Refactor(Training): new outcome types, representative pipeline steps, bet-sizing (#187)

* refactor(Training): added InferenceResult & TrainedModel types

* refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc.

* fix(Pipeline): getting it to compile

* refactor(WalkForward): separate preprocessing step

* feat(Pipeline): separate out transformations processing step

* refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step

* refactor(WalkForward): moved functions to separate folder

* fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster)

* fix(Tests): and evaluation

* fix(Tests): for realz

* fix(Inference): preloading everything now, renamed primary models to directional models

* fix(BetSizing): was running transformations on the wrong data, oops

* fix(BetSizing): concatenated on the wrong axis accidentally

* fix(Reporting): able to use the new Stats type

* fix(BetSizing): renamed int column names

* fix(Portfolio): name the column properly

* fix(Reporting): rename the correct Series, lol

* fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index

* fix(WalkForward): accidentally using the wrong index

* fix(WalkForward): use the correct indicies to fetch last model/transformations

* fix(CI): changed the name of the results

training/walk_forward/process_transformations_parallel.py

@@ -0,0 +1,46 @@
+import pandas as pd
+from training.types import TransformationsOverTime
+from utils.helpers import get_first_valid_return_index
+from tqdm import tqdm
+from transformations.base import Transformation
+from typing import Optional
+from data_loader.types import ForwardReturnSeries, XDataFrame, ySeries
+import ray
+
+def walk_forward_process_transformations(
+                        X: XDataFrame,
+                        y: ySeries,
+                        forward_returns: ForwardReturnSeries,
+                        expanding_window: bool,
+                        window_size: int,
+                        retrain_every: int,
+                        from_index: Optional[pd.Timestamp],
+                        transformations: list[Transformation],
+                    ) -> TransformationsOverTime:
+    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(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)
+    
+    processed_transformations = ray.get([preprocess_transformations_window.remote(X, y, expanding_window, window_size, transformations, first_nonzero_return, index) for index in range(train_from, train_till, retrain_every)])
+        
+    for transformation, index_time in processed_transformations:
+        for transformation_index, transformation in enumerate(transformation):
+            transformations_over_time[transformation_index][X.index[index_time]] = transformation
+
+    return transformations_over_time
+
+@ray.remote
+def preprocess_transformations_window(X: XDataFrame, y: ySeries, expanding_window: bool, window_size: int, transformations: list[Transformation], first_nonzero_return: int, index: int) -> tuple[list[Transformation], int]:
+    train_window_start = X.index[first_nonzero_return] if expanding_window else X.index[index - window_size - 1]
+    train_window_end = X.index[index - 1]
+
+    X_expanding_window = X[train_window_start:train_window_end]
+    y_expanding_window = y[train_window_start:train_window_end]
+
+    current_transformations = [t.clone() for t in transformations]
+    for transformation in current_transformations:
+        X_expanding_window = transformation.fit_transform(X_expanding_window, y_expanding_window)
+
+    return (current_transformations, index)