"""
Indicator Agent for technical indicator calculation and interpretation.

This agent computes technical indicators (RSI, MACD, Stochastic) and provides
interpretation of their values for trading decisions.
"""

import json
import logging
import time
from typing import Any, Dict, 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 INDICATOR_AGENT_PROMPT
from graph.state.agent_state import add_agent_message, update_analysis_result
from graph.state.trading_state import TechnicalWorkflowState
from utils.charts.chart_generator import ChartGenerator
from utils.formatters.educational_content import (
    generate_macd_explanation,
    generate_rsi_explanation,
    generate_stochastic_explanation,
)
from utils.indicators import calculate_macd, calculate_rsi, calculate_stochastic
from utils.indicators.macd import (
    find_macd_crossovers,
    find_macd_divergence,
    interpret_macd,
)
from utils.indicators.rsi import find_rsi_divergence, interpret_rsi
from utils.indicators.stochastic import find_stochastic_crossovers, interpret_stochastic
from utils.investment_style_helpers import (
    get_investment_style_from_state,
    get_technical_analysis_style_context,
)
from utils.llm.provider_factory import LLMProviderFactory


class IndicatorAgent:
    """
    Technical Indicator Agent.

    Responsibilities:
    - Calculate RSI, MACD, Stochastic Oscillator
    - Interpret indicator values (overbought/oversold, bullish/bearish)
    - Detect divergences and crossovers
    - Provide trading signals based on indicators
    """

    AGENT_NAME = "indicator_agent"

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

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

        # Initialize LLM - 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"],
        )

        # Indicator parameters
        self.indicator_params = self.config["indicator_parameters"]

        # Initialize chart generator
        self.chart_generator = ChartGenerator()

    def run(self, state: TechnicalWorkflowState) -> TechnicalWorkflowState:
        """
        Execute indicator analysis.

        Args:
            state: Current workflow state

        Returns:
            Updated state with indicator 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 indicator calculation")

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

            # Calculate indicators
            indicators_result = self._calculate_indicators(df)

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

            # Generate charts and educational notes (User Story 5)
            chart_paths = []
            educational_notes = []

            try:
                # Check if educational mode is enabled
                config = state.get("config", {})
                educational_mode = (
                    config.get("educational_mode", False)
                    if isinstance(config, dict)
                    else False
                )

                # Generate RSI chart
                if "rsi" in indicators_result and "value" in indicators_result["rsi"]:
                    rsi_value = indicators_result["rsi"]["value"]
                    rsi_series_dict = indicators_result["rsi"].get("series")

                    if rsi_series_dict is not None:
                        # Convert dict back to Series
                        rsi_series = pd.Series(rsi_series_dict)

                        fig, filepath = self.chart_generator.generate_rsi_chart(
                            df=df,
                            rsi_series=rsi_series,
                            ticker=ticker,
                            timeframe=timeframe,
                            rsi_period=self.indicator_params["rsi_period"],
                            save=True,
                        )
                        if filepath:
                            chart_paths.append(filepath)
                        self.chart_generator.close_figure(fig)

                    # Add educational note if enabled
                    if educational_mode:
                        educational_notes.append(
                            f"**RSI**: {generate_rsi_explanation(rsi_value)}"
                        )

                # Generate MACD chart
                if "macd" in indicators_result:
                    macd_data = indicators_result["macd"]
                    series_dict = macd_data.get("series", {})

                    logger.info(
                        f"MACD data available: series_dict keys = {list(series_dict.keys()) if series_dict else 'None'}"
                    )

                    if (
                        series_dict
                        and "macd" in series_dict
                        and "signal" in series_dict
                        and "histogram" in series_dict
                    ):
                        # Convert dicts back to Series
                        macd_series = pd.Series(series_dict["macd"])
                        signal_series = pd.Series(series_dict["signal"])
                        histogram_series = pd.Series(series_dict["histogram"])

                        logger.info(
                            f"Generating MACD chart: macd_len={len(macd_series)}, signal_len={len(signal_series)}, hist_len={len(histogram_series)}"
                        )

                        fig, filepath = self.chart_generator.generate_macd_chart(
                            df=df,
                            macd=macd_series,
                            signal=signal_series,
                            histogram=histogram_series,
                            ticker=ticker,
                            timeframe=timeframe,
                            save=True,
                        )
                        logger.info(f"MACD chart generated: filepath={filepath}")
                        if filepath:
                            chart_paths.append(filepath)
                        self.chart_generator.close_figure(fig)
                    else:
                        logger.warning(
                            f"MACD chart skipped - missing series data. series_dict keys: {list(series_dict.keys()) if series_dict else 'None'}"
                        )

                    # Add educational note if enabled
                    if (
                        educational_mode
                        and "macd" in macd_data
                        and "signal" in macd_data
                        and "histogram" in macd_data
                    ):
                        educational_notes.append(
                            f"**MACD**: {generate_macd_explanation(macd_data['macd'], macd_data['signal'], macd_data['histogram'])}"
                        )

                # Generate Stochastic chart
                if "stochastic" in indicators_result:
                    stoch_data = indicators_result["stochastic"]
                    series_dict = stoch_data.get("series", {})

                    if series_dict and "k" in series_dict and "d" in series_dict:
                        # Convert dicts back to Series
                        k_series = pd.Series(series_dict["k"])
                        d_series = pd.Series(series_dict["d"])

                        fig, filepath = self.chart_generator.generate_stochastic_chart(
                            df=df,
                            k_series=k_series,
                            d_series=d_series,
                            ticker=ticker,
                            timeframe=timeframe,
                            save=True,
                        )
                        if filepath:
                            chart_paths.append(filepath)
                        self.chart_generator.close_figure(fig)

                    # Add educational note if enabled
                    if educational_mode and "k" in stoch_data and "d" in stoch_data:
                        educational_notes.append(
                            f"**Stochastic**: {generate_stochastic_explanation(stoch_data['k'], stoch_data['d'])}"
                        )

            except Exception as chart_error:
                logger.warning(
                    json.dumps(
                        {
                            "agent": self.AGENT_NAME,
                            "action": "chart_generation_warning",
                            "ticker": ticker,
                            "error": str(chart_error),
                            "timestamp": time.time(),
                        }
                    )
                )

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

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

            # Append educational notes to interpretation if available
            if educational_notes:
                interpretation += "\n\n### 📚 Educational Notes\n\n" + "\n\n".join(
                    educational_notes
                )

            # Update state
            new_state = update_analysis_result(state, "indicators", indicators_result)
            new_state = add_agent_message(
                new_state,
                self.AGENT_NAME,
                interpretation,
                metadata={
                    "indicators": indicators_result,
                    "chart_paths": chart_paths,
                    "educational_mode": educational_mode,
                },
            )

            execution_time = time.time() - start_time
            logger.info(
                json.dumps(
                    {
                        "agent": self.AGENT_NAME,
                        "action": "complete",
                        "ticker": ticker,
                        "timeframe": timeframe,
                        "execution_time": execution_time,
                        "indicators_calculated": list(indicators_result.keys()),
                        "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 calculating indicators: {str(e)}",
                metadata={"error": True},
            )
            return error_state

    def _calculate_indicators(self, df: pd.DataFrame) -> Dict[str, Any]:
        """
        Calculate all technical indicators.

        Args:
            df: OHLC DataFrame

        Returns:
            Dict with indicator results
        """
        result = {}

        # RSI
        try:
            rsi_series = calculate_rsi(
                df,
                period=self.indicator_params["rsi_period"],
            )
            current_rsi = float(rsi_series.iloc[-1])
            rsi_interpretation = interpret_rsi(current_rsi)

            # Try to find divergences, but don't fail if it doesn't work
            try:
                rsi_divergence = find_rsi_divergence(df, rsi_series)
            except Exception:
                rsi_divergence = {"bullish": [], "bearish": []}

            result["rsi"] = {
                "value": current_rsi,
                "interpretation": rsi_interpretation,
                "divergences": rsi_divergence,
                "series": rsi_series.to_dict(),  # For charting
            }
        except Exception as e:
            result["rsi"] = {"error": str(e)}

        # MACD
        try:
            logger.info(f"Calculating MACD with {len(df)} data points")
            macd, signal, hist = calculate_macd(
                df,
                fast_period=self.indicator_params["macd_fast"],
                slow_period=self.indicator_params["macd_slow"],
                signal_period=self.indicator_params["macd_signal"],
            )
            logger.info(
                f"MACD calculation succeeded: macd_len={len(macd)}, valid_values={(~pd.isna(macd)).sum()}"
            )

            current_macd = float(macd.iloc[-1]) if not pd.isna(macd.iloc[-1]) else None
            current_signal = (
                float(signal.iloc[-1]) if not pd.isna(signal.iloc[-1]) else None
            )
            current_hist = float(hist.iloc[-1]) if not pd.isna(hist.iloc[-1]) else None

            prev_hist = (
                float(hist.iloc[-2])
                if len(hist) > 1 and not pd.isna(hist.iloc[-2])
                else None
            )

            macd_interpretation = interpret_macd(
                current_macd, current_signal, current_hist, prev_hist
            )

            # Try to find crossovers and divergences, but don't fail if it doesn't work
            try:
                macd_crossovers = find_macd_crossovers(macd, signal)
            except Exception:
                macd_crossovers = {"bullish": [], "bearish": []}

            try:
                macd_divergence = find_macd_divergence(df, macd)
            except Exception:
                macd_divergence = {"bullish": [], "bearish": []}

            result["macd"] = {
                "macd": current_macd,
                "signal": current_signal,
                "histogram": current_hist,
                "interpretation": macd_interpretation,
                "crossovers": macd_crossovers,
                "divergences": macd_divergence,
                "series": {
                    "macd": macd.to_dict(),
                    "signal": signal.to_dict(),
                    "histogram": hist.to_dict(),
                },
            }
        except Exception as e:
            logger.error(f"MACD calculation failed: {str(e)}")
            result["macd"] = {"error": str(e)}

        # Stochastic Oscillator
        try:
            k_series, d_series = calculate_stochastic(
                df,
                k_period=self.indicator_params["stoch_k_period"],
                d_period=self.indicator_params["stoch_d_period"],
            )

            current_k = (
                float(k_series.iloc[-1]) if not pd.isna(k_series.iloc[-1]) else None
            )
            current_d = (
                float(d_series.iloc[-1]) if not pd.isna(d_series.iloc[-1]) else None
            )

            prev_k = (
                float(k_series.iloc[-2])
                if len(k_series) > 1 and not pd.isna(k_series.iloc[-2])
                else None
            )
            prev_d = (
                float(d_series.iloc[-2])
                if len(d_series) > 1 and not pd.isna(d_series.iloc[-2])
                else None
            )

            stoch_interpretation = interpret_stochastic(
                current_k, current_d, prev_k, prev_d
            )
            stoch_crossovers = find_stochastic_crossovers(k_series, d_series)

            result["stochastic"] = {
                "k": current_k,
                "d": current_d,
                "interpretation": stoch_interpretation,
                "crossovers": stoch_crossovers,
                "series": {
                    "k": k_series.to_dict(),
                    "d": d_series.to_dict(),
                },
            }
        except Exception as e:
            result["stochastic"] = {"error": str(e)}

        return result

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

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

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

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

        # RSI
        if "rsi" in indicators and "value" in indicators["rsi"]:
            rsi = indicators["rsi"]
            summary_parts.append(
                f"- RSI({self.indicator_params['rsi_period']}): {rsi['value']:.2f}"
            )
            summary_parts.append(f"  {rsi['interpretation']}")
            if rsi.get("divergences", {}).get("bullish"):
                summary_parts.append(
                    f"  Bullish divergences detected at indices: {rsi['divergences']['bullish']}"
                )
            if rsi.get("divergences", {}).get("bearish"):
                summary_parts.append(
                    f"  Bearish divergences detected at indices: {rsi['divergences']['bearish']}"
                )

        # MACD
        if "macd" in indicators and "macd" in indicators["macd"]:
            macd = indicators["macd"]
            summary_parts.append(
                f"- MACD: {macd['macd']:.4f}, Signal: {macd['signal']:.4f}, Histogram: {macd['histogram']:.4f}"
            )
            summary_parts.append(f"  {macd['interpretation']}")
            if macd.get("crossovers", {}).get("bullish"):
                summary_parts.append(
                    f"  Recent bullish crossovers at indices: {macd['crossovers']['bullish'][-3:]}"
                )
            if macd.get("crossovers", {}).get("bearish"):
                summary_parts.append(
                    f"  Recent bearish crossovers at indices: {macd['crossovers']['bearish'][-3:]}"
                )

        # Stochastic
        if "stochastic" in indicators and "k" in indicators["stochastic"]:
            stoch = indicators["stochastic"]
            summary_parts.append(
                f"- Stochastic: %K={stoch['k']:.2f}, %D={stoch['d']:.2f}"
            )
            summary_parts.append(f"  {stoch['interpretation']}")

        indicator_summary = "\n".join(summary_parts)

        # Get investment style context
        style_context = get_technical_analysis_style_context(investment_style)

        # LLM prompt with specialized indicator template
        system_prompt = f"""{INDICATOR_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 technical indicators for {ticker} ({timeframe} timeframe) and provide a comprehensive technical analysis following the template structure:

{indicator_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
        """
        # Assuming data is stored as dict with columns
        # This will be properly implemented when we serialize in the workflow
        df = pd.DataFrame(data)
        if "Date" in df.columns:
            df["Date"] = pd.to_datetime(df["Date"])
            df = df.set_index("Date")
        return df