fix(Config): adjusted parameters to 5 minute timeframe

config/preprocess.py

@@ -96,7 +96,10 @@ def __preprocess_event_labeller_config(config_dict: dict) -> dict:
 def __preprocess_transformations_config(config_dict: dict) -> dict:
     transformations = [
         get_scaler(config_dict["scaler"]),
-        get_pca(config_dict["dimensionality_reduction_ratio"], config_dict["sliding_window_size"]),
+        get_pca(
+            config_dict["dimensionality_reduction_ratio"],
+            config_dict["sliding_window_size"],
+        ),
         get_rfe(config_dict["n_features_to_select"]),
     ]
     transformations = [x for x in transformations if x is not None]

config/presets.py

@@ -1,42 +1,7 @@
 from .types import RawConfig, Config
 
 
-def get_default_ensemble_config() -> RawConfig:
-
-    classification_models = [
-        "LogisticRegression_two_class",
-        "LDA",
-        "NB",
-        "RFC",
-        "XGB_two_class",
-        "LGBM",
-        "StaticMom",
-    ]
-    meta_models = ["LogisticRegression_two_class", "LGBM"]
-
-    return RawConfig(
-        dimensionality_reduction_ratio=0.5,
-        n_features_to_select=30,
-        sliding_window_size=380,
-        retrain_every=10,
-        scaler="minmax",  # 'normalize' 'minmax' 'standardize'
-        assets=["daily_crypto"],
-        target_asset="BTC_USD",
-        other_assets=["daily_etf"],
-        exogenous_data=["daily_glassnode"],
-        load_non_target_asset=True,
-        own_features=["level_2", "date_days", "lags_up_to_5"],
-        other_features=["level_2", "lags_up_to_5"],
-        exogenous_features=["z_score"],
-        directional_models=classification_models,
-        meta_models=meta_models,
-        event_filter="cusum_vol",
-        labeling="two_class",
-        forecasting_horizon=100,
-    )
-
-
-def get_lightweight_ensemble_config() -> RawConfig:
+def get_default_config() -> RawConfig:
 
     classification_models = [
         "LogisticRegression_two_class",
@@ -58,9 +23,9 @@ def get_lightweight_ensemble_config() -> RawConfig:
         target_asset="BTC_USD",
         other_assets=[],
         exogenous_data=[],
-        load_non_target_asset=False,
-        own_features=["level_1"],
-        other_features=[],
+        load_non_target_asset=True,
+        own_features=["level_2"],
+        other_features=["z_score"],
         exogenous_features=[],
         directional_models=classification_models,
         meta_models=meta_models,

data_loader/process.py

@@ -18,7 +18,4 @@ def check_data(X: XDataFrame, config: Config) -> bool:
 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 *2) + 100
-    )
+    return samples_to_train > (config.sliding_window_size * 2) + 100

feature_extractors/feature_extractor_presets.py

@@ -22,8 +22,8 @@ __presets = dict(
     debug_future_lookahead=[("debug_future", feature_debug_future_lookahead, [1])],
     single_mom=[("mom", feature_mom, [30])],
     single_vol=[("vol", feature_vol, [30])],
-    mom=[("mom", feature_mom, [10, 20, 30, 60, 90])],
-    vol=[("vol", feature_vol, [10, 20, 30, 60])],
+    mom=[("mom", feature_mom, [100, 300, 600, 900, 1800])],
+    vol=[("vol", feature_vol, [100, 300, 600, 1800])],
     lags_up_to_5=[("lag", feature_lag, [1, 2, 3, 4, 5])],
     lags_up_to_10=[("lag", feature_lag, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])],
     date_all=[
@@ -35,13 +35,13 @@ __presets = dict(
         ("day_of_week", feature_day_of_week, [0]),
         ("day_of_month", feature_day_of_month, [0]),
     ],
-    roc=[("roc", feature_ROC, [10, 30])],
-    rsi=[("rsi", feature_ROC, [10, 30, 100])],
-    stod=[("stod", feature_STOD, [10, 30, 200])],
-    stok=[("stok", feature_STOK, [10, 30, 200])],
-    fracdiff=[("fracdiff", feature_fractional_differentiation, [10, 30])],
-    fracdiff_log=[("fracdiff_log", feature_fractional_differentiation_log, [10, 30])],
-    z_score=[("z_score", feature_expanding_zscore, [10])],
+    roc=[("roc", feature_ROC, [100, 300])],
+    rsi=[("rsi", feature_ROC, [100, 300, 1000])],
+    stod=[("stod", feature_STOD, [100, 300, 2000])],
+    stok=[("stok", feature_STOK, [100, 300, 2000])],
+    fracdiff=[("fracdiff", feature_fractional_differentiation, [100, 300])],
+    fracdiff_log=[("fracdiff_log", feature_fractional_differentiation_log, [100, 300])],
+    z_score=[("z_score", feature_expanding_zscore, [100])],
 )
 
 presets = __presets | dict(

run_inference.py

@@ -4,10 +4,7 @@ from reporting.saving import load_models
 
 from run_pipeline import run_pipeline
 from config.types import Config, RawConfig
-from config.presets import (
-    get_default_ensemble_config,
-    get_lightweight_ensemble_config,
-)
+from config.presets import get_default_config
 from labeling.process import label_data
 import pandas as pd
 
@@ -84,6 +81,4 @@ def __inference(config: Config, pipeline_outcome: PipelineOutcome):
 
 
 if __name__ == "__main__":
-    run_inference(
-        preload_models=True, fallback_raw_config=get_lightweight_ensemble_config()
-    )
+    run_inference(preload_models=True, fallback_raw_config=get_default_config())

run_pipeline.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from config.types import Config, RawConfig
 from config.preprocess import preprocess_config
-from config.presets import get_default_ensemble_config, get_lightweight_ensemble_config
+from config.presets import get_default_config
 
 from data_loader.load import load_data
 from data_loader.process import check_data
@@ -109,5 +109,5 @@ if __name__ == "__main__":
         project_name="price-prediction",
         with_wandb=False,
         sweep=False,
-        raw_config=get_lightweight_ensemble_config(),
+        raw_config=get_default_config(),
     )