because we forward-shift the target (see what happens if you call .shift[-1] on a pd.Series)
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dfs = dfs.iloc[:-forecasting_horizon]
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forward_returns = forward_returns.iloc[:-forecasting_horizon]
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X = dfs.drop(columns=[target_col])
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y = dfs[target_col]
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return X, y, forward_returns
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@ray.remote
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def __load_df(data_source: DataSource,
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prefix: str,
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returns: Literal['price', 'returns', 'log_returns'],
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feature_extractors: list[tuple[str, FeatureExtractor, list[int]]],
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narrow_format: bool = False) -> pd.DataFrame:
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df = pd.read_csv(os.path.join(data_source[0], data_source[1] + '.csv'), header=0, index_col=0).fillna(0)
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if returns == 'log_returns':
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df['returns'] = np.log(df['close']).diff(1)
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elif returns == 'price':
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df['returns'] = df['close']
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else:
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df['returns'] = df['close'].pct_change()
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df = __apply_feature_extractors(df, log_returns=True if returns == 'log_returns' else False, feature_extractors = feature_extractors)
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df = df.replace([np.inf, -np.inf], 0.)
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df = df.drop(columns=['open', 'high', 'low', 'close'])
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# we're not ready for this just yet
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if 'volume' in df.columns:
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df = df.drop(columns=['volume'])
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if narrow_format:
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df["ticker"] = np.repeat(prefix, df.shape[0])
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else:
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df.columns = [prefix + "_" + c if 'date' not in c else c for c in df.columns]
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return df
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def __apply_feature_extractors(df: pd.DataFrame,
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log_returns: bool,
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feature_extractors: list[tuple[str, FeatureExtractor, list[int]]]) -> pd.DataFrame:
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for name, extractor, periods in feature_extractors:
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for period in periods:
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features = extractor(df, period, log_returns)
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if type(features) == pd.DataFrame:
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df = pd.concat([df, features], axis=1)
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elif type(features) == pd.Series:
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df[name + '_' + str(period)] = extractor(df, period, log_returns)
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else:
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assert False, "Feature extractor must return a pd.DataFrame or pd.Series"
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return df
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def __create_target_cum_forward_returns(df: pd.DataFrame, source_column: str, period: int) -> pd.Series:
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assert period > 0
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return df[source_column].shift(-period)
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def __create_target_classes(df: pd.DataFrame, source_column: str, period: int, no_of_classes: Literal["two", "three"]) -> pd.Series:
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assert period > 0
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def get_class_binary(x: float) -> int:
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return -1 if x <= 0.0 else 1
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def get_class_threeway_balanced(series: pd.Series) -> pd.Series:
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def get_bins_threeway(x):
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bins = pd.qcut(df[source_column], 3, retbins=True, duplicates = 'drop')[1]
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if len(bins) != 4:
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# if we don't have enough data for the quantiles, we'll need to add hard-coded values
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lower_bound = bins[0]
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upper_bound = bins[-1]
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bins = [lower_bound] + [-0.02, 0.02] + [upper_bound]
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return bins
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bins = get_bins_threeway(series)
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def map_class_threeway(current_value):
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lower_threshold = bins[1]
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upper_threshold = bins[2]
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if current_value <= lower_threshold:
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return -1
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elif current_value > lower_threshold and current_value < upper_threshold:
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return 0
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else:
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return 1
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return series.map(map_class_threeway)
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def get_class_threeway_imbalanced(series: pd.Series) -> pd.Series:
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def get_bins_threeway(x):
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bins = pd.qcut(df[source_column], 4, retbins=True, duplicates = 'drop')[1]
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if len(bins) != 5:
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# if we don't have enough data for the quantiles, we'll need to add hard-coded values
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lower_bound = bins[0]
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upper_bound = bins[-1]
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bins = [lower_bound] + [-0.02, 0.0, 0.02] + [upper_bound]
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return bins
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bins = get_bins_threeway(series)
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def map_class_threeway(current_value):
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lower_threshold = bins[1]
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upper_threshold = bins[3]
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if current_value <= lower_threshold:
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return -1
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elif current_value > lower_threshold and current_value < upper_threshold:
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return 0
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else:
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return 1
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return series.map(map_class_threeway)
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target_column = df[source_column].shift(-period)
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if no_of_classes == "three-balanced":
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return get_class_threeway_balanced(target_column)
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elif no_of_classes == "three-imbalanced":
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return get_class_threeway_imbalanced(target_column)
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else:
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return target_column.map(get_class_binary)
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def datasource_to_file(data_source: DataSource) -> str:
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return data_source[0] + '/' + data_source[1] + '.csv'
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# These are needed for the portfolio feature, maybe we can do this in a more elegant way
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# def load_crypto_only_returns(path: str, index_column: Literal['date', 'int'], returns: Literal['price', 'returns']) -> pd.DataFrame:
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# files = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and 'USD' in f and not f.startswith('.')]
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# dfs = [__load_df(
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# path=os.path.join(path,f),
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# prefix=f.split('.')[0],
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# returns=returns,
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# feature_extractors=[],
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# narrow_format=False,
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# ) for f in files]
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# dfs = pd.concat(dfs, axis=1)
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# dfs = dfs.applymap(lambda x: np.nan if x == 0 else x)
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# dfs.index = pd.DatetimeIndex(dfs.index)
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# dfs.columns = [column.split('_')[0] for column in dfs.columns]
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# if index_column == 'int':
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# dfs.reset_index(drop=True, inplace=True)
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# return dfs
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# def load_crypto_assets_availability(path: str, index_column: Literal['date', 'int']) -> pd.DataFrame:
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# return load_crypto_only_returns(path, index_column, 'returns').applymap(lambda x: 0 if x == 0.0 or x == 0 or np.isnan(x) else 1)
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