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# -*- coding: utf-8 -*-
"""risk_analysis.py
Automatically generated by Colab.
Original file is located at
    https://colab.research.google.com/drive/10u2Di5_droisNYuq_KYAmdgVHixe6oVi
"""

# risk_analysis.py
# Functions for calculating risk metrics and correlations.

import pandas as pd
import numpy as np
import traceback
import plotly.graph_objects as go
from utils import create_empty_figure # Import helper

def get_drawdown_table(returns: pd.Series, top: int = 5) -> pd.DataFrame:
    """
    Calculates drawdown periods and statistics from a series of returns.
    Args:
        returns: Series of daily returns with a DatetimeIndex.
        top: Number of top drawdowns (by magnitude) to return.
    Returns:
        DataFrame containing information about the top drawdown periods:
        'Peak Date', 'Valley Date', 'End Date', 'Duration (Days)', 'Max Drawdown (%)'.
        Returns an empty DataFrame if input is invalid or no drawdowns occur.
    """
    # Input validation
    if returns is None or not isinstance(returns, pd.Series) or returns.empty:
        # print("Drawdown calculation skipped: Input returns series is invalid or empty.")
        return pd.DataFrame()
    if not isinstance(returns.index, pd.DatetimeIndex):
        # print("Drawdown calculation skipped: Input returns series index is not DatetimeIndex.")
        return pd.DataFrame()

    # Create a DataFrame from the returns series
    df = returns.to_frame(name='returns')

    # Ensure returns are numeric, drop non-numeric values
    df['returns'] = pd.to_numeric(df['returns'], errors='coerce')
    df.dropna(subset=['returns'], inplace=True)
    if df.empty:
        # print("Drawdown calculation skipped: No valid numeric returns.")
        return pd.DataFrame()

    # Calculate cumulative returns (compounded)
    df['Cumulative'] = (1 + df['returns']).cumprod()
    # Calculate the running maximum cumulative return (high watermark)
    df['HighWatermark'] = df['Cumulative'].cummax()
    # Calculate drawdown as the percentage decline from the high watermark
    df['Drawdown'] = (df['Cumulative'] / df['HighWatermark']) - 1

    # Identify drawdown periods
    in_drawdown = False # Flag to track if currently in a drawdown
    periods = []        # List to store completed drawdown period dictionaries
    current_period = {} # Dictionary to store details of the ongoing drawdown
    peak_idx = df.index[0] # Initialize peak index to the start

    for idx, row in df.iterrows():
        # Update the peak index if a new high watermark is reached
        # Use .loc for safe index-based comparison, especially with potential duplicate indices
        if row['Cumulative'] >= df.loc[peak_idx, 'Cumulative']:
            peak_idx = idx

        is_dd = row['Drawdown'] < 0 # Check if currently in a drawdown state

        # Start of a new drawdown period
        if not in_drawdown and is_dd:
            in_drawdown = True
            current_period = {
                'Peak Date': peak_idx,          # Date the drawdown started (previous peak)
                'Valley Date': idx,             # Date the maximum drawdown was reached (initially the start)
                'End Date': pd.NaT,             # Date the drawdown ended (recovered to peak) - initially NaT
                'Max Drawdown (%)': row['Drawdown'], # The maximum drawdown percentage (initially the current DD)
                'Duration (Days)': 0            # Duration of the drawdown - calculated at the end
            }
        # Inside an ongoing drawdown period
        elif in_drawdown:
            # Update valley date and max drawdown if a lower point is reached
            if row['Drawdown'] < current_period['Max Drawdown (%)']:
                current_period['Valley Date'] = idx
                current_period['Max Drawdown (%)'] = row['Drawdown']

            # End of the current drawdown period (recovered)
            if not is_dd: # Recovered when Drawdown is no longer negative (or zero)
                in_drawdown = False
                current_period['End Date'] = idx # Mark the recovery date

                # Calculate duration (using business days if possible, else calendar days)
                start_date = current_period['Peak Date']
                end_date = current_period['End Date']
                if pd.notna(start_date) and pd.notna(end_date):
                    try:
                        # Attempt to use business days for duration
                        duration = len(pd.bdate_range(start=start_date, end=end_date))
                    except Exception: # Fallback to calendar days if bdate_range fails (e.g., non-standard dates)
                         duration = (end_date - start_date).days + 1 # Inclusive of start/end day
                    current_period['Duration (Days)'] = duration
                else:
                    current_period['Duration (Days)'] = np.nan # Duration is NaN if dates are invalid

                periods.append(current_period) # Add the completed period to the list
                current_period = {} # Reset for the next potential drawdown

    # Handle the case where the series ends while still in a drawdown
    if in_drawdown:
        start_date = current_period['Peak Date']
        end_date = df.index[-1] # End date is the last date in the series
        if pd.notna(start_date) and pd.notna(end_date):
             try:
                 duration = len(pd.bdate_range(start=start_date, end=end_date))
             except Exception:
                 duration = (end_date - start_date).days + 1
             current_period['Duration (Days)'] = duration
        else:
             current_period['Duration (Days)'] = np.nan
        # 'End Date' remains NaT as recovery hasn't happened by the end of the data
        periods.append(current_period)

    # If no drawdown periods were identified
    if not periods:
        return pd.DataFrame()

    # Create DataFrame from the identified periods
    drawdown_df = pd.DataFrame(periods)

    # Sort by the magnitude of the drawdown (most negative first) and select the top N
    drawdown_df = drawdown_df.sort_values(by='Max Drawdown (%)', ascending=True).head(top)

    # Format the Max Drawdown column as percentage
    drawdown_df['Max Drawdown (%)'] = drawdown_df['Max Drawdown (%)'].map('{:.2%}'.format)

    # Format date columns to YYYY-MM-DD strings for display
    for col in ['Peak Date', 'Valley Date', 'End Date']:
        if col in drawdown_df.columns:
             # Ensure conversion to datetime first, then format
             drawdown_df[col] = pd.to_datetime(drawdown_df[col]).dt.strftime('%Y-%m-%d')

    # Select and order columns for the final output table
    cols_to_select = ['Peak Date', 'Valley Date', 'End Date', 'Duration (Days)', 'Max Drawdown (%)']
    # Ensure only existing columns are selected (e.g., 'End Date' might be all NaT if never recovered)
    existing_cols = [col for col in cols_to_select if col in drawdown_df.columns]

    return drawdown_df[existing_cols]


def calculate_manual_risk_stats(returns_series):
    """
    Calculates various risk and performance metrics manually using pandas based on daily returns.
    Args:
        returns_series: A pandas Series of daily percentage returns with a DatetimeIndex.
    Returns:
        A dictionary containing:
        - monthly_returns_table_for_heatmap: DataFrame pivoted for monthly return heatmap (values as percentages).
        - monthly_perf_stats: DataFrame with summary stats for monthly returns.
        - rolling_vol_df: DataFrame containing rolling annualized volatility calculations (with 'Time' column).
        - rolling_vol_stats: DataFrame summarizing min/max/mean rolling volatility.
        - drawdown_table: DataFrame with top drawdown periods (from get_drawdown_table).
        - status: A string indicating the status of the analysis.
    """
    # Initialize results dictionary with default empty structures
    analysis_results = {
        "monthly_returns_table_for_heatmap": pd.DataFrame(),
        "monthly_perf_stats": pd.DataFrame(columns=['Metric', 'Value']),
        "rolling_vol_df": pd.DataFrame(),
        "rolling_vol_stats": pd.DataFrame(columns=['Window', 'Min Vol', 'Max Vol', 'Mean Vol']),
        "drawdown_table": pd.DataFrame(),
        "status": "Analysis skipped." # Default status
    }

    # --- Input Validation ---
    if returns_series is None or not isinstance(returns_series, pd.Series) or returns_series.empty or len(returns_series) < 2:
        analysis_results["status"] = "Analysis skipped: Insufficient/invalid returns data."
        return analysis_results
    if not isinstance(returns_series.index, pd.DatetimeIndex):
        analysis_results["status"] = "Analysis skipped: Returns index is not DatetimeIndex."
        return analysis_results

    try:
        status_parts = [] # To collect status messages for different parts

        # Ensure returns are numeric and index is UTC DatetimeIndex
        returns_series = pd.to_numeric(returns_series, errors='coerce').dropna()
        if returns_series.empty or len(returns_series) < 2:
             analysis_results["status"] = "Analysis skipped: No valid numeric returns after cleaning."
             return analysis_results

        if returns_series.index.tz is None:
            returns_series = returns_series.tz_localize('UTC')
        elif returns_series.index.tz != 'UTC':
            returns_series = returns_series.tz_convert('UTC')

        # --- Monthly Returns Analysis ---
        # Resample daily returns to monthly, calculating compounded monthly return
        # The lambda function calculates (1+r1)*(1+r2)*...*(1+rn) - 1 for each month
        monthly_rets = returns_series.resample('M').apply(lambda x: (1 + x).prod() - 1)

        if not monthly_rets.empty:
            # Create table for heatmap: Year rows, Month columns
            monthly_ret_table_df = pd.DataFrame({'returns': monthly_rets})
            monthly_ret_table_df['Year'] = monthly_ret_table_df.index.year
            monthly_ret_table_df['Month'] = monthly_ret_table_df.index.strftime('%b') # Month abbreviation (Jan, Feb, ...)
            # Pivot the table
            monthly_heatmap_data = monthly_ret_table_df.pivot_table(index='Year', columns='Month', values='returns')
            # Order columns chronologically
            month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
            present_months = [m for m in month_order if m in monthly_heatmap_data.columns]
            monthly_heatmap_data = monthly_heatmap_data[present_months]
            # Sort index (Year) ascending
            monthly_heatmap_data.sort_index(ascending=True, inplace=True)
            # Store as percentages for the heatmap plot
            analysis_results["monthly_returns_table_for_heatmap"] = monthly_heatmap_data * 100

            # Monthly Performance Statistics
            monthly_stats = {
                "Min": f"{monthly_rets.min():.2%}",
                "Max": f"{monthly_rets.max():.2%}",
                "Mean": f"{monthly_rets.mean():.2%}",
                "Positive Months": (monthly_rets > 0).sum(),
                "Negative Months": (monthly_rets <= 0).sum()
            }
            analysis_results["monthly_perf_stats"] = pd.DataFrame(list(monthly_stats.items()), columns=['Metric', 'Value'])
            status_parts.append("Monthly stats OK.")
        else:
            status_parts.append("Monthly stats skipped (no monthly data).")


        # --- Rolling Volatility Analysis ---
        vol_df = pd.DataFrame(index=returns_series.index) # Initialize DF to store rolling vol results
        vol_stats_list = [] # List to store summary stats for each window
        # Define windows (label: number of trading days)
        windows = {'3M': 63, '6M': 126, '12M': 252}
        vol_calculated = False
        for label, window in windows.items():
            # Check if there's enough data for the window
            if len(returns_series) >= window:
                try:
                    # Calculate rolling standard deviation
                    # min_periods ensures calculation starts even if window isn't full yet (adjust as needed)
                    rolling_std = returns_series.rolling(window=window, min_periods=window // 2).std()
                    # Annualize the volatility (multiply by sqrt of trading days per year)
                    rolling_vol = rolling_std * np.sqrt(252)
                    # Store the result in the DataFrame
                    vol_df[f'vol_{label}'] = rolling_vol
                    # Calculate summary stats for this window's volatility
                    if not rolling_vol.dropna().empty: # Check if there are valid vol values
                        vol_stats_list.append({
                            "Window": label,
                            "Min Vol": f"{rolling_vol.min():.2%}",
                            "Max Vol": f"{rolling_vol.max():.2%}",
                            "Mean Vol": f"{rolling_vol.mean():.2%}"
                        })
                        vol_calculated = True
                except Exception as vol_e:
                    print(f"Error calculating rolling volatility for window {label}: {vol_e}")
                    status_parts.append(f"Rolling Vol ({label}) Error.")

        # Store the rolling volatility DataFrame (reset index to get 'Time' column for plotting)
        if not vol_df.empty:
            analysis_results["rolling_vol_df"] = vol_df.reset_index()

        # Store the summary statistics if any were calculated
        if vol_stats_list:
            analysis_results["rolling_vol_stats"] = pd.DataFrame(vol_stats_list)
            status_parts.append("Rolling Vol OK.")
        elif not vol_calculated and "Error" not in " ".join(status_parts): # If no vol calculated and no errors reported
             status_parts.append("Rolling Vol skipped (insufficient data for windows).")


        # --- Drawdown Table Calculation ---
        try:
            analysis_results["drawdown_table"] = get_drawdown_table(returns_series, top=5)
            if not analysis_results["drawdown_table"].empty:
                 status_parts.append("Drawdown Table OK.")
            else:
                 status_parts.append("Drawdown Table: No drawdowns found or error.")
        except Exception as dd_e:
             print(f"Error calculating drawdown table: {dd_e}")
             traceback.print_exc()
             status_parts.append("Drawdown Table Error.")


        # --- Final Status ---
        analysis_results["status"] = " ".join(status_parts) if status_parts else "Analysis completed (no specific issues)."

    except Exception as e:
        # Catch-all for any unexpected error during the entire analysis
        error_msg = f"Error during manual risk analysis: {e}"
        print(error_msg)
        traceback.print_exc()
        analysis_results["status"] = f"Manual risk analysis failed: {e}"

    return analysis_results


def calculate_correlation(all_results):
    """
    Calculates the correlation matrix for the daily returns of multiple strategies
    and optionally includes the benchmark.
    Args:
        all_results: A dictionary where keys are strategy filenames and values are
                     the result dictionaries obtained from process_single_file.
                     These results should contain 'equity_df' and optionally 'benchmark_df'.
    Returns:
        A tuple containing:
        - correlation_matrix: DataFrame of the Pearson correlation coefficients.
        - heatmap_fig: Plotly heatmap figure of the correlation matrix.
        - corr_status: String message indicating the status of the correlation calculation.
    """
    # Default outputs
    default_corr_matrix = pd.DataFrame()
    default_heatmap = create_empty_figure("Correlation Heatmap (Insufficient Data)")
    corr_status = "Correlation analysis skipped."

    equity_data_all = {} # Dictionary to store equity series {filename: Series}
    benchmark_data = None # To store the first valid benchmark series found
    valid_strategies_count = 0 # Count strategies with valid equity data

    # --- Extract Equity and Benchmark Data ---
    for filename, results in all_results.items():
        if results.get("error"): # Skip files that had processing errors
             print(f"Skipping {filename} for correlation due to processing error.")
             continue

        equity_df = results.get("equity_df") # DataFrame with 'Time', 'Equity'
        bench_df = results.get("benchmark_df") # DataFrame with 'Time', 'Benchmark'

        # Check for valid equity data
        if equity_df is not None and not equity_df.empty and \
           'Time' in equity_df.columns and 'Equity' in equity_df.columns and \
           pd.api.types.is_datetime64_any_dtype(equity_df['Time']):

            # Set 'Time' as index, select 'Equity', remove duplicate indices
            df_eq = equity_df.set_index('Time')['Equity']
            df_eq = df_eq[~df_eq.index.duplicated(keep='first')]

            # Ensure index is UTC
            if df_eq.index.tz is None: df_eq = df_eq.tz_localize('UTC')
            elif df_eq.index.tz != 'UTC': df_eq = df_eq.tz_convert('UTC')

            if not df_eq.empty:
                equity_data_all[filename] = df_eq
                valid_strategies_count += 1

                # Try to grab the benchmark data from the *first* strategy that has it
                if benchmark_data is None and bench_df is not None and not bench_df.empty and \
                   'Time' in bench_df.columns and 'Benchmark' in bench_df.columns and \
                   pd.api.types.is_datetime64_any_dtype(bench_df['Time']):

                    df_b = bench_df.set_index('Time')['Benchmark']
                    df_b = df_b[~df_b.index.duplicated(keep='first')]

                    # Ensure index is UTC
                    if df_b.index.tz is None: df_b = df_b.tz_localize('UTC')
                    elif df_b.index.tz != 'UTC': df_b = df_b.tz_convert('UTC')

                    if not df_b.empty:
                        benchmark_data = df_b
                        print(f"Using benchmark data from {filename} for correlation.")
        else:
            print(f"Skipping {filename} for correlation: Invalid or empty equity_df or Time column.")

    # --- Check if enough data for correlation ---
    # Need at least 1 strategy for correlation (against itself or benchmark)
    # Need at least 2 strategies if no benchmark is available
    if valid_strategies_count == 0:
         corr_status = "Correlation skipped: No valid strategy equity data found."
         return default_corr_matrix, default_heatmap, corr_status
    if valid_strategies_count == 1 and benchmark_data is None:
         corr_status = "Correlation skipped: Only one strategy and no benchmark data."
         # Return the single equity series maybe? Or just empty. Empty is safer.
         return default_corr_matrix, default_heatmap, corr_status


    # --- Combine Data and Calculate Returns ---
    # Combine all valid equity series into a single DataFrame
    combined_equity = pd.concat(equity_data_all, axis=1, join='outer') # Use outer join to keep all dates

    # Add benchmark data if available
    if benchmark_data is not None:
        combined_equity['Benchmark'] = benchmark_data

    # Sort by index (Time)
    combined_equity = combined_equity.sort_index()

    # Forward-fill missing values (common for aligning different start/end dates)
    # Consider alternatives like backward fill or interpolation if ffill isn't appropriate
    combined_equity_filled = combined_equity.ffill()

    # Calculate daily percentage returns
    daily_returns = combined_equity_filled.pct_change()

    # Handle potential infinite values resulting from division by zero (e.g., price was 0)
    daily_returns.replace([np.inf, -np.inf], np.nan, inplace=True)

    # Drop rows with any NaN values (typically the first row after pct_change, and any rows affected by NaNs)
    daily_returns.dropna(inplace=True)

    # Check if enough overlapping data remains after cleaning
    if daily_returns.empty or len(daily_returns) < 2:
        corr_status = "Correlation skipped: Not enough overlapping daily data points after cleaning."
        return default_corr_matrix, default_heatmap, corr_status

    # --- Calculate Correlation Matrix ---
    try:
        correlation_matrix = daily_returns.corr(method='pearson') # Can change method if needed ('kendall', 'spearman')
        corr_status = f"Correlation calculated for {valid_strategies_count} strategies"
        if benchmark_data is not None:
            corr_status += " and Benchmark."
        else:
            corr_status += "."
    except Exception as corr_e:
         print(f"Error calculating correlation matrix: {corr_e}")
         traceback.print_exc()
         corr_status = f"Correlation calculation failed: {corr_e}"
         return default_corr_matrix, default_heatmap, corr_status


    # --- Generate Correlation Heatmap Figure ---
    heatmap_fig = create_empty_figure("Correlation Heatmap") # Default empty
    try:
        heatmap_fig = go.Figure(data=go.Heatmap(
            z=correlation_matrix.values,
            x=correlation_matrix.columns,
            y=correlation_matrix.columns,
            colorscale='RdBu', # Red-Blue diverging scale is good for correlation
            zmin=-1, zmax=1,   # Set scale limits to -1 and 1
            colorbar=dict(title='Correlation')
        ))
        heatmap_fig.update_layout(
            title='Strategy (+Benchmark) Daily Return Correlation',
            xaxis_tickangle=-45, # Angle labels for better readability if many strategies
            yaxis_autorange='reversed' # Often preferred for matrices
        )

        # Add text annotations (correlation values) to the heatmap cells
        for i in range(len(correlation_matrix.columns)):
            for j in range(len(correlation_matrix.columns)):
                corr_value = correlation_matrix.iloc[i, j]
                if pd.notna(corr_value):
                    # Choose text color based on background intensity for better contrast
                    text_color = "white" if abs(corr_value) > 0.5 else "black"
                    heatmap_fig.add_annotation(
                        x=correlation_matrix.columns[j],
                        y=correlation_matrix.columns[i],
                        text=f"{corr_value:.2f}", # Format to 2 decimal places
                        showarrow=False,
                        font=dict(color=text_color)
                    )
    except Exception as e:
        print(f"Error creating correlation heatmap figure: {e}")
        traceback.print_exc()
        heatmap_fig = create_empty_figure("Error Creating Correlation Heatmap") # Update title on error

    return correlation_matrix, heatmap_fig, corr_status

	# -- coding: utf-8 --
	"""risk_analysis.py

	Automatically generated by Colab.

	Original file is located at
	https://colab.research.google.com/drive/10u2Di5_droisNYuq_KYAmdgVHixe6oVi
	"""

	# risk_analysis.py
	# Functions for calculating risk metrics and correlations.

	import pandas as pd
	import numpy as np
	import traceback
	import plotly.graph_objects as go
	from utils import create_empty_figure # Import helper

	def get_drawdown_table(returns: pd.Series, top: int = 5) -> pd.DataFrame:
	"""
	Calculates drawdown periods and statistics from a series of returns.

	Args:
	returns: Series of daily returns with a DatetimeIndex.
	top: Number of top drawdowns (by magnitude) to return.

	Returns:
	DataFrame containing information about the top drawdown periods:
	'Peak Date', 'Valley Date', 'End Date', 'Duration (Days)', 'Max Drawdown (%)'.
	Returns an empty DataFrame if input is invalid or no drawdowns occur.
	"""
	# Input validation
	if returns is None or not isinstance(returns, pd.Series) or returns.empty:
	# print("Drawdown calculation skipped: Input returns series is invalid or empty.")
	return pd.DataFrame()
	if not isinstance(returns.index, pd.DatetimeIndex):
	# print("Drawdown calculation skipped: Input returns series index is not DatetimeIndex.")
	return pd.DataFrame()

	# Create a DataFrame from the returns series
	df = returns.to_frame(name='returns')

	# Ensure returns are numeric, drop non-numeric values
	df['returns'] = pd.to_numeric(df['returns'], errors='coerce')
	df.dropna(subset=['returns'], inplace=True)
	if df.empty:
	# print("Drawdown calculation skipped: No valid numeric returns.")
	return pd.DataFrame()

	# Calculate cumulative returns (compounded)
	df['Cumulative'] = (1 + df['returns']).cumprod()
	# Calculate the running maximum cumulative return (high watermark)
	df['HighWatermark'] = df['Cumulative'].cummax()
	# Calculate drawdown as the percentage decline from the high watermark
	df['Drawdown'] = (df['Cumulative'] / df['HighWatermark']) - 1

	# Identify drawdown periods
	in_drawdown = False # Flag to track if currently in a drawdown
	periods = [] # List to store completed drawdown period dictionaries
	current_period = {} # Dictionary to store details of the ongoing drawdown
	peak_idx = df.index[0] # Initialize peak index to the start

	for idx, row in df.iterrows():
	# Update the peak index if a new high watermark is reached
	# Use .loc for safe index-based comparison, especially with potential duplicate indices
	if row['Cumulative'] >= df.loc[peak_idx, 'Cumulative']:
	peak_idx = idx

	is_dd = row['Drawdown'] < 0 # Check if currently in a drawdown state

	# Start of a new drawdown period
	if not in_drawdown and is_dd:
	in_drawdown = True
	current_period = {
	'Peak Date': peak_idx, # Date the drawdown started (previous peak)
	'Valley Date': idx, # Date the maximum drawdown was reached (initially the start)
	'End Date': pd.NaT, # Date the drawdown ended (recovered to peak) - initially NaT
	'Max Drawdown (%)': row['Drawdown'], # The maximum drawdown percentage (initially the current DD)
	'Duration (Days)': 0 # Duration of the drawdown - calculated at the end
	}
	# Inside an ongoing drawdown period
	elif in_drawdown:
	# Update valley date and max drawdown if a lower point is reached
	if row['Drawdown'] < current_period['Max Drawdown (%)']:
	current_period['Valley Date'] = idx
	current_period['Max Drawdown (%)'] = row['Drawdown']

	# End of the current drawdown period (recovered)
	if not is_dd: # Recovered when Drawdown is no longer negative (or zero)
	in_drawdown = False
	current_period['End Date'] = idx # Mark the recovery date

	# Calculate duration (using business days if possible, else calendar days)
	start_date = current_period['Peak Date']
	end_date = current_period['End Date']
	if pd.notna(start_date) and pd.notna(end_date):
	try:
	# Attempt to use business days for duration
	duration = len(pd.bdate_range(start=start_date, end=end_date))
	except Exception: # Fallback to calendar days if bdate_range fails (e.g., non-standard dates)
	duration = (end_date - start_date).days + 1 # Inclusive of start/end day
	current_period['Duration (Days)'] = duration
	else:
	current_period['Duration (Days)'] = np.nan # Duration is NaN if dates are invalid

	periods.append(current_period) # Add the completed period to the list
	current_period = {} # Reset for the next potential drawdown

	# Handle the case where the series ends while still in a drawdown
	if in_drawdown:
	start_date = current_period['Peak Date']
	end_date = df.index[-1] # End date is the last date in the series
	if pd.notna(start_date) and pd.notna(end_date):
	try:
	duration = len(pd.bdate_range(start=start_date, end=end_date))
	except Exception:
	duration = (end_date - start_date).days + 1
	current_period['Duration (Days)'] = duration
	else:
	current_period['Duration (Days)'] = np.nan
	# 'End Date' remains NaT as recovery hasn't happened by the end of the data
	periods.append(current_period)

	# If no drawdown periods were identified
	if not periods:
	return pd.DataFrame()

	# Create DataFrame from the identified periods
	drawdown_df = pd.DataFrame(periods)

	# Sort by the magnitude of the drawdown (most negative first) and select the top N
	drawdown_df = drawdown_df.sort_values(by='Max Drawdown (%)', ascending=True).head(top)

	# Format the Max Drawdown column as percentage
	drawdown_df['Max Drawdown (%)'] = drawdown_df['Max Drawdown (%)'].map('{:.2%}'.format)

	# Format date columns to YYYY-MM-DD strings for display
	for col in ['Peak Date', 'Valley Date', 'End Date']:
	if col in drawdown_df.columns:
	# Ensure conversion to datetime first, then format
	drawdown_df[col] = pd.to_datetime(drawdown_df[col]).dt.strftime('%Y-%m-%d')

	# Select and order columns for the final output table
	cols_to_select = ['Peak Date', 'Valley Date', 'End Date', 'Duration (Days)', 'Max Drawdown (%)']
	# Ensure only existing columns are selected (e.g., 'End Date' might be all NaT if never recovered)
	existing_cols = [col for col in cols_to_select if col in drawdown_df.columns]

	return drawdown_df[existing_cols]


	def calculate_manual_risk_stats(returns_series):
	"""
	Calculates various risk and performance metrics manually using pandas based on daily returns.

	Args:
	returns_series: A pandas Series of daily percentage returns with a DatetimeIndex.

	Returns:
	A dictionary containing:
	- monthly_returns_table_for_heatmap: DataFrame pivoted for monthly return heatmap (values as percentages).
	- monthly_perf_stats: DataFrame with summary stats for monthly returns.
	- rolling_vol_df: DataFrame containing rolling annualized volatility calculations (with 'Time' column).
	- rolling_vol_stats: DataFrame summarizing min/max/mean rolling volatility.
	- drawdown_table: DataFrame with top drawdown periods (from get_drawdown_table).
	- status: A string indicating the status of the analysis.
	"""
	# Initialize results dictionary with default empty structures
	analysis_results = {
	"monthly_returns_table_for_heatmap": pd.DataFrame(),
	"monthly_perf_stats": pd.DataFrame(columns=['Metric', 'Value']),
	"rolling_vol_df": pd.DataFrame(),
	"rolling_vol_stats": pd.DataFrame(columns=['Window', 'Min Vol', 'Max Vol', 'Mean Vol']),
	"drawdown_table": pd.DataFrame(),
	"status": "Analysis skipped." # Default status
	}

	# --- Input Validation ---
	if returns_series is None or not isinstance(returns_series, pd.Series) or returns_series.empty or len(returns_series) < 2:
	analysis_results["status"] = "Analysis skipped: Insufficient/invalid returns data."
	return analysis_results
	if not isinstance(returns_series.index, pd.DatetimeIndex):
	analysis_results["status"] = "Analysis skipped: Returns index is not DatetimeIndex."
	return analysis_results

	try:
	status_parts = [] # To collect status messages for different parts

	# Ensure returns are numeric and index is UTC DatetimeIndex
	returns_series = pd.to_numeric(returns_series, errors='coerce').dropna()
	if returns_series.empty or len(returns_series) < 2:
	analysis_results["status"] = "Analysis skipped: No valid numeric returns after cleaning."
	return analysis_results

	if returns_series.index.tz is None:
	returns_series = returns_series.tz_localize('UTC')
	elif returns_series.index.tz != 'UTC':
	returns_series = returns_series.tz_convert('UTC')

	# --- Monthly Returns Analysis ---
	# Resample daily returns to monthly, calculating compounded monthly return
	# The lambda function calculates (1+r1)(1+r2)...*(1+rn) - 1 for each month
	monthly_rets = returns_series.resample('M').apply(lambda x: (1 + x).prod() - 1)

	if not monthly_rets.empty:
	# Create table for heatmap: Year rows, Month columns
	monthly_ret_table_df = pd.DataFrame({'returns': monthly_rets})
	monthly_ret_table_df['Year'] = monthly_ret_table_df.index.year
	monthly_ret_table_df['Month'] = monthly_ret_table_df.index.strftime('%b') # Month abbreviation (Jan, Feb, ...)
	# Pivot the table
	monthly_heatmap_data = monthly_ret_table_df.pivot_table(index='Year', columns='Month', values='returns')
	# Order columns chronologically
	month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
	present_months = [m for m in month_order if m in monthly_heatmap_data.columns]
	monthly_heatmap_data = monthly_heatmap_data[present_months]
	# Sort index (Year) ascending
	monthly_heatmap_data.sort_index(ascending=True, inplace=True)
	# Store as percentages for the heatmap plot
	analysis_results["monthly_returns_table_for_heatmap"] = monthly_heatmap_data * 100

	# Monthly Performance Statistics
	monthly_stats = {
	"Min": f"{monthly_rets.min():.2%}",
	"Max": f"{monthly_rets.max():.2%}",
	"Mean": f"{monthly_rets.mean():.2%}",
	"Positive Months": (monthly_rets > 0).sum(),
	"Negative Months": (monthly_rets <= 0).sum()
	}
	analysis_results["monthly_perf_stats"] = pd.DataFrame(list(monthly_stats.items()), columns=['Metric', 'Value'])
	status_parts.append("Monthly stats OK.")
	else:
	status_parts.append("Monthly stats skipped (no monthly data).")


	# --- Rolling Volatility Analysis ---
	vol_df = pd.DataFrame(index=returns_series.index) # Initialize DF to store rolling vol results
	vol_stats_list = [] # List to store summary stats for each window
	# Define windows (label: number of trading days)
	windows = {'3M': 63, '6M': 126, '12M': 252}
	vol_calculated = False
	for label, window in windows.items():
	# Check if there's enough data for the window
	if len(returns_series) >= window:
	try:
	# Calculate rolling standard deviation
	# min_periods ensures calculation starts even if window isn't full yet (adjust as needed)
	rolling_std = returns_series.rolling(window=window, min_periods=window // 2).std()
	# Annualize the volatility (multiply by sqrt of trading days per year)
	rolling_vol = rolling_std * np.sqrt(252)
	# Store the result in the DataFrame
	vol_df[f'vol_{label}'] = rolling_vol
	# Calculate summary stats for this window's volatility
	if not rolling_vol.dropna().empty: # Check if there are valid vol values
	vol_stats_list.append({
	"Window": label,
	"Min Vol": f"{rolling_vol.min():.2%}",
	"Max Vol": f"{rolling_vol.max():.2%}",
	"Mean Vol": f"{rolling_vol.mean():.2%}"
	})
	vol_calculated = True
	except Exception as vol_e:
	print(f"Error calculating rolling volatility for window {label}: {vol_e}")
	status_parts.append(f"Rolling Vol ({label}) Error.")

	# Store the rolling volatility DataFrame (reset index to get 'Time' column for plotting)
	if not vol_df.empty:
	analysis_results["rolling_vol_df"] = vol_df.reset_index()

	# Store the summary statistics if any were calculated
	if vol_stats_list:
	analysis_results["rolling_vol_stats"] = pd.DataFrame(vol_stats_list)
	status_parts.append("Rolling Vol OK.")
	elif not vol_calculated and "Error" not in " ".join(status_parts): # If no vol calculated and no errors reported
	status_parts.append("Rolling Vol skipped (insufficient data for windows).")


	# --- Drawdown Table Calculation ---
	try:
	analysis_results["drawdown_table"] = get_drawdown_table(returns_series, top=5)
	if not analysis_results["drawdown_table"].empty:
	status_parts.append("Drawdown Table OK.")
	else:
	status_parts.append("Drawdown Table: No drawdowns found or error.")
	except Exception as dd_e:
	print(f"Error calculating drawdown table: {dd_e}")
	traceback.print_exc()
	status_parts.append("Drawdown Table Error.")


	# --- Final Status ---
	analysis_results["status"] = " ".join(status_parts) if status_parts else "Analysis completed (no specific issues)."

	except Exception as e:
	# Catch-all for any unexpected error during the entire analysis
	error_msg = f"Error during manual risk analysis: {e}"
	print(error_msg)
	traceback.print_exc()
	analysis_results["status"] = f"Manual risk analysis failed: {e}"

	return analysis_results


	def calculate_correlation(all_results):
	"""
	Calculates the correlation matrix for the daily returns of multiple strategies
	and optionally includes the benchmark.

	Args:
	all_results: A dictionary where keys are strategy filenames and values are
	the result dictionaries obtained from process_single_file.
	These results should contain 'equity_df' and optionally 'benchmark_df'.

	Returns:
	A tuple containing:
	- correlation_matrix: DataFrame of the Pearson correlation coefficients.
	- heatmap_fig: Plotly heatmap figure of the correlation matrix.
	- corr_status: String message indicating the status of the correlation calculation.
	"""
	# Default outputs
	default_corr_matrix = pd.DataFrame()
	default_heatmap = create_empty_figure("Correlation Heatmap (Insufficient Data)")
	corr_status = "Correlation analysis skipped."

	equity_data_all = {} # Dictionary to store equity series {filename: Series}
	benchmark_data = None # To store the first valid benchmark series found
	valid_strategies_count = 0 # Count strategies with valid equity data

	# --- Extract Equity and Benchmark Data ---
	for filename, results in all_results.items():
	if results.get("error"): # Skip files that had processing errors
	print(f"Skipping {filename} for correlation due to processing error.")
	continue

	equity_df = results.get("equity_df") # DataFrame with 'Time', 'Equity'
	bench_df = results.get("benchmark_df") # DataFrame with 'Time', 'Benchmark'

	# Check for valid equity data
	if equity_df is not None and not equity_df.empty and \
	'Time' in equity_df.columns and 'Equity' in equity_df.columns and \
	pd.api.types.is_datetime64_any_dtype(equity_df['Time']):

	# Set 'Time' as index, select 'Equity', remove duplicate indices
	df_eq = equity_df.set_index('Time')['Equity']
	df_eq = df_eq[~df_eq.index.duplicated(keep='first')]

	# Ensure index is UTC
	if df_eq.index.tz is None: df_eq = df_eq.tz_localize('UTC')
	elif df_eq.index.tz != 'UTC': df_eq = df_eq.tz_convert('UTC')

	if not df_eq.empty:
	equity_data_all[filename] = df_eq
	valid_strategies_count += 1

	# Try to grab the benchmark data from the first strategy that has it
	if benchmark_data is None and bench_df is not None and not bench_df.empty and \
	'Time' in bench_df.columns and 'Benchmark' in bench_df.columns and \
	pd.api.types.is_datetime64_any_dtype(bench_df['Time']):

	df_b = bench_df.set_index('Time')['Benchmark']
	df_b = df_b[~df_b.index.duplicated(keep='first')]

	# Ensure index is UTC
	if df_b.index.tz is None: df_b = df_b.tz_localize('UTC')
	elif df_b.index.tz != 'UTC': df_b = df_b.tz_convert('UTC')

	if not df_b.empty:
	benchmark_data = df_b
	print(f"Using benchmark data from {filename} for correlation.")
	else:
	print(f"Skipping {filename} for correlation: Invalid or empty equity_df or Time column.")

	# --- Check if enough data for correlation ---
	# Need at least 1 strategy for correlation (against itself or benchmark)
	# Need at least 2 strategies if no benchmark is available
	if valid_strategies_count == 0:
	corr_status = "Correlation skipped: No valid strategy equity data found."
	return default_corr_matrix, default_heatmap, corr_status
	if valid_strategies_count == 1 and benchmark_data is None:
	corr_status = "Correlation skipped: Only one strategy and no benchmark data."
	# Return the single equity series maybe? Or just empty. Empty is safer.
	return default_corr_matrix, default_heatmap, corr_status


	# --- Combine Data and Calculate Returns ---
	# Combine all valid equity series into a single DataFrame
	combined_equity = pd.concat(equity_data_all, axis=1, join='outer') # Use outer join to keep all dates

	# Add benchmark data if available
	if benchmark_data is not None:
	combined_equity['Benchmark'] = benchmark_data

	# Sort by index (Time)
	combined_equity = combined_equity.sort_index()

	# Forward-fill missing values (common for aligning different start/end dates)
	# Consider alternatives like backward fill or interpolation if ffill isn't appropriate
	combined_equity_filled = combined_equity.ffill()

	# Calculate daily percentage returns
	daily_returns = combined_equity_filled.pct_change()

	# Handle potential infinite values resulting from division by zero (e.g., price was 0)
	daily_returns.replace([np.inf, -np.inf], np.nan, inplace=True)

	# Drop rows with any NaN values (typically the first row after pct_change, and any rows affected by NaNs)
	daily_returns.dropna(inplace=True)

	# Check if enough overlapping data remains after cleaning
	if daily_returns.empty or len(daily_returns) < 2:
	corr_status = "Correlation skipped: Not enough overlapping daily data points after cleaning."
	return default_corr_matrix, default_heatmap, corr_status

	# --- Calculate Correlation Matrix ---
	try:
	correlation_matrix = daily_returns.corr(method='pearson') # Can change method if needed ('kendall', 'spearman')
	corr_status = f"Correlation calculated for {valid_strategies_count} strategies"
	if benchmark_data is not None:
	corr_status += " and Benchmark."
	else:
	corr_status += "."
	except Exception as corr_e:
	print(f"Error calculating correlation matrix: {corr_e}")
	traceback.print_exc()
	corr_status = f"Correlation calculation failed: {corr_e}"
	return default_corr_matrix, default_heatmap, corr_status


	# --- Generate Correlation Heatmap Figure ---
	heatmap_fig = create_empty_figure("Correlation Heatmap") # Default empty
	try:
	heatmap_fig = go.Figure(data=go.Heatmap(
	z=correlation_matrix.values,
	x=correlation_matrix.columns,
	y=correlation_matrix.columns,
	colorscale='RdBu', # Red-Blue diverging scale is good for correlation
	zmin=-1, zmax=1, # Set scale limits to -1 and 1
	colorbar=dict(title='Correlation')
	))
	heatmap_fig.update_layout(
	title='Strategy (+Benchmark) Daily Return Correlation',
	xaxis_tickangle=-45, # Angle labels for better readability if many strategies
	yaxis_autorange='reversed' # Often preferred for matrices
	)

	# Add text annotations (correlation values) to the heatmap cells
	for i in range(len(correlation_matrix.columns)):
	for j in range(len(correlation_matrix.columns)):
	corr_value = correlation_matrix.iloc[i, j]
	if pd.notna(corr_value):
	# Choose text color based on background intensity for better contrast
	text_color = "white" if abs(corr_value) > 0.5 else "black"
	heatmap_fig.add_annotation(
	x=correlation_matrix.columns[j],
	y=correlation_matrix.columns[i],
	text=f"{corr_value:.2f}", # Format to 2 decimal places
	showarrow=False,
	font=dict(color=text_color)
	)
	except Exception as e:
	print(f"Error creating correlation heatmap figure: {e}")
	traceback.print_exc()
	heatmap_fig = create_empty_figure("Error Creating Correlation Heatmap") # Update title on error

	return correlation_matrix, heatmap_fig, corr_status