feat: Implement imputed bar handling in TradingEnv (skip, hold, penalty) and tests

gru_sac_predictor/src/pipeline_stages/data_processing.py

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+# Stage functions for loading, initial preprocessing, feature engineering, label generation, and splitting 
+import logging
+import sys # Added for sys.exit
+from datetime import datetime, timezone # Added for datetime
+import pandas as pd
+import numpy as np
+from typing import Tuple, Optional, Any, List, Dict # Added List and Dict
+import matplotlib.pyplot as plt # Added for plotting
+import seaborn as sns # Added for plotting
+
+# --- Component Imports --- #
+# Assuming DataLoader is in the parent directory's src
+# This might need adjustment based on actual project structure
+# Using relative import assuming pipeline_stages is sibling to other src modules
+from ..data_loader import DataLoader, fill_missing_bars
+from ..feature_engineer import FeatureEngineer # Added FeatureEngineer import
+from ..io_manager import IOManager # Added IOManager import
+from ..metrics import calculate_sharpe_ratio # For potential baseline comparison
+
+# --- Local Imports --- #
+# Import the label generation function we moved here
+# Removed duplicate import: from .data_processing import generate_direction_labels
+
+# Assuming tensorflow is installed and available
+try:
+    from tensorflow.keras.utils import to_categorical
+except ImportError:
+    logging.warning("TensorFlow/Keras not found. Ternary label one-hot encoding will fail.")
+    # Define a placeholder if keras is not available
+    def to_categorical(*args, **kwargs):
+        raise NotImplementedError("Keras 'to_categorical' is unavailable.")
+
+logger = logging.getLogger(__name__) # Use module-level logger
+
+# --- Refactored Label Generation Logic (Moved from trading_pipeline.py) --- #
+def generate_direction_labels(df: pd.DataFrame, config: dict) -> Tuple[pd.DataFrame, str, pd.Series, Optional[pd.Series]]:
+    """
+    Calculates forward returns and generates binary, soft binary, or ternary direction labels.
+    Also returns the raw forward returns and the epsilon series used for ternary flat definition.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing at least a 'close' column and DatetimeIndex.
+        config (dict): Pipeline configuration dictionary, expecting keys under 'gru' and 'data'.
+
+    Returns:
+        tuple[pd.DataFrame, str, pd.Series, Optional[pd.Series]]:
+            - DataFrame with added forward return and direction label columns (and NaNs dropped based on labels).
+            - Name of the generated direction label column.
+            - Series containing the calculated forward log returns (`fwd_log_ret`).
+            - Series containing the calculated epsilon (`eps`) threshold if ternary, else None.
+    """
+    if 'close' not in df.columns:
+        raise ValueError("'close' column missing in input DataFrame for label generation.")
+
+    gru_cfg = config.get('gru', {})
+    data_cfg = config.get('data', {})
+    horizon = gru_cfg.get('prediction_horizon', 5)
+    use_ternary = gru_cfg.get('use_ternary', False) # Check if ternary flag is set
+
+    target_ret_col = f'fwd_log_ret_{horizon}'
+    eps_series: Optional[pd.Series] = None # Initialize eps
+
+    # --- Calculate Forward Log Return --- #
+    shifted_close = df['close'].shift(-horizon)
+    fwd_returns = np.log(shifted_close / df['close'])
+    df[target_ret_col] = fwd_returns
+
+    # --- Generate Direction Label (Binary/Soft or Ternary) --- #
+    if use_ternary:
+        k = gru_cfg.get('flat_sigma_multiplier', 0.25)
+        target_dir_col = f'direction_label3_{horizon}'
+        logger.info(f"Generating ternary labels ({target_dir_col}) with k={k}...")
+
+        sigma_n = fwd_returns.rolling(window=horizon, min_periods=max(1, horizon//2)).std()
+        eps = k * sigma_n
+        eps_series = eps # Store the calculated eps series
+
+        conditions = [fwd_returns > eps, fwd_returns < -eps]
+        choices = [2, 0] # 2=up, 0=down
+        ordinal_labels = np.select(conditions, choices, default=1).astype(int) # 1=flat
+
+        # --- Log Distribution & Check Balance --- #
+        df['_ordinal_label_temp'] = ordinal_labels
+        valid_mask_for_dist = ~np.isnan(eps) & ~np.isnan(fwd_returns)
+        ordinal_labels_valid = df.loc[valid_mask_for_dist, '_ordinal_label_temp']
+
+        if not ordinal_labels_valid.empty:
+            counts = np.bincount(ordinal_labels_valid, minlength=3)
+            total_valid = len(ordinal_labels_valid)
+            if total_valid > 0: # Avoid division by zero
+                dist_pct = counts / total_valid * 100
+                log_msg = (f"Label dist (n={total_valid}): "
+                           f"Down(0)={dist_pct[0]:.1f}%, Flat(1)={dist_pct[1]:.1f}%, Up(2)={dist_pct[2]:.1f}%")
+                logger.info(log_msg)
+
+                min_pct_threshold = 10.0 # As per implementation
+                if any(p < min_pct_threshold for p in dist_pct):
+                    error_msg = f"Label imbalance detected! Min class percentage is {np.min(dist_pct):.1f}% (Threshold: {min_pct_threshold}%). Check data or flat_sigma_multiplier (k={k})."
+                    logger.error(error_msg)
+                    print(f"ERROR: {error_msg}") # Also print for visibility
+            else:
+                logger.warning("Label distribution check skipped: total valid labels is zero.")
+        else:
+            logger.warning("Could not calculate label distribution (no valid sigma or returns).")
+        # --- End Distribution Check --- #
+
+        # --- One-hot encode --- #
+        try:
+            y_cat_full = np.full((len(df), 3), np.nan, dtype=np.float32)
+            if not ordinal_labels_valid.empty:
+                 y_cat_valid = to_categorical(ordinal_labels_valid, num_classes=3)
+                 y_cat_full[valid_mask_for_dist] = y_cat_valid.astype(np.float32)
+            else:
+                 logger.warning("No valid ordinal labels to one-hot encode.")
+
+            # Assign the list of arrays (or NaNs) - using list avoids mixed type issues later
+            df[target_dir_col] = [list(row) if not np.all(np.isnan(row)) else np.nan for row in y_cat_full]
+
+        except NotImplementedError as nie:
+             logger.error(f"Ternary label generation failed: {nie}. Keras 'to_categorical' is unavailable. Please install tensorflow.", exc_info=True)
+             raise # Re-raise exception to halt pipeline
+        except Exception as e:
+            logger.error(f"Error during one-hot encoding: {e}", exc_info=True)
+            raise # Re-raise exception to halt pipeline if encoding fails
+        finally:
+            if '_ordinal_label_temp' in df.columns:
+                 df.drop(columns=['_ordinal_label_temp'], inplace=True)
+        # --- End One-hot Encoding --- #
+
+    else: # Binary / Soft Binary
+        target_dir_col = f'direction_label_{horizon}'
+        label_smoothing = data_cfg.get('label_smoothing', 0.0)
+        if not (0.0 <= label_smoothing < 1.0):
+            logger.warning(f"Invalid label_smoothing value ({label_smoothing}). Must be in [0.0, 1.0). Disabling smoothing.")
+            label_smoothing = 0.0
+
+        if label_smoothing > 0.0:
+            high_label = 1.0 - label_smoothing / 2.0
+            low_label = label_smoothing / 2.0
+            logger.info(f"Applying label smoothing: {label_smoothing:.2f} -> labels [{low_label:.2f}, {high_label:.2f}] for {target_dir_col}")
+            df[target_dir_col] = np.where(fwd_returns > 0, high_label, low_label).astype(np.float32)
+        else:
+            logger.info(f"Using hard binary labels (0.0 / 1.0) for {target_dir_col}")
+            df[target_dir_col] = (fwd_returns > 0).astype(np.float32)
+
+    # --- Drop Rows with NaN Targets --- #
+    initial_rows = len(df)
+
+    # Create mask for NaNs in the direction column
+    if use_ternary:
+        # Check if elements are np.nan (since we assign np.nan for rows with no valid labels)
+        nan_mask_dir = df[target_dir_col].isna()
+    else:
+        nan_mask_dir = df[target_dir_col].isna()
+
+    nan_mask_combined = df[target_ret_col].isna() | nan_mask_dir
+
+    df_clean = df[~nan_mask_combined].copy()
+
+    final_rows = len(df_clean)
+    if final_rows < initial_rows:
+        logger.info(f"Dropped {initial_rows - final_rows} rows due to NaN targets (horizon={horizon}).")
+
+    if df_clean.empty:
+         logger.error("DataFrame is empty after defining labels and dropping NaNs. Exiting.")
+         # Returning empty DataFrame, caller should handle exit
+         return pd.DataFrame(), target_dir_col, pd.Series(dtype=float), None # Return empty series/None on failure
+
+    # Return the cleaned df, target col name, and the *original* full fwd_returns and eps series
+    # Need to return the original series aligned with the original df index *before* cleaning
+    # So the caller can align them with the features *after* cleaning df_clean
+    return df_clean, target_dir_col, fwd_returns, eps_series
+# --- End Label Generation --- # 
+
+# --- Stage 1: Load and Preprocess Data (Moved from TradingPipeline.load_and_preprocess_data) --- #
+def load_and_preprocess(
+    data_loader: DataLoader,
+    io: Optional[IOManager],
+    run_id: str,
+    config: Dict[str, Any]
+) -> Tuple[Optional[pd.DataFrame], Optional[Dict[str, Any]]]:
+    """
+    Loads the full raw dataset using DataLoader and performs initial checks.
+
+    Args:
+        data_loader: Initialized DataLoader instance.
+        io: IOManager instance (optional).
+        run_id: Current run ID.
+        config: Pipeline configuration dictionary.
+
+    Returns:
+        Tuple containing:
+        - DataFrame with raw loaded data, or None on failure.
+        - Dictionary summarizing the loading process, or None on failure.
+    """
+    logger.info("--- Stage: Loading and Preprocessing Data ---")
+    data_cfg = config.get('data', {})
+    
+    # --- Extract necessary parameters from config --- #
+    ticker = data_cfg.get('ticker')
+    exchange = data_cfg.get('exchange')
+    start_date = data_cfg.get('start_date')
+    end_date = data_cfg.get('end_date')
+    interval = data_cfg.get('interval', '1min') # Default to 1min
+    vol_sampling = data_cfg.get('volatility_sampling', {}).get('enabled', False)
+    vol_window = data_cfg.get('volatility_sampling', {}).get('window', 30)
+    vol_quantile = data_cfg.get('volatility_sampling', {}).get('quantile', 0.5)
+    
+    # Validate required parameters
+    if not all([ticker, exchange, start_date, end_date]):
+        logger.error("Missing required data parameters in config: ticker, exchange, start_date, end_date")
+        return None, None
+    # --- End Parameter Extraction --- #
+
+    load_summary = {
+        'ticker': ticker,
+        'exchange': exchange,
+        'start_date_req': start_date,
+        'end_date_req': end_date,
+        'interval_req': interval,
+        'vol_sampling_enabled': vol_sampling,
+        'vol_window': vol_window,
+        'vol_quantile': vol_quantile,
+    }
+
+    try:
+        logger.info(f"Loading data for {ticker} ({exchange}) from {start_date} to {end_date}, interval {interval}")
+        # --- Pass extracted parameters to load_data --- #
+        df_raw = data_loader.load_data(
+            ticker=ticker,
+            exchange=exchange,
+            start_date=start_date,
+            end_date=end_date,
+            interval=interval,
+            vol_sampling=vol_sampling,
+            vol_window=vol_window,
+            vol_quantile=vol_quantile
+        )
+        # --- End Pass Parameters --- #
+
+        if df_raw is None or df_raw.empty:
+            logger.error("Data loading returned empty DataFrame or failed.")
+            return None, load_summary
+
+        # --- Fill Missing Bars (Step 2.5 from prompts/missing_data.txt) --- #
+        if io is None:
+            logger.error("IOManager is required for fill_missing_bars reporting. Cannot proceed.")
+            return None, load_summary
+        try:
+            df_filled = fill_missing_bars(df_raw, config, io, logger)
+            if df_filled is None or df_filled.empty:
+                logger.error("fill_missing_bars returned empty DataFrame or failed.")
+                return None, load_summary
+            df_raw = df_filled # Replace df_raw with the filled version
+            logger.info("Missing bars handled successfully.")
+        except ValueError as e:
+             logger.error(f"Error during missing bar handling: {e}. Halting processing.")
+             return None, load_summary
+        except Exception as e:
+             logger.error(f"Unexpected error during missing bar handling: {e}. Halting processing.", exc_info=True)
+             return None, load_summary
+        # --- End Fill Missing Bars --- #
+
+        # Calculate memory usage and log info
+        mem_usage = df_raw.memory_usage(deep=True).sum() / (1024**2)
+        if load_summary:
+            logger.info(f"Data loading summary: {load_summary}")
+        else:
+            logger.warning("No load summary returned by DataLoader.")
+        logger.info(f"Loaded data: {df_raw.shape[0]} rows, {df_raw.shape[1]} columns. Memory: {mem_usage:.2f} MB")
+        logger.info(f"Time range: {df_raw.index.min()} to {df_raw.index.max()}")
+
+        # --- V3 Output Contract: Stage 1 Artifacts --- #
+        if io:
+            if load_summary:
+                save_summary = load_summary.copy() # Don't modify original
+                save_summary['run_id'] = run_id
+                save_summary['timestamp_utc'] = datetime.now(timezone.utc).isoformat()
+                # TODO: Finalize summary content (add counts, NaN info etc.)
+                logger.info("Saving preprocess summary...")
+                io.save_json(save_summary, "preprocess_summary", use_txt=True) # Spec wants .txt
+            
+            # Save head of preprocessed data
+            if df_raw is not None and not df_raw.empty:
+                 logger.info("Saving head of preprocessed data (first 20 rows)...")
+                 io.save_df(df_raw.head(20), "head_preprocessed") 
+            else:
+                 logger.warning("Skipping saving head_preprocessed: DataFrame is empty or None.")
+
+        else:
+            logger.warning("IOManager not available, skipping saving of Stage 1 artifacts (preprocess_summary, head_preprocessed).")
+        # --- End V3 Output Contract ---
+
+        # --- V3 Output Contract: Stage 2 Artifact (Label Histogram) --- #
+        # TODO: Move this plotting logic to evaluation stage or after split, needs y_train.
+        # if io and config.get('control', {}).get('generate_plots', True):
+        #     logger.info("Generating training label distribution histogram... [SKIPPED IN CURRENT STAGE]")
+            # ... (Original plotting code removed from here)
+        # --- End V3 Output Contract ---
+
+        return df_raw, load_summary 
+
+    except Exception as e:
+        logger.error(f"Error during data loading: {e}", exc_info=True)
+        return None, None
+
+# --- Stage 2: Engineer Features (Moved from TradingPipeline.engineer_features) --- #
+def engineer_features_for_fold(
+    df: pd.DataFrame,
+    feature_engineer: FeatureEngineer,
+    io: Optional[IOManager], # Added IOManager for saving figure
+    config: Dict[str, Any], # Added config for plot settings
+    target_col: Optional[str] = None # Added target column name for sorting correlation
+) -> pd.DataFrame:
+    """Adds features using FeatureEngineer, handles NaNs, and saves correlation heatmap for a fold.
+
+    Args:
+        df (pd.DataFrame): Input DataFrame for the fold (typically raw data).
+        feature_engineer (FeatureEngineer): Initialized FeatureEngineer instance.
+        io (Optional[IOManager]): IOManager instance for saving artifacts.
+        config (Dict[str, Any]): Pipeline configuration dictionary.
+        target_col (Optional[str]): Name of the target column to sort correlations by (e.g., 'fwd_log_ret_5').
+
+    Returns:
+        pd.DataFrame: DataFrame with engineered features, NaNs dropped.
+                     Returns an empty DataFrame if input is empty or result is empty.
+    """
+    logger.info("--- Stage: Engineering Features --- ")
+    if df is None or df.empty:
+        logger.error("Input DataFrame is empty. Cannot engineer features.")
+        return pd.DataFrame() # Return empty DataFrame to indicate failure
+
+    if feature_engineer is None:
+        logger.error("FeatureEngineer not initialized. Cannot engineer features.")
+        # Or raise an error? For now return empty
+        return pd.DataFrame()
+
+    # Add base features (cyclical, imbalance, TA)
+    df_engineered = feature_engineer.add_base_features(df.copy())
+
+    # --- V3 Output Contract: Feature Correlation Heatmap --- #
+    # Generate heatmap *before* dropping NaNs to capture full feature set correlations
+    # if io and config.get('control', {}).get('generate_plots', True): # Check if plotting is enabled
+    if io: # Assume generate_plots is implicitly true if io is provided
+        try:
+            logger.info("Generating feature correlation heatmap...")
+            numeric_cols = df_engineered.select_dtypes(include=np.number).columns
+            if len(numeric_cols) < 2:
+                 logger.warning("Skipping correlation heatmap: Less than 2 numeric columns found.")
+            else:
+                corr_matrix = df_engineered[numeric_cols].corr(method='pearson')
+
+                # Get plot settings from config
+                output_cfg = config.get('output', {})
+                fig_size = output_cfg.get('figure_size', [16, 9])
+                plot_style = output_cfg.get('plot_style', 'seaborn-v0_8-darkgrid')
+                annot_threshold = output_cfg.get('corr_annot_threshold', 0.5)
+                plot_footer = output_cfg.get('plot_footer', "© GRU-SAC v3")
+
+                plt.style.use(plot_style)
+                fig, ax = plt.subplots(figsize=fig_size)
+
+                sort_features = False
+                if target_col and target_col in corr_matrix.columns:
+                    # Sort by absolute correlation with the target
+                    target_corr = corr_matrix[target_col].abs().sort_values(ascending=False)
+                    sorted_cols = target_corr.index.tolist()
+                    corr_matrix_sorted = corr_matrix.loc[sorted_cols, sorted_cols]
+                    sort_features = True
+                else:
+                    if target_col:
+                         logger.warning(f"Target column '{target_col}' not found in correlation matrix. Heatmap will not be sorted by target correlation.")
+                    corr_matrix_sorted = corr_matrix # Use original matrix if no target or not found
+
+                sns.heatmap(
+                    corr_matrix_sorted,
+                    annot=False, # Annotations can be messy; spec only requires > threshold
+                    cmap='coolwarm', # Diverging palette centered at 0
+                    center=0,
+                    linewidths=0.5,
+                    cbar=True,
+                    square=True, # Ensure square cells
+                    ax=ax
+                )
+
+                # Annotate cells where absolute correlation > threshold (from config)
+                for i in range(corr_matrix_sorted.shape[0]):
+                    for j in range(corr_matrix_sorted.shape[1]):
+                        if abs(corr_matrix_sorted.iloc[i, j]) > annot_threshold and i != j:
+                            ax.text(j + 0.5, i + 0.5, f'{corr_matrix_sorted.iloc[i, j]:.2f}',
+                                    ha='center', va='center', color='black', fontsize=8)
+
+                title = "Feature Correlation Heatmap (Pearson)"
+                if sort_features:
+                    title += f" - Sorted by |ρ| vs '{target_col}'"
+                ax.set_title(title, fontsize=14)
+                plt.xticks(rotation=90, fontsize=8)
+                plt.yticks(rotation=0, fontsize=8)
+
+                # Add footer (from config)
+                if plot_footer: # Only add if footer is not empty
+                    plt.figtext(0.99, 0.01, plot_footer, ha="right", va="bottom", fontsize=8, color='gray')
+
+                # Save figure using IOManager
+                io.save_figure(fig, "feature_corr_heatmap", section='figures') # Saved to results/<run_id>/figures/
+                plt.close(fig) # Close figure after saving
+                logger.info("Saved feature correlation heatmap.")
+
+        except Exception as e:
+            logger.error(f"Failed to generate or save feature correlation heatmap: {e}", exc_info=True)
+    else:
+        logger.warning("IOManager not provided or plotting disabled, skipping feature correlation heatmap.")
+    # --- End V3 Output Contract --- #
+
+    # --- REMOVE Aggressive DropNA --- #
+    # Dropping all rows with any NaN here is too aggressive, especially with long lookback features.
+    # NaN handling should occur within feature calculation methods (bfill/ffill/fillna(0))
+    # and critically during label definition (where rows without valid labels are dropped).
+    # initial_rows = len(df_engineered)
+    # df_engineered.dropna(inplace=True)
+    # rows_dropped = initial_rows - len(df_engineered)
+    # if rows_dropped > 0:
+    #      logger.warning(f"Dropped {rows_dropped} rows with NaN values after feature engineering.")
+    # --- End REMOVE --- #
+
+    # Check if dataframe became empty *after feature calculation and internal NaN handling*
+    # (Though ideally internal handling should prevent this)
+    if df_engineered.empty:
+         logger.error("DataFrame is empty after feature engineering (check internal NaN handling in FeatureEngineer)." )
+         return pd.DataFrame() # Return empty DataFrame
+
+    logger.info(f"Feature engineering complete. Shape: {df_engineered.shape}")
+    return df_engineered 
+
+# --- Stage 3: Define Labels and Align (Moved from TradingPipeline.define_labels_and_align) --- #
+def define_labels_and_align_fold(
+    df_engineered: pd.DataFrame, 
+    config: dict
+) -> Tuple[pd.DataFrame, str, List[str], pd.Series, Optional[pd.Series]]:
+    """Defines prediction labels, aligns with features, and separates targets for a fold.
+    Also returns the raw forward returns and epsilon series used for filtering baselines.
+
+    Args:
+        df_engineered (pd.DataFrame): DataFrame with engineered features for the fold.
+        config (dict): Pipeline configuration dictionary.
+
+    Returns:
+        Tuple[pd.DataFrame, str, List[str], pd.Series, Optional[pd.Series]]:
+            - df_labeled_aligned: DataFrame with labels generated and features/targets aligned (NaNs dropped).
+            - target_dir_col: Name of the direction label column.
+            - target_cols: List containing names of all target columns (ret + dir).
+            - fwd_returns_aligned: Series of forward returns aligned with df_labeled_aligned.
+            - eps_aligned: Series of epsilon threshold aligned with df_labeled_aligned (or None).
+            Returns (pd.DataFrame(), "", [], pd.Series(), None) on failure or empty input.
+    """
+    logger.info("--- Stage: Defining Labels and Aligning --- ")
+    if df_engineered is None or df_engineered.empty:
+        logger.error("Engineered data (DataFrame) is empty. Cannot define labels.")
+        return pd.DataFrame(), "", [], pd.Series(dtype=float), None
+
+    # --- Call the label generation function (already in this module) --- #
+    try:
+        # generate_direction_labels modifies the DataFrame in place and returns it
+        # It also returns the original fwd_returns and eps series (aligned with df_engineered)
+        df_clean, target_dir_col, fwd_returns_orig, eps_orig = generate_direction_labels(
+            df_engineered.copy(), # Pass a copy to avoid modifying original outside this scope if needed
+            config
+        )
+    except Exception as e:
+         logger.error(f"Label generation failed: {e}.", exc_info=True)
+         return pd.DataFrame(), "", [], pd.Series(dtype=float), None
+
+    if df_clean.empty:
+        logger.error("Label generation resulted in an empty DataFrame.")
+        return pd.DataFrame(), "", [], pd.Series(dtype=float), None
+    # --- End Label Generation Call --- #
+
+    # --- Determine Target Columns --- #
+    horizon = config.get('gru', {}).get('prediction_horizon', 5)
+    target_ret_col = f'fwd_log_ret_{horizon}'
+    # target_dir_col is returned by generate_direction_labels
+    target_cols = [target_ret_col, target_dir_col]
+
+    # Ensure the columns actually exist after generation and cleaning
+    if not all(col in df_clean.columns for col in target_cols):
+         # Log which columns are actually present for debugging
+         present_cols = df_clean.columns.tolist()
+         logger.error(f"Generated label/return columns ({target_cols}) not found in DataFrame after label generation. Present columns: {present_cols}")
+         return pd.DataFrame(), "", [], pd.Series(dtype=float), None
+    # --- End Determine Target Columns --- #
+
+    # --- Align fwd_returns_orig and eps_orig with the cleaned DataFrame --- #
+    fwd_returns_aligned = fwd_returns_orig.loc[df_clean.index]
+    eps_aligned = eps_orig.loc[df_clean.index] if eps_orig is not None else None
+    # --- End Alignment --- #
+
+    # Note: Separation of X and y happens in the splitting function now
+    # We just need to return the fully labeled/aligned DataFrame and target column names.
+    logger.info(f"Labels defined and aligned. Shape: {df_clean.shape}")
+
+    # Return the aligned DataFrame and the aligned supplementary series
+    return df_clean, target_dir_col, target_cols, fwd_returns_aligned, eps_aligned 
+
+# --- Stage 4: Split Data (Moved from TradingPipeline.split_data) --- #
+def split_data_fold(
+    df_labeled_aligned: pd.DataFrame,
+    fwd_returns_aligned: pd.Series,
+    eps_aligned: Optional[pd.Series],
+    config: dict,
+    target_columns: List[str],
+    target_dir_col: str,
+    fold_dates: Optional[Tuple] = None,
+    current_fold: Optional[int] = None # For logging
+) -> Tuple[
+    # Features
+    pd.DataFrame, pd.DataFrame, pd.DataFrame,
+    # Original Targets
+    pd.DataFrame, pd.DataFrame, pd.DataFrame,
+    # Original Full DataFrames
+    pd.DataFrame, pd.DataFrame, pd.DataFrame,
+    # Ordinal Direction Target (Train only)
+    pd.Series,
+    # Forward Returns (Train/Val)
+    pd.Series, Optional[pd.Series],
+    # Epsilon (Train/Val)
+    Optional[pd.Series], Optional[pd.Series],
+    # Ordinal Direction Labels (Val)
+    Optional[pd.Series]
+]:
+    """Splits features, targets, fwd returns, and epsilon for a given fold.
+
+    Args:
+        df_labeled_aligned (pd.DataFrame): Labeled and aligned data for the entire fold period.
+        fwd_returns_aligned (pd.Series): Forward returns aligned with df_labeled_aligned.
+        eps_aligned (Optional[pd.Series]): Epsilon threshold aligned with df_labeled_aligned.
+        config (dict): Pipeline configuration.
+        target_columns (List[str]): Names of all target columns (e.g., ['fwd_log_ret_5', 'direction_label_5']).
+        target_dir_col (str): Name of the specific direction target column.
+        fold_dates (Optional[Tuple]): Tuple of (train_start, train_end, val_start, val_end, test_start, test_end) for WF.
+        current_fold (Optional[int]): Fold number for logging.
+
+    Returns:
+        Tuple containing the split dataframes/series:
+        (X_train_raw, X_val_raw, X_test_raw, # Features
+         y_train, y_val, y_test, # Targets
+         df_train_original, df_val_original, df_test_original, # Original DFs
+         y_dir_train_ordinal, # Ordinal Direction Labels (Train)
+         fwd_ret_train, fwd_ret_val, # Forward Returns (Train/Val)
+         eps_train, eps_val, # Epsilon (Train/Val, Optional)
+         y_dir_val_ordinal) # Ordinal Direction Labels (Val, Optional)
+         Returns tuple of Nones if splitting fails.
+    """
+    fold_label = f"Fold {current_fold}" if current_fold is not None else "Split"
+    logger.info(f"--- {fold_label}: Stage: Splitting Data --- ")
+
+    if df_labeled_aligned is None or df_labeled_aligned.empty:
+        logger.error(f"Fold {fold_label}: Input data for splitting is empty.")
+        # Return Nones to indicate failure (update count based on new returns)
+        return (None,) * 14
+
+    # --- Temporarily add fwd_ret and eps to DataFrame for easier splitting --- #
+    temp_fwd_ret_col = '__temp_fwd_ret__'
+    temp_eps_col = '__temp_eps__'
+    df_split_input = df_labeled_aligned.copy()
+    df_split_input[temp_fwd_ret_col] = fwd_returns_aligned
+    if eps_aligned is not None:
+        df_split_input[temp_eps_col] = eps_aligned
+    # --- End Temp Add --- #
+
+    if not isinstance(df_split_input.index, pd.DatetimeIndex):
+         logger.error(f"{fold_label}: Data index must be DatetimeIndex for splitting. Aborting.")
+         raise SystemExit(f"{fold_label}: Index is not DatetimeIndex in split_data.")
+
+    if not target_columns:
+         logger.error(f"{fold_label}: Target columns list is empty. Aborting.")
+         raise SystemExit(f"{fold_label}: Target columns missing in split_data.")
+    if not target_dir_col:
+         logger.error(f"{fold_label}: Target direction column name is empty. Aborting.")
+         raise SystemExit(f"{fold_label}: Target direction column missing in split_data.")
+
+    # Ensure target columns exist before trying to drop/select them
+    cols_to_drop = [col for col in target_columns if col in df_split_input.columns]
+    if len(cols_to_drop) != len(target_columns):
+        missing_targets = set(target_columns) - set(cols_to_drop)
+        logger.error(f"{fold_label}: Expected target columns {missing_targets} not found in input DataFrame. Aborting.")
+        raise SystemExit(f"{fold_label}: Missing target columns in split_data input.")
+
+    # Exclude temporary columns from feature_cols
+    feature_cols = df_split_input.columns.difference(cols_to_drop + [temp_fwd_ret_col, temp_eps_col])
+    if feature_cols.empty:
+        logger.error(f"{fold_label}: No feature columns remain after excluding targets and temp cols. Aborting.")
+        raise SystemExit(f"{fold_label}: No feature columns found in split_data.")
+
+    # --- Determine if ternary mode is active --- #
+    use_ternary = config.get('gru', {}).get('use_ternary', False)
+    # --- End Determine Ternary --- #
+
+    # Initialize split results
+    X_train_raw, X_val_raw, X_test_raw = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
+    y_train, y_val, y_test = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
+    df_train_original, df_val_original, df_test_original = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
+    fwd_ret_train, fwd_ret_val = pd.Series(dtype=float), pd.Series(dtype=float)
+    eps_train, eps_val = None, None
+    y_dir_train_raw_format, y_dir_val_raw_format = pd.Series(dtype=object), pd.Series(dtype=object) # Store raw labels before converting
+
+    # Split based on Walk-Forward dates or ratios
+    if fold_dates and len(fold_dates) == 6 and all(fold_dates): # Check for valid WF tuple
+        train_start, train_end, val_start, val_end, test_start, test_end = fold_dates
+        logger.info(f"  Splitting using Walk-Forward dates: Train=[{train_start}, {train_end}), Val=[{val_start}, {val_end}), Test=[{test_start}, {test_end})")
+
+        # Slicing logic
+        df_train_original = df_split_input.loc[train_start:train_end]
+        df_val_original = df_split_input.loc[val_start:val_end]
+        df_test_original = df_split_input.loc[test_start:test_end] if test_start else pd.DataFrame()
+
+    else: # Single split using ratios
+        split_cfg = config.get('split_ratios', {})
+        train_ratio = split_cfg.get('train', 0.7)
+        val_ratio = split_cfg.get('validation', 0.15)
+        test_ratio = round(1.0 - train_ratio - val_ratio, 2)
+        logger.info(f"  Splitting using ratios: Train={train_ratio:.2f}, Val={val_ratio:.2f}, Test={test_ratio:.2f}")
+
+        total_len = len(df_split_input)
+        train_end_idx = int(total_len * train_ratio)
+        val_end_idx = int(total_len * (train_ratio + val_ratio))
+
+        df_train_original = df_split_input.iloc[:train_end_idx]
+        df_val_original = df_split_input.iloc[train_end_idx:val_end_idx]
+        df_test_original = df_split_input.iloc[val_end_idx:]
+
+    # --- Extract components from split DataFrames --- #
+    if not df_train_original.empty:
+        X_train_raw = df_train_original[feature_cols]
+        y_train = df_train_original[target_columns]
+        y_dir_train_raw_format = df_train_original[target_dir_col]
+        fwd_ret_train = df_train_original[temp_fwd_ret_col]
+        if temp_eps_col in df_train_original:
+            eps_train = df_train_original[temp_eps_col]
+
+    if not df_val_original.empty:
+        X_val_raw = df_val_original[feature_cols]
+        y_val = df_val_original[target_columns]
+        fwd_ret_val = df_val_original[temp_fwd_ret_col]
+        if temp_eps_col in df_val_original:
+            eps_val = df_val_original[temp_eps_col]
+
+    if not df_test_original.empty:
+        X_test_raw = df_test_original[feature_cols]
+        y_test = df_test_original[target_columns]
+    # --- End Extraction --- #
+
+    # --- Extract Ordinal Labels if Ternary --- #
+    y_dir_train_ordinal = None
+    if not y_train.empty: # Check if training data exists
+        if use_ternary:
+            valid_mask = y_dir_train_raw_format.notna() & y_dir_train_raw_format.apply(lambda x: isinstance(x, list) and len(x) == 3)
+            if valid_mask.any():
+                 ordinal_values = y_dir_train_raw_format[valid_mask].apply(np.argmax)
+                 y_dir_train_ordinal = pd.Series(np.nan, index=y_dir_train_raw_format.index)
+                 y_dir_train_ordinal[valid_mask] = ordinal_values
+                 logger.info(f"{fold_label}: Extracted ordinal labels (0, 1, 2) for feature selection. Count: {valid_mask.sum()}")
+            else:
+                 logger.warning(f"{fold_label}: No valid list-based ternary labels found in y_dir_train_raw_format to convert to ordinal.")
+                 y_dir_train_ordinal = pd.Series(dtype=np.float64) # Return empty series
+        else:
+            y_dir_train_ordinal = y_dir_train_raw_format.astype(int) # Ensure integer type
+    else:
+        y_dir_train_ordinal = pd.Series(dtype=int) # Empty series if no train data
+    # --- End Extract Ordinal Labels --- #
+
+    # --- Extract Ordinal Validation Labels if Ternary --- #
+    y_dir_val_ordinal = None
+    if not y_val.empty: # Check if validation data exists
+        if use_ternary:
+            # Use y_dir_val_raw_format which holds the lists/NaNs
+            valid_mask_val = y_dir_val_raw_format.notna() & y_dir_val_raw_format.apply(lambda x: isinstance(x, list) and len(x) == 3)
+            if valid_mask_val.any():
+                 ordinal_values_val = y_dir_val_raw_format[valid_mask_val].apply(np.argmax)
+                 y_dir_val_ordinal = pd.Series(np.nan, index=y_dir_val_raw_format.index)
+                 y_dir_val_ordinal[valid_mask_val] = ordinal_values_val
+                 logger.info(f"{fold_label}: Extracted ordinal validation labels. Count: {valid_mask_val.sum()}")
+            else:
+                 logger.warning(f"{fold_label}: No valid ternary labels found in y_dir_val_raw_format.")
+                 y_dir_val_ordinal = pd.Series(dtype=np.float64)
+        else:
+            # Use y_dir_val_raw_format which holds 0.0/1.0
+            y_dir_val_ordinal = y_dir_val_raw_format.astype(int)
+    else:
+        y_dir_val_ordinal = pd.Series(dtype=int) # Empty series if no validation data
+    # --- End Extract Ordinal Validation Labels --- #
+
+    # Log split shapes and check for empty splits
+    logger.info(f"Data split complete for {fold_label}:")
+    logger.info(f"  Train: X={X_train_raw.shape}, y={y_train.shape}, fwd_ret={fwd_ret_train.shape}, eps={eps_train.shape if eps_train is not None else 'None'} ({X_train_raw.index.min()} to {X_train_raw.index.max()})" if not X_train_raw.empty else "  Train: EMPTY")
+    logger.info(f"  Val:   X={X_val_raw.shape}, y={y_val.shape}, fwd_ret={fwd_ret_val.shape}, eps={eps_val.shape if eps_val is not None else 'None'} ({X_val_raw.index.min()} to {X_val_raw.index.max()})" if not X_val_raw.empty else "  Val:   EMPTY")
+    logger.info(f"  Test:  X=({X_test_raw.shape if X_test_raw is not None else 'None'}), y=({y_test.shape if y_test is not None else 'None'}) ({df_test_original.index.min() if df_test_original is not None and not df_test_original.empty else 'N/A'} to {df_test_original.index.max() if df_test_original is not None and not df_test_original.empty else 'N/A'})" )
+
+    # Check required splits are non-empty
+    if X_train_raw.empty or X_val_raw.empty:
+        logger.error(f"Fold {current_fold}: Data splitting resulted in empty train or validation set. Aborting fold.")
+        raise SystemExit(f"Fold {current_fold}: Empty train or validation split detected.")
+
+    return (
+        X_train_raw, X_val_raw, X_test_raw, # Features
+        y_train, y_val, y_test, # Targets
+        df_train_original, df_val_original, df_test_original, # Original DFs
+        y_dir_train_ordinal, # Ordinal Direction Labels (Train)
+        fwd_ret_train, fwd_ret_val, # Forward Returns (Train/Val)
+        eps_train, eps_val, # Epsilon (Train/Val, Optional)
+        y_dir_val_ordinal # Ordinal Direction Labels (Val, Optional)
+    )

gru_sac_predictor/src/pipeline_stages/sequence_creation.py

@@ -0,0 +1,182 @@
+# Stage functions for creating GRU input sequences
+import logging
+import sys
+import numpy as np
+import pandas as pd
+from typing import Tuple, Dict, Optional, List
+import json # Added for saving artefact
+
+# Assuming IOManager is importable from parent src directory
+from ..io_manager import IOManager 
+
+logger = logging.getLogger(__name__)
+
+def create_sequences_fold(
+    X_data: pd.DataFrame,
+    y_data: pd.DataFrame,
+    target_names: List[str], # e.g., ['mu', 'dir3'] or ['mu', 'dir']
+    lookback: int,
+    name: str, # e.g., "Train", "Validation", "Test"
+    config: dict, # For gru.drop_imputed_sequences
+    io: Optional[IOManager] # For saving artefact
+) -> Tuple[Optional[np.ndarray], Optional[Dict], Optional[pd.Index], int]:
+    """
+    Transforms pruned, scaled feature DataFrame into 3D sequences for GRU input
+    and extracts corresponding targets for a specific data split (Train/Val/Test).
+    Handles dropping sequences containing imputed bars based on config.
+
+    Args:
+        X_data (pd.DataFrame): Pruned, scaled features for the split.
+        y_data (pd.DataFrame): Targets for the split.
+        target_names (List[str]): List of target column names in y_data (e.g., ['mu', 'dir3']).
+        lookback (int): Sequence length.
+        name (str): Name of the split (e.g., "Train", "Validation", "Test") for logging.
+        config (dict): Pipeline configuration dictionary.
+        io (Optional[IOManager]): IOManager instance for saving artefacts.
+
+    Returns:
+        Tuple containing:
+        - X_seq (np.ndarray or None): 3D feature sequences.
+        - y_seq_dict (Dict or None): Dictionary of target sequences.
+        - target_indices (pd.Index or None): Timestamps corresponding to the targets.
+        - dropped_count (int): Number of sequences dropped due to imputed bars.
+        Returns (None, None, None, 0) if sequence creation fails or data is insufficient.
+        Raises SystemExit on critical errors (e.g., misalignment).
+    """
+    logger.info(f"--- Creating {name} Sequences ---")
+    use_ternary = config.get('gru', {}).get('use_ternary', False)
+    drop_imputed = config.get('gru', {}).get('drop_imputed_sequences', False)
+    imputed_col_name = 'bar_imputed' # Assuming this is the column name
+
+    # --- Input Validation --- #
+    if X_data is None or y_data is None or X_data.empty or y_data.empty:
+        logger.error(f"{name}: Missing or empty features/targets for sequence creation.")
+        return None, None, None, 0
+
+    # Check for bar_imputed column
+    if imputed_col_name not in X_data.columns:
+         logger.error(f"{name}: Required column '{imputed_col_name}' not found in features. Cannot handle imputed sequences.")
+         # Decide whether to proceed without it or raise error - raising for now
+         raise SystemExit(f"{name}: '{imputed_col_name}' column missing. Sequence creation halted.")
+
+    # Strict Anti-Leakage Check
+    try:
+        assert X_data.index.equals(y_data.index), \
+            f"{name}: Features and targets indices misaligned!"
+    except AssertionError as e:
+        logger.error(f"Data alignment check failed: {e}. Potential data leakage. Aborting.")
+        raise SystemExit(f"{name}: {e}")
+
+    # Check target columns exist
+    if not all(col in y_data.columns for col in target_names):
+         missing_targets = set(target_names) - set(y_data.columns)
+         logger.error(f"{name}: Target columns {missing_targets} not found in y_data. Aborting.")
+         raise SystemExit(f"{name}: Missing target columns for sequencing.")
+    # --- End Input Validation --- #
+
+    # Convert DataFrames to numpy for potential speedup, keep index access
+    features_np = X_data.values
+    imputed_flag_np = X_data[imputed_col_name].values.astype(bool) # Ensure boolean type
+    # Extract targets based on target_names
+    targets_dict_np = {name: y_data[name].values for name in target_names}
+
+    X_seq_list, y_seq_dict_list = [], {name: [] for name in target_names}
+    mask_seq_list = [] # To store the imputed flag sequences
+    target_indices = []
+
+    if len(X_data) <= lookback:
+         logger.warning(f"{name}: DataFrame length ({len(X_data)}) is not greater than lookback ({lookback}). Cannot create sequences.")
+         return None, None, None, 0
+
+    for i in range(lookback, len(features_np)):
+        # Feature window: [i-lookback, i)
+        X_seq_list.append(features_np[i - lookback : i])
+        mask_seq_list.append(imputed_flag_np[i - lookback : i])
+
+        # Targets correspond to index i
+        for t_name in target_names:
+             target_val = targets_dict_np[t_name][i]
+             # Special handling for potential list/array type in ternary labels
+             if use_ternary and 'dir' in t_name and isinstance(target_val, list):
+                 target_val = np.array(target_val, dtype=np.float32)
+             y_seq_dict_list[t_name].append(target_val)
+             
+        target_indices.append(y_data.index[i]) # Get index corresponding to target
+
+    if not X_seq_list: # Check if any sequences were created
+         logger.warning(f"{name}: No sequences were generated (length <= lookback?).")
+         return None, None, None, 0
+
+    # Convert lists to numpy arrays
+    X_seq = np.array(X_seq_list, dtype=np.float32)
+    mask_seq = np.array(mask_seq_list, dtype=bool)
+    target_indices_pd = pd.Index(target_indices)
+    y_seq_dict_np = {}
+    for t_name in target_names:
+        try:
+            # Attempt to stack; requires consistent shapes
+            if use_ternary and 'dir' in t_name:
+                 y_seq_dict_np[t_name] = np.stack(y_seq_dict_list[t_name]).astype(np.float32)
+            else: # Assuming other targets are scalar
+                 y_seq_dict_np[t_name] = np.array(y_seq_dict_list[t_name], dtype=np.float32)
+        except ValueError as e:
+            logger.error(f"{name}: Error stacking target '{t_name}': {e}. Check target consistency (especially ternary).", exc_info=True)
+            shapes = [getattr(item, 'shape', type(item)) for item in y_seq_dict_list[t_name]]
+            from collections import Counter
+            logger.error(f"Target shapes/types found: {Counter(shapes)}")
+            raise SystemExit(f"{name}: Inconsistent target shapes for '{t_name}' during sequence creation.") from e
+
+    orig_n = X_seq.shape[0]
+    dropped_count = 0
+
+    # Conditionally drop sequences containing imputed bars
+    if drop_imputed:
+        logger.info(f"{name}: Dropping sequences containing imputed bars (drop_imputed_sequences=True)...")
+        valid_mask = ~mask_seq.any(axis=1)
+        X_seq = X_seq[valid_mask]
+        mask_seq = mask_seq[valid_mask] # Keep mask aligned, though not explicitly used later
+        for t_name in target_names:
+             y_seq_dict_np[t_name] = y_seq_dict_np[t_name][valid_mask]
+        target_indices_pd = target_indices_pd[valid_mask]
+        
+        dropped_count = orig_n - X_seq.shape[0]
+        logger.info(f"{name}: Generated {orig_n} sequences, dropped {dropped_count} containing imputed bars. Remaining: {X_seq.shape[0]}")
+
+        # Save summary artifact
+        if io:
+             summary_data = {
+                 "split_name": name,
+                 "total_sequences_generated": orig_n,
+                 "sequences_dropped_imputed": dropped_count,
+                 "sequences_remaining": X_seq.shape[0],
+                 "drop_imputed_sequences_config": drop_imputed
+             }
+             try:
+                 filename = f"imputed_sequence_summary_{name.lower()}.json"
+                 io.save_json(summary_data, filename, section='results', indent=4)
+                 logger.info(f"Saved imputed sequence summary to results/{filename}")
+             except Exception as e:
+                 logger.error(f"Failed to save imputed sequence summary for {name}: {e}")
+        else:
+             logger.warning(f"IOManager not available, cannot save imputed sequence summary for {name}.")
+             
+    else:
+         logger.info(f"{name}: Generated {orig_n} sequences. Keeping sequences with imputed bars (drop_imputed_sequences=False).")
+
+    # Final checks
+    if X_seq.shape[0] == 0:
+         logger.error(f"{name}: No valid sequences remaining after potential filtering. Aborting.")
+         return None, None, None, dropped_count # Return 0 count if no sequences left
+
+    # --- REMOVE: Final Dictionary Mapping (Let GRU handler manage this) --- #
+    # final_y_seq_dict = {
+    #     'mu': y_seq_dict_np['ret'], # Map 'ret' to 'mu'
+    #     'dir3': y_seq_dict_np['dir3'] # Keep 'dir3' as is
+    # }
+    # --- END REMOVE --- #
+
+    # Log final shapes
+    logger.info(f"Sequence shapes created for {name}:")
+    logger.info(f"  X={X_seq.shape}, y_keys={list(y_seq_dict_np.keys())}, indices={len(target_indices_pd)}")
+
+    return X_seq, y_seq_dict_np, target_indices_pd, dropped_count

gru_sac_predictor/src/trading_env.py

@@ -6,6 +6,8 @@ Uses pre-calculated GRU predictions (mu, sigma, p_cal) and actual returns.
 import numpy as np
 import pandas as pd
 import logging
+import gymnasium as gym
+from omegaconf import DictConfig # Added for config typing
 
 env_logger = logging.getLogger(__name__)
 
@@ -15,6 +17,8 @@ class TradingEnv:
                  sigma_predictions: np.ndarray,
                  p_cal_predictions: np.ndarray,
                  actual_returns: np.ndarray,
+                 bar_imputed_flags: np.ndarray, # Added imputed flags
+                 config: DictConfig, # Added config
                  initial_capital: float = 10000.0,
                  transaction_cost: float = 0.0005,
                  reward_scale: float = 100.0,
@@ -27,18 +31,22 @@ class TradingEnv:
             sigma_predictions: Predicted volatility (σ̂ = exp(log σ̂)).
             p_cal_predictions: Calibrated probability of price increase (p_cal).
             actual_returns: Actual log returns (y_ret).
+            bar_imputed_flags: Boolean array indicating if a bar was imputed.
+            config: OmegaConf configuration object.
             initial_capital: Starting capital for simulation (used notionally in reward).
             transaction_cost: Fractional cost per trade.
             reward_scale: Multiplier for the reward signal.
             action_penalty_lambda: Coefficient for the action magnitude penalty (λ).
         """
-        assert len(mu_predictions) == len(sigma_predictions) == len(p_cal_predictions) == len(actual_returns), \
+        assert len(mu_predictions) == len(sigma_predictions) == len(p_cal_predictions) == len(actual_returns) == len(bar_imputed_flags), \
-            "All input arrays must have the same length"
+            "All input arrays (predictions, returns, imputed_flags) must have the same length"
 
         self.mu = mu_predictions
         self.sigma = sigma_predictions
         self.p_cal = p_cal_predictions
         self.actual_returns = actual_returns
+        self.bar_imputed = bar_imputed_flags.astype(bool) # Store imputed flags
+        self.config = config # Store config
 
         self.initial_capital = initial_capital
         self.transaction_cost = transaction_cost
@@ -65,20 +73,36 @@ class TradingEnv:
         self.state_dim = 5
         self.action_dim = 1
 
+        # --- Define Gym Spaces ---
+        self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(self.action_dim,), dtype=np.float32)
+        self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self.state_dim,), dtype=np.float32)
+        # --- End Define Gym Spaces ---
+
         env_logger.info(f"TradingEnv initialized with {self.n_steps} steps.")
 
     def _get_state(self) -> np.ndarray:
         """Construct the state vector for the current step."""
         if self.current_step >= self.n_steps:
-             # Handle episode end - return a dummy state or zeros
              return np.zeros(self.state_dim, dtype=np.float32)
 
         mu_t = self.mu[self.current_step]
         sigma_t = self.sigma[self.current_step]
         p_cal_t = self.p_cal[self.current_step]
 
-        edge_t = 2 * p_cal_t - 1
+        # Calculate edge based on p_cal shape (binary vs ternary)
-        z_score_t = np.abs(mu_t) / (sigma_t + 1e-9)
+        if isinstance(p_cal_t, (np.ndarray, list)) and len(p_cal_t) == 3:
+            # Ternary: edge = max(P(up), P(down)) - P(flat)
+            # Assuming order [Down, Flat, Up] for p_cal_t
+            edge_t = max(p_cal_t[2], p_cal_t[0]) - p_cal_t[1]
+        elif isinstance(p_cal_t, (float, np.number)):
+            # Binary: edge = 2 * P(up) - 1
+            edge_t = 2 * p_cal_t - 1
+        else:
+            env_logger.error(f"Unexpected type/shape for p_cal_t at step {self.current_step}: {p_cal_t}. Using edge=0.")
+            edge_t = 0.0
+
+        _EPS = 1e-9 # Define epsilon locally
+        z_score_t = np.abs(mu_t) / (sigma_t + _EPS)
 
         # State uses position *before* the action for this step is taken
         state = np.array([
@@ -108,11 +132,48 @@ class TradingEnv:
         Returns:
             tuple: (next_state, reward, done, info_dict)
         """
+        info = {'capital': self.current_capital, 'position': self.current_position, 'is_imputed_step_skipped': False}
+
         if self.current_step >= self.n_steps:
             # Should not happen if 'done' is handled correctly, but as safeguard
             env_logger.warning("Step called after environment finished.")
-            return self._get_state(), 0.0, True, {}
+            return self._get_state(), 0.0, True, info
 
+        # --- Handle Imputed Bar --- #
+        imputed = self.bar_imputed[self.current_step]
+        if imputed:
+            mode = self.config.sac.imputed_handling
+            env_logger.debug(f"SAC step {self.current_step} on imputed bar: handling={mode}")
+            if mode == "skip":
+                self.current_step += 1
+                next_state = self._get_state() # Get state for the *next* actual step
+                # Return 0 reward, not done, but indicate skip for buffer handling
+                info['is_imputed_step_skipped'] = True 
+                return next_state, 0.0, False, info
+            elif mode == "hold":
+                # Action is forced to maintain current position
+                action = self.current_position 
+            elif mode == "penalty":
+                # Calculate reward penalty based on config
+                target_position_penalty = np.clip(action, -1.0, 1.0)
+                reward = -self.config.sac.action_penalty * (target_position_penalty - self.current_position)**2
+                # Update position based on agent's intended action (clipped)
+                self.current_position = target_position_penalty
+                # Update capital notionally (no actual return, only cost if implemented)
+                # Cost is implicitly 0 here as there's no trade size if pos doesn't change
+                # If penalty mode allowed position change, cost would apply.
+                # For simplicity, we don't add cost here for the penalty step.
+                self.current_step += 1
+                next_state = self._get_state()
+                scaled_reward = reward * self.reward_scale # Scale the penalty
+                done = self.current_step >= self.n_steps
+                info['capital'] = self.current_capital
+                info['position'] = self.current_position
+                return next_state, scaled_reward, done, info
+            # else: default behavior (treat as normal bar) - implicitly handled by falling through
+        # --- End Handle Imputed Bar --- #
+
+        # --- Normal Step Logic (if not imputed or handling mode allows fallthrough like 'hold') --- #
         # Action is the TARGET position for the *end* of this step
         target_position = np.clip(action, -1.0, 1.0)
         trade_size = target_position - self.current_position
@@ -150,7 +211,9 @@ class TradingEnv:
         done = self.current_step >= self.n_steps or self.current_capital <= 0
 
         next_state = self._get_state()
-        info = {'capital': self.current_capital, 'position': self.current_position}
+        # Update info dict (capital/position might have changed in normal step)
+        info['capital'] = self.current_capital
+        info['position'] = self.current_position 
 
         # Log step details periodically
         # if self.current_step % 1000 == 0:

gru_sac_predictor/tests/test_sequence_creation.py

@@ -0,0 +1,186 @@
+import pytest
+import pandas as pd
+import numpy as np
+from omegaconf import OmegaConf
+from unittest.mock import MagicMock
+import os
+import tempfile
+import json
+
+# Adjust the import path based on your project structure
+from gru_sac_predictor.src.pipeline_stages.sequence_creation import create_sequences_fold
+from gru_sac_predictor.src.io_manager import IOManager # Adjust path if needed
+
+# --- Test Fixtures ---
+
+@pytest.fixture
+def sample_data_with_imputed():
+    """Creates sample X and y dataframes with a 'bar_imputed' column."""
+    dates = pd.to_datetime(pd.date_range('2023-01-01', periods=20, freq='T'))
+    lookback = 5
+    n_features_orig = 3
+    n_samples = len(dates)
+
+    # Features (including bar_imputed)
+    X_data = pd.DataFrame(
+        np.random.randn(n_samples, n_features_orig), 
+        index=dates, 
+        columns=[f'feat_{i}' for i in range(n_features_orig)]
+    )
+    # Add bar_imputed column - mark some bars as imputed
+    imputed_flags = np.zeros(n_samples, dtype=bool)
+    imputed_flags[2] = True # Imputed within first potential sequence
+    imputed_flags[8] = True # Imputed within a later potential sequence
+    imputed_flags[15] = True # Imputed near the end
+    X_data['bar_imputed'] = imputed_flags
+
+    # Targets (mu and dir3)
+    y_data = pd.DataFrame({
+        'mu': np.random.randn(n_samples),
+        'dir3': [list(row) for row in np.eye(3)[np.random.randint(0, 3, n_samples)]] # Example one-hot
+    }, index=dates)
+
+    return X_data, y_data
+
+@pytest.fixture
+def base_config():
+    """Creates a base OmegaConf config for testing sequence creation."""
+    conf = OmegaConf.create({
+        'gru': {
+            'lookback': 5,
+            'use_ternary': True, # Matches sample_data_with_imputed
+            'drop_imputed_sequences': True # Default to True for testing dropping
+        },
+        # Add other necessary sections if needed
+    })
+    return conf
+
+@pytest.fixture
+def mock_io_manager():
+    """Creates a mock IOManager for testing artefact saving."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        mock_io = MagicMock(spec=IOManager)
+        mock_io.results_dir = tmpdir
+        saved_jsons = {}
+        def mock_save_json(data, filename, **kwargs):
+            filepath = os.path.join(tmpdir, filename)
+            saved_jsons[filename] = data
+            with open(filepath, 'w') as f:
+                json.dump(data, f, **kwargs)
+        mock_io.save_json.side_effect = mock_save_json
+        mock_io.get_artifact_path.side_effect = lambda filename: os.path.join(tmpdir, filename)
+        mock_io._saved_jsons = saved_jsons 
+        yield mock_io
+
+# --- Test Functions ---
+
+def test_sequence_creation_shapes(sample_data_with_imputed, base_config, mock_io_manager):
+    X_data, y_data = sample_data_with_imputed
+    lookback = base_config.gru.lookback
+    n_features = X_data.shape[1]
+    n_samples = len(X_data)
+    expected_n_seq = n_samples - lookback
+
+    # Test without dropping imputed
+    cfg_no_drop = base_config.copy()
+    cfg_no_drop.gru.drop_imputed_sequences = False
+
+    X_seq, y_seq_dict, indices, dropped_count = create_sequences_fold(
+        X_data=X_data, y_data=y_data, target_names=['mu', 'dir3'], 
+        lookback=lookback, name="TestSplit", config=cfg_no_drop, io=mock_io_manager
+    )
+
+    assert dropped_count == 0
+    assert X_seq is not None
+    assert y_seq_dict is not None
+    assert indices is not None
+    assert X_seq.shape == (expected_n_seq, lookback, n_features)
+    assert 'mu' in y_seq_dict and y_seq_dict['mu'].shape == (expected_n_seq,)
+    assert 'dir3' in y_seq_dict and y_seq_dict['dir3'].shape == (expected_n_seq, 3)
+    assert len(indices) == expected_n_seq
+    # Check first target index corresponds to lookback-th original index
+    assert indices[0] == X_data.index[lookback]
+    # Check last target index corresponds to last original index
+    assert indices[-1] == X_data.index[-1]
+
+def test_sequence_dropping_imputed(sample_data_with_imputed, base_config, mock_io_manager):
+    X_data, y_data = sample_data_with_imputed
+    lookback = base_config.gru.lookback
+    n_samples = len(X_data)
+    expected_n_seq_orig = n_samples - lookback
+
+    # Config with dropping enabled (default in fixture)
+    cfg_drop = base_config
+
+    X_seq, y_seq_dict, indices, dropped_count = create_sequences_fold(
+        X_data=X_data.copy(), y_data=y_data.copy(), target_names=['mu', 'dir3'], 
+        lookback=lookback, name="TestDrop", config=cfg_drop, io=mock_io_manager
+    )
+
+    assert X_seq is not None
+    assert y_seq_dict is not None
+    assert indices is not None
+    
+    # Determine which original sequences should have been dropped
+    # A sequence starting at index i uses data from [i, i+lookback-1]
+    # The target corresponds to index i+lookback
+    # We need to check the imputed flag in the range [i, i+lookback-1] for each potential sequence target index i+lookback
+    
+    # Original target indices range from index `lookback` to `n_samples - 1`
+    should_be_dropped_mask = np.zeros(expected_n_seq_orig, dtype=bool)
+    imputed_flags_np = X_data['bar_imputed'].values
+    for seq_idx in range(expected_n_seq_orig):
+        # The features for this sequence are from original indices [seq_idx, seq_idx + lookback - 1]
+        feature_indices_range = slice(seq_idx, seq_idx + lookback)
+        if np.any(imputed_flags_np[feature_indices_range]):
+            should_be_dropped_mask[seq_idx] = True
+            
+    expected_dropped_count = np.sum(should_be_dropped_mask)
+    expected_remaining_count = expected_n_seq_orig - expected_dropped_count
+
+    assert dropped_count == expected_dropped_count
+    assert X_seq.shape[0] == expected_remaining_count
+    assert y_seq_dict['mu'].shape[0] == expected_remaining_count
+    assert y_seq_dict['dir3'].shape[0] == expected_remaining_count
+    assert len(indices) == expected_remaining_count
+    
+    # Check that the remaining indices are correct (weren't marked for dropping)
+    original_indices = X_data.index[lookback:]
+    expected_remaining_indices = original_indices[~should_be_dropped_mask]
+    pd.testing.assert_index_equal(indices, expected_remaining_indices)
+
+    # Check artifact saving
+    assert 'imputed_sequence_summary_testdrop.json' in mock_io_manager._saved_jsons
+    report_data = mock_io_manager._saved_jsons['imputed_sequence_summary_testdrop.json']
+    assert report_data['total_sequences_generated'] == expected_n_seq_orig
+    assert report_data['sequences_dropped_imputed'] == expected_dropped_count
+    assert report_data['sequences_remaining'] == expected_remaining_count
+
+def test_sequence_creation_no_imputed_col(sample_data_with_imputed, base_config, mock_io_manager):
+    X_data, y_data = sample_data_with_imputed
+    X_data_no_imputed = X_data.drop(columns=['bar_imputed'])
+    lookback = base_config.gru.lookback
+
+    with pytest.raises(SystemExit) as excinfo:
+        create_sequences_fold(
+            X_data=X_data_no_imputed, y_data=y_data, target_names=['mu', 'dir3'], 
+            lookback=lookback, name="TestNoImputedCol", config=base_config, io=mock_io_manager
+        )
+    assert "'bar_imputed' column missing" in str(excinfo.value)
+
+def test_sequence_creation_insufficient_data(sample_data_with_imputed, base_config, mock_io_manager):
+    X_data, y_data = sample_data_with_imputed
+    lookback = base_config.gru.lookback
+    # Create data shorter than lookback
+    X_short = X_data.iloc[:lookback-1]
+    y_short = y_data.iloc[:lookback-1]
+
+    X_seq, y_seq_dict, indices, dropped_count = create_sequences_fold(
+        X_data=X_short, y_data=y_short, target_names=['mu', 'dir3'], 
+        lookback=lookback, name="TestShort", config=base_config, io=mock_io_manager
+    )
+
+    assert X_seq is None
+    assert y_seq_dict is None
+    assert indices is None
+    assert dropped_count == 0 

gru_sac_predictor/tests/test_trading_env.py

@@ -0,0 +1,166 @@
+import pytest
+import numpy as np
+from omegaconf import OmegaConf
+
+# Adjust import path based on structure
+from gru_sac_predictor.src.trading_env import TradingEnv
+
+# --- Test Fixtures ---
+
+@pytest.fixture
+def sample_env_data():
+    """Provides sample data for initializing the TradingEnv."""
+    n_steps = 10
+    data = {
+        'mu_predictions': np.random.randn(n_steps) * 0.001,
+        'sigma_predictions': np.abs(np.random.randn(n_steps) * 0.002 + 0.005),
+        'p_cal_predictions': np.random.rand(n_steps),
+        'actual_returns': np.random.randn(n_steps) * 0.0015,
+        'bar_imputed_flags': np.array([False, False, True, False, True, True, False, False, True, False], dtype=bool)
+    }
+    return data
+
+@pytest.fixture
+def base_env_config():
+    """Base configuration for the environment."""
+    return OmegaConf.create({
+        'sac': {
+            'imputed_handling': 'skip', # Default test mode
+            'action_penalty': 0.05
+        },
+        'environment': {
+            'initial_capital': 10000.0,
+            'transaction_cost': 0.0005,
+            'reward_scale': 100.0,
+            'action_penalty_lambda': 0.0 # Usually overridden by transaction_cost calc
+        }
+    })
+
+@pytest.fixture
+def trading_env_instance(sample_env_data, base_env_config):
+    """Creates a TradingEnv instance with default 'skip' mode."""
+    return TradingEnv(**sample_env_data, config=base_env_config)
+
+# --- Test Functions ---
+
+def test_env_initialization(trading_env_instance, sample_env_data):
+    assert trading_env_instance.n_steps == len(sample_env_data['actual_returns'])
+    assert trading_env_instance.current_step == 0
+    assert trading_env_instance.current_position == 0.0
+    assert np.array_equal(trading_env_instance.bar_imputed, sample_env_data['bar_imputed_flags'])
+
+def test_env_reset(trading_env_instance):
+    # Take a few steps
+    trading_env_instance.step(0.5)
+    trading_env_instance.step(-0.2)
+    assert trading_env_instance.current_step > 0
+    # Reset
+    initial_state = trading_env_instance.reset()
+    assert trading_env_instance.current_step == 0
+    assert trading_env_instance.current_position == 0.0
+    assert initial_state is not None
+    assert initial_state.shape == (trading_env_instance.state_dim,)
+
+def test_env_step_normal(trading_env_instance):
+    # Test a normal step (step 0 is not imputed)
+    initial_pos = trading_env_instance.current_position
+    action = 0.7
+    next_state, reward, done, info = trading_env_instance.step(action)
+
+    assert trading_env_instance.current_step == 1
+    assert trading_env_instance.current_position == action # Position updates to action
+    assert not info['is_imputed_step_skipped']
+    assert not done
+    assert next_state is not None
+    # Reward calculation is complex, just check type/sign if needed
+    assert isinstance(reward, float)
+
+def test_env_step_imputed_skip(trading_env_instance, sample_env_data):
+    # Step 2 is imputed in sample_env_data
+    trading_env_instance.step(0.5) # Step 0
+    trading_env_instance.step(0.6) # Step 1
+    assert trading_env_instance.current_step == 2
+    initial_pos_before_imputed = trading_env_instance.current_position
+
+    # Action for the imputed step (should be ignored by 'skip')
+    action_imputed = 0.9
+    next_state, reward, done, info = trading_env_instance.step(action_imputed)
+
+    # Should skip step 2 and now be at step 3
+    assert trading_env_instance.current_step == 3 
+    # Position should NOT have changed from step 1
+    assert trading_env_instance.current_position == initial_pos_before_imputed
+    assert reward == 0.0 # Skip gives 0 reward
+    assert not done
+    assert info['is_imputed_step_skipped'] == True # Crucial check for buffer
+    # Check that the returned state is for step 3
+    expected_state_step_3 = trading_env_instance._get_state() # Get state now that we are at step 3
+    np.testing.assert_array_almost_equal(next_state, expected_state_step_3)
+
+def test_env_step_imputed_hold(sample_env_data, base_env_config):
+    cfg = base_env_config.copy()
+    cfg.sac.imputed_handling = 'hold'
+    env = TradingEnv(**sample_env_data, config=cfg)
+
+    # Step 2 is imputed
+    env.step(0.5) # Step 0
+    env.step(0.6) # Step 1
+    assert env.current_step == 2
+    position_before_imputed = env.current_position
+
+    # Action for the imputed step (should be overridden by 'hold')
+    action_imputed = -0.5 
+    next_state, reward, done, info = env.step(action_imputed)
+
+    # Should process step 2 and move to step 3
+    assert env.current_step == 3
+    # Position should be the same as before the step
+    assert env.current_position == position_before_imputed
+    assert not info['is_imputed_step_skipped']
+    assert not done
+    # Reward should be calculated based on holding the position
+    expected_pnl = position_before_imputed * (np.exp(sample_env_data['actual_returns'][2]) - 1)
+    expected_cost = 0 # No trade size if holding
+    expected_penalty = 0 # No penalty in hold mode
+    expected_raw_reward = expected_pnl - expected_cost - expected_penalty
+    expected_scaled_reward = expected_raw_reward * cfg.environment.reward_scale
+    assert np.isclose(reward, expected_scaled_reward)
+
+def test_env_step_imputed_penalty(sample_env_data, base_env_config):
+    cfg = base_env_config.copy()
+    cfg.sac.imputed_handling = 'penalty'
+    cfg.sac.action_penalty = 0.1 # Use a specific penalty for testing
+    env = TradingEnv(**sample_env_data, config=cfg)
+
+    # Step 2 is imputed
+    env.step(0.5) # Step 0
+    env.step(0.6) # Step 1
+    assert env.current_step == 2
+    position_before_imputed = env.current_position # Should be 0.6
+
+    # Action for the imputed step
+    action_imputed = -0.2 
+    next_state, reward, done, info = env.step(action_imputed)
+
+    # Should process step 2 and move to step 3
+    assert env.current_step == 3
+    # Position should update to the *agent's* action
+    assert env.current_position == np.clip(action_imputed, -1.0, 1.0)
+    assert not info['is_imputed_step_skipped']
+    assert not done
+    
+    # Reward calculation is ONLY the penalty
+    expected_raw_reward = -cfg.sac.action_penalty * (action_imputed - position_before_imputed)**2
+    expected_scaled_reward = expected_raw_reward * cfg.environment.reward_scale
+    assert np.isclose(reward, expected_scaled_reward)
+
+def test_env_done_condition(trading_env_instance, sample_env_data):
+    n_steps = len(sample_env_data['actual_returns'])
+    # Step through the environment
+    done = False
+    for i in range(n_steps):
+        _, _, done, _ = trading_env_instance.step(np.random.uniform(-1, 1))
+        if i < n_steps - 1:
+            assert not done
+        else:
+            assert done # Should be done on the last step