feat(Events): added EventFilter, EventLabeller (#186)

2026-07-27 18:57:55 +00:00 · 2022-01-26 23:22:43 +01:00
parent 1042c82333
commit 42a1bc59cb
65 changed files with 759 additions and 276571 deletions
@@ -137,3 +137,5 @@ lightning_logs/

 results.csv
 models/saved/
+
+data/minute_crypto/**
@@ -9,7 +9,7 @@ from pytorch_forecasting.metrics import QuantileLoss
 import sys
 sys.path.insert(0, '..')

-from data_loader.load_data import load_files
+from data_loader import load_files

 print("success")

@@ -1,5 +1,5 @@

-from utils.types import DataCollection
+from data_loader.types import DataCollection

 def hash_data_config(data_config: dict) -> str:
    
@@ -12,10 +12,7 @@ def hash_data_config(data_config: dict) -> str:
        hash_data_collection(data_config['exogenous_data']),
        data_config['target_asset'][0] + data_config['target_asset'][1],
        data_config['load_non_target_asset'],
-        data_config['log_returns'],
-        data_config['forecasting_horizon'],
        hash_feature_extractors(data_config['own_features']),
        hash_feature_extractors(data_config['other_features']),
        hash_feature_extractors(data_config['exogenous_features']),
-        data_config['no_of_classes'],
    ]))
@@ -1,16 +1,20 @@
-from config.config import Config, RawConfig
+from .types import Config, RawConfig
 from utils.helpers import flatten
 from feature_extractors.feature_extractor_presets import presets as feature_extractor_presets
-from models.model_map import get_model_map
+from models.model_map import get_model
 from data_loader.collections import data_collections
-
+from labeling.eventfilters_map import eventfilters_map
+from labeling.labellers_map import labellers_map

 def preprocess_config(raw_config: RawConfig) -> Config:
    config_dict = vars(raw_config)
    config_dict = __preprocess_model_config(config_dict)
    config_dict = __preprocess_feature_extractors_config(config_dict)
    config_dict = __preprocess_data_collections_config(config_dict)
+    config_dict = __preprocess_event_filter_config(config_dict)
+    config_dict = __preprocess_event_labeller_config(config_dict)

+    config_dict['no_of_classes'] = 'two'
    config = Config(**config_dict)
    validate_config(config)
    return config
@@ -24,17 +28,15 @@ def __preprocess_feature_extractors_config(data_dict: dict) -> dict:
    return data_dict

 def __preprocess_model_config(model_config:dict) -> dict:
-    model_map = get_model_map(model_config)
-    model_config['primary_models'] = [(model_name, model_map['primary_models'][model_name]) for model_name in model_config['primary_models']]
+    model_config['primary_models'] = [(model_name, get_model(model_name)) for model_name in model_config['primary_models']]
    if len(model_config['meta_labeling_models']) > 0:
-        model_config['meta_labeling_models'] = [(model_name, model_map['primary_models'][model_name]) for model_name in  model_config['meta_labeling_models']]
+        model_config['meta_labeling_models'] = [(model_name, get_model(model_name)) for model_name in  model_config['meta_labeling_models']]
    if model_config['ensemble_model'] is not None:
-        model_config['ensemble_model'] = (model_config['ensemble_model'], model_map['ensemble_models'][model_config['ensemble_model']])
+        model_config['ensemble_model'] = (model_config['ensemble_model'], get_model(model_config['ensemble_model']))

    return model_config

 def __preprocess_data_collections_config(data_dict: dict) -> dict:
-    data_dict = data_dict.copy()
    keys = ['assets', 'other_assets', 'exogenous_data']
    for key in keys:
        preset_names = data_dict[key]
@@ -44,6 +46,14 @@ def __preprocess_data_collections_config(data_dict: dict) -> dict:
    data_dict['target_asset'] = target_asset
    return data_dict

+def __preprocess_event_filter_config(data_dict: dict) -> dict:
+    data_dict['event_filter'] = eventfilters_map[data_dict['event_filter']]
+    return data_dict
+
+def __preprocess_event_labeller_config(data_dict: dict) -> dict:
+    data_dict['labeling'] = labellers_map[data_dict['labeling']]
+    return data_dict
+    

 def validate_config(config: Config):
    # We need to make sure there's only one output from the pipeline
@@ -1,68 +1,4 @@
-
-from pydantic import BaseModel
-from typing import Literal, Optional
-from models.base import Model
-from utils.types import DataCollection, DataSource, FeatureExtractor
-
-# RawConfig is needed to ensure we can declare config presets here with static typing, we then convert it to Config
-class RawConfig(BaseModel):
-    primary_models_meta_labeling: bool
-    dimensionality_reduction: bool
-    n_features_to_select: int
-    expanding_window_base: bool
-    expanding_window_meta_labeling: bool
-    sliding_window_size_base: int
-    sliding_window_size_meta_labeling: int
-    retrain_every: int
-    scaler: Literal['normalize', 'minmax', 'standardize']
-
-    assets: list[str]
-    target_asset: str
-    other_assets: list[str]
-    exogenous_data: list[str]
-    load_non_target_asset: bool
-    log_returns: bool
-    forecasting_horizon: int
-    own_features: list[str]
-    other_features: list[str]
-    exogenous_features: list[str]
-    no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']
-
-    primary_models: list[str]
-    meta_labeling_models: list[str]
-    ensemble_model: Optional[str]
-
-
-class Config(BaseModel):
-    primary_models_meta_labeling: bool
-    dimensionality_reduction: bool
-    n_features_to_select: int
-    expanding_window_base: bool
-    expanding_window_meta_labeling: bool
-    sliding_window_size_base: int
-    sliding_window_size_meta_labeling: int
-    retrain_every: int
-    scaler: Literal['normalize', 'minmax', 'standardize']
-
-    assets: DataCollection
-    target_asset: DataSource
-    other_assets: DataCollection
-    exogenous_data: DataCollection
-    load_non_target_asset: bool
-    log_returns: bool
-    forecasting_horizon: int
-    own_features: list[tuple[str, FeatureExtractor, list[int]]]
-    other_features: list[tuple[str, FeatureExtractor, list[int]]]
-    exogenous_features: list[tuple[str, FeatureExtractor, list[int]]]
-    no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']
-
-    primary_models: list[tuple[str, Model]]
-    meta_labeling_models: list[tuple[str, Model]]
-    ensemble_model: Optional[tuple[str, Model]]
-
-    class Config:
-        arbitrary_types_allowed = True
-
+from .types import RawConfig, Config

 def get_dev_config() -> RawConfig:
  
@@ -85,16 +21,16 @@ def get_dev_config() -> RawConfig:
        other_assets = [],
        exogenous_data = [],
        load_non_target_asset= True,
-        log_returns= True,
-        forecasting_horizon = 1,
        own_features = ['level_2', 'date_days'],
        other_features = ['single_mom'],
        exogenous_features = ['z_score'],
-        no_of_classes= 'two',

        primary_models = classification_models,
        meta_labeling_models = [],
-        ensemble_model = None
+        ensemble_model = None,
+
+        event_filter = 'none',
+        labeling = 'two_class'
    )


@@ -121,16 +57,16 @@ def get_default_ensemble_config() -> RawConfig:
        other_assets = ['daily_etf'],
        exogenous_data = ['daily_glassnode'],
        load_non_target_asset= True,
-        log_returns= True,
-        forecasting_horizon = 1,
        own_features = ['level_2', 'date_days', 'lags_up_to_5'],
        other_features = ['level_2', 'lags_up_to_5'],
        exogenous_features = ['z_score'],
-        no_of_classes= 'two',

        primary_models = classification_models,
        meta_labeling_models = meta_labeling_models,
-        ensemble_model = ensemble_model
+        ensemble_model = ensemble_model,
+
+        event_filter = 'cusum_vol',
+        labeling = 'two_class'
    )


@@ -158,16 +94,16 @@ def get_lightweight_ensemble_config() -> RawConfig:
        other_assets = ['daily_etf'],
        exogenous_data = ['daily_glassnode'],
        load_non_target_asset= True,
-        log_returns= True,
-        forecasting_horizon = 1,
        own_features = ['level_2' ],
        other_features = ['level_2'],
        exogenous_features = ['z_score'],
-        no_of_classes= 'two',

        primary_models = classification_models,
        meta_labeling_models = meta_labeling_models,
-        ensemble_model = ensemble_model
+        ensemble_model = ensemble_model,
+
+        event_filter = 'none',
+        labeling = 'two_class'
    )


@@ -34,12 +34,8 @@ parameters:
      value: 'minmax'
    no_of_classes:
      value: 'two'
-    forecasting_horizon: 
-      value: 1
    load_non_target_asset: 
      value: True
-    log_returns: 
-      value: True
    primary_models: 
      distribution: categorical
      values:
@@ -39,13 +39,9 @@ parameters:
    no_of_classes:
      values: ['two', 'three-balanced', 'three-imbalanced']
      distribution: categorical
-    forecasting_horizon: 
-      value: 1
    load_non_target_asset: 
      values: [True, False]
      distribution: categorical
-    log_returns: 
-      value: True
    primary_models: 
      value: ["LogisticRegression_two_class", "LDA", "KNN", "CART", "NB", "AB", "RFC", "StaticMom"]
    meta_labeling_models: 
@@ -34,12 +34,8 @@ parameters:
      value: 'minmax'
    no_of_classes:
      value: 'two'
-    forecasting_horizon: 
-      value: 1
    load_non_target_asset: 
      value: True
-    log_returns: 
-      value: True
    primary_models: 
      values: [['LogisticRegression_two_class'], ['SVC'], ['LDA'], ['KNN'], ['CART'], ['MNB'], ['NB'], ['AB'], ['RFC'], ['XGB_two_class'], ['LGBM']]
      distribution: categorical
@@ -0,0 +1,68 @@
+from pydantic import BaseModel
+from typing import Literal, Optional
+from labeling.types import EventFilter
+from models.base import Model
+from data_loader.types import DataCollection, DataSource
+from feature_extractors.types import FeatureExtractor, ScalerTypes
+from labeling.types import EventFilter, EventLabeller
+
+
+# RawConfig is needed to ensure we can declare config presets here with static typing, we then convert it to Config
+class RawConfig(BaseModel):
+    primary_models_meta_labeling: bool
+    dimensionality_reduction: bool
+    n_features_to_select: int
+    expanding_window_base: bool
+    expanding_window_meta_labeling: bool
+    sliding_window_size_base: int
+    sliding_window_size_meta_labeling: int
+    retrain_every: int
+    scaler: Literal['normalize', 'minmax', 'standardize']
+
+    assets: list[str]
+    target_asset: str
+    other_assets: list[str]
+    exogenous_data: list[str]
+    load_non_target_asset: bool
+    own_features: list[str]
+    other_features: list[str]
+    exogenous_features: list[str]
+    event_filter: Literal['none', 'cusum_vol', 'cusum_fixed']
+    labeling: Literal['two_class', 'three_class_balanced', 'three_class_imbalanced']
+
+    primary_models: list[str]
+    meta_labeling_models: list[str]
+    ensemble_model: Optional[str]
+
+
+class Config(BaseModel):
+    primary_models_meta_labeling: bool
+    dimensionality_reduction: bool
+    n_features_to_select: int
+    expanding_window_base: bool
+    expanding_window_meta_labeling: bool
+    sliding_window_size_base: int
+    sliding_window_size_meta_labeling: int
+    retrain_every: int
+    scaler: Literal['normalize', 'minmax', 'standardize']
+
+    assets: DataCollection
+    target_asset: DataSource
+    other_assets: DataCollection
+    exogenous_data: DataCollection
+    load_non_target_asset: bool
+    own_features: list[tuple[str, FeatureExtractor, list[int]]]
+    other_features: list[tuple[str, FeatureExtractor, list[int]]]
+    exogenous_features: list[tuple[str, FeatureExtractor, list[int]]]
+    event_filter: EventFilter
+    labeling: EventLabeller
+    no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']
+
+    primary_models: list[tuple[str, Model]]
+    meta_labeling_models: list[tuple[str, Model]]
+    ensemble_model: Optional[tuple[str, Model]]
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
@@ -0,0 +1,2 @@
+from .collections import data_collections
+from .load import load_data, load_only_returns
@@ -1,4 +1,4 @@
-from utils.types import Path, FileName, DataSource, DataCollection
+from .types import Path, FileName, DataSource, DataCollection
 from utils.helpers import flatten

 def transform_to_data_collection(path: str, file_names: list[str]) -> DataCollection:
@@ -10,7 +10,7 @@ __daily_crypto = ["ADA_USD", "BCH_USD", "BNB_USD", "BTC_USD", "DOT_USD", "ETC_US

 __daily_crypto_lightweight = ["ADA_USD", "BCH_USD"]

-__hourly_crypto = ["BTC_USD", "DASH_USD", "ETC_USD", "ETH_USD", "LTC_USD", "TRX_USD", "XLM_USD", "XMR_USD", "XRP_USD"]
+__minute_crypto = ["BTC_USD", "DASH_USD", "ETC_USD", "ETH_USD", "LTC_USD", "TRX_USD", "XLM_USD", "XMR_USD"]

 __daily_glassnode = ['rhodl_ratio',
    # 'cvdd',
@@ -45,7 +45,7 @@ data_collections = dict(
    daily_crypto = transform_to_data_collection("data/daily_crypto", __daily_crypto),
    daily_crypto_lightweight = transform_to_data_collection("data/daily_crypto", __daily_crypto_lightweight),
    daily_etf = transform_to_data_collection("data/daily_etf", __daily_etf),
-    hourly_crypto = transform_to_data_collection("data/hourly_crypto", __hourly_crypto),
+    minute_crypto = transform_to_data_collection("data/minute_crypto", __minute_crypto),
    daily_glassnode =transform_to_data_collection("data/daily_glassnode",  __daily_glassnode),
 )

@@ -0,0 +1,151 @@
+import pandas as pd
+import numpy as np
+from .types import DataSource
+from feature_extractors.types import FeatureExtractor
+from utils.helpers import drop_columns_if_exist
+from data_loader.collections import DataCollection
+from typing import Literal
+import ray
+import os
+from config.hashing import hash_data_config
+from .types import XDataFrame, ReturnSeries, ForwardReturnSeries
+from diskcache import Cache
+cache = Cache(".cachedir/data")
+
+
+
+def load_data(**kwargs) -> tuple[XDataFrame, ReturnSeries, ForwardReturnSeries]:
+    hashed = hash_data_config(kwargs)
+    if hashed in cache:
+        return cache.get(hashed)
+    else:
+        return_value = __load_data(**kwargs)
+        cache[hashed] = return_value
+        return return_value
+
+def __load_data(assets: DataCollection,
+            other_assets: DataCollection,
+            exogenous_data: DataCollection,
+            target_asset: DataSource,
+            load_non_target_asset: bool,
+            own_features: list[tuple[str, FeatureExtractor, list[int]]],
+            other_features: list[tuple[str, FeatureExtractor, list[int]]],
+            exogenous_features: list[tuple[str, FeatureExtractor, list[int]]],
+        ) -> tuple[XDataFrame, ReturnSeries, ForwardReturnSeries]:
+    """
+    Loads asset data from the specified path.
+    Returns:
+        - DataFrame `X` with all the training data
+        - Series `returns` with only the returns
+        - Series `forward_returns` with the target asset returns shifted by 1 day
+    """
+
+    target_file = [f for f in assets if f[1].startswith(target_asset[1])]
+    assert len(target_file) == 1, "There should be exactly one target file"
+    other_files = [f for f in assets if load_non_target_asset == True and f[1].startswith(target_asset[1]) == False]
+    files = other_files + other_assets
+    
+    target_asset_future = [__load_df.remote(
+        data_source=data_source,
+        prefix=data_source[1],
+        returns='log_returns',
+        feature_extractors=own_features,
+    ) for data_source in target_file]
+    target_asset_df = ray.get(target_asset_future)
+    
+    target_asset_only_returns_future = __load_df.remote(
+        data_source=target_file[0],
+        prefix=target_file[0][1],
+        returns='returns',
+        feature_extractors=[],
+    )
+    df_target_asset_only_returns = ray.get(target_asset_only_returns_future)
+
+    asset_futures = [__load_df.remote(
+        data_source=data_source,
+        prefix=data_source[1],
+        returns='log_returns',
+        feature_extractors=other_features,
+    ) for data_source in files]
+    asset_dfs = ray.get(asset_futures)
+
+    exogenous_futures = [__load_df.remote(
+        data_source=data_source,
+        prefix=data_source[1],
+        returns='none',
+        feature_extractors=exogenous_features,
+    ) for data_source in exogenous_data]
+    exogenous_dfs = ray.get(exogenous_futures)
+
+    X = target_asset_df + asset_dfs + exogenous_dfs
+    X = pd.concat([df.sort_index().reindex(X[0].index) for df in X], axis=1).fillna(0.)
+
+    X.index = pd.DatetimeIndex(X.index)
+    
+    ## Create target 
+    returns = df_target_asset_only_returns[target_asset[1] + '_returns']
+    returns.index = pd.DatetimeIndex(X.index)
+    forward_returns = __create_target_cum_forward_returns(returns, 1)
+    forward_returns.index = pd.DatetimeIndex(X.index)
+    # we need to null out the last forward returns row, because when doing forward-shifting, we automatically get the first row, which is definitely incorrect
+    forward_returns[forward_returns.index[-1]] = 0.
+    
+    return X, returns, forward_returns 
+
+
+@ray.remote
+def __load_df(data_source: DataSource,
+            prefix: str,
+            returns: Literal['none', 'price', 'returns', 'log_returns'],
+            feature_extractors: list[tuple[str, FeatureExtractor, list[int]]]) -> pd.DataFrame:
+    df = pd.read_csv(os.path.join(data_source[0], data_source[1] + '.csv'), header=0, index_col=0).fillna(0)
+
+    if returns == 'log_returns':
+        df['returns'] = np.log(df['close']).diff(1)
+    elif returns == 'price':
+        df['returns'] = df['close']
+    elif returns == 'returns':
+        df['returns'] = df['close'].pct_change()
+
+    df = __apply_feature_extractors(df, feature_extractors = feature_extractors)
+
+    df = df.replace([np.inf, -np.inf], 0.)
+    df = drop_columns_if_exist(df, ['open', 'high', 'low', 'close', 'volume'])
+    
+    df.columns = [prefix + "_" + c if 'date' not in c else c for c in df.columns]
+    return df
+
+
+def __apply_feature_extractors(df: pd.DataFrame, feature_extractors: list[tuple[str, FeatureExtractor, list[int]]]) -> pd.DataFrame:
+
+    for name, extractor, periods in feature_extractors:
+        for period in periods:
+            features = extractor(df, period)
+            if type(features) == pd.DataFrame:
+                df = pd.concat([df, features], axis=1)
+            elif type(features) == pd.Series:
+                df[name + '_' + str(period)] = features
+            else:
+                assert False, "Feature extractor must return a pd.DataFrame or pd.Series"
+    return df
+
+
+def __create_target_cum_forward_returns(series: pd.Series, period: int) -> pd.Series:
+    assert period > 0
+    return series.shift(-period).copy()
+
+
+def load_only_returns(assets: DataCollection, returns: Literal['price', 'returns']) -> pd.DataFrame:
+
+    assets_future = [__load_df.remote(
+        data_source=data_source,
+        prefix=data_source[1],
+        returns=returns,
+        feature_extractors=[],
+    ) for data_source in assets]
+    dfs = ray.get(assets_future)
+
+    dfs = pd.concat(dfs, axis=1)
+    dfs.index = pd.DatetimeIndex(dfs.index)
+
+    return dfs
@@ -1,229 +0,0 @@
-import pandas as pd
-import numpy as np
-from utils.types import DataSource, FeatureExtractor
-from utils.helpers import deduplicate_indexes, drop_columns_if_exist
-from data_loader.collections import DataCollection
-from typing import Literal
-import ray
-import os
-from config.hashing import hash_data_config
-from diskcache import Cache
-cache = Cache(".cachedir/data")
-
-def load_data(**kwargs) -> tuple[pd.DataFrame, pd.Series, pd.Series]:
-    hashed = hash_data_config(kwargs)
-    if hashed in cache:
-        return cache.get(hashed)
-    else:
-        return_value = __load_data(**kwargs)
-        cache[hashed] = return_value
-        return return_value
-
-def __load_data(assets: DataCollection,
-            other_assets: DataCollection,
-            exogenous_data: DataCollection,
-            target_asset: DataSource,
-            load_non_target_asset: bool,
-            log_returns: bool,
-            forecasting_horizon: int,
-            own_features: list[tuple[str, FeatureExtractor, list[int]]],
-            other_features: list[tuple[str, FeatureExtractor, list[int]]],
-            exogenous_features: list[tuple[str, FeatureExtractor, list[int]]],
-            no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
-        ) -> tuple[pd.DataFrame, pd.Series, pd.Series]:
-    """
-    Loads asset data from the specified path.
-    Returns:
-        - DataFrame `X` with all the training data
-        - Series `y` with the target asset returns shifted by 1 day OR if it's a classification problem, the target class)
-        - Series `forward_returns` with the target asset returns shifted by 1 day
-    """
-
-    target_file = [f for f in assets if f[1].startswith(target_asset[1])]
-    assert len(target_file) == 1, "There should be exactly one target file"
-    other_files = [f for f in assets if load_non_target_asset == True and f[1].startswith(target_asset[1]) == False]
-    files = other_files + other_assets
-    
-    target_asset_future = [__load_df.remote(
-        data_source=data_source,
-        prefix=data_source[1],
-        returns='log_returns' if log_returns else 'returns',
-        feature_extractors=own_features,
-    ) for data_source in target_file]
-    target_asset_df = ray.get(target_asset_future)
-    
-    target_asset_only_returns_future = __load_df.remote(
-        data_source=target_file[0],
-        prefix=target_file[0][1],
-        returns='returns',
-        feature_extractors=[],
-    )
-    df_target_asset_only_returns = ray.get(target_asset_only_returns_future)
-
-    asset_futures = [__load_df.remote(
-        data_source=data_source,
-        prefix=data_source[1],
-        returns='log_returns' if log_returns else 'returns',
-        feature_extractors=other_features,
-    ) for data_source in files]
-    asset_dfs = ray.get(asset_futures)
-
-    exogenous_futures = [__load_df.remote(
-        data_source=data_source,
-        prefix=data_source[1],
-        returns='none',
-        feature_extractors=exogenous_features,
-    ) for data_source in exogenous_data]
-    exogenous_dfs = ray.get(exogenous_futures)
-
-    dfs = target_asset_df + asset_dfs + exogenous_dfs
-    dfs = [deduplicate_indexes(df) for df in dfs]
-    target_df = dfs[0]
-    dfs = pd.concat([df.sort_index().reindex(target_df.index) for df in dfs], axis=1).fillna(0.)
-
-    dfs.index = pd.DatetimeIndex(dfs.index)
-    
-    ## Create target 
-    target_col = 'target'
-    returns_col = target_asset[1] + '_returns'
-    forward_returns = __create_target_cum_forward_returns(df_target_asset_only_returns, returns_col, forecasting_horizon)
-    forward_returns.index = pd.DatetimeIndex(dfs.index)
-    dfs[target_col] = __create_target_classes(dfs, returns_col, forecasting_horizon, no_of_classes)
-    # 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) 
-    dfs = dfs.iloc[:-forecasting_horizon]
-    forward_returns = forward_returns.iloc[:-forecasting_horizon]
-    
-    X = dfs.drop(columns=[target_col])
-    y = dfs[target_col]
-
-    return X, y, forward_returns
-
-
-@ray.remote
-def __load_df(data_source: DataSource,
-            prefix: str,
-            returns: Literal['none', 'price', 'returns', 'log_returns'],
-            feature_extractors: list[tuple[str, FeatureExtractor, list[int]]]) -> pd.DataFrame:
-    df = pd.read_csv(os.path.join(data_source[0], data_source[1] + '.csv'), header=0, index_col=0).fillna(0)
-
-    if returns == 'log_returns':
-        df['returns'] = np.log(df['close']).diff(1)
-    elif returns == 'price':
-        df['returns'] = df['close']
-    elif returns == 'returns':
-        df['returns'] = df['close'].pct_change()
-
-    df = __apply_feature_extractors(df, log_returns=True if returns == 'log_returns' else False, feature_extractors = feature_extractors)
-
-    df = df.replace([np.inf, -np.inf], 0.)
-    df = drop_columns_if_exist(df, ['open', 'high', 'low', 'close', 'volume'])
-    
-    df.columns = [prefix + "_" + c if 'date' not in c else c for c in df.columns]
-    return df
-
-
-def __apply_feature_extractors(df: pd.DataFrame,
-                            log_returns: bool,
-                            feature_extractors: list[tuple[str, FeatureExtractor, list[int]]]) -> pd.DataFrame:
-
-    for name, extractor, periods in feature_extractors:
-        for period in periods:
-            features = extractor(df, period, log_returns)
-            if type(features) == pd.DataFrame:
-                df = pd.concat([df, features], axis=1)
-            elif type(features) == pd.Series:
-                df[name + '_' + str(period)] = features
-            else:
-                assert False, "Feature extractor must return a pd.DataFrame or pd.Series"
-    return df
-
-
-def __create_target_cum_forward_returns(df: pd.DataFrame, source_column: str, period: int) -> pd.Series:
-    assert period > 0
-    return df[source_column].shift(-period)
-
-
-def __create_target_classes(df: pd.DataFrame, source_column: str, period: int, no_of_classes: Literal["two", "three"]) -> pd.Series:
-    assert period > 0
-
-    def get_class_binary(x: float) -> int:
-        return -1 if x <= 0.0 else 1
-
-    def get_class_threeway_balanced(series: pd.Series) -> pd.Series:
-
-        def get_bins_threeway(x):
-            bins = pd.qcut(df[source_column], 3, retbins=True, duplicates = 'drop')[1]
-            
-            if len(bins) != 4:
-                # if we don't have enough data for the quantiles, we'll need to add hard-coded values
-                lower_bound = bins[0]
-                upper_bound = bins[-1]
-                bins = [lower_bound] + [-0.02, 0.02] + [upper_bound]
-            return bins
-        bins = get_bins_threeway(series)
-
-        def map_class_threeway(current_value):
-            lower_threshold = bins[1]
-            upper_threshold = bins[2]
-            if current_value <= lower_threshold:
-                return -1
-            elif current_value > lower_threshold and current_value < upper_threshold:
-                return 0
-            else:
-                return 1
-        return series.map(map_class_threeway)
-
-    def get_class_threeway_imbalanced(series: pd.Series) -> pd.Series:
-
-        def get_bins_threeway(x):
-            bins = pd.qcut(df[source_column], 4, retbins=True, duplicates = 'drop')[1]
-            
-            if len(bins) != 5:
-                # if we don't have enough data for the quantiles, we'll need to add hard-coded values
-                lower_bound = bins[0]
-                upper_bound = bins[-1]
-                bins = [lower_bound] + [-0.02, 0.0, 0.02] + [upper_bound]
-            return bins
-        bins = get_bins_threeway(series)
-
-        def map_class_threeway(current_value):
-            lower_threshold = bins[1]
-            upper_threshold = bins[3]
-            if current_value <= lower_threshold:
-                return -1
-            elif current_value > lower_threshold and current_value < upper_threshold:
-                return 0
-            else:
-                return 1
-        return series.map(map_class_threeway)
-
-    target_column = df[source_column].shift(-period)
-    
-    if no_of_classes == "three-balanced":
-        return get_class_threeway_balanced(target_column)
-    elif no_of_classes == "three-imbalanced":
-        return get_class_threeway_imbalanced(target_column)
-    else:
-        return target_column.map(get_class_binary)
-
-
-def datasource_to_file(data_source: DataSource) -> str:
-    return data_source[0] + '/' + data_source[1] + '.csv'
-
-
-
-def load_only_returns(assets: DataCollection, returns: Literal['price', 'returns']) -> pd.DataFrame:
-
-    assets_future = [__load_df.remote(
-        data_source=data_source,
-        prefix=data_source[1],
-        returns=returns,
-        feature_extractors=[],
-    ) for data_source in assets]
-    target_asset_df = ray.get(assets_future)
-
-    dfs = [deduplicate_indexes(df) for df in target_asset_df]
-    dfs = pd.concat(dfs, axis=1)
-    dfs.index = pd.DatetimeIndex(dfs.index)
-
-    return dfs
@@ -0,0 +1,19 @@
+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_labeling + 100
@@ -1,13 +0,0 @@
-import pandas as pd
-from config.config import Config
-from utils.helpers import has_enough_samples_to_train
-import warnings
-
-def check_data(X:pd.DataFrame, y:pd.Series, config: Config):
-    """ Returns True if data is valid, else returns False."""
-    
-    if has_enough_samples_to_train(X, y, config) == False:
-        warnings.warn("Not enough samples to train")
-        return False
-    
-    return True
@@ -0,0 +1,11 @@
+import pandas as pd
+
+Path = str
+FileName = str
+DataSource = tuple[Path, FileName]
+DataCollection = list[DataSource]
+
+ReturnSeries = pd.Series
+ForwardReturnSeries = pd.Series
+XDataFrame = pd.DataFrame
+ySeries = pd.Series
@@ -0,0 +1,3 @@
+import pandas as pd
+
+def deduplicate_indexes(df: pd.DataFrame) -> pd.DataFrame: return df[~df.index.duplicated(keep='last')]
@@ -34,4 +34,5 @@ dependencies:
    - alphalens-reloaded
    - vectorbt
    - pydantic
+    - pandera[mypy]
 prefix: /usr/local/anaconda3/envs/quant
@@ -25,7 +25,7 @@
   "source": [
    "import pandas as pd\n",
    "import pandas_ta as ta\n",
-    "from config.config import get_default_ensemble_config\n",
+    "from config import get_default_ensemble_config\n",
    "from config.preprocess import preprocess_config\n",
    "from data_loader.load_data import load_data\n",
    "import seaborn as sns\n",
@@ -35,7 +35,7 @@
    "model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)\n",
    "\n",
    "config.target_asset'] = config.assets'][0]\n",
-    "X, y, target_returns = load_data(**data_config)"
+    "X, returns, forward_returns = load_data(**data_config)"
   ]
  },
  {
@@ -1,7 +1,7 @@
-from feature_extractors.feature_extractors import feature_lag, feature_mom, feature_ROC, feature_RSI, feature_STOD, feature_STOK, feature_expanding_zscore, feature_vol, feature_day_of_month, feature_day_of_week, feature_month, feature_debug_future_lookahead
-from utils.types import FeatureExtractorConfig
-from utils.helpers import flatten
-from feature_extractors.fractional_differentiation import feature_fractional_differentiation, feature_fractional_differentiation_log
+
+from .types import FeatureExtractorConfig
+from .feature_extractors import feature_lag, feature_mom, feature_ROC, feature_RSI, feature_STOD, feature_STOK, feature_expanding_zscore, feature_vol, feature_day_of_month, feature_day_of_week, feature_month, feature_debug_future_lookahead
+from .fractional_differentiation import feature_fractional_differentiation, feature_fractional_differentiation_log

 __presets = dict(
    debug_future_lookahead = [('debug_future', feature_debug_future_lookahead, [1])],
@@ -3,46 +3,43 @@ import numpy as np
 from feature_extractors.utils import get_close_low_high
 from feature_extractors.utils import apply_log_if_necessary_series

-def feature_debug_future_lookahead(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_debug_future_lookahead(df: pd.DataFrame, period: int) -> pd.Series:
    return df['returns'].shift(-period)

-def feature_lag(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_lag(df: pd.DataFrame, period: int) -> pd.Series:
    assert period > 0
    return df['returns'].shift(period)

-def feature_expanding_zscore(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_expanding_zscore(df: pd.DataFrame, period: int) -> pd.Series:
    close = df['close']
    return (close - close.expanding(period).mean()) / close.expanding(period).std()

-def feature_day_of_week(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
+def feature_day_of_week(df: pd.DataFrame, period: int) -> pd.DataFrame:
    return pd.get_dummies(pd.DatetimeIndex(df.index).dayofweek, drop_first=True, prefix="date_day_week").set_index(df.index)

-def feature_day_of_month(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
+def feature_day_of_month(df: pd.DataFrame, period: int) -> pd.DataFrame:
    return pd.get_dummies(pd.DatetimeIndex(df.index).day, drop_first=True, prefix="date_day_month").set_index(df.index)

-def feature_month(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.DataFrame:
+def feature_month(df: pd.DataFrame, period: int) -> pd.DataFrame:
    return pd.get_dummies(pd.DatetimeIndex(df.index).month, drop_first=True, prefix="date_month").set_index(df.index)

-def feature_vol(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_vol(df: pd.DataFrame, period: int) -> pd.Series:
    return df['returns'].rolling(period).std() * (252**0.5)

-def feature_mom(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
-    if is_log_return:
-        return np.log(df['close']).diff(period)
-    else:
-        return df['close'].pct_change(period)
+def feature_mom(df: pd.DataFrame, period: int) -> pd.Series:
+    return np.log(df['close']).diff(period)

-def feature_STOK(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_STOK(df: pd.DataFrame, period: int) -> pd.Series:
    close, low, high = get_close_low_high(df)

    STOK = ((close - low.rolling(period).min()) / (high.rolling(period).max() - low.rolling(period).min())) * 100
    return apply_log_if_necessary_series(STOK, "stok")

-def feature_STOD(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
-    stok = feature_STOK(df, period, is_log_return)
+def feature_STOD(df: pd.DataFrame, period: int) -> pd.Series:
+    stok = feature_STOK(df, period)
    return stok.rolling(3).mean()

-def feature_RSI(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_RSI(df: pd.DataFrame, period: int) -> pd.Series:
    returns = df['returns']
    delta = returns.diff().dropna()
    u=delta*0
@@ -55,7 +52,7 @@ def feature_RSI(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series
    d.ewm(com=period-1, adjust=False).mean()
    return apply_log_if_necessary_series(100-100/(1+rs), "rsi")

-def feature_ROC(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_ROC(df: pd.DataFrame, period: int) -> pd.Series:
    returns = df['returns']
    M = returns.diff(period - 1)
    N = returns.shift(period - 1)
@@ -3,13 +3,13 @@ import pandas as pd
 import numpy as np
 from feature_extractors.utils import apply_log_if_necessary_series

-def feature_fractional_differentiation(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_fractional_differentiation(df: pd.DataFrame, period: int) -> pd.Series:
    frac_diff = FracdiffStat(window = period)
    input_series = df["close"].to_numpy().reshape(-1, 1)
    result = frac_diff.fit_transform(input_series)
    return pd.Series(result.squeeze(), index = df.index)

-def feature_fractional_differentiation_log(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_fractional_differentiation_log(df: pd.DataFrame, period: int) -> pd.Series:
    series = feature_fractional_differentiation(df, period, is_log_return)
    return apply_log_if_necessary_series(series, "fracdiff")
    
@@ -2,7 +2,7 @@ import pandas_ta as ta
 import pandas as pd
 from feature_extractors.utils import get_close_low_high

-def feature_EBSW(df: pd.DataFrame, period: int, is_log_return: bool) -> pd.Series:
+def feature_EBSW(df: pd.DataFrame, period: int) -> pd.Series:
    close, low, high = get_close_low_high(df)

    return ta.ebsw(close, period)
@@ -3,12 +3,7 @@ import pandas as pd

 Period = int
 IsLogReturn = bool
-FeatureExtractor = Callable[[pd.DataFrame, Period, IsLogReturn], Union[pd.DataFrame, pd.Series]]
+FeatureExtractor = Callable[[pd.DataFrame, Period], Union[pd.DataFrame, pd.Series]]
 Name = str
 FeatureExtractorConfig = tuple[Name, FeatureExtractor, list[Period]]
-Path = str
-FileName = str
-DataSource = tuple[Path, FileName]
-DataCollection = list[DataSource]
-
-ScalerTypes = Literal['normalize', 'minmax', 'standardize', 'none']
+ScalerTypes = Literal['normalize', 'minmax', 'standardize', 'none']
@@ -0,0 +1,60 @@
+
+
+from ..types import EventFilter
+from data_loader.types import ReturnSeries
+import pandas as pd
+
+class CUSUMVolatilityEventFilter(EventFilter):
+
+    def __init__(self, vol_period: int):
+        self.vol_period = vol_period
+
+    def get_event_start_times(self, returns: ReturnSeries) -> pd.DatetimeIndex:
+
+        rolling_vol = returns.rolling(self.vol_period).std() * 0.15
+
+        filtered_indices = []
+        pos_threshold = 0
+        neg_threshold = 0
+        diff = returns.diff()
+        for index in diff.index[1:]:
+            pos_threshold, neg_threshold = (
+                max(0, pos_threshold + diff.loc[index]),
+                min(0, neg_threshold + diff.loc[index]),
+            )
+
+            if neg_threshold < -rolling_vol[index]:
+                neg_threshold = 0
+                filtered_indices.append(index)
+
+            elif pos_threshold > rolling_vol[index]:
+                pos_threshold = 0
+                filtered_indices.append(index)
+
+        return pd.DatetimeIndex(filtered_indices)
+
+class CUSUMFixedEventFilter(EventFilter):
+
+    def __init__(self, threshold: float):
+        self.threshold = threshold
+
+    def get_event_start_times(self, returns: ReturnSeries) -> pd.DatetimeIndex:
+        filtered_indices = []
+        pos_threshold = 0
+        neg_threshold = 0
+        diff = returns.diff()
+        for index in diff.index[1:]:
+            pos_threshold, neg_threshold = (
+                max(0, pos_threshold + diff.loc[index]),
+                min(0, neg_threshold + diff.loc[index]),
+            )
+
+            if neg_threshold < -self.threshold:
+                neg_threshold = 0
+                filtered_indices.append(index)
+
+            elif pos_threshold > self.threshold:
+                pos_threshold = 0
+                filtered_indices.append(index)
+
+        return pd.DatetimeIndex(filtered_indices)
@@ -0,0 +1,9 @@
+from ..types import EventFilter
+from data_loader.types import ReturnSeries
+import pandas as pd
+
+class NoEventFilter(EventFilter):
+
+    def get_event_start_times(self, returns: ReturnSeries) -> pd.DatetimeIndex:
+        return returns.index
+
@@ -0,0 +1,8 @@
+from .event_filters.nofilter import NoEventFilter
+from .event_filters.cusum import CUSUMVolatilityEventFilter, CUSUMFixedEventFilter
+
+eventfilters_map = dict(
+    none = NoEventFilter(),
+    cusum_vol = CUSUMVolatilityEventFilter(vol_period = 20),
+    cusum_fixed = CUSUMFixedEventFilter(threshold = 0.05)
+)
@@ -0,0 +1,44 @@
+from data_loader.types import ForwardReturnSeries
+from ..types import EventLabeller, EventsDataFrame
+import pandas as pd
+
+class FixedTimeHorionThreeClassBalancedEventLabeller(EventLabeller):
+
+    time_horizon: int
+
+    def __init__(self, time_horizon: int = 1):
+        self.time_horizon = time_horizon
+
+    def label_events(self, event_start_times: pd.DatetimeIndex, forward_returns: ForwardReturnSeries) -> EventsDataFrame:
+
+        event_candidates = forward_returns[event_start_times]
+
+        def get_bins_threeway(x):
+            bins = pd.qcut(event_candidates, 3, retbins=True, duplicates = 'drop')[1]
+
+            if len(bins) != 4:
+                # if we don't have enough data for the quantiles, we'll need to add hard-coded values
+                lower_bound = bins[0]
+                upper_bound = bins[-1]
+                bins = [lower_bound] + [-0.02, 0.02] + [upper_bound]
+            return bins
+        bins = get_bins_threeway(event_candidates)
+
+        def map_class_threeway(current_value):
+            lower_threshold = bins[1]
+            upper_threshold = bins[2]
+            if current_value <= lower_threshold:
+                return -1
+            elif current_value > lower_threshold and current_value < upper_threshold:
+                return 0
+            else:
+                return 1
+        labels = event_candidates.map(map_class_threeway)
+        
+        return pd.DataFrame({
+            'start': event_start_times,
+            'end': event_start_times + pd.Timedelta(days=self.time_horizon),
+            'label': labels,
+            'returns': forward_returns[event_start_times]
+        })
+
@@ -0,0 +1,44 @@
+from ..types import EventLabeller, EventsDataFrame, ForwardReturnSeries
+import pandas as pd
+
+class FixedTimeHorionThreeClassImbalancedEventLabeller(EventLabeller):
+
+    time_horizon: int
+
+    def __init__(self, time_horizon: int = 1):
+        self.time_horizon = time_horizon
+
+    def label_events(self, event_start_times: pd.DatetimeIndex, forward_returns: ForwardReturnSeries) -> EventsDataFrame:
+
+        event_candidates = forward_returns[event_start_times]
+
+        def get_bins_threeway(x):
+            bins = pd.qcut(event_candidates, 4, retbins=True, duplicates = 'drop')[1]
+
+            if len(bins) != 5:
+                # if we don't have enough data for the quantiles, we'll need to add hard-coded values
+                lower_bound = bins[0]
+                upper_bound = bins[-1]
+                bins = [lower_bound] + [-0.02, 0.0, 0.02] + [upper_bound]
+            return bins
+        bins = get_bins_threeway(event_candidates)
+
+        def map_class_threeway(current_value):
+            lower_threshold = bins[1]
+            upper_threshold = bins[3]
+            if current_value <= lower_threshold:
+                return -1
+            elif current_value > lower_threshold and current_value < upper_threshold:
+                return 0
+            else:
+                return 1
+        labels = event_candidates.map(map_class_threeway)
+        
+        return pd.DataFrame({
+            'start': event_start_times,
+            'end': event_start_times + pd.Timedelta(days=self.time_horizon),
+            'label': labels,
+            'returns': forward_returns[event_start_times]
+        })
+
+
@@ -0,0 +1,29 @@
+
+
+from ..types import EventLabeller, EventsDataFrame, ForwardReturnSeries
+import pandas as pd
+
+class FixedTimeHorionTwoClassEventLabeller(EventLabeller):
+
+    time_horizon: int
+
+    def __init__(self, time_horizon: int = 1):
+        self.time_horizon = time_horizon
+
+    def label_events(self, event_start_times: pd.DatetimeIndex, forward_returns: ForwardReturnSeries) -> EventsDataFrame:
+
+        event_candidates = forward_returns[event_start_times]
+
+        def get_class_binary(x: float) -> int:
+            return -1 if x <= 0.0 else 1
+        labels = event_candidates.map(get_class_binary)
+        
+        return pd.DataFrame({
+            'start': event_start_times,
+            'end': event_start_times + pd.Timedelta(days=self.time_horizon),
+            'label': labels,
+            'returns': forward_returns[event_start_times]
+        })
+
+
+
@@ -0,0 +1,9 @@
+from .labellers.fixed_time_three_class_balanced import FixedTimeHorionThreeClassBalancedEventLabeller
+from .labellers.fixed_time_three_class_imbalanced import FixedTimeHorionThreeClassImbalancedEventLabeller
+from .labellers.fixed_time_two_class import FixedTimeHorionTwoClassEventLabeller
+
+labellers_map = dict(
+    two_class = FixedTimeHorionTwoClassEventLabeller(),
+    three_class_balanced = FixedTimeHorionThreeClassBalancedEventLabeller(),
+    three_class_imbalanced = FixedTimeHorionThreeClassImbalancedEventLabeller()
+)
@@ -0,0 +1,17 @@
+from .types import EventFilter, EventLabeller, EventsDataFrame
+from data_loader.types import ForwardReturnSeries, XDataFrame, ReturnSeries, ySeries
+
+def label_data(
+            event_filter: EventFilter,
+            event_labeller: EventLabeller,
+            X: XDataFrame,
+            returns: ReturnSeries,
+            forward_returns: ForwardReturnSeries) -> tuple[EventsDataFrame, XDataFrame, ySeries, ForwardReturnSeries]:
+    event_start_times = event_filter.get_event_start_times(returns)
+    events = event_labeller.label_events(event_start_times, forward_returns)
+    
+    X = X.filter(items = events.index, axis = 0)
+    y = events['label']
+    forward_returns = events['returns']
+
+    return events, X, y, forward_returns
@@ -0,0 +1,28 @@
+from data_loader.types import ReturnSeries, ForwardReturnSeries
+from abc import ABC, abstractmethod
+import pandas as pd
+import pandera as pa
+from pandera.typing import DataFrame, Series
+
+class EventFilter(ABC):
+
+    @abstractmethod
+    def get_event_start_times(self, returns: ReturnSeries) -> pd.DatetimeIndex:
+        raise NotImplementedError
+
+
+class EventSchema(pa.SchemaModel):
+    start: Series[pd.Timestamp]
+    end: Series[pd.Timestamp]
+    label: Series[int]
+    returns: Series[float]
+
+EventsDataFrame = DataFrame[EventSchema]
+
+
+class EventLabeller(ABC):
+
+    @abstractmethod
+    def label_events(self, event_start_times: pd.DatetimeIndex, forward_returns: ForwardReturnSeries) -> EventsDataFrame:
+        raise NotImplementedError
+
@@ -3,8 +3,6 @@ from typing import Literal, Optional, Union
 from abc import ABC, abstractmethod
 import numpy as np

-# import numpy as np
-
 class Model(ABC):

    method: Literal["regression", "classification"]
@@ -1,100 +1,92 @@
 from models.sklearn import SKLearnModel
 from sklearnex.ensemble import RandomForestClassifier
 from sklearnex.ensemble import RandomForestRegressor
+from .base import Model

 default_feature_selector_classification = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
 default_feature_selector_regression = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1), 'regression')

-def get_model_map(config:dict):
+def get_model(model_name: str) -> Model:

-    model_map = {
-        "primary_models": dict(),
-        "ensemble_models": dict(),
-    }
-    
-    combined_list = config['primary_models'] + config['meta_labeling_models'] + [config['ensemble_model']]
-    for model_name in combined_list:
-        if model_name == 'LinearRegression':
-            from sklearn.linear_model import LinearRegression
-            model_map['primary_models']['LR'] = SKLearnModel(LinearRegression(n_jobs=-1), 'regression')
-        elif model_name == 'Lasso':
-            from sklearn.linear_model import Lasso
-            model_map['primary_models']['Lasso'] = SKLearnModel(Lasso(alpha=100, random_state=1), 'regression')
-        elif model_name == 'Ridge':
-            from sklearn.linear_model import Ridge
-            model_map['primary_models']['Ridge'] = SKLearnModel(Ridge(alpha=0.1), 'regression')
-        elif model_name == 'BayesianRidge':
-            from sklearn.linear_model import BayesianRidge
-            model_map['primary_models']['BayesianRidge'] = SKLearnModel(BayesianRidge(), 'regression')
-        elif model_name == 'KNN':
-            from sklearnex.neighbors import KNeighborsRegressor
-            model_map['primary_models']['KNN'] = SKLearnModel(KNeighborsRegressor(n_neighbors=25), 'regression')
-        elif model_name == 'AB':
-            from sklearn.ensemble import AdaBoostRegressor
-            model_map['primary_models']['AB'] = SKLearnModel(AdaBoostRegressor(random_state=1), 'regression')
-        elif model_name == 'MLP':
-            from sklearn.neural_network import MLPRegressor
-            model_map['primary_models']['MLP'] = SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000), 'regression')
-        elif model_name == 'RFR':
-            # from sklearn.ensemble import RandomForestRegressor
-            model_map['primary_models']['RFR'] = SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1), 'regression')
-        elif model_name == 'SVR':
-            from sklearnex.svm import SVR
-            model_map['primary_models']['SVR'] = SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1), 'regression')
-        elif model_name == 'StaticNaive':
-            from models.naive import StaticNaiveModel
-            model_map['primary_models']['StaticNaive'] = StaticNaiveModel()
-        elif model_name == 'DNN':
-            from models.neural import LightningNeuralNetModel
-            from models.pytorch.neural_nets import MultiLayerPerceptron
-            import torch.nn.functional as F
-            model_map['primary_models']['DNN'] = LightningNeuralNetModel(
-                MultiLayerPerceptron(
-                    hidden_layers_ratio = [1.0], 
-                    probabilities = False, 
-                    loss_function = F.mse_loss), 
-                max_epochs=15
-            )
-        
-        elif model_name == 'LogisticRegression_two_class':
-            from sklearn.linear_model import LogisticRegression
-            model_map['primary_models']['LogisticRegression_two_class'] = SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000), 'classification')
-        elif model_name == 'LogisticRegression_three_class':
-            from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
-            model_map['primary_models']['LogisticRegression_three_class'] = SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1), 'classification')
-        elif model_name == 'LDA':
-            from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-            model_map['primary_models']['LDA'] = SKLearnModel(LinearDiscriminantAnalysis(), 'classification')
-        elif model_name == 'KNN':
-            from sklearn.neighbors import KNeighborsClassifier
-            model_map['primary_models']['KNN'] = SKLearnModel(KNeighborsClassifier(), 'classification')
-        elif model_name == 'CART':
-            from sklearn.tree import DecisionTreeClassifier
-            model_map['primary_models']['CART'] = SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1), 'classification')
-        elif model_name == 'NB':
-            from sklearn.naive_bayes import GaussianNB
-            model_map['primary_models']['NB'] = SKLearnModel(GaussianNB(), 'classification')
-        elif model_name == 'AB':
-            from sklearn.ensemble import AdaBoostClassifier
-            model_map['primary_models']['AB'] = SKLearnModel(AdaBoostClassifier(n_estimators=15), 'classification')
-        elif model_name == 'RFC':
-            model_map['primary_models']['RFC'] = SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
-        elif model_name == 'SVC':
-            from sklearn.svm import SVC
-            model_map['primary_models']['SVC'] = SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1), 'classification')
-        elif model_name == 'XGB_two_class':
-            from xgboost import XGBClassifier
-            from models.xgboost import XGBoostModel
-            model_map['primary_models']['XGB_two_class'] = XGBoostModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', use_label_encoder= False, eval_metric='mlogloss'))
-        elif model_name == 'LGBM':
-            from lightgbm import LGBMClassifier
-            model_map['primary_models']['LGBM'] = SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
-        elif model_name == 'StaticMom':
-            from models.momentum import StaticMomentumModel
-            model_map['primary_models']['StaticMom'] = StaticMomentumModel(allow_short=True)
-        elif model_name == 'Average':
-            from models.average import StaticAverageModel
-            model_map['ensemble_models']['Average'] = StaticAverageModel()
-    
-    return model_map
+    if model_name == 'LinearRegression':
+        from sklearn.linear_model import LinearRegression
+        return SKLearnModel(LinearRegression(n_jobs=-1), 'regression')
+    elif model_name == 'Lasso':
+        from sklearn.linear_model import Lasso
+        return SKLearnModel(Lasso(alpha=100, random_state=1), 'regression')
+    elif model_name == 'Ridge':
+        from sklearn.linear_model import Ridge
+        return SKLearnModel(Ridge(alpha=0.1), 'regression')
+    elif model_name == 'BayesianRidge':
+        from sklearn.linear_model import BayesianRidge
+        return SKLearnModel(BayesianRidge(), 'regression')
+    elif model_name == 'KNN':
+        from sklearnex.neighbors import KNeighborsRegressor
+        return SKLearnModel(KNeighborsRegressor(n_neighbors=25), 'regression')
+    elif model_name == 'AB':
+        from sklearn.ensemble import AdaBoostRegressor
+        return SKLearnModel(AdaBoostRegressor(random_state=1), 'regression')
+    elif model_name == 'MLP':
+        from sklearn.neural_network import MLPRegressor
+        return SKLearnModel(MLPRegressor(hidden_layer_sizes=(100,20), max_iter=1000), 'regression')
+    elif model_name == 'RFR':
+        return SKLearnModel(RandomForestRegressor(n_jobs=-1, max_depth=20, random_state=1), 'regression')
+    elif model_name == 'SVR':
+        from sklearnex.svm import SVR
+        return SKLearnModel(SVR(kernel='rbf', C=1e3, gamma=0.1), 'regression')
+    elif model_name == 'StaticNaive':
+        from models.naive import StaticNaiveModel
+        return StaticNaiveModel()
+    elif model_name == 'DNN':
+        from models.neural import LightningNeuralNetModel
+        from models.pytorch.neural_nets import MultiLayerPerceptron
+        import torch.nn.functional as F
+        return LightningNeuralNetModel(
+            MultiLayerPerceptron(
+                hidden_layers_ratio = [1.0], 
+                probabilities = False, 
+                loss_function = F.mse_loss), 
+            max_epochs=15
+        )

+    elif model_name == 'LogisticRegression_two_class':
+        from sklearn.linear_model import LogisticRegression
+        return SKLearnModel(LogisticRegression(C=10, random_state=1, solver='liblinear', max_iter=1000), 'classification')
+    elif model_name == 'LogisticRegression_three_class':
+        from sklearnex.linear_model import LogisticRegression as LogisticRegression_EX
+        return SKLearnModel(LogisticRegression_EX(C=10, random_state=1, max_iter=1000, n_jobs=-1), 'classification')
+    elif model_name == 'LDA':
+        from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+        return SKLearnModel(LinearDiscriminantAnalysis(), 'classification')
+    elif model_name == 'KNN':
+        from sklearn.neighbors import KNeighborsClassifier
+        return SKLearnModel(KNeighborsClassifier(), 'classification')
+    elif model_name == 'CART':
+        from sklearn.tree import DecisionTreeClassifier
+        return SKLearnModel(DecisionTreeClassifier(max_depth=15, random_state=1), 'classification')
+    elif model_name == 'NB':
+        from sklearn.naive_bayes import GaussianNB
+        return SKLearnModel(GaussianNB(), 'classification')
+    elif model_name == 'AB':
+        from sklearn.ensemble import AdaBoostClassifier
+        return SKLearnModel(AdaBoostClassifier(n_estimators=15), 'classification')
+    elif model_name == 'RFC':
+        return SKLearnModel(RandomForestClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
+    elif model_name == 'SVC':
+        from sklearn.svm import SVC
+        return SKLearnModel(SVC(kernel='rbf', C=1e3, probability=True, random_state=1), 'classification')
+    elif model_name == 'XGB_two_class':
+        from xgboost import XGBClassifier
+        from models.xgboost import XGBoostModel
+        return XGBoostModel(XGBClassifier(n_jobs=-1, max_depth = 20, random_state=1, objective='binary:logistic', use_label_encoder= False, eval_metric='mlogloss'))
+    elif model_name == 'LGBM':
+        from lightgbm import LGBMClassifier
+        return SKLearnModel(LGBMClassifier(n_jobs=-1, max_depth=20, random_state=1), 'classification')
+    elif model_name == 'StaticMom':
+        from models.momentum import StaticMomentumModel
+        return StaticMomentumModel(allow_short=True)
+    elif model_name == 'Average':
+        from models.average import StaticAverageModel
+        return StaticAverageModel()
+    else:
+        raise Exception(f'Model {model_name} not found')
@@ -1,7 +1,7 @@
 from reporting.wandb import send_report_to_wandb
 import pandas as pd
 from utils.helpers import weighted_average
-from config.config import Config 
+from config.types import Config 

 def report_results(results:pd.DataFrame, all_predictions:pd.DataFrame, config: Config, wandb, sweep: bool, project_name:str):

@@ -1,6 +1,6 @@
 import pickle
 import datetime
-from config.config import Config
+from config.types import Config
 from typing import Optional, Union
 import os
 import warnings
@@ -1,7 +1,6 @@
 from __future__ import annotations
+from dataclasses import dataclass
 import pandas as pd
-from models.base import Model
-from typing import Optional, Union


 class Reporting:
@@ -14,12 +13,11 @@ class Reporting:
    def get_results(self) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, Reporting.Asset]:
        return self.results, self.all_predictions, self.all_probabilities, self.asset
    
-
+    @dataclass
    class Single_Model:
-        def __init__(self, model_name: str, model_over_time: pd.Series, transformations_over_time: list[pd.Series]):
-            self.model_name: str = model_name
-            self.model_over_time: pd.Series = model_over_time
-            self.transformations_over_time: list[pd.Series] = transformations_over_time
+        model_name: str
+        model_over_time: pd.Series
+        transformations_over_time: list[pd.Series]


    class Training_Step: 
@@ -37,8 +35,9 @@ class Reporting:
                structured_dict[model.model_name] = [(x.model_name, x.model_over_time, x.transformations_over_time) for x in self.metalabeling[i]]
            return structured_dict

+    @dataclass
    class Asset():
-        def __init__(self, ticker: str, primary: Reporting.Training_Step, secondary: Reporting.Training_Step):
-            self.name: str = ticker
-            self.primary: Reporting.Training_Step = primary
-            self.secondary: Reporting.Training_Step = secondary 
+        name: str
+        primary: Reporting.Training_Step
+        secondary: Reporting.Training_Step 
+
@@ -1,5 +1,5 @@
 import pandas as pd
-from config.config import RawConfig
+from config.types import RawConfig
 from typing import Optional
 from utils.helpers import weighted_average

@@ -1,11 +1,11 @@
 from run_pipeline import run_pipeline
-from config.config import get_default_ensemble_config, get_dev_config
+from config import get_default_ensemble_config, get_dev_config
 import pandas as pd

 all_results = []
 all_predictions = []
 for index in range(6):
-    _, _, _, _, results_1, predictions_1, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config= get_default_ensemble_config)
+    _, _, _, _, results_1, predictions_1, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, config= get_default_ensemble_config())
    all_results.append(results_1)
    all_predictions.append(predictions_1)

@@ -1,24 +1,21 @@
 import pandas as pd
 import ssl
 from tqdm import tqdm
+from data_loader.utils import deduplicate_indexes

 base_url = "https://www.cryptodatadownload.com/cdd/"

 exchange_name = "Bitfinex"
+period = "minute" # 1h
 files_to_download = [
- exchange_name + '_TRXUSD_1h.csv',
- exchange_name + '_ETHUSD_1h.csv',
- exchange_name + '_XLMUSD_1h.csv',
- exchange_name + '_XMRUSD_1h.csv',
- exchange_name + '_LTCUSD_1h.csv',
-#  exchange_name + '_FILUSD_1h.csv',
- exchange_name + '_DASHUSD_1h.csv',
-#  exchange_name + '_LINKUSD_1h.csv',
-#  exchange_name + '_SOLUSD_1h.csv',
- exchange_name + '_BTCUSD_1h.csv',
- exchange_name + '_ETCUSD_1h.csv',
-#  exchange_name + '_VETUSD_1h.csv',
-#  exchange_name + '_DOTUSD_1h.csv'
+ exchange_name + '_TRXUSD_' + period + '.csv',
+ exchange_name + '_ETHUSD_' + period + '.csv',
+ exchange_name + '_XLMUSD_' + period + '.csv',
+ exchange_name + '_XMRUSD_' + period + '.csv',
+ exchange_name + '_LTCUSD_' + period + '.csv',
+ exchange_name + '_DASHUSD_' + period + '.csv',
+ exchange_name + '_BTCUSD_' + period + '.csv',
+ exchange_name + '_ETCUSD_' + period + '.csv',
 ]


@@ -31,4 +28,5 @@ for file in tqdm(files_to_download):
    data.rename({'Volume USD': 'volume'}, axis=1, inplace=True)
    data.index.rename('time', inplace=True)
    target_file = file.split('_')[1].replace('USD', '') + '_USD'
-    data.to_csv(f"data/hourly_crypto/{target_file}.csv")
+    data = deduplicate_indexes(data)
+    data.to_csv(f"data/minute_crypto/{target_file}.csv")
@@ -1,21 +1,22 @@
-from data_loader.load_data import load_data
-from data_loader.process_data import check_data
+from data_loader import load_data
+from data_loader.process import check_data
 from reporting.saving import load_models

 from run_pipeline import run_pipeline
-from config.config import Config, get_dev_config, get_default_ensemble_config, get_lightweight_ensemble_config
-
+from config.types import Config, RawConfig
+from config.presets import get_dev_config, get_default_ensemble_config, get_lightweight_ensemble_config
+from labeling.process import label_data
 from typing import Callable, Optional
 from reporting.types import Reporting
 from training.training_steps import primary_step, secondary_step
 import pandas as pd
 import warnings

-def run_inference(preload_models:bool, get_config:Callable):
+def run_inference(preload_models:bool, raw_config: RawConfig):
    if preload_models:
        all_models, config = load_models(None)
    else:
-        all_models, config, _, _, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_config)
+        all_models, config, _, _, _ = run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=raw_config)
    
    __inference(config, all_models.primary, all_models.secondary)

@@ -25,35 +26,35 @@ def __inference(config: Config, primary_models: Optional[Reporting.Training_Step
    asset = config.target_asset
    
    # 1. Load data, check for validity and process data
-    X, y, target_returns = load_data(
+    X, returns, forward_returns = load_data(
        assets = config.assets,
        other_assets = config.other_assets,
        exogenous_data = config.exogenous_data,
        target_asset = config.target_asset,
        load_non_target_asset = config.load_non_target_asset,
-        log_returns = config.log_returns,
-        forecasting_horizon = config.forecasting_horizon,
        own_features = config.own_features,
        other_features = config.other_features,
        exogenous_features = config.exogenous_features,
-        no_of_classes = config.no_of_classes,
    )
-    assert check_data(X, y, config) == True, "Data is not valid. Cancelling Inference." 
+    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)

    inference_from: pd.Timestamp = X.index[len(X.index) - 2]
+
    # 2. Train a Primary model with optional metalabeling for each asset
-    training_step_primary, current_predictions = primary_step(X, y, target_returns, config, reporting, from_index = inference_from, preloaded_training_step = primary_models)
+    training_step_primary, current_predictions = primary_step(X, y, forward_returns, config, reporting, from_index = inference_from, preloaded_training_step = primary_models)

    # 3. Train an Ensemble model with optional metalabeling for each asset
    if secondary_models is not None:
        warnings.warn("Secondary models are not specified.")
-        training_step_secondary = secondary_step(X, y, current_predictions, target_returns, config, reporting, from_index = inference_from, preloaded_training_step = secondary_models)
+        training_step_secondary = secondary_step(X, y, current_predictions, forward_returns, config, reporting, from_index = inference_from, preloaded_training_step = secondary_models)

    # 4. Save the models
-    reporting.asset = Reporting.Asset(ticker=asset[1], primary=training_step_primary, secondary=training_step_secondary)
+    reporting.asset = Reporting.Asset(name=asset[1], primary=training_step_primary, secondary=training_step_secondary)

    return reporting


 if __name__ == '__main__':
-    run_inference(preload_models=True, get_config=get_lightweight_ensemble_config)
+    run_inference(preload_models=True, raw_config=get_lightweight_ensemble_config())
@@ -1,5 +1,5 @@
 from run_pipeline import run_pipeline
-from config.config import get_dev_config
+from config.presets import get_dev_config


-run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_dev_config)
+run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_dev_config())
@@ -1,27 +1,29 @@
 import pandas as pd
 from typing import Callable, Optional

-from data_loader.load_data import load_data
-from data_loader.process_data import check_data
+from config.types import Config, RawConfig
+from config.preprocess import preprocess_config, validate_config
+from config.presets import get_default_ensemble_config, get_lightweight_ensemble_config
+
+from data_loader.load import load_data
+from data_loader.process import check_data
+
+from labeling.process import label_data

 from reporting.wandb import launch_wandb, override_config_with_wandb_values
 from reporting.reporting import report_results

 from reporting.saving import save_models
-
-from config.config import Config, get_default_ensemble_config, get_lightweight_ensemble_config
-from config.preprocess import validate_config, preprocess_config
+from reporting.types import Reporting

 from training.training_steps import primary_step, secondary_step

-from reporting.types import Reporting
-
 import ray
 ray.init()


-def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Reporting.Asset, Config, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
-    wandb, config = __setup_config(project_name, with_wandb, sweep, get_config)
+def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[Reporting.Asset, Config, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    wandb, config = __setup_config(project_name, with_wandb, sweep, raw_config)
    reporting = __run_training(config) 
    results, all_predictions, all_probabilities, all_models = reporting.get_results()
    report_results(results, all_predictions, config, wandb, sweep, project_name)
@@ -30,8 +32,7 @@ def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config: Ca
    return all_models, config, results, all_predictions, all_probabilities
    

-def __setup_config(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Optional[object], Config]:
-    raw_config = get_config() 
+def __setup_config(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[Optional[object], Config]:
    wandb = None
    if with_wandb: 
        wandb = launch_wandb(project_name=project_name, default_config=raw_config, sweep=sweep)
@@ -48,29 +49,30 @@ def __run_training(config: Config):
    reporting = Reporting()
    
    # 1. Load data, check for validity
-    X, y, target_returns = load_data(
+    X, returns, forward_returns = load_data(
        assets = config.assets,
        other_assets = config.other_assets,
        exogenous_data = config.exogenous_data,
        target_asset = config.target_asset,
        load_non_target_asset = config.load_non_target_asset,
-        log_returns = config.log_returns,
-        forecasting_horizon = config.forecasting_horizon,
        own_features = config.own_features,
        other_features = config.other_features,
        exogenous_features = config.exogenous_features,
-        no_of_classes = config.no_of_classes,
    )
-    assert check_data(X, y, config) == True, "Data is not valid." 

-    # 2. Train a Primary model with optional metalabeling for each asset
-    training_step_primary, current_predictions = primary_step(X, y, target_returns, config, reporting, from_index = None)
+    assert check_data(X, config) == True, "Data is not valid." 
+
+    # 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 a Primary model with optional metalabeling for each asset
+    training_step_primary, current_predictions = primary_step(X, y, forward_returns, config, reporting, from_index = None)
    
-    # 3. Train an Ensemble model with optional metalabeling for each asset
-    training_step_secondary = secondary_step(X, y, current_predictions, target_returns, config, reporting, from_index = None)
+    # 4. Train an Ensemble model with optional metalabeling for each asset
+    training_step_secondary = secondary_step(X, y, current_predictions, forward_returns, config, reporting, from_index = None)
    
-    # 4. Save the models
-    reporting.asset = Reporting.Asset(ticker= config.target_asset[1], primary=training_step_primary, secondary=training_step_secondary)
+    # 5. Save the models
+    reporting.asset = Reporting.Asset(name = config.target_asset[1], primary = training_step_primary, secondary = training_step_secondary)
    
    return reporting
    
@@ -78,4 +80,4 @@ def __run_training(config: Config):

    
 if __name__ == '__main__':
-    run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, get_config=get_default_ensemble_config)
+    run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_default_ensemble_config())
@@ -1,8 +1,7 @@
 #%%
 import pandas as pd
 import numpy as np
-from data_loader.load_data import load_only_returns
-from data_loader.collections import data_collections
+from data_loader import load_only_returns, data_collections

 from utils.helpers import get_first_valid_return_index
 import alphalens
@@ -109,9 +108,10 @@ first_index = get_first_valid_return_index(predictions[predictions.columns[0]])
 predictions = predictions.iloc[first_index:]

 close = load_only_returns(data_collections['daily_crypto'], 'price')
-close = close.iloc[first_index:-1]
+close = close.iloc[first_index:]
 close.columns = [col.replace("_returns", "") for col in close.columns]
 close = close[predictions.columns]
+close = close.filter(items = predictions.index, axis = 0)

 availability = close.applymap(lambda x: 0 if x == 0.0 or x == 0 or np.isnan(x) else 1)

@@ -1,4 +1,4 @@
 from run_pipeline import run_pipeline
-from config.config import get_default_ensemble_config
+from config.presets import get_default_ensemble_config

-run_pipeline(project_name='price-prediction', with_wandb = True, sweep = True, get_config= get_default_ensemble_config)
+run_pipeline(project_name='price-prediction', with_wandb = True, sweep = True, raw_config= get_default_ensemble_config())
@@ -73,7 +73,7 @@ def test_evaluation():
        model=model,
        X=X,
        y=y,
-        target_returns=y,
+        forward_returns=y,
        expanding_window=False,
        window_size=window_length,
        retrain_every=10,
@@ -94,12 +94,12 @@ def test_evaluation():
    for i in range(window_length+2, no_of_rows):
        assert predictions[i] == y[i]

-    fake_target_returns = y * 0.1
+    fake_forward_returns = y * 0.1
    processed_predictions_to_match_returns = predictions * 0.1

    result = evaluate_predictions(
        model_name='test',
-        target_returns=fake_target_returns,
+        forward_returns=fake_forward_returns,
        y_pred=processed_predictions_to_match_returns,
        y_true=y,
        no_of_classes='two',
@@ -71,7 +71,7 @@ def test_walk_forward_train_test():
        model=model,
        X=X,
        y=y,
-        target_returns=y,
+        forward_returns=y,
        expanding_window=False,
        window_size=window_length,
        retrain_every=10,
@@ -5,14 +5,14 @@ import pandas as pd
 from models.base import Model
 from reporting.types import Reporting
 from typing import Union, Optional
-from config.config import Config
+from config.types import Config

 def train_meta_labeling_model(
                            target_asset: str,
                            X: pd.DataFrame,
                            input_predictions: pd.Series,
                            y: pd.Series,
-                            target_returns: pd.Series,
+                            forward_returns: pd.Series,
                            models: list[tuple[str, Model]],
                            config: Config,
                            model_suffix: str,
@@ -30,7 +30,7 @@ def train_meta_labeling_model(
        ticker_to_predict = "prediction_correct",
        X = meta_X,
        y = meta_y,
-        target_returns = target_returns,
+        forward_returns = forward_returns,
        models = models,
        expanding_window = config.expanding_window_meta_labeling,
        sliding_window_size = config.sliding_window_size_meta_labeling,
@@ -52,7 +52,7 @@ def train_meta_labeling_model(

    meta_result = evaluate_predictions(
        model_name = "Meta",
-        target_returns = target_returns,
+        forward_returns = forward_returns,
        y_pred = avg_predictions_with_sizing,
        y_true = y,
        no_of_classes = 'two',
@@ -3,8 +3,7 @@ from typing import Literal, Optional, Union
 from training.walk_forward import walk_forward_train, walk_forward_inference
 from utils.evaluate import evaluate_predictions
 from models.base import Model
-from utils.scaler import get_scaler
-from utils.types import ScalerTypes
+from transformations.scaler import get_scaler, ScalerTypes
 from reporting.types import Reporting
 from transformations.rfe import RFETransformation
 from transformations.pca import PCATransformation
@@ -13,7 +12,7 @@ def train_primary_model(
                    ticker_to_predict: str,
                    X: pd.DataFrame,
                    y: pd.Series,
-                    target_returns: pd.Series,
+                    forward_returns: pd.Series,
                    models: list[tuple[str, Model]],
                    expanding_window: bool,
                    sliding_window_size: int,
@@ -46,7 +45,7 @@ def train_primary_model(
                model = model,
                X = X,
                y = y,
-                target_returns = target_returns,
+                forward_returns = forward_returns,
                expanding_window = expanding_window,
                window_size = sliding_window_size,
                retrain_every = retrain_every,
@@ -76,7 +75,7 @@ def train_primary_model(
        assert len(preds) == len(y)
        result = evaluate_predictions(
            model_name = model_name,
-            target_returns = target_returns,
+            forward_returns = forward_returns,
            y_pred = preds,
            y_true = y,
            no_of_classes=no_of_classes,
@@ -7,13 +7,13 @@ from training.meta_labeling import train_meta_labeling_model

 from reporting.types import Reporting
 from typing import Union, Optional
-from config.config import Config 
+from config.types import Config 


 def primary_step(
                X: pd.DataFrame,
                y: pd.Series,
-                target_returns: pd.Series,
+                forward_returns: pd.Series,
                config: Config,
                reporting: Reporting,
                from_index: Optional[pd.Timestamp],
@@ -26,7 +26,7 @@ def primary_step(
        ticker_to_predict = config.target_asset[1],
        X = X,
        y = y,
-        target_returns = target_returns,
+        forward_returns = forward_returns,
        models = config.primary_models,
        expanding_window = config.expanding_window_base,
        sliding_window_size = config.sliding_window_size_base,
@@ -50,7 +50,7 @@ def primary_step(
                X = X,
                input_predictions= primary_model_predictions,
                y = y,
-                target_returns = target_returns,
+                forward_returns = forward_returns,
                model_suffix = 'meta',
                models = config.meta_labeling_models,
                config = config,
@@ -75,7 +75,7 @@ def secondary_step(
                X:pd.DataFrame,
                y:pd.Series,
                current_predictions:pd.DataFrame,
-                target_returns:pd.Series,
+                forward_returns:pd.Series,
                config: Config,
                reporting: Reporting,
                from_index: Optional[pd.Timestamp],
@@ -89,7 +89,7 @@ def secondary_step(
            ticker_to_predict = config.target_asset[1],
            X = current_predictions,
            y = y,
-            target_returns = target_returns,
+            forward_returns = forward_returns,
            models = [config.ensemble_model],
            expanding_window = False,
            sliding_window_size = 1,
@@ -117,7 +117,7 @@ def secondary_step(
                X = X,
                input_predictions= ensemble_predictions,
                y = y,
-                target_returns = target_returns,
+                forward_returns = forward_returns,
                models = config.meta_labeling_models,
                config = config,
                model_suffix = 'ensemble',
@@ -11,7 +11,7 @@ def walk_forward_train(
                        model: Model,
                        X: pd.DataFrame,
                        y: pd.Series,
-                        target_returns: pd.Series,
+                        forward_returns: pd.Series,
                        expanding_window: bool,
                        window_size: int,
                        retrain_every: int,
@@ -23,7 +23,7 @@ def walk_forward_train(
    models_over_time = pd.Series(index=y.index).rename(model_name)
    transformations_over_time = [pd.Series(index=y.index).rename(t.get_name()) for t in transformations]

-    first_nonzero_return = max(get_first_valid_return_index(target_returns), get_first_valid_return_index(X.iloc[:,0]), get_first_valid_return_index(y))
+    first_nonzero_return = max(get_first_valid_return_index(forward_returns), get_first_valid_return_index(X.iloc[:,0]), get_first_valid_return_index(y))
    train_from = first_nonzero_return + window_size + 1 if from_index is None else X.index.to_list().index(from_index)
    train_till = len(y)
    iterations_before_retrain = 0
@@ -26,6 +26,3 @@ class Transformation(ABC):
        raise NotImplementedError
    

-
-    
-    
@@ -1,6 +1,8 @@
 from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler
-from transformations.sklearn import SKLearnTransformation
-from utils.types import ScalerTypes
+from .sklearn import SKLearnTransformation
+from typing import Literal
+
+ScalerTypes = Literal['normalize', 'minmax', 'standardize']

 def get_scaler(type: ScalerTypes) -> SKLearnTransformation:
    if type == 'normalize':
@@ -12,23 +12,23 @@ def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.002) ->
    return (signal * returns) - costs


-def __preprocess(target_returns: pd.Series, y_pred: pd.Series, y_true: pd.Series, no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'], discretize: bool) -> pd.DataFrame:
+def __preprocess(forward_returns: pd.Series, y_pred: pd.Series, y_true: pd.Series, no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'], discretize: bool) -> pd.DataFrame:
    y_pred.name = 'y_pred'
-    target_returns.name = 'target_returns'
-    df = pd.concat([y_pred, target_returns],axis=1).dropna()
+    forward_returns.name = 'forward_returns'
+    df = pd.concat([y_pred, forward_returns],axis=1).dropna()

    discretize_func = get_discretize_function(no_of_classes)
    # make sure that we evaluate binary/three-way predictions even if the model is a regression
    df['sign_pred'] = df.y_pred.apply(discretize_func) if discretize else df.y_pred
    df['sign_true'] = y_true

-    df['result'] = backtest(df.target_returns, df.sign_pred)
+    df['result'] = backtest(df.forward_returns, df.sign_pred)

    return df

 def evaluate_predictions(
                        model_name: str,
-                        target_returns: pd.Series,
+                        forward_returns: pd.Series,
                        y_pred: pd.Series,
                        y_true: pd.Series,
                        no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
@@ -36,12 +36,12 @@ def evaluate_predictions(
                        discretize: bool = False,
                        ) -> pd.Series:
    # ignore the predictions until we see a non-zero returns (and definitely skip the first sliding_window_size)
-    evaluate_from = max(get_first_valid_return_index(target_returns), get_first_valid_return_index(y_pred))
+    evaluate_from = max(get_first_valid_return_index(forward_returns), get_first_valid_return_index(y_pred))
    
-    target_returns = pd.Series(target_returns[evaluate_from:])
+    forward_returns = pd.Series(forward_returns[evaluate_from:])
    y_pred = pd.Series(y_pred[evaluate_from:])

-    df = __preprocess(target_returns, y_pred, y_true, no_of_classes, discretize)
+    df = __preprocess(forward_returns, y_pred, y_true, no_of_classes, discretize)
    
    scorecard = pd.Series()
    
@@ -51,7 +51,7 @@ def evaluate_predictions(
    scorecard.loc['no_of_samples'] = no_of_samples
    sharpe = sharpe_ratio(df.result)
    scorecard.loc['sharpe'] = sharpe
-    benchmark_sharpe = sharpe_ratio(df.target_returns)
+    benchmark_sharpe = sharpe_ratio(df.forward_returns)
    scorecard.loc['benchmark_sharpe'] = benchmark_sharpe
    scorecard.loc['prob_sharpe'] = probabilistic_sharpe_ratio(sharpe, benchmark_sharpe, no_of_samples)
    scorecard.loc['sortino'] = sortino(df.result)
@@ -3,9 +3,6 @@ import numpy as np
 import os
 import string
 import random
-from typing import Union
-from config.config import Config
-

 def get_files_from_dir(path: str) -> list[str]:
    return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and not f.startswith('.')]
@@ -19,10 +16,7 @@ def get_first_valid_return_index(series: pd.Series) -> int:
        return 0
    return nested_result[0]

-def has_enough_samples_to_train(X: pd.DataFrame, y: pd.Series, config: Config) -> bool:
-    first_valid_index = get_first_valid_return_index(X.iloc[:,0])
-    samples_to_train = len(y) - first_valid_index
-    return samples_to_train > config.sliding_window_size_base + config.sliding_window_size_meta_labeling + 100
+

 def flatten(list_of_lists: list) -> list:
    return [item for sublist in list_of_lists for item in sublist]
@@ -39,8 +33,6 @@ def weighted_average(df: pd.DataFrame, weights_source: str) -> pd.Series:

    return mean_df

-def deduplicate_indexes(df: pd.DataFrame) -> pd.DataFrame: return df[~df.index.duplicated(keep='last')]
-
 def drop_columns_if_exist(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    for column in columns:
        if column in df.columns:
				`@@ -26,6 +26,3 @@ class Transformation(ABC):`
				`raise NotImplementedError`