"""
Forward Performance Calculator
Calculates historical forward returns and signal outcomes.
Uses ONLY future candles (after the chart's final candle) for these calculations.
"""

import numpy as np
import pandas as pd
from typing import Dict, Any, Optional, List


def calculate_forward_performance(
    full_df: pd.DataFrame,
    chart_end_idx: int,
    candles_ahead: list = [1, 5, 10, 20],
) -> Dict[str, Any]:
    """
    Calculate forward performance after the chart's final candle.

    Parameters
    ----------
    full_df : DataFrame with the full OHLCV series.
    chart_end_idx : Index of the last candle in the chart window.
    candles_ahead : List of forward periods to calculate.

    Returns
    -------
    Dict with forward returns, MFE, MAE, max drawdown, future volatility.
    """
    n = len(full_df)
    if chart_end_idx >= n - 1:
        return {"insufficient_future_data": True}

    close_price = full_df["close"].iloc[chart_end_idx]
    if close_price <= 0:
        close_price = 1e-10

    result = {"insufficient_future_data": False}

    # Forward returns
    for period in candles_ahead:
        future_idx = chart_end_idx + period
        if future_idx < n:
            future_price = full_df["close"].iloc[future_idx]
            ret = (future_price - close_price) / close_price
            result[f"return_{period}c"] = round(float(ret), 6)

    # Maximum Favorable Excursion (MFE) and Maximum Adverse Excursion (MAE)
    max_available = min(50, n - chart_end_idx - 1)
    if max_available > 0:
        future_highs = full_df["high"].iloc[chart_end_idx + 1:chart_end_idx + 1 + max_available]
        future_lows = full_df["low"].iloc[chart_end_idx + 1:chart_end_idx + 1 + max_available]

        max_favorable = (future_highs.max() - close_price) / close_price
        max_adverse = (future_lows.min() - close_price) / close_price

        result["max_favorable_excursion"] = round(float(max_favorable), 6)
        result["max_adverse_excursion"] = round(float(max_adverse), 6)

        # Maximum drawdown from chart end
        future_closes = full_df["close"].iloc[chart_end_idx:chart_end_idx + 1 + max_available]
        peak = future_closes.expanding().max()
        drawdown = (future_closes - peak) / peak
        result["max_drawdown"] = round(float(drawdown.min()), 6)

        # Future volatility
        if len(future_closes) > 1:
            log_returns = np.log(future_closes / future_closes.shift(1)).dropna()
            result["future_volatility"] = round(float(log_returns.std() * np.sqrt(252)), 6)

    return result


def classify_signal_outcome(
    signal_type: str,
    forward_perf: Dict[str, Any],
    threshold: float = 0.01,
) -> str:
    """
    Classify the historical outcome of a technical signal.

    Parameters
    ----------
    signal_type : Type of signal (breakout, reversal, pattern, etc.)
    forward_perf : Forward performance dict from calculate_forward_performance.
    threshold : Minimum return to consider a signal successful (1%).

    Returns
    -------
    Outcome classification string.
    """
    if forward_perf.get("insufficient_future_data"):
        return "unknown"

    ret_10c = forward_perf.get("return_10c", 0)
    ret_20c = forward_perf.get("return_20c", 0)
    mfe = forward_perf.get("max_favorable_excursion", 0)
    mae = forward_perf.get("max_adverse_excursion", 0)

    if signal_type == "breakout":
        if ret_10c > threshold and mfe > threshold * 1.5:
            return "confirmed_breakout"
        elif ret_10c < -threshold:
            return "failed_breakout"
        elif mfe > threshold and ret_10c > 0:
            return "breakout_retest_success"
        elif mfe > threshold and ret_10c <= 0:
            return "breakout_retest_failure"
        else:
            return "neutral"

    elif signal_type == "reversal":
        if ret_20c > threshold * 2:
            return "successful"
        elif ret_20c > threshold:
            return "partially_successful"
        elif ret_20c < -threshold:
            return "failed"
        else:
            return "neutral"

    elif signal_type == "pattern":
        if mfe > threshold * 2 and ret_10c > threshold:
            return "successful"
        elif mfe > threshold and ret_10c > 0:
            return "partially_successful"
        elif mae < -threshold and ret_10c < 0:
            return "failed"
        else:
            return "neutral"

    else:
        if ret_10c > threshold:
            return "successful"
        elif ret_10c < -threshold:
            return "failed"
        else:
            return "neutral"


def calculate_technical_score(
    trend: Dict[str, Any],
    market_structure: Dict[str, Any],
    indicators: Dict[str, Any],
    volume_analysis: Dict[str, Any],
    patterns: List[Dict[str, Any]],
    breakout: Dict[str, Any],
) -> Dict[str, Any]:
    """
    Calculate a composite technical score and bias.
    """
    score = 0
    breakdown = {}

    # Trend contribution (-3 to +3)
    trend_map = {
        "strong_bullish": 3, "bullish": 2, "weak_bullish": 1,
        "sideways": 0,
        "weak_bearish": -1, "bearish": -2, "strong_bearish": -3,
    }
    trend_score = trend_map.get(trend.get("classification", "sideways"), 0)
    score += trend_score
    breakdown["trend"] = trend_score

    # Market structure contribution (-2 to +2)
    struct_map = {"bullish": 2, "bearish": -2, "mixed": 0, "neutral": 0}
    struct_score = struct_map.get(market_structure.get("classification", "neutral"), 0)
    if market_structure.get("break_of_structure"):
        struct_score += 1 if struct_score >= 0 else -1
    score += struct_score
    breakdown["market_structure"] = struct_score

    # Momentum contribution (-2 to +2)
    rsi = indicators.get("rsi", {})
    rsi_score = 0
    if rsi.get("zone") in ("overbought",):
        rsi_score = -1
    elif rsi.get("zone") in ("oversold",):
        rsi_score = 1
    elif rsi.get("zone") == "neutral_bullish":
        rsi_score = 1
    elif rsi.get("zone") == "neutral_bearish":
        rsi_score = -1

    macd = indicators.get("macd", {})
    macd_score = 1 if "bullish" in macd.get("state", "") else (-1 if "bearish" in macd.get("state", "") else 0)
    momentum_score = rsi_score + macd_score
    score += momentum_score
    breakdown["momentum"] = momentum_score

    # Volume contribution (-1 to +1)
    vol = volume_analysis.get("current_volume_vs_average", 1.0)
    vol_score = 0
    if vol > 1.5:
        vol_score = 1
    elif vol < 0.5:
        vol_score = -1
    score += vol_score
    breakdown["volume"] = vol_score

    # Moving average contribution (-2 to +2)
    ema = indicators.get("ema_structure", {})
    ma_score = 0
    if ema.get("alignment") == "bullish":
        ma_score = 2
    elif ema.get("alignment") == "bearish":
        ma_score = -2
    score += ma_score
    breakdown["moving_averages"] = ma_score

    # Pattern contribution (-1 to +1)
    pattern_score = 0
    for p in patterns:
        name = p.get("pattern", "")
        if name in ("double_bottom", "bull_flag", "ascending_triangle", "morning_star",
                     "inverse_head_and_shoulders", "cup_and_handle"):
            pattern_score = 1
        elif name in ("double_top", "bear_flag", "descending_triangle", "evening_star",
                       "head_and_shoulders", "inverse_cup_and_handle"):
            pattern_score = -1
    score += pattern_score
    breakdown["pattern"] = pattern_score

    # Breakout contribution (-1 to +1)
    breakout_score = 0
    if breakout.get("status") == "confirmed_breakout":
        breakout_score = 1
    elif breakout.get("status") == "confirmed_breakdown":
        breakout_score = -1
    elif breakout.get("status") == "false_breakout":
        breakout_score = -1
    elif breakout.get("status") == "false_breakdown":
        breakout_score = 1
    score += breakout_score
    breakdown["breakout"] = breakout_score

    # Classify bias
    if score >= 7:
        bias = "strong_bullish"
    elif score >= 4:
        bias = "bullish"
    elif score >= 2:
        bias = "slightly_bullish"
    elif score >= -1:
        bias = "neutral"
    elif score >= -3:
        bias = "slightly_bearish"
    elif score >= -6:
        bias = "bearish"
    else:
        bias = "strong_bearish"

    return {
        "score": score,
        "bias": bias,
        "breakdown": breakdown,
    }


def calculate_confidence(
    trend: Dict[str, Any],
    market_structure: Dict[str, Any],
    indicators: Dict[str, Any],
    patterns: List[Dict[str, Any]],
    volume_analysis: Dict[str, Any],
) -> float:
    """
    Calculate a confidence score (0.0 - 1.0) for the technical analysis.
    """
    factors = []

    # Signal agreement
    signals = []
    if "bullish" in trend.get("classification", ""):
        signals.append(1)
    elif "bearish" in trend.get("classification", ""):
        signals.append(-1)
    else:
        signals.append(0)

    rsi_zone = indicators.get("rsi", {}).get("zone", "")
    if rsi_zone in ("neutral_bullish", "oversold"):
        signals.append(1)
    elif rsi_zone in ("neutral_bearish", "overbought"):
        signals.append(-1)
    else:
        signals.append(0)

    macd_state = indicators.get("macd", {}).get("state", "")
    if "bullish" in macd_state:
        signals.append(1)
    elif "bearish" in macd_state:
        signals.append(-1)
    else:
        signals.append(0)

    # Agreement ratio
    non_zero = [s for s in signals if s != 0]
    if non_zero:
        agreement = abs(sum(non_zero)) / len(non_zero)
    else:
        agreement = 0.5
    factors.append(agreement)

    # Pattern quality
    if patterns:
        strong_patterns = sum(1 for p in patterns if p.get("strength") == "strong")
        factors.append(min(strong_patterns * 0.3, 1.0))
    else:
        factors.append(0.3)

    # Market structure clarity
    struct_class = market_structure.get("classification", "neutral")
    if struct_class in ("bullish", "bearish"):
        factors.append(0.8)
    else:
        factors.append(0.4)

    # Volume confirmation
    vol_ratio = volume_analysis.get("current_volume_vs_average", 1.0)
    if vol_ratio > 1.5:
        factors.append(0.8)
    elif vol_ratio > 0.8:
        factors.append(0.6)
    else:
        factors.append(0.3)

    # ADX strength
    adx = indicators.get("adx", {}).get("value", 20)
    if adx > 40:
        factors.append(0.9)
    elif adx > 25:
        factors.append(0.7)
    else:
        factors.append(0.4)

    confidence = round(float(np.mean(factors)), 2)
    return min(max(confidence, 0.0), 1.0)