"""
Pattern Agent for chart pattern recognition and analysis.

This agent identifies candlestick patterns, chart patterns, and support/resistance levels
using both algorithmic detection and LLM vision analysis.
"""

import json
import logging
import time
from typing import Any, Dict, List, Optional

import pandas as pd
from langchain_core.messages import HumanMessage, SystemMessage

# Configure logger
logger = logging.getLogger(__name__)

from config.default_config import DEFAULT_CONFIG
from config.models import AGENT_MODELS
from config.prompt_templates import PATTERN_AGENT_PROMPT
from graph.state.agent_state import add_agent_message, update_analysis_result
from graph.state.trading_state import TechnicalWorkflowState
from utils.charts.annotations import ChartAnnotations
from utils.investment_style_helpers import (
    get_investment_style_from_state,
    get_technical_analysis_style_context,
)
from utils.llm.provider_factory import LLMProviderFactory


class PatternAgent:
    """
    Chart Pattern Recognition Agent.

    Responsibilities:
    - Identify candlestick patterns (doji, hammer, engulfing, etc.)
    - Detect chart patterns (triangles, channels, head-and-shoulders)
    - Find support and resistance levels
    - Analyze pattern significance and reliability
    """

    AGENT_NAME = "pattern_agent"

    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """
        Initialize Pattern Agent.

        Args:
            config: Optional configuration override
        """
        self.config = config or DEFAULT_CONFIG

        # Initialize LLM (needs vision capability for chart analysis) - use runtime provider override if available
        from config.models import DEFAULT_MODELS_BY_PROVIDER

        model_config = AGENT_MODELS[self.AGENT_NAME]
        runtime_provider = self.config.get("llm_provider", model_config["provider"])

        # If provider is overridden but model is not, use default model for that provider
        if "llm_provider" in self.config and "llm_model" not in self.config:
            runtime_model = DEFAULT_MODELS_BY_PROVIDER.get(
                runtime_provider, model_config["model"]
            )
        else:
            runtime_model = self.config.get("llm_model", model_config["model"])

        self.llm = LLMProviderFactory.create(
            provider=runtime_provider,
            model=runtime_model,
            temperature=model_config["temperature"],
        )

    def _get_timeframe_significance(self, timeframe: str) -> Dict[str, Any]:
        """
        Get timeframe significance level for pattern analysis.

        Args:
            timeframe: Timeframe string (1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w)

        Returns:
            Dict with significance info
        """
        timeframe_map = {
            "1m": {"weight": 0.3, "label": "1-minute", "scope": "scalping"},
            "5m": {"weight": 0.4, "label": "5-minute", "scope": "scalping"},
            "15m": {"weight": 0.5, "label": "15-minute", "scope": "day trading"},
            "30m": {"weight": 0.6, "label": "30-minute", "scope": "day trading"},
            "1h": {"weight": 0.7, "label": "1-hour", "scope": "swing trading"},
            "4h": {"weight": 0.8, "label": "4-hour", "scope": "swing trading"},
            "1d": {"weight": 0.9, "label": "daily", "scope": "position trading"},
            "1w": {"weight": 1.0, "label": "weekly", "scope": "long-term"},
        }
        return timeframe_map.get(
            timeframe, {"weight": 0.5, "label": timeframe, "scope": "intraday"}
        )

    def run(self, state: TechnicalWorkflowState) -> TechnicalWorkflowState:
        """
        Execute pattern recognition.

        Args:
            state: Current workflow state

        Returns:
            Updated state with pattern analysis
        """
        start_time = time.time()
        ticker = state.get("ticker", "UNKNOWN")
        timeframe = state.get("timeframe", "UNKNOWN")

        logger.info(
            json.dumps(
                {
                    "agent": self.AGENT_NAME,
                    "action": "start",
                    "ticker": ticker,
                    "timeframe": timeframe,
                    "timestamp": time.time(),
                }
            )
        )

        try:
            # Extract market data
            market_data = state["market_data"]
            if not market_data.get("ohlc_data"):
                raise ValueError("No OHLC data available for pattern recognition")

            # Convert serialized DataFrame back to pandas DataFrame
            df = self._deserialize_dataframe(market_data["ohlc_data"])

            # Get timeframe significance for pattern annotation
            timeframe_info = self._get_timeframe_significance(timeframe)

            # Detect patterns with timeframe context
            patterns_result = self._detect_patterns(df, timeframe, timeframe_info)

            # Find support/resistance levels
            levels = ChartAnnotations.find_support_resistance_levels(
                df, window=20, num_levels=3
            )
            patterns_result["support_levels"] = levels["support"]
            patterns_result["resistance_levels"] = levels["resistance"]

            # Get investment style from state
            investment_style = get_investment_style_from_state(state)

            # Pattern agent does not generate separate charts
            # The main candlestick chart above the tabs shows the pricing data
            # Pattern agent only provides textual analysis of detected patterns
            chart_paths = []
            educational_notes = []
            config_dict = state.get("config", {})
            educational_mode = config_dict.get("educational_mode", False)

            chart_patterns = patterns_result.get("chart_patterns", [])
            candlestick_patterns = patterns_result.get("candlestick_patterns", [])

            # Generate educational notes for detected patterns if enabled
            if educational_mode:
                for pattern in candlestick_patterns[:10]:  # Limit to top 10 patterns
                    try:
                        from utils.formatters.educational_content import (
                            generate_pattern_explanation,
                        )

                        pattern_name = (
                            pattern.get("pattern", "")
                            .lower()
                            .replace(" ", "_")
                            .replace("-", "_")
                        )
                        explanation = generate_pattern_explanation(pattern_name)
                        educational_notes.append(
                            f"**{pattern.get('pattern')}**:\n{explanation}"
                        )
                    except Exception as e:
                        logger.warning(f"Failed to generate educational note: {e}")

            # Extract cost tracker from state
            cost_tracker = state.get("_cost_tracker")

            # Interpret patterns using LLM
            interpretation = self._interpret_with_llm(
                state["ticker"],
                state["timeframe"],
                patterns_result,
                df,
                investment_style,
                cost_tracker,
            )

            # Update state
            new_state = update_analysis_result(state, "patterns", patterns_result)
            new_state = add_agent_message(
                new_state,
                self.AGENT_NAME,
                interpretation,
                metadata={
                    "patterns": patterns_result,
                    "charts": chart_paths,  # Changed from "chart_paths" to "charts" for UI compatibility
                    "educational_notes": "\n\n".join(educational_notes)
                    if educational_notes
                    else None,
                },
            )

            execution_time = time.time() - start_time
            logger.info(
                json.dumps(
                    {
                        "agent": self.AGENT_NAME,
                        "action": "complete",
                        "ticker": ticker,
                        "timeframe": timeframe,
                        "execution_time": execution_time,
                        "candlestick_patterns": len(
                            patterns_result.get("candlestick_patterns", [])
                        ),
                        "chart_patterns": len(
                            patterns_result.get("chart_patterns", [])
                        ),
                        "timestamp": time.time(),
                    }
                )
            )

            return new_state

        except Exception as e:
            execution_time = time.time() - start_time
            logger.error(
                json.dumps(
                    {
                        "agent": self.AGENT_NAME,
                        "action": "error",
                        "ticker": ticker,
                        "timeframe": timeframe,
                        "execution_time": execution_time,
                        "error": str(e),
                        "timestamp": time.time(),
                    }
                )
            )

            # Add error message to state
            error_state = add_agent_message(
                state,
                self.AGENT_NAME,
                f"Error detecting patterns: {str(e)}",
                metadata={"error": True},
            )
            return error_state

    def _detect_patterns(
        self, df: pd.DataFrame, timeframe: str, timeframe_info: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Detect candlestick and chart patterns with timeframe significance.

        Args:
            df: OHLC DataFrame
            timeframe: Timeframe string (e.g., "1d", "4h")
            timeframe_info: Timeframe significance info

        Returns:
            Dict with pattern results including timeframe context
        """
        result = {
            "candlestick_patterns": [],
            "chart_patterns": [],
            "support_levels": [],
            "resistance_levels": [],
            "trend_lines": [],
            "timeframe_context": {
                "timeframe": timeframe,
                "label": timeframe_info["label"],
                "scope": timeframe_info["scope"],
                "weight": timeframe_info["weight"],
            },
        }

        # Detect candlestick patterns with timeframe context
        candlestick_patterns = self._detect_candlestick_patterns(
            df, timeframe, timeframe_info
        )
        result["candlestick_patterns"] = candlestick_patterns

        # Detect chart patterns with timeframe context
        chart_patterns = self._detect_chart_patterns(df, timeframe, timeframe_info)
        result["chart_patterns"] = chart_patterns

        return result

    def _detect_candlestick_patterns(
        self, df: pd.DataFrame, timeframe: str, timeframe_info: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """
        Detect common candlestick patterns with timeframe significance.

        Args:
            df: OHLC DataFrame
            timeframe: Timeframe string
            timeframe_info: Timeframe significance info

        Returns:
            List of detected patterns with timeframe context
        """
        patterns = []

        # Look at last 10 candles for patterns
        window = min(10, len(df))
        recent_df = df.iloc[-window:]

        for i in range(len(recent_df)):
            idx = recent_df.index[i]
            row = recent_df.iloc[i]

            open_price = row["open"]
            close_price = row["close"]
            high = row["high"]
            low = row["low"]

            body_size = abs(close_price - open_price)
            total_range = high - low

            # Doji pattern (small body)
            if total_range > 0 and body_size / total_range < 0.1:
                patterns.append(
                    {
                        "name": "Doji",
                        "location": len(df) - window + i,
                        "date": str(idx),
                        "signal": "neutral",
                        "confidence": 0.7
                        * timeframe_info["weight"],  # Adjust confidence by timeframe
                        "description": f"Indecision in market on {timeframe_info['label']} chart, potential reversal",
                        "timeframe": timeframe,
                        "timeframe_label": timeframe_info["label"],
                        "significance": timeframe_info["scope"],
                    }
                )

            # Hammer pattern (bullish reversal)
            if total_range > 0:
                upper_wick = high - max(open_price, close_price)
                lower_wick = min(open_price, close_price) - low

                if lower_wick > 2 * body_size and upper_wick < body_size:
                    patterns.append(
                        {
                            "name": "Hammer",
                            "location": len(df) - window + i,
                            "date": str(idx),
                            "signal": "bullish",
                            "confidence": 0.75 * timeframe_info["weight"],
                            "description": f"Bullish reversal signal on {timeframe_info['label']} chart, sellers exhausted",
                            "timeframe": timeframe,
                            "timeframe_label": timeframe_info["label"],
                            "significance": timeframe_info["scope"],
                        }
                    )

                # Shooting Star pattern (bearish reversal)
                if upper_wick > 2 * body_size and lower_wick < body_size:
                    patterns.append(
                        {
                            "name": "Shooting Star",
                            "location": len(df) - window + i,
                            "date": str(idx),
                            "signal": "bearish",
                            "confidence": 0.75 * timeframe_info["weight"],
                            "description": f"Bearish reversal signal on {timeframe_info['label']} chart, buyers exhausted",
                            "timeframe": timeframe,
                            "timeframe_label": timeframe_info["label"],
                            "significance": timeframe_info["scope"],
                        }
                    )

            # Engulfing patterns (need previous candle)
            if i > 0:
                prev_row = recent_df.iloc[i - 1]
                prev_open = prev_row["open"]
                prev_close = prev_row["close"]

                # Bullish engulfing
                if (
                    prev_close < prev_open  # Previous candle bearish
                    and close_price > open_price  # Current candle bullish
                    and open_price < prev_close  # Opens below previous close
                    and close_price > prev_open
                ):  # Closes above previous open
                    patterns.append(
                        {
                            "name": "Bullish Engulfing",
                            "location": len(df) - window + i,
                            "date": str(idx),
                            "signal": "bullish",
                            "confidence": 0.8 * timeframe_info["weight"],
                            "description": f"Strong bullish reversal on {timeframe_info['label']} chart, buyers taking control",
                            "timeframe": timeframe,
                            "timeframe_label": timeframe_info["label"],
                            "significance": timeframe_info["scope"],
                        }
                    )

                # Bearish engulfing
                if (
                    prev_close > prev_open  # Previous candle bullish
                    and close_price < open_price  # Current candle bearish
                    and open_price > prev_close  # Opens above previous close
                    and close_price < prev_open
                ):  # Closes below previous open
                    patterns.append(
                        {
                            "name": "Bearish Engulfing",
                            "location": len(df) - window + i,
                            "date": str(idx),
                            "signal": "bearish",
                            "confidence": 0.8 * timeframe_info["weight"],
                            "description": f"Strong bearish reversal on {timeframe_info['label']} chart, sellers taking control",
                            "timeframe": timeframe,
                            "timeframe_label": timeframe_info["label"],
                            "significance": timeframe_info["scope"],
                        }
                    )

        return patterns

    def _detect_chart_patterns(
        self, df: pd.DataFrame, timeframe: str, timeframe_info: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """
        Detect chart patterns like triangles, channels, head-and-shoulders.

        This is a simplified algorithmic approach. In production, this would
        use more sophisticated pattern recognition algorithms or TA-Lib.

        Args:
            df: OHLC DataFrame
            timeframe: Timeframe string
            timeframe_info: Timeframe significance info

        Returns:
            List of detected chart patterns with timeframe context
        """
        patterns = []

        # Check for ascending triangle (flat resistance, rising support)
        if len(df) >= 20:
            recent_highs = df["high"].iloc[-20:]
            recent_lows = df["low"].iloc[-20:]

            # Flat top (resistance)
            high_std = recent_highs.std()
            high_mean = recent_highs.mean()

            # Rising lows (support)
            first_half_lows = recent_lows.iloc[:10].mean()
            second_half_lows = recent_lows.iloc[10:].mean()

            if (
                high_std / high_mean < 0.02
                and second_half_lows > first_half_lows * 1.01
            ):
                patterns.append(
                    {
                        "type": "Ascending Triangle",
                        "confidence": 0.65 * timeframe_info["weight"],
                        "signal": "bullish",
                        "description": f"Bullish continuation pattern on {timeframe_info['label']} chart, breakout likely upward",
                        "resistance": float(high_mean),
                        "support_trend": "rising",
                        "timeframe": timeframe,
                        "timeframe_label": timeframe_info["label"],
                        "significance": timeframe_info["scope"],
                    }
                )

            # Descending triangle (flat support, falling resistance)
            low_std = recent_lows.std()
            low_mean = recent_lows.mean()

            first_half_highs = recent_highs.iloc[:10].mean()
            second_half_highs = recent_highs.iloc[10:].mean()

            if (
                low_std / low_mean < 0.02
                and second_half_highs < first_half_highs * 0.99
            ):
                patterns.append(
                    {
                        "type": "Descending Triangle",
                        "confidence": 0.65 * timeframe_info["weight"],
                        "signal": "bearish",
                        "description": f"Bearish continuation pattern on {timeframe_info['label']} chart, breakout likely downward",
                        "support": float(low_mean),
                        "resistance_trend": "falling",
                        "timeframe": timeframe,
                        "timeframe_label": timeframe_info["label"],
                        "significance": timeframe_info["scope"],
                    }
                )

        # Check for head-and-shoulders pattern
        if len(df) >= 30:
            head_shoulder_pattern = self._detect_head_and_shoulders(df, timeframe_info)
            if head_shoulder_pattern:
                patterns.append(head_shoulder_pattern)
                logger.info(
                    f"Detected head-and-shoulders pattern: {head_shoulder_pattern}"
                )
            else:
                logger.debug("No head-and-shoulders pattern detected")

        # Check for double-bottom or double-top patterns
        if len(df) >= 20:
            double_patterns = self._detect_double_bottom_top(df, timeframe_info)
            if double_patterns:
                logger.info(
                    f"Detected {len(double_patterns)} double-bottom/top patterns"
                )
                patterns.extend(double_patterns)
            else:
                logger.debug("No double-bottom/top patterns detected")

        logger.info(f"Total chart patterns detected: {len(patterns)}")
        return patterns

    def _detect_head_and_shoulders(
        self, df: pd.DataFrame, timeframe_info: Dict[str, Any]
    ) -> Optional[Dict[str, Any]]:
        """
        Detect head-and-shoulders pattern.

        Args:
            df: OHLC DataFrame
            timeframe_info: Timeframe significance info

        Returns:
            Pattern dict if detected, None otherwise
        """
        # Look at last 30 candlesticks
        window = df.iloc[-30:]
        highs = window["high"].values
        lows = window["low"].values

        # Find local maxima (potential shoulders and head)
        import numpy as np
        from scipy.signal import argrelextrema

        try:
            peaks = argrelextrema(highs, np.greater, order=3)[0]

            if len(peaks) >= 3:
                # Check if we have a head-and-shoulders pattern
                # (left shoulder, head, right shoulder pattern)
                for i in range(len(peaks) - 2):
                    left_idx = peaks[i]
                    head_idx = peaks[i + 1]
                    right_idx = peaks[i + 2]

                    left_price = highs[left_idx]
                    head_price = highs[head_idx]
                    right_price = highs[right_idx]

                    # Head should be higher than both shoulders
                    # Shoulders should be roughly equal (within 3%)
                    if (
                        head_price > left_price
                        and head_price > right_price
                        and abs(left_price - right_price) / left_price < 0.03
                    ):
                        # Find neckline (lows between peaks)
                        valley1_idx = left_idx + np.argmin(lows[left_idx:head_idx])
                        valley2_idx = head_idx + np.argmin(lows[head_idx:right_idx])

                        neckline_price = (lows[valley1_idx] + lows[valley2_idx]) / 2

                        return {
                            "type": "Head and Shoulders",
                            "confidence": 0.70 * timeframe_info["weight"],
                            "signal": "bearish",
                            "description": f"Bearish reversal pattern on {timeframe_info['label']} chart, breakdown likely if neckline breaks",
                            "timeframe": window.index[0].strftime("%Y-%m-%d"),
                            "timeframe_label": timeframe_info["label"],
                            "significance": timeframe_info["scope"],
                            "points": {
                                "left_shoulder": int(left_idx),
                                "head": int(head_idx),
                                "right_shoulder": int(right_idx),
                                "neckline": [
                                    (valley1_idx, neckline_price),
                                    (valley2_idx, neckline_price),
                                ],
                            },
                        }
        except Exception:
            pass  # scipy not available or pattern not found

        return None

    def _detect_double_bottom_top(
        self, df: pd.DataFrame, timeframe_info: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """
        Detect double-bottom or double-top patterns.

        Args:
            df: OHLC DataFrame
            timeframe_info: Timeframe significance info

        Returns:
            List of detected patterns
        """
        patterns = []
        window = df.iloc[-20:]

        try:
            import numpy as np
            from scipy.signal import argrelextrema

            # Detect double bottom (two lows at similar levels)
            lows = window["low"].values
            troughs = argrelextrema(lows, np.less, order=2)[0]

            if len(troughs) >= 2:
                # Check last two troughs for double bottom
                first_idx = troughs[-2]
                second_idx = troughs[-1]
                first_price = lows[first_idx]
                second_price = lows[second_idx]

                # Prices should be within 2% of each other
                if abs(first_price - second_price) / first_price < 0.02:
                    # Find resistance (peak between the troughs)
                    middle_peak_idx = first_idx + np.argmax(
                        window["high"].values[first_idx:second_idx]
                    )
                    resistance = window["high"].values[middle_peak_idx]

                    patterns.append(
                        {
                            "type": "Double Bottom",
                            "confidence": 0.70 * timeframe_info["weight"],
                            "signal": "bullish",
                            "description": f"Bullish reversal pattern on {timeframe_info['label']} chart, breakout likely if resistance breaks",
                            "timeframe": window.index[0].strftime("%Y-%m-%d"),
                            "timeframe_label": timeframe_info["label"],
                            "significance": timeframe_info["scope"],
                            "points": {
                                "first": int(first_idx),
                                "second": int(second_idx),
                                "resistance_support": float(resistance),
                            },
                        }
                    )

            # Detect double top (two highs at similar levels)
            highs = window["high"].values
            peaks = argrelextrema(highs, np.greater, order=2)[0]

            if len(peaks) >= 2:
                # Check last two peaks for double top
                first_idx = peaks[-2]
                second_idx = peaks[-1]
                first_price = highs[first_idx]
                second_price = highs[second_idx]

                # Prices should be within 2% of each other
                if abs(first_price - second_price) / first_price < 0.02:
                    # Find support (trough between the peaks)
                    middle_trough_idx = first_idx + np.argmin(
                        window["low"].values[first_idx:second_idx]
                    )
                    support = window["low"].values[middle_trough_idx]

                    patterns.append(
                        {
                            "type": "Double Top",
                            "confidence": 0.70 * timeframe_info["weight"],
                            "signal": "bearish",
                            "description": f"Bearish reversal pattern on {timeframe_info['label']} chart, breakdown likely if support breaks",
                            "timeframe": window.index[0].strftime("%Y-%m-%d"),
                            "timeframe_label": timeframe_info["label"],
                            "significance": timeframe_info["scope"],
                            "points": {
                                "first": int(first_idx),
                                "second": int(second_idx),
                                "resistance_support": float(support),
                            },
                        }
                    )
        except Exception:
            pass  # scipy not available or patterns not found

        return patterns

    def _interpret_with_llm(
        self,
        ticker: str,
        timeframe: str,
        patterns: Dict[str, Any],
        df: pd.DataFrame,
        investment_style: Optional[str] = None,
        cost_tracker=None,
    ) -> str:
        """
        Use LLM to interpret pattern significance.

        Args:
            ticker: Asset ticker
            timeframe: Analysis timeframe
            patterns: Detected patterns
            df: OHLC DataFrame
            investment_style: Investment style for context
            cost_tracker: Optional cost tracker for tracking LLM costs

        Returns:
            LLM interpretation string
        """
        current_price = float(df["close"].iloc[-1])

        summary_parts = [
            f"Asset: {ticker}",
            f"Timeframe: {timeframe}",
            f"Current Price: ${current_price:.2f}",
            "",
            "Pattern Analysis:",
        ]

        # Candlestick patterns
        if patterns.get("candlestick_patterns"):
            summary_parts.append("\nCandlestick Patterns:")
            for pattern in patterns["candlestick_patterns"]:
                summary_parts.append(
                    f"- {pattern['name']} ({pattern['signal']}, confidence: {pattern['confidence']:.0%})"
                )
                summary_parts.append(f"  {pattern['description']}")
        else:
            summary_parts.append("\nNo significant candlestick patterns detected")

        # Chart patterns
        if patterns.get("chart_patterns"):
            summary_parts.append("\nChart Patterns:")
            for pattern in patterns["chart_patterns"]:
                summary_parts.append(
                    f"- {pattern['type']} ({pattern['signal']}, confidence: {pattern['confidence']:.0%})"
                )
                summary_parts.append(f"  {pattern['description']}")
        else:
            summary_parts.append("\nNo major chart patterns detected")

        # Support/Resistance levels
        if patterns.get("support_levels"):
            summary_parts.append(
                f"\nSupport Levels: {[f'${s:.2f}' for s in patterns['support_levels']]}"
            )
        if patterns.get("resistance_levels"):
            summary_parts.append(
                f"Resistance Levels: {[f'${r:.2f}' for r in patterns['resistance_levels']]}"
            )

        pattern_summary = "\n".join(summary_parts)

        # Get investment style context
        style_context = get_technical_analysis_style_context(investment_style)

        # LLM prompt with specialized pattern template
        system_prompt = f"""{PATTERN_AGENT_PROMPT}

Investment Style Context:
{style_context}

IMPORTANT: Your response MUST follow the exact structure shown in the template above, including:
- Markdown section headers (##)
- Data tables with proper markdown table syntax (| pipes)
- Bullet-pointed insights (-)
- Numbered summary points (1., 2., 3.)
- Clear conclusion with trading implication"""

        user_prompt = f"""Analyze the following pattern data for {ticker} ({timeframe} timeframe) and provide a comprehensive technical analysis following the template structure:

{pattern_summary}

Generate your response following the exact template structure with all sections, tables, bullet points, and numbered summary."""

        # Call LLM with cost tracking callback
        messages = [
            SystemMessage(content=system_prompt),
            HumanMessage(content=user_prompt),
        ]

        # Create callback if cost tracker is available
        if cost_tracker:
            callback = cost_tracker.get_callback(agent_name=self.AGENT_NAME)
            response = self.llm.invoke(messages, config={"callbacks": [callback]})
        else:
            response = self.llm.invoke(messages)

        return response.content

    def _deserialize_dataframe(self, data: Dict[str, Any]) -> pd.DataFrame:
        """
        Convert serialized data back to DataFrame.

        Args:
            data: Serialized DataFrame data

        Returns:
            pandas DataFrame
        """
        df = pd.DataFrame(data)
        if "Date" in df.columns:
            df["Date"] = pd.to_datetime(df["Date"])
            df = df.set_index("Date")
        return df