from __future__ import annotations

import math
import re
from typing import Any, Dict, List, Optional, Tuple

import numpy as np
import pandas as pd

AI_SCORING_VERSION = "2026-05-05-path-direction-v2"
TECHNICAL_SCORING_VERSION = "2026-05-18-performance-votes-v12"
ANALYSIS_PRESENTATION_VERSION = "2026-05-16-gauge-view-v2"

EMA_PAIR_SEQUENCE: List[Tuple[int, int]] = [
    (1, 2),
    (2, 5),
    (5, 10),
    (10, 20),
    (20, 50),
    (50, 100),
    (100, 200),
]

EMA_PAIR_THRESHOLD_PCT: Dict[str, float] = {
    "ema_1_2": 0.05,
    "ema_2_5": 0.08,
    "ema_5_10": 0.10,
    "ema_10_20": 0.14,
    "ema_20_50": 0.22,
    "ema_50_100": 0.35,
    "ema_100_200": 0.55,
}

ADX_GAUGE_PERIOD = 10
PERFORMANCE_LOOKBACKS: Tuple[int, ...] = (1, 2, 3, 5, 7, 30, 90, 180, 365)
PERFORMANCE_VOTE_WEIGHTS: Dict[int, float] = {
    2: 2.0,
}
PERFORMANCE_NEUTRAL_EPSILON_PCT = 1e-9

__all__ = [
    "AI_SCORING_VERSION",
    "ANALYSIS_PRESENTATION_VERSION",
    "TECHNICAL_SCORING_VERSION",
    "_apply_forecast_amplitude_calibration",
    "_build_analysis_presentation",
    "_apply_forecast_path_texture",
    "_build_dashboard_payload",
    "_build_raw_ohlc4_bundle",
    "_build_regime_texture_template",
    "_build_trade_analysis",
    "_calc_ai_forecast_score",
    "_calc_ma_score",
    "_calc_performance_score",
    "_calc_price_action_levels",
    "_calc_pivot_points",
    "_calc_summary_score_v2",
    "_calc_technical_score_v2",
    "_calc_vote_gauge",
    "_clamp",
    "_derive_target_amplitude_profile",
    "_forecast_path_metrics",
    "_gauge_to_signal",
    "_momentum",
    "_osc_action",
    "_pct",
    "_score_forecast_context_candidate",
    "compute_indicators",
]

def _ema(arr: np.ndarray, period: int) -> np.ndarray:
    """Vectorized EMA using NumPy (replaces loops)."""
    if len(arr) == 0: return np.array([], dtype=float)
    alpha = 2.0 / (period + 1.0)
    # Use pandas ewm for robust vectorized calculation (v6.0)
    return pd.Series(arr).ewm(alpha=alpha, adjust=False).mean().values

def _rsi(close: np.ndarray, period: int = 14) -> np.ndarray:
    """Vectorized RSI using NumPy/Pandas."""
    delta = np.diff(close)
    gain = np.where(delta > 0, delta, 0.0)
    loss = np.where(delta < 0, -delta, 0.0)
    
    avg_gain = pd.Series(gain).ewm(alpha=1.0/period, adjust=False).mean()
    avg_loss = pd.Series(loss).ewm(alpha=1.0/period, adjust=False).mean()

    rs = avg_gain / avg_loss.replace(0, np.nan)
    rsi = 100 - (100 / (1 + rs))
    rsi = rsi.where(avg_loss > 0, 100.0)
    rsi = rsi.where(avg_gain > 0, 0.0)
    rsi = rsi.where(~((avg_gain == 0) & (avg_loss == 0)), 50.0)
    # Prepend NaN to match original array length
    return np.concatenate([[np.nan], rsi.values])

def _bollinger(close: np.ndarray, period=20, k=2.0) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """Vectorized Bollinger Bands."""
    s = pd.Series(close)
    mid = s.rolling(window=period).mean()
    std = s.rolling(window=period).std(ddof=0)
    return (mid + k*std).values, mid.values, (mid - k*std).values


def _macd(close: np.ndarray, fast=12, slow=26, signal=9
          ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    ema_fast = _ema(close, fast)
    ema_slow = _ema(close, slow)
    macd_line = ema_fast - ema_slow
    sig_line  = _ema(np.where(np.isnan(macd_line), 0, macd_line), signal)
    histogram  = macd_line - sig_line
    return macd_line, sig_line, histogram


def _atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, period=14) -> np.ndarray:
    """Vectorized ATR."""
    tr = np.maximum(high[1:] - low[1:], 
                    np.maximum(np.abs(high[1:] - close[:-1]), 
                               np.abs(low[1:] - close[:-1])))
    tr = np.concatenate([[np.nan], tr])
    return pd.Series(tr).ewm(alpha=1.0/period, adjust=False).mean().values


def _stoch_rsi(close: np.ndarray, rsi_period=14, stoch_period=14,
               smooth_k=3, smooth_d=3) -> Tuple[np.ndarray, np.ndarray]:
    """Vectorized Stochastic RSI."""
    rsi_vals = pd.Series(_rsi(close, rsi_period))
    roll_min = rsi_vals.rolling(window=stoch_period).min()
    roll_max = rsi_vals.rolling(window=stoch_period).max()
    
    k = 100 * (rsi_vals - roll_min) / (roll_max - roll_min).replace(0, np.inf)
    k_smooth = k.rolling(window=smooth_k).mean()
    d_smooth = k_smooth.rolling(window=smooth_d).mean()
    return k_smooth.values, d_smooth.values


def _stoch_kd(
    high: np.ndarray,
    low: np.ndarray,
    close: np.ndarray,
    k_period: int = 14,
    smooth_k: int = 3,
    d_period: int = 3,
) -> Tuple[np.ndarray, np.ndarray]:
    """Standard stochastic oscillator computed from price high/low/close."""
    high_s = pd.Series(high)
    low_s = pd.Series(low)
    close_s = pd.Series(close)
    hh = high_s.rolling(window=k_period).max()
    ll = low_s.rolling(window=k_period).min()
    raw_k = 100.0 * (close_s - ll) / (hh - ll).replace(0, np.nan)
    k = raw_k.rolling(window=smooth_k).mean()
    d = k.rolling(window=d_period).mean()
    return k.values, d.values


def _sma(arr: np.ndarray, period: int) -> np.ndarray:
    """Vectorized Simple Moving Average."""
    if len(arr) == 0: return np.array([], dtype=float)
    return pd.Series(arr).rolling(window=period).mean().values


def _cci(high: np.ndarray, low: np.ndarray, close: np.ndarray, period: int = 20) -> np.ndarray:
    """Vectorized Commodity Channel Index."""
    tp = (high + low + close) / 3.0
    tp_series = pd.Series(tp)
    sma = tp_series.rolling(window=period).mean()
    # Optimized MAD calculation (Vectorized)
    mad = tp_series.rolling(window=period).apply(lambda x: np.abs(x - x.mean()).mean(), raw=True)
    return ((tp_series - sma) / (0.015 * mad.replace(0, np.inf))).values


def _adx(high: np.ndarray, low: np.ndarray, close: np.ndarray, period: int = 14) -> tuple:
    """Vectorized Average Directional Index (v6.0)."""
    plus_dm = np.where((high[1:] - high[:-1] > low[:-1] - low[1:]) & (high[1:] - high[:-1] > 0), high[1:] - high[:-1], 0.0)
    minus_dm = np.where((low[:-1] - low[1:] > high[1:] - high[:-1]) & (low[:-1] - low[1:] > 0), low[:-1] - low[1:], 0.0)
    tr = np.maximum(high[1:] - low[1:], np.maximum(np.abs(high[1:] - close[:-1]), np.abs(low[1:] - close[:-1])))
    
    # Pad first index
    plus_dm = np.concatenate([[0.0], plus_dm])
    minus_dm = np.concatenate([[0.0], minus_dm])
    tr = np.concatenate([[0.0], tr])
    
    tr_sum = pd.Series(tr).ewm(alpha=1.0/period, adjust=False).mean()
    plus_di = 100 * pd.Series(plus_dm).ewm(alpha=1.0/period, adjust=False).mean() / tr_sum.replace(0, np.inf)
    minus_di = 100 * pd.Series(minus_dm).ewm(alpha=1.0/period, adjust=False).mean() / tr_sum.replace(0, np.inf)
    
    dx = 100 * np.abs(plus_di - minus_di) / (plus_di + minus_di).replace(0, np.inf)
    adx = dx.ewm(alpha=1.0/period, adjust=False).mean()
    return adx.values, plus_di.values, minus_di.values


def _awesome_oscillator(high: np.ndarray, low: np.ndarray) -> np.ndarray:
    """Awesome Oscillator = SMA(5, median) - SMA(34, median)."""
    median = (high + low) / 2.0
    sma5 = _sma(median, 5)
    sma34 = _sma(median, 34)
    return sma5 - sma34


def _momentum(close: np.ndarray, period: int = 10) -> np.ndarray:
    """Classic momentum oscillator centered at 0."""
    base = pd.Series(close).shift(period)
    return (pd.Series(close) - base).values


def _roc(close: np.ndarray, period: int = 12) -> np.ndarray:
    """Rate of Change in percentage."""
    base = pd.Series(close).shift(period)
    return (pd.Series(close) / base.replace(0, np.nan) - 1.0).mul(100.0).values


def _trix(close: np.ndarray, period: int = 18) -> np.ndarray:
    """Triple EMA oscillator in percentage."""
    ema1 = pd.Series(_ema(close, period))
    ema2 = ema1.ewm(span=period, adjust=False).mean()
    ema3 = ema2.ewm(span=period, adjust=False).mean()
    return ema3.pct_change().mul(100.0).values


def _ppo(close: np.ndarray, fast: int = 12, slow: int = 26) -> np.ndarray:
    """Percentage Price Oscillator."""
    ema_fast = _ema(close, fast)
    ema_slow = _ema(close, slow)
    return ((ema_fast - ema_slow) / np.where(np.abs(ema_slow) < 1e-8, np.nan, ema_slow) * 100.0)


def _cmo(close: np.ndarray, period: int = 14) -> np.ndarray:
    """Chande Momentum Oscillator."""
    delta = pd.Series(close).diff()
    up = delta.clip(lower=0.0).rolling(period).sum()
    down = (-delta.clip(upper=0.0)).rolling(period).sum()
    return ((up - down) / (up + down).replace(0, np.nan) * 100.0).values


def _dpo(close: np.ndarray, period: int = 20) -> np.ndarray:
    """Detrended Price Oscillator."""
    offset = int(period / 2) + 1
    sma = pd.Series(close).rolling(period).mean()
    return (pd.Series(close) - sma.shift(offset)).values


def _aroon_oscillator(high: np.ndarray, low: np.ndarray, period: int = 25) -> np.ndarray:
    """Aroon Oscillator = Aroon Up - Aroon Down."""
    hs = pd.Series(high)
    ls = pd.Series(low)
    aroon_up = hs.rolling(period).apply(lambda x: ((period - 1 - (len(x) - 1 - int(np.argmax(x)))) / (period - 1)) * 100.0, raw=True)
    aroon_down = ls.rolling(period).apply(lambda x: ((period - 1 - (len(x) - 1 - int(np.argmin(x)))) / (period - 1)) * 100.0, raw=True)
    return (aroon_up - aroon_down).values


def _tsi(close: np.ndarray, long_period: int = 25, short_period: int = 13) -> np.ndarray:
    """True Strength Index."""
    delta = pd.Series(close).diff()
    abs_delta = delta.abs()
    ema1 = delta.ewm(span=long_period, adjust=False).mean()
    ema2 = ema1.ewm(span=short_period, adjust=False).mean()
    abs_ema1 = abs_delta.ewm(span=long_period, adjust=False).mean()
    abs_ema2 = abs_ema1.ewm(span=short_period, adjust=False).mean()
    return (ema2 / abs_ema2.replace(0, np.nan) * 100.0).values


def _demarker(high: np.ndarray, low: np.ndarray, period: int = 14) -> np.ndarray:
    """DeMarker oscillator in range 0..100."""
    high_delta = pd.Series(high).diff()
    low_delta = -pd.Series(low).diff()
    demax = high_delta.clip(lower=0.0)
    demin = low_delta.clip(lower=0.0)
    demax_avg = demax.rolling(period).sum()
    demin_avg = demin.rolling(period).sum()
    denom = (demax_avg + demin_avg).replace(0, np.nan)
    return (demax_avg / denom * 100.0).values


def _williams_r(high: np.ndarray, low: np.ndarray, close: np.ndarray,
                period: int = 14) -> np.ndarray:
    """Vectorized Williams %R."""
    s = pd.Series(close)
    hh = pd.Series(high).rolling(window=period).max()
    ll = pd.Series(low).rolling(window=period).min()
    wr = -100 * (hh - s) / (hh - ll).replace(0, np.inf)
    return wr.values


def _bull_bear_power(high: np.ndarray, low: np.ndarray, close: np.ndarray,
                     period: int = 13) -> Tuple[np.ndarray, np.ndarray]:
    """Return Bull Power and Bear Power separately for sign-based scoring."""
    ema_val = _ema(close, period)
    return (high - ema_val), (low - ema_val)


def _ultimate_oscillator(high: np.ndarray, low: np.ndarray, close: np.ndarray,
                         p1: int = 7, p2: int = 14, p3: int = 28) -> np.ndarray:
    """Vectorized Ultimate Oscillator (v6.0)."""
    n = len(close)
    if n < p3 + 1: return np.full(n, np.nan)

    prev_close = pd.Series(close).shift(1)
    tl = np.minimum(low, prev_close.values)
    th = np.maximum(high, prev_close.values)
    
    bp = pd.Series(close - tl)
    tr = pd.Series(th - tl)
    
    def _avg(p):
        return bp.rolling(p).sum() / tr.rolling(p).sum().replace(0, np.inf)

    avg1 = _avg(p1)
    avg2 = _avg(p2)
    avg3 = _avg(p3)
    
    uo = 100 * (4 * avg1 + 2 * avg2 + avg3) / 7.0
    return uo.values


def _ichimoku_base(high: np.ndarray, low: np.ndarray, period: int = 26) -> np.ndarray:
    """Vectorized Ichimoku Base Line."""
    hh = pd.Series(high).rolling(window=period).max()
    ll = pd.Series(low).rolling(window=period).min()
    return ((hh + ll) / 2.0).values


def _ichimoku_cloud(high: np.ndarray, low: np.ndarray) -> Dict[str, np.ndarray]:
    """Basic Ichimoku lines for cloud-position scoring."""
    hs = pd.Series(high)
    ls = pd.Series(low)
    tenkan = ((hs.rolling(9).max() + ls.rolling(9).min()) / 2.0).values
    kijun = ((hs.rolling(26).max() + ls.rolling(26).min()) / 2.0).values
    span_a = ((pd.Series(tenkan) + pd.Series(kijun)) / 2.0).values
    span_b = ((hs.rolling(52).max() + ls.rolling(52).min()) / 2.0).values
    return {
        "tenkan": tenkan,
        "kijun": kijun,
        "span_a": span_a,
        "span_b": span_b,
    }


def _vwma(close: np.ndarray, volume: np.ndarray, period: int = 20) -> np.ndarray:
    """Vectorized Volume Weighted Moving Average."""
    cv = pd.Series(close * volume)
    v  = pd.Series(volume)
    return (cv.rolling(period).sum() / v.rolling(period).sum().replace(0, np.inf)).values


def _wma(arr: np.ndarray, period: int) -> np.ndarray:
    """Weighted moving average used by Hull MA."""
    if len(arr) == 0:
        return np.array([], dtype=float)
    weights = np.arange(1, period + 1, dtype=float)
    return pd.Series(arr).rolling(window=period).apply(
        lambda x: float(np.dot(x, weights) / weights.sum()),
        raw=True,
    ).values


def _hull_ma(close: np.ndarray, period: int = 9) -> np.ndarray:
    """Hull Moving Average."""
    half = max(period // 2, 1)
    sqrt_p = max(int(math.sqrt(period)), 1)
    wma_half = _wma(close, half)
    wma_full = _wma(close, period)
    diff = 2 * wma_half - wma_full
    hull = _wma(np.where(np.isnan(diff), close, diff), sqrt_p)
    return hull


def _price_action_config(interval: str) -> Tuple[int, int]:
    config_map = {
        "1m": (2, 120),
        "5m": (2, 140),
        "15m": (3, 180),
        "1h": (4, 260),
        "4h": (4, 320),
        "1d": (5, 360),
        "1w": (5, 260),
    }
    return config_map.get(interval, (4, 240))


def _cluster_price_action_candidates(
    candidates: List[Tuple[float, float, int]],
    tolerance: float,
) -> List[Dict[str, Any]]:
    clusters: List[Dict[str, Any]] = []
    for price, weight, candle_index in sorted(candidates, key=lambda item: item[0]):
        cluster = next(
            (item for item in clusters if abs(item["price"] - price) <= tolerance),
            None,
        )
        if cluster is None:
            clusters.append(
                {
                    "price": float(price),
                    "weight": float(weight),
                    "touches": 1,
                    "last_index": int(candle_index),
                }
            )
            continue

        total_weight = float(cluster["weight"]) + float(weight)
        cluster["price"] = (
            (float(cluster["price"]) * float(cluster["weight"])) + (float(price) * float(weight))
        ) / max(total_weight, 1e-8)
        cluster["weight"] = total_weight
        cluster["touches"] = int(cluster["touches"]) + 1
        cluster["last_index"] = max(int(cluster["last_index"]), int(candle_index))
    return clusters


def _dedupe_sorted_levels(
    levels: List[Dict[str, Any]],
    *,
    tolerance: float,
) -> List[Dict[str, Any]]:
    deduped: List[Dict[str, Any]] = []
    for level in levels:
        if deduped and abs(float(deduped[-1]["price"]) - float(level["price"])) <= tolerance:
            continue
        deduped.append(level)
    return deduped


def _calc_price_action_levels(
    data: List[Dict[str, Any]],
    current_price: float,
    interval: str,
) -> Dict[str, Any]:
    if not data:
        rounded_price = round(float(current_price), 6)
        return {
            "method": "price_action",
            "timeframe": interval,
            "current_price": rounded_price,
            "summary": "Khong du du lieu de xac dinh cac moc khang cu va ho tro theo Price Action.",
            "data": [
                {"level": "KC2", "price": rounded_price, "display_value": _format_analysis_value(rounded_price)},
                {"level": "KC1", "price": rounded_price, "display_value": _format_analysis_value(rounded_price)},
                {"level": "Giá hiện tại", "price": rounded_price, "display_value": _format_analysis_value(rounded_price)},
                {"level": "HT1", "price": rounded_price, "display_value": _format_analysis_value(rounded_price)},
                {"level": "HT2", "price": rounded_price, "display_value": _format_analysis_value(rounded_price)},
            ],
        }

    highs = np.array([float(row["high"]) for row in data], dtype=float)
    lows = np.array([float(row["low"]) for row in data], dtype=float)
    closes = np.array([float(row["close"]) for row in data], dtype=float)
    pivot_window, lookback = _price_action_config(interval)
    recent_start = max(0, len(closes) - min(len(closes), lookback))
    recent_highs = highs[recent_start:]
    recent_lows = lows[recent_start:]
    recent_closes = closes[recent_start:]

    atr_value = _latest_finite_value(_atr(recent_highs, recent_lows, recent_closes, period=14))
    median_range = float(np.nanmedian(recent_highs - recent_lows)) if len(recent_highs) else 0.0
    cluster_tolerance = max(
        float(atr_value or 0.0) * 0.35,
        median_range * 0.22,
        abs(float(current_price)) * 0.0015,
        1e-6,
    )
    extension_step = max(
        float(atr_value or 0.0),
        median_range,
        abs(float(current_price)) * 0.003,
        cluster_tolerance * 1.5,
    )

    resistance_candidates: List[Tuple[float, float, int]] = []
    support_candidates: List[Tuple[float, float, int]] = []

    recent_count = len(recent_closes)
    for idx in range(pivot_window, max(pivot_window, recent_count - pivot_window)):
        high_slice = recent_highs[idx - pivot_window : idx + pivot_window + 1]
        low_slice = recent_lows[idx - pivot_window : idx + pivot_window + 1]
        high_price = float(recent_highs[idx])
        low_price = float(recent_lows[idx])
        recency_weight = 1.0 + (idx / max(recent_count, 1))

        if high_price >= float(np.max(high_slice)):
            resistance_candidates.append((high_price, recency_weight, recent_start + idx))
        if low_price <= float(np.min(low_slice)):
            support_candidates.append((low_price, recency_weight, recent_start + idx))

    fallback_spans = [10, 20, 50, 100, min(recent_count, lookback)]
    for span in fallback_spans:
        if span <= 0 or recent_count < span:
            continue
        resistance_candidates.append((float(np.max(recent_highs[-span:])), 0.75, len(data) - 1))
        support_candidates.append((float(np.min(recent_lows[-span:])), 0.75, len(data) - 1))

    resistance_clusters = _cluster_price_action_candidates(resistance_candidates, cluster_tolerance)
    support_clusters = _cluster_price_action_candidates(support_candidates, cluster_tolerance)

    resistance_levels = _dedupe_sorted_levels(
        sorted(
            [
                cluster
                for cluster in resistance_clusters
                if float(cluster["price"]) > float(current_price) + (cluster_tolerance * 0.12)
            ],
            key=lambda item: float(item["price"]),
        ),
        tolerance=cluster_tolerance * 0.55,
    )
    support_levels = _dedupe_sorted_levels(
        sorted(
            [
                cluster
                for cluster in support_clusters
                if float(cluster["price"]) < float(current_price) - (cluster_tolerance * 0.12)
            ],
            key=lambda item: float(item["price"]),
            reverse=True,
        ),
        tolerance=cluster_tolerance * 0.55,
    )

    while len(resistance_levels) < 2:
        base_price = float(resistance_levels[-1]["price"]) if resistance_levels else float(current_price)
        resistance_levels.append(
            {
                "price": base_price + extension_step,
                "weight": 0.0,
                "touches": 0,
                "last_index": len(data) - 1,
            }
        )
    while len(support_levels) < 2:
        base_price = float(support_levels[-1]["price"]) if support_levels else float(current_price)
        support_levels.append(
            {
                "price": base_price - extension_step,
                "weight": 0.0,
                "touches": 0,
                "last_index": len(data) - 1,
            }
        )

    kc1 = float(resistance_levels[0]["price"])
    kc2 = float(resistance_levels[1]["price"])
    ht1 = float(support_levels[0]["price"])
    ht2 = float(support_levels[1]["price"])

    if current_price >= kc1:
        summary = (
            "Gia hien tai dang ap sat hoac vuot len tren KC1. "
            "Neu duy tri duoc dong luc, KC2 la moc can theo doi tiep theo."
        )
    elif current_price <= ht1:
        summary = (
            "Gia hien tai dang ap sat hoac nam duoi HT1. "
            "Neu ap luc ban tiep tuc, HT2 la vung ho tro can quan sat them."
        )
    else:
        summary = (
            "Gia hien tai dang nam giua HT1 va KC1. "
            "Day la vung can theo doi phan ung gia truoc khi xac nhan huong di tiep theo."
        )

    def _row(level: str, price: float) -> Dict[str, Any]:
        rounded_price = round(float(price), 6)
        distance_pct = 0.0 if current_price == 0 else ((rounded_price - float(current_price)) / float(current_price)) * 100.0
        return {
            "level": level,
            "price": rounded_price,
            "display_value": _format_analysis_value(rounded_price),
            "distance_pct": round(distance_pct, 2),
        }

    rows = [
        _row("KC2", kc2),
        _row("KC1", kc1),
        _row("Giá hiện tại", float(current_price)),
        _row("HT1", ht1),
        _row("HT2", ht2),
    ]
    return {
        "method": "price_action",
        "timeframe": interval,
        "current_price": round(float(current_price), 6),
        "summary": summary,
        "data": rows,
    }


def _calc_pivot_points(
    data: List[Dict[str, Any]],
    current_price: float,
    interval: str,
) -> Dict[str, Any]:
    """Compatibility wrapper for old callers. New logic uses Price Action only."""
    return _calc_price_action_levels(data, current_price, interval)


# ─────────────────────────────────────────────────────────────────────────────
# TradingView-style action classification helpers
# ─────────────────────────────────────────────────────────────────────────────

def _latest_finite_value(values: Any) -> Optional[float]:
    """Return the latest finite float from a scalar or array-like input without rounding."""
    if values is None:
        return None
    if isinstance(values, (float, int)):
        value = float(values)
        return None if math.isnan(value) else value
    if isinstance(values, pd.Series):
        values = values.values
    if not hasattr(values, "__len__") or len(values) == 0:
        return None
    value = float(values[-1])
    return None if math.isnan(value) else value


def _format_analysis_value(value: Optional[float]) -> str:
    """Format a technical value for UI without losing useful precision."""
    if value is None or math.isnan(float(value)):
        return "—"

    abs_value = abs(float(value))
    if abs_value >= 100:
        decimals = 2
    elif abs_value >= 10:
        decimals = 3
    elif abs_value >= 1:
        decimals = 4
    elif abs_value >= 0.1:
        decimals = 6
    elif abs_value >= 0.01:
        decimals = 7
    else:
        decimals = 8
    return f"{float(value):.{decimals}f}".rstrip("0").rstrip(".")


def _osc_action(name: str, value: float, **kw) -> str:
    """Classify oscillator value as 'Mua' / 'Bán' / 'Trung lập'."""
    if value is None or math.isnan(value):
        return "Trung lập"
    atr = max(float(kw.get("atr", 0.0) or 0.0), 1e-8)

    def _bounded_action(
        current: float,
        min_value: float,
        max_value: float,
        *,
        bullish_high: bool = True,
    ) -> str:
        span = max(max_value - min_value, 1e-8)
        ratio = _clamp((current - min_value) / span, 0.0, 1.0)
        if not bullish_high:
            ratio = 1.0 - ratio
        if ratio > (2.0 / 3.0):
            return "Mua"
        if ratio < (1.0 / 3.0):
            return "Bán"
        return "Trung lập"

    def _zero_line_action(current: float, epsilon: float) -> str:
        band = max(float(epsilon), 1e-8)
        if current > band:
            return "Mua"
        if current < -band:
            return "Bán"
        return "Trung lập"

    if name == "rsi":
        return _bounded_action(value, 0.0, 100.0)
    if name in {"stoch", "stoch_rsi", "ultimate", "demarker"}:
        return _bounded_action(value, 0.0, 100.0)
    if name == "williams":
        return _bounded_action(value, -100.0, 0.0, bullish_high=True)
    if name in {"aroon", "cmo", "tsi"}:
        return _bounded_action(value, -100.0, 100.0)
    if name == "cci":
        return "Mua" if value > 50 else "Bán" if value < -50 else "Trung lập"
    if name == "momentum":
        return _zero_line_action(value, kw.get("epsilon", atr * 0.025))
    if name == "roc":
        return _zero_line_action(value, kw.get("epsilon", 0.01))
    if name == "trix":
        return _zero_line_action(value, kw.get("epsilon", 0.01))
    if name == "ppo":
        return _zero_line_action(value, kw.get("epsilon", 0.01))
    if name == "dpo":
        return _zero_line_action(value, kw.get("epsilon", atr * 0.02))
    if name == "adx":
        plus_di = kw.get("plus_di", 0)
        minus_di = kw.get("minus_di", 0)
        if value < 20:
            return "Trung lập"
        return "Mua" if plus_di > minus_di else "Bán"
    if name == "ao":
        return _zero_line_action(value, kw.get("epsilon", atr * 0.02))
    if name == "macd":
        return _zero_line_action(value, kw.get("epsilon", atr * 0.02))
    if name == "bbp":
        bull_power = kw.get("bull_power")
        bear_power = kw.get("bear_power")
        epsilon = max(float(kw.get("epsilon", atr * 0.02)), 1e-8)
        if bull_power is None or bear_power is None:
            return "Trung lập"
        if bull_power > epsilon and bear_power > epsilon:
            return "Mua"
        if bull_power < -epsilon and bear_power < -epsilon:
            return "Bán"
        return "Trung lập"
    return "Trung lập"


def _ema_pair_action(fast_val: Optional[float], slow_val: Optional[float], pair_key: str = "") -> str:
    """EMA chain classification using raw spread instead of rounded display values."""
    if fast_val is None or slow_val is None:
        return "Trung lập"
    if math.isnan(fast_val) or math.isnan(slow_val):
        return "Trung lập"
    spread_pct = _pct(fast_val, slow_val)
    neutral_band_pct = max(_ema_pair_threshold_pct(pair_key) * 0.08, 0.0005)
    if spread_pct > neutral_band_pct:
        return "Mua"
    if spread_pct < -neutral_band_pct:
        return "Bán"
    return "Trung lập"


def _price_vs_level_action(
    price: Optional[float],
    level: Optional[float],
    *,
    neutral_band_pct: float = 0.08,
) -> str:
    """Simple trend classification from price relative to a pure-price reference line."""
    if price is None or level is None:
        return "Trung lập"
    if math.isnan(price) or math.isnan(level):
        return "Trung lập"
    spread_pct = _pct(price, level)
    band = max(float(neutral_band_pct), 0.0005)
    if spread_pct > band:
        return "Mua"
    if spread_pct < -band:
        return "Bán"
    return "Trung lập"


def _ema_pair_threshold_pct(pair_key: str) -> float:
    return EMA_PAIR_THRESHOLD_PCT.get(pair_key, 0.18)


def compute_indicators(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Compute a full suite of technical indicators on OHLCV data."""
    if len(data) < 30:
        return {"error": "Insufficient data (need ≥30 candles)"}

    closes = np.array([d["close"] for d in data], dtype=float)
    highs  = np.array([d["high"]  for d in data], dtype=float)
    lows   = np.array([d["low"]   for d in data], dtype=float)
    vols   = np.array([d["volume"]for d in data], dtype=float)
    times  = [d["time"] for d in data]

    def _last(arr):
        v = arr[-1]
        return None if math.isnan(v) else round(float(v), 8)

    def _series(arr, n: int = 50):
        out = []
        window = arr[-min(n, len(arr)):]
        offset = len(arr) - len(window)
        for i, v in enumerate(window):
            idx = offset + i
            out.append({"time": times[idx], "value": None if math.isnan(v) else round(float(v), 8)})
        return out

    ema9   = _ema(closes, 9)
    ema21  = _ema(closes, 21)
    ema50  = _ema(closes, 50)
    ema200 = _ema(closes, 200)
    rsi14  = _rsi(closes, 14)
    macd_l, macd_s, macd_h = _macd(closes)
    bb_u, bb_m, bb_l       = _bollinger(closes)
    atr14  = _atr(highs, lows, closes, 14)
    stoch_k, stoch_d       = _stoch_rsi(closes)
    roc12  = _roc(closes, 12)
    trix18 = _trix(closes, 18)
    ppo12  = _ppo(closes, 12, 26)
    cmo14  = _cmo(closes, 14)
    dpo20  = _dpo(closes, 20)
    aroon25 = _aroon_oscillator(highs, lows, 25)
    tsi25  = _tsi(closes, 25, 13)

    # Volume SMA 20 (Vectorized v6.0)
    vol_sma = _sma(vols, 20)

    last_close = closes[-1]
    last_atr   = _last(atr14) or 0

    return {
        "ema": {
            "ema9":   _last(ema9),   "ema21":  _last(ema21),
            "ema50":  _last(ema50),  "ema200": _last(ema200),
        },
        "rsi": {
            "value":  _last(rsi14),
            "signal": (
                "overbought" if (_last(rsi14) or 50) > 70
                else "oversold" if (_last(rsi14) or 50) < 30
                else "neutral"
            ),
        },
        "macd": {
            "macd":      _last(macd_l),
            "signal":    _last(macd_s),
            "histogram": _last(macd_h),
            "cross":     (
                "bullish" if (_last(macd_h) or 0) > 0
                else "bearish" if (_last(macd_h) or 0) < 0
                else "neutral"
            ),
        },
        "bollinger": {
            "upper":  _last(bb_u),
            "middle": _last(bb_m),
            "lower":  _last(bb_l),
            "bandwidth": round(((_last(bb_u) or 0) - (_last(bb_l) or 0)) / ((_last(bb_m) or 1)), 6),
        },
        "atr": {
            "value":   _last(atr14),
            "pct":     round(last_atr / last_close * 100, 4) if last_close else None,
        },
        "stoch_rsi": {
            "k": _last(stoch_k),
            "d": _last(stoch_d),
            "signal": (
                "overbought" if (_last(stoch_k) or 50) > 80
                else "oversold" if (_last(stoch_k) or 50) < 20
                else "neutral"
            ),
        },
        "roc": {
            "value": _last(roc12),
            "signal": "bullish" if (_last(roc12) or 0) > 1.0 else "bearish" if (_last(roc12) or 0) < -1.0 else "neutral",
        },
        "trix": {
            "value": _last(trix18),
            "signal": "bullish" if (_last(trix18) or 0) > 0 else "bearish" if (_last(trix18) or 0) < 0 else "neutral",
        },
        "ppo": {
            "value": _last(ppo12),
            "signal": "bullish" if (_last(ppo12) or 0) > 0.35 else "bearish" if (_last(ppo12) or 0) < -0.35 else "neutral",
        },
        "cmo": {
            "value": _last(cmo14),
            "signal": "bullish" if (_last(cmo14) or 0) > 20 else "bearish" if (_last(cmo14) or 0) < -20 else "neutral",
        },
        "dpo": {
            "value": _last(dpo20),
            "signal": "bullish" if (_last(dpo20) or 0) > 0 else "bearish" if (_last(dpo20) or 0) < 0 else "neutral",
        },
        "aroon": {
            "value": _last(aroon25),
            "signal": "bullish" if (_last(aroon25) or 0) > 25 else "bearish" if (_last(aroon25) or 0) < -25 else "neutral",
        },
        "tsi": {
            "value": _last(tsi25),
            "signal": "bullish" if (_last(tsi25) or 0) > 5 else "bearish" if (_last(tsi25) or 0) < -5 else "neutral",
        },
        "volume": {
            "last":       round(float(vols[-1]), 2),
            "sma20":      round(float(vol_sma[-1]), 2),
            "above_avg":  bool(vols[-1] > vol_sma[-1]),
        },
        "trend": {
            "ema_bullish_stack": bool(
                all(x is not None for x in [_last(ema9),_last(ema21),_last(ema50)]) and
                _last(ema9) > _last(ema21) > _last(ema50)   # type: ignore
            ),
            "above_ema200": bool(_last(ema200) is not None and last_close > (_last(ema200) or 0)),
            "close":        round(float(last_close), 8),
            "short_momentum_ref": float(closes[max(0, len(closes)-6)]) if len(closes) else float(last_close),
        },
        "short_momentum_ref": float(closes[max(0, len(closes)-6)]) if len(closes) else float(last_close),
        "series": {
            "ema9":   _series(ema9),
            "ema21":  _series(ema21),
            "ema50":  _series(ema50),
            "bb_upper": _series(bb_u),
            "bb_mid":   _series(bb_m),
            "bb_lower": _series(bb_l),
        },
    }


# ─────────────────────────────────────────────────────────────────────────────
# UTILITIES
# ─────────────────────────────────────────────────────────────────────────────

def _clamp(v: float, lo: float, hi: float) -> float:
    return max(lo, min(hi, v))


def _pct(a: float, b: float) -> float:
    """(a - b) / b * 100, an toàn với b = 0."""
    return (a - b) / b * 100.0 if b != 0 else 0.0


def _safe(v: Optional[float], default: float = 0.0) -> float:
    if v is None or math.isnan(v) or math.isinf(v):
        return default
    return float(v)

def _classify_regime(indicators: Dict[str, Any], last_close: float, atr_pct: float) -> Tuple[str, str]:
    """Phân loại trạng thái thị trường dựa trên biến động và xu hướng."""
    rsi = _safe(indicators["rsi"].get("value"), 50.0)
    ema9 = _safe(indicators["ema"].get("ema9"), last_close)
    ema50 = _safe(indicators["ema"].get("ema50"), last_close)
    ema200 = _safe(indicators["ema"].get("ema200"), last_close)
    
    # Logic 8 trạng thái
    if atr_pct > 3.5:
        if rsi > 60: return "volatile_bull", "Tăng trưởng biến động cao"
        if rsi < 40: return "volatile_bear", "Sụt giảm biến động cao"
        return "high_chaos", "Thị trường hỗn loạn / Vol cao"
    
    if last_close > ema200 and ema9 > ema50:
        if rsi > 70: return "overextended_bull", "Tăng trưởng quá mức (Overbought)"
        return "stable_bull", "Xu hướng tăng ổn định"
    
    if last_close < ema200 and ema9 < ema50:
        if rsi < 30: return "capitulation", "Hoảng loạn / Quá bán (Oversold)"
        return "stable_bear", "Xu hướng giảm ổn định"
    
    if abs(_pct(ema9, ema50)) < 0.5:
        return "tight_range", "Tích lũy biên độ hẹp (Squeeze)"
        
    return "sideways", "Thị trường đi ngang (Sideway)"

OSC_WEIGHTS = {
    "rsi": 2.1,
    "macd": 2.4,
    "stoch_rsi": 1.1,
    "stoch": 1.2,
    "cci": 1.7,
    "adx": 1.8,
    "williams": 1.5,
    "ultimate": 1.1,
    "bbp": 1.0,
    "ao": 0.9,
    "momentum": 1.6,
    "roc": 1.3,
    "trix": 1.4,
    "ppo": 1.4,
    "cmo": 1.2,
    "dpo": 0.8,
    "aroon": 1.3,
    "tsi": 1.4,
    "demarker": 1.2,
}

MA_WEIGHT_MAP = {
    "ema_1_2": 0.8,
    "ema_2_5": 1.0,
    "ema_5_10": 1.1,
    "ema_10_20": 1.35,
    "ema_20_50": 1.8,
    "ema_50_100": 2.2,
    "ema_100_200": 2.7,
    "ichimoku_base_26": 1.9,
}

def _extract_osc_key(label: str) -> str:
    l = label.lower()
    if "sức mạnh tương đối" in l or ("rsi" in l and "nhanh" not in l): return "rsi"
    if "macd" in l: return "macd"
    if "stochastic %k" in l: return "stoch"
    if "nhanh" in l or "stoch_rsi" in l: return "stoch_rsi"
    if "kênh hàng hóa" in l or "cci" in l: return "cci"
    if "định hướng" in l or "adx" in l: return "adx"
    if "williams" in l: return "williams"
    if "ultimate" in l: return "ultimate"
    if "bbp" in l or "sức mạnh giá" in l: return "bbp"
    if "ao" in l: return "ao"
    if "xung lượng" in l or "momentum" in l: return "momentum"
    if "roc" in l: return "roc"
    if "trix" in l: return "trix"
    if "ppo" in l: return "ppo"
    if "cmo" in l: return "cmo"
    if "dpo" in l: return "dpo"
    if "aroon" in l: return "aroon"
    if "tsi" in l: return "tsi"
    if "demarker" in l: return "demarker"
    return "unknown"

def _get_ma_weight(label: str) -> float:
    l = label.lower()
    if l in MA_WEIGHT_MAP:
        return MA_WEIGHT_MAP[l]
    pair = re.findall(r"\d+", l)
    if len(pair) >= 2:
        return MA_WEIGHT_MAP.get(f"ema_{pair[0]}_{pair[1]}", 1.0)
    return 1.0

def _gauge_to_signal(gauge: float, interval: str = "1h") -> str:
    """Strict 3-level signal converter."""
    if gauge >= 66.66: return "Mua"
    if gauge > 33.33: return "Trung lập"
    return "Bán"


def _gauge_to_normalized_score(gauge: float) -> float:
    """Convert a 0..100 gauge into a -1..1 frontend-friendly scale."""
    return _clamp((float(gauge) - 50.0) / 50.0, -1.0, 1.0)


def _forecast_path_metrics(p50_path: np.ndarray, last_close: float) -> Dict[str, float]:
    """Measure forecast quality from the full path, not only the final endpoint."""
    if len(p50_path) == 0 or abs(last_close) <= 1e-8:
        return {
            "weighted_return_pct": 0.0,
            "final_return_pct": 0.0,
            "path_consistency": 50.0,
            "monotonicity": 50.0,
            "max_adverse_excursion_pct": 0.0,
            "mean_step_return_pct": 0.0,
        }

    p50_safe = np.array(p50_path, dtype=float)
    if not np.isfinite(p50_safe).all():
        last_valid = float(last_close)
        for i in range(len(p50_safe)):
            if not np.isfinite(p50_safe[i]):
                p50_safe[i] = last_valid
            else:
                last_valid = p50_safe[i]

    ret_path = ((p50_safe / last_close) - 1.0) * 100.0
    step_weights = np.linspace(1.0, 0.65, len(ret_path))
    weighted_ret_pct = float(np.average(ret_path, weights=step_weights))
    final_ret_pct = float(ret_path[-1])
    final_sign = 0 if abs(final_ret_pct) < 0.05 else (1 if final_ret_pct > 0 else -1)

    if len(ret_path) > 1 and final_sign != 0:
        signed_steps = [
            1.0 if np.sign(curr - prev) == final_sign else 0.0
            for prev, curr in zip(ret_path[:-1], ret_path[1:])
            if abs(curr - prev) >= 0.02
        ]
        path_consistency = float(sum(signed_steps) / len(signed_steps)) if signed_steps else 0.5
    else:
        path_consistency = 0.5

    if len(ret_path) > 1:
        monotonicity = float(np.mean(np.diff(ret_path) >= 0)) if final_sign >= 0 else float(np.mean(np.diff(ret_path) <= 0))
    else:
        monotonicity = 0.5

    if final_sign > 0:
        adverse = abs(float(np.min(ret_path)))
    elif final_sign < 0:
        adverse = abs(float(np.max(ret_path)))
    else:
        adverse = max(abs(float(np.min(ret_path))), abs(float(np.max(ret_path))))

    mean_step_return_pct = float(np.mean(np.diff(ret_path))) if len(ret_path) > 1 else final_ret_pct
    return {
        "weighted_return_pct": round(weighted_ret_pct, 2),
        "final_return_pct": round(final_ret_pct, 2),
        "path_consistency": round(path_consistency * 100.0, 1),
        "monotonicity": round(monotonicity * 100.0, 1),
        "max_adverse_excursion_pct": round(adverse, 2),
        "mean_step_return_pct": round(mean_step_return_pct, 3),
    }


def _forecast_band_profile(
    p10_path: np.ndarray,
    p50_path: np.ndarray,
    p90_path: np.ndarray,
) -> Dict[str, float]:
    """Summarize uncertainty bands across the full forecast path."""
    if len(p50_path) == 0:
        return {
            "avg_band_pct": 0.0,
            "end_band_pct": 0.0,
            "band_stability_pct": 0.0,
            "band_step_change_pct": 0.0,
        }

    safe_mid = np.maximum(np.abs(p50_path), 1e-8)
    band_pct = np.maximum(((p90_path - p10_path) / safe_mid) * 100.0, 0.0)
    band_step_changes = np.abs(np.diff(band_pct)) if len(band_pct) > 1 else np.array([0.0], dtype=float)
    return {
        "avg_band_pct": round(float(np.mean(band_pct)), 4),
        "end_band_pct": round(float(band_pct[-1]), 4),
        "band_stability_pct": round(float(np.std(band_pct)), 4),
        "band_step_change_pct": round(float(np.mean(band_step_changes)), 4),
    }


def _forecast_path_responsiveness(p50_path: np.ndarray, last_close: float) -> Dict[str, float]:
    """Quantify how much the forecast path actually moves relative to the anchor."""
    if len(p50_path) == 0 or abs(last_close) <= 1e-8:
        return {
            "range_pct": 0.0,
            "step_abs_mean_pct": 0.0,
            "step_abs_max_pct": 0.0,
        }

    ret_path = ((p50_path / last_close) - 1.0) * 100.0
    diffs = np.diff(ret_path) if len(ret_path) > 1 else np.array([ret_path[-1]], dtype=float)
    return {
        "range_pct": round(float(np.max(ret_path) - np.min(ret_path)), 4),
        "step_abs_mean_pct": round(float(np.mean(np.abs(diffs))), 4),
        "step_abs_max_pct": round(float(np.max(np.abs(diffs))), 4),
    }


def _recent_ohlc4_abs_step_pct(ohlc4: np.ndarray, window: int = 20) -> float:
    """Use recent OHLC4 movement as a volatility-aware floor for forecast responsiveness."""
    if len(ohlc4) < 2:
        return 0.0

    base = np.maximum(np.abs(ohlc4[:-1]), 1e-8)
    returns = np.diff(ohlc4) / base * 100.0
    recent = returns[-min(window, len(returns)) :]
    return round(float(np.mean(np.abs(recent))), 4) if len(recent) else 0.0


def _future_window_path_stats(
    ohlc4: np.ndarray,
    anchor_index: int,
    horizon: int,
) -> Optional[Dict[str, float]]:
    """Measure the realized future path that followed a historical anchor point."""
    if anchor_index < 0 or horizon <= 0:
        return None
    if anchor_index + horizon >= len(ohlc4):
        return None

    baseline = float(ohlc4[anchor_index])
    future_path = np.asarray(ohlc4[anchor_index + 1 : anchor_index + 1 + horizon], dtype=float)
    responsiveness = _forecast_path_responsiveness(future_path, baseline)
    final_return_pct = (
        ((future_path[-1] / max(abs(baseline), 1e-8)) - 1.0) * 100.0
        if len(future_path)
        else 0.0
    )
    return {
        "range_pct": float(responsiveness["range_pct"]),
        "step_abs_mean_pct": float(responsiveness["step_abs_mean_pct"]),
        "step_abs_max_pct": float(responsiveness["step_abs_max_pct"]),
        "final_return_pct": float(final_return_pct),
    }


def _rolling_future_path_targets(
    ohlc4: np.ndarray,
    horizon: int,
    max_windows: int = 48,
) -> Optional[Dict[str, float]]:
    """Summarize realized future-path amplitudes from recent historical anchors."""
    if len(ohlc4) < horizon + 2:
        return None

    last_anchor = len(ohlc4) - horizon - 1
    start_anchor = max(0, last_anchor - max_windows + 1)
    stats: List[Dict[str, float]] = []
    for anchor_index in range(start_anchor, last_anchor + 1):
        path_stats = _future_window_path_stats(ohlc4, anchor_index, horizon)
        if path_stats is not None:
            stats.append(path_stats)

    if not stats:
        return None

    weights = np.linspace(0.6, 1.0, len(stats), dtype=float)
    range_vals = np.array([item["range_pct"] for item in stats], dtype=float)
    step_vals = np.array([item["step_abs_mean_pct"] for item in stats], dtype=float)
    return {
        "window_count": len(stats),
        "range_pct": round(float(np.average(range_vals, weights=weights)), 4),
        "step_abs_mean_pct": round(float(np.average(step_vals, weights=weights)), 4),
    }


def _context_regime_signature(ohlc4_window: np.ndarray) -> Dict[str, float]:
    """Describe the local market regime of an OHLC4 window using scale-free features."""
    if len(ohlc4_window) < 2:
        return {
            "net_change_pct": 0.0,
            "vol_pct": 0.0,
            "abs_step_mean_pct": 0.0,
            "range_pct": 0.0,
        }

    base = np.maximum(np.abs(ohlc4_window[:-1]), 1e-8)
    returns = np.diff(ohlc4_window) / base * 100.0
    first = max(abs(float(ohlc4_window[0])), 1e-8)
    return {
        "net_change_pct": float(((ohlc4_window[-1] / first) - 1.0) * 100.0),
        "vol_pct": float(np.std(returns)) if len(returns) > 1 else 0.0,
        "abs_step_mean_pct": float(np.mean(np.abs(returns))) if len(returns) else 0.0,
        "range_pct": float(((np.max(ohlc4_window) - np.min(ohlc4_window)) / first) * 100.0),
    }


def _regime_signature_distance(current: Dict[str, float], candidate: Dict[str, float]) -> float:
    """Weighted distance between two market-regime signatures."""
    terms = (
        ("net_change_pct", 0.34, 0.40),
        ("vol_pct", 0.28, 0.18),
        ("abs_step_mean_pct", 0.22, 0.12),
        ("range_pct", 0.16, 0.30),
    )
    distance = 0.0
    for key, weight, floor in terms:
        lhs = float(current.get(key, 0.0))
        rhs = float(candidate.get(key, 0.0))
        scale = max(abs(lhs), abs(rhs), floor)
        distance += weight * (abs(lhs - rhs) / scale)
    return float(distance)


def _regime_matched_future_targets(
    ohlc4: np.ndarray,
    context_len: int,
    horizon: int,
    search_limit: int = 720,
    top_k: int = 18,
) -> Optional[Dict[str, float]]:
    """Find historical windows with a similar regime and measure their realized future amplitudes."""
    max_anchor = len(ohlc4) - horizon - 1
    if max_anchor < 32:
        return None

    signature_len = max(32, min(int(context_len), 96, max_anchor + 1))
    if len(ohlc4) < signature_len + horizon + 1:
        return None

    current_signature = _context_regime_signature(
        np.asarray(ohlc4[-signature_len:], dtype=float)
    )
    min_anchor = signature_len - 1
    start_anchor = max(min_anchor, max_anchor - search_limit + 1)
    matches: List[Tuple[float, Dict[str, float]]] = []

    for anchor_index in range(start_anchor, max_anchor + 1):
        context_window = np.asarray(
            ohlc4[anchor_index - signature_len + 1 : anchor_index + 1],
            dtype=float,
        )
        candidate_signature = _context_regime_signature(context_window)
        path_stats = _future_window_path_stats(ohlc4, anchor_index, horizon)
        if path_stats is None:
            continue
        distance = _regime_signature_distance(current_signature, candidate_signature)
        matches.append((distance, path_stats))

    if not matches:
        return None

    matches.sort(key=lambda item: item[0])
    chosen = matches[: min(top_k, len(matches))]
    weights = np.array([1.0 / (item[0] + 0.08) for item in chosen], dtype=float)
    range_vals = np.array([item[1]["range_pct"] for item in chosen], dtype=float)
    step_vals = np.array([item[1]["step_abs_mean_pct"] for item in chosen], dtype=float)
    return {
        "match_count": len(chosen),
        "signature_len": signature_len,
        "range_pct": round(float(np.average(range_vals, weights=weights)), 4),
        "step_abs_mean_pct": round(float(np.average(step_vals, weights=weights)), 4),
    }


def _build_regime_texture_template(
    ohlc4: np.ndarray,
    context_len: int,
    horizon: int,
    search_limit: int = 720,
    top_k: int = 18,
) -> Optional[Dict[str, Any]]:
    """Build a step-return template from regime-matched historical futures."""
    max_anchor = len(ohlc4) - horizon - 1
    if max_anchor < 32:
        return None

    signature_len = max(32, min(int(context_len), 96, max_anchor + 1))
    if len(ohlc4) < signature_len + horizon + 1:
        return None

    current_signature = _context_regime_signature(
        np.asarray(ohlc4[-signature_len:], dtype=float)
    )
    min_anchor = signature_len - 1
    start_anchor = max(min_anchor, max_anchor - search_limit + 1)
    matches: List[Tuple[float, np.ndarray, Dict[str, float]]] = []

    for anchor_index in range(start_anchor, max_anchor + 1):
        context_window = np.asarray(
            ohlc4[anchor_index - signature_len + 1 : anchor_index + 1],
            dtype=float,
        )
        candidate_signature = _context_regime_signature(context_window)
        distance = _regime_signature_distance(current_signature, candidate_signature)

        baseline = float(ohlc4[anchor_index])
        future_path = np.asarray(
            ohlc4[anchor_index + 1 : anchor_index + 1 + horizon],
            dtype=float,
        )
        if len(future_path) != horizon:
            continue

        prev_values = np.concatenate(([baseline], future_path[:-1]))
        step_returns = ((future_path / np.maximum(np.abs(prev_values), 1e-8)) - 1.0) * 100.0
        path_stats = _forecast_path_responsiveness(future_path, baseline)
        matches.append((distance, step_returns, path_stats))

    if not matches:
        return None

    matches.sort(key=lambda item: item[0])
    chosen = matches[: min(top_k, len(matches))]
    weights = np.array([1.0 / (distance + 0.08) for distance, _, _ in chosen], dtype=float)
    step_matrix = np.vstack([step_returns for _, step_returns, _ in chosen])
    averaged_steps = np.average(step_matrix, axis=0, weights=weights)

    best_distance = float("inf")
    best_steps: Optional[np.ndarray] = None
    best_score = float("-inf")
    for distance, step_returns, path_stats in chosen[: min(6, len(chosen))]:
        step_abs_mean_pct = float(np.mean(np.abs(step_returns))) if len(step_returns) else 0.0
        range_pct = float(path_stats.get("range_pct", 0.0))
        texture_score = (
            step_abs_mean_pct * 0.60
            + range_pct * 0.40
            - (distance * 0.12)
        )
        if texture_score > best_score:
            best_score = texture_score
            best_distance = distance
            best_steps = step_returns

    if best_steps is None:
        best_steps = averaged_steps
        best_distance = float(chosen[0][0])

    blended_steps = (best_steps * 0.72) + (averaged_steps * 0.28)
    step_abs_mean_pct = float(np.mean(np.abs(blended_steps))) if len(blended_steps) else 0.0
    simulated_prices = [baseline]
    price = baseline
    for step_return in blended_steps:
        price = max(0.0, price * (1.0 + (float(step_return) / 100.0)))
        simulated_prices.append(price)
    simulated_path = np.asarray(simulated_prices[1:], dtype=float)
    range_pct = _forecast_path_responsiveness(simulated_path, baseline)["range_pct"] if len(simulated_path) else 0.0
    return {
        "step_returns_pct": blended_steps,
        "step_abs_mean_pct": round(step_abs_mean_pct, 4),
        "range_pct": round(float(range_pct), 4),
        "match_count": len(chosen),
        "signature_len": signature_len,
        "best_match_distance": round(best_distance, 4),
    }


def _derive_target_amplitude_profile(
    ohlc4: np.ndarray,
    context_len: int,
    horizon: int,
) -> Dict[str, Any]:
    """Blend recent and regime-matched history into a target amplitude profile."""
    recent_abs_step_pct = _recent_ohlc4_abs_step_pct(ohlc4, window=20)
    rolling_targets = _rolling_future_path_targets(
        ohlc4,
        horizon=horizon,
        max_windows=max(24, min(72, horizon * 6)),
    )
    regime_targets = _regime_matched_future_targets(
        ohlc4,
        context_len=context_len,
        horizon=horizon,
    )

    step_terms: List[Tuple[float, float]] = []
    if recent_abs_step_pct > 0:
        step_terms.append((recent_abs_step_pct, 0.24))
    if rolling_targets is not None:
        step_terms.append((float(rolling_targets["step_abs_mean_pct"]), 0.31))
    if regime_targets is not None:
        step_terms.append((float(regime_targets["step_abs_mean_pct"]), 0.45))

    if step_terms:
        target_step_abs_mean_pct = sum(value * weight for value, weight in step_terms) / sum(
            weight for _, weight in step_terms
        )
    else:
        target_step_abs_mean_pct = 0.0

    range_terms: List[Tuple[float, float]] = []
    if rolling_targets is not None:
        range_terms.append((float(rolling_targets["range_pct"]), 0.42))
    if regime_targets is not None:
        range_terms.append((float(regime_targets["range_pct"]), 0.58))
    if not range_terms and target_step_abs_mean_pct > 0:
        fallback_range = target_step_abs_mean_pct * max(2.0, min(math.sqrt(max(horizon, 1)) * 1.6, 4.5))
        range_terms.append((fallback_range, 1.0))

    target_range_pct = sum(value * weight for value, weight in range_terms) / sum(
        weight for _, weight in range_terms
    ) if range_terms else 0.0

    return {
        "target_step_abs_mean_pct": round(float(target_step_abs_mean_pct), 4),
        "target_range_pct": round(float(target_range_pct), 4),
        "recent_abs_step_pct": round(float(recent_abs_step_pct), 4),
        "rolling_targets": rolling_targets,
        "regime_targets": regime_targets,
    }


def _build_raw_ohlc4_bundle(
    model_output: Dict[str, Any],
    last_ohlc4: float,
) -> Dict[str, Any]:
    """Build the OHLC4-path bundle directly from raw TimesFM output."""
    raw_p10 = np.array(model_output["p10"], dtype=float)
    raw_p50 = np.array(model_output["p50"], dtype=float)
    raw_p90 = np.array(model_output["p90"], dtype=float)

    path_metrics = _forecast_path_metrics(raw_p50, last_ohlc4)
    avg_band_pct = float(
        np.mean((raw_p90 - raw_p10) / np.maximum(np.abs(raw_p50), 1e-8)) * 100.0
    ) if len(raw_p50) else 0.0
    band_certainty = math.exp(-avg_band_pct / 4.0)
    path_consistency = path_metrics["path_consistency"] / 100.0
    monotonicity = path_metrics["monotonicity"] / 100.0
    move_pct = abs(path_metrics["final_return_pct"])

    confidence = (
        28.0
        + band_certainty * 34.0
        + path_consistency * 18.0
        + monotonicity * 12.0
        + min(move_pct, 4.0) * 2.0
    )
    confidence = max(20.0, min(95.0, confidence))

    final_sign = 0 if abs(path_metrics["final_return_pct"]) < 0.05 else (1 if path_metrics["final_return_pct"] > 0 else -1)
    weighted_sign = 0 if abs(path_metrics["weighted_return_pct"]) < 0.05 else (1 if path_metrics["weighted_return_pct"] > 0 else -1)
    agreement = final_sign == 0 or weighted_sign == 0 or final_sign == weighted_sign

    return {
        "p10": raw_p10,
        "p50": raw_p50,
        "p90": raw_p90,
        "model_weight": 1.0,
        "anchor_weight": 0.0,
        "agreement": agreement,
        "scale": 1.0,
        "confidence": round(confidence, 2),
        "model_bias_pct": 0.0,
        "path_metrics": path_metrics,
        "mode": "raw_timesfm_ohlc4",
    }


def _apply_forecast_amplitude_calibration(
    raw_bundle: Dict[str, Any],
    last_ohlc4: float,
    target_profile: Dict[str, Any],
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """Scale raw TimesFM OHLC4 paths so their step/range amplitude stays realistic."""
    raw_p10 = np.asarray(raw_bundle.get("p10", []), dtype=float)
    raw_p50 = np.asarray(raw_bundle.get("p50", []), dtype=float)
    raw_p90 = np.asarray(raw_bundle.get("p90", []), dtype=float)
    raw_responsiveness = _forecast_path_responsiveness(raw_p50, last_ohlc4)

    raw_step = max(float(raw_responsiveness["step_abs_mean_pct"]), 1e-6)
    raw_range = max(float(raw_responsiveness["range_pct"]), 1e-6)
    target_step = max(float(target_profile.get("target_step_abs_mean_pct") or 0.0), 0.0)
    target_range = max(float(target_profile.get("target_range_pct") or 0.0), 0.0)

    ratios: List[Tuple[float, float]] = []
    if target_step > 0:
        ratios.append((target_step / raw_step, 0.62))
    if target_range > 0:
        ratios.append((target_range / raw_range, 0.38))

    if ratios:
        log_scale = sum(weight * math.log(max(ratio, 1e-6)) for ratio, weight in ratios) / sum(
            weight for _, weight in ratios
        )
        scale = math.exp(log_scale)
    else:
        scale = 1.0

    scale = float(np.clip(scale, 0.82, 2.35))
    if raw_step >= target_step * 0.92 and raw_range >= target_range * 0.88:
        scale = float(np.clip(scale, 0.9, 1.25))

    calibrated_p10 = np.maximum(0.0, last_ohlc4 + ((raw_p10 - last_ohlc4) * scale))
    calibrated_p50 = np.maximum(0.0, last_ohlc4 + ((raw_p50 - last_ohlc4) * scale))
    calibrated_p90 = np.maximum(0.0, last_ohlc4 + ((raw_p90 - last_ohlc4) * scale))

    calibrated_bundle = _build_raw_ohlc4_bundle(
        {
            "p10": calibrated_p10,
            "p50": calibrated_p50,
            "p90": calibrated_p90,
        },
        last_ohlc4,
    )
    calibrated_bundle["mode"] = "timesfm_ohlc4_vol_calibrated"

    calibration_meta = {
        "enabled": True,
        "version": "volatility_regime_v1",
        "scale": round(scale, 4),
        "raw_path": raw_responsiveness,
        "calibrated_path": _forecast_path_responsiveness(calibrated_p50, last_ohlc4),
        "targets": target_profile,
    }
    return calibrated_bundle, calibration_meta


def _apply_forecast_path_texture(
    base_bundle: Dict[str, Any],
    last_ohlc4: float,
    target_profile: Dict[str, Any],
    texture_template: Optional[Dict[str, Any]],
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """Inject regime-matched step texture so each future point is updated sequentially."""
    base_p10 = np.asarray(base_bundle.get("p10", []), dtype=float)
    base_p50 = np.asarray(base_bundle.get("p50", []), dtype=float)
    base_p90 = np.asarray(base_bundle.get("p90", []), dtype=float)
    base_path = _forecast_path_responsiveness(base_p50, last_ohlc4)

    texture_meta: Dict[str, Any] = {
        "enabled": True,
        "version": "historical_step_texture_v2",
        "applied": False,
        "blend_alpha": 0.0,
        "base_path": base_path,
        "textured_path": base_path,
        "template": {
            "match_count": 0,
            "signature_len": 0,
            "step_abs_mean_pct": 0.0,
            "range_pct": 0.0,
        },
    }
    if texture_template is None:
        return base_bundle, texture_meta

    template_steps = np.asarray(texture_template.get("step_returns_pct", []), dtype=float)
    if len(template_steps) != len(base_p50):
        return base_bundle, texture_meta

    template_step = max(float(texture_template.get("step_abs_mean_pct") or 0.0), 1e-6)
    template_range = max(float(texture_template.get("range_pct") or 0.0), 1e-6)
    target_step = max(float(target_profile.get("target_step_abs_mean_pct") or 0.0), 0.0)
    target_range = max(float(target_profile.get("target_range_pct") or 0.0), 0.0)
    base_step = float(base_path["step_abs_mean_pct"])
    base_range = float(base_path["range_pct"])
    base_monotonicity = float(base_bundle.get("path_metrics", {}).get("monotonicity", 50.0))

    step_gap = max((target_step * 0.94) - base_step, 0.0)
    range_gap = max((target_range * 0.78) - base_range, 0.0)
    if base_monotonicity >= 88.0:
        step_gap = max(step_gap, target_step * 0.16)
        range_gap = max(range_gap, target_range * 0.10)

    if step_gap <= 0.0 and range_gap <= 0.0:
        texture_meta["template"] = {
            "match_count": int(texture_template.get("match_count") or 0),
            "signature_len": int(texture_template.get("signature_len") or 0),
            "step_abs_mean_pct": round(float(texture_template.get("step_abs_mean_pct") or 0.0), 4),
            "range_pct": round(float(texture_template.get("range_pct") or 0.0), 4),
        }
        return base_bundle, texture_meta

    ratios: List[float] = []
    if step_gap > 0.0:
        ratios.append(step_gap / template_step)
    if range_gap > 0.0:
        ratios.append(range_gap / template_range)
    blend_alpha = float(np.clip(np.median(ratios) if ratios else 0.0, 0.0, 0.82))

    def _count_direction_flips(series: np.ndarray) -> int:
        if len(series) < 2:
            return 0
        signs = [int(np.sign(delta)) for delta in series if abs(delta) >= 0.015]
        return sum(1 for prev, curr in zip(signs[:-1], signs[1:]) if prev != curr)

    template_flips = _count_direction_flips(template_steps)
    if base_monotonicity >= 92.0 and template_flips >= 1:
        blend_alpha = max(blend_alpha, 0.28)

    def _step_returns_from_path(path: np.ndarray, anchor_price: float) -> np.ndarray:
        prev_values = np.concatenate(([anchor_price], path[:-1]))
        return ((path / np.maximum(np.abs(prev_values), 1e-8)) - 1.0) * 100.0

    def _rebuild_from_step_returns(path: np.ndarray, step_returns_pct: np.ndarray) -> np.ndarray:
        rebuilt: List[float] = []
        prev_price = float(last_ohlc4)
        for raw_price, textured_step in zip(path, step_returns_pct):
            raw_step = ((float(raw_price) / max(abs(prev_price), 1e-8)) - 1.0) * 100.0
            raw_factor = max(1e-6, 1.0 + (raw_step / 100.0))
            target_factor = max(1e-6, 1.0 + (float(textured_step) / 100.0))
            factor_ratio = target_factor / raw_factor
            next_price = max(0.0, float(raw_price) * factor_ratio)
            rebuilt.append(next_price)
            prev_price = next_price
        return np.asarray(rebuilt, dtype=float)

    base_steps_p50 = _step_returns_from_path(base_p50, last_ohlc4)
    textured_steps_p50 = (base_steps_p50 * (1.0 - blend_alpha)) + (template_steps * blend_alpha)

    if base_monotonicity >= 92.0 and template_flips >= 1:
        textured_flips = _count_direction_flips(textured_steps_p50)
        while textured_flips < 1 and blend_alpha < 0.92:
            blend_alpha = min(blend_alpha * 1.16, 0.92)
            textured_steps_p50 = (base_steps_p50 * (1.0 - blend_alpha)) + (template_steps * blend_alpha)
            textured_flips = _count_direction_flips(textured_steps_p50)

    textured_p10 = _rebuild_from_step_returns(base_p10, textured_steps_p50)
    textured_p50 = _rebuild_from_step_returns(base_p50, textured_steps_p50)
    textured_p90 = _rebuild_from_step_returns(base_p90, textured_steps_p50)
    textured_bundle = _build_raw_ohlc4_bundle(
        {
            "p10": textured_p10,
            "p50": textured_p50,
            "p90": textured_p90,
        },
        last_ohlc4,
    )
    textured_path = _forecast_path_responsiveness(
        np.asarray(textured_bundle.get("p50", []), dtype=float),
        last_ohlc4,
    )

    if target_step > 0.0 and float(textured_path["step_abs_mean_pct"]) > target_step * 1.28:
        shrink = (target_step * 1.28) / max(float(textured_path["step_abs_mean_pct"]), 1e-6)
        blend_alpha *= shrink
        textured_steps_p50 = (base_steps_p50 * (1.0 - blend_alpha)) + (template_steps * blend_alpha)
        textured_p10 = _rebuild_from_step_returns(base_p10, textured_steps_p50)
        textured_p50 = _rebuild_from_step_returns(base_p50, textured_steps_p50)
        textured_p90 = _rebuild_from_step_returns(base_p90, textured_steps_p50)
        textured_bundle = _build_raw_ohlc4_bundle(
            {
                "p10": textured_p10,
                "p50": textured_p50,
                "p90": textured_p90,
            },
            last_ohlc4,
        )
        textured_path = _forecast_path_responsiveness(
            np.asarray(textured_bundle.get("p50", []), dtype=float),
            last_ohlc4,
        )

    textured_bundle["mode"] = "timesfm_ohlc4_textured"
    texture_meta.update(
        {
            "applied": blend_alpha > 0.0,
            "blend_alpha": round(blend_alpha, 4),
            "textured_path": textured_path,
            "template": {
                "match_count": int(texture_template.get("match_count") or 0),
                "signature_len": int(texture_template.get("signature_len") or 0),
                "step_abs_mean_pct": round(float(texture_template.get("step_abs_mean_pct") or 0.0), 4),
                "range_pct": round(float(texture_template.get("range_pct") or 0.0), 4),
            },
        }
    )
    return textured_bundle, texture_meta


def _score_forecast_context_candidate(
    analysis_bundle: Dict[str, Any],
    last_ohlc4: float,
    recent_abs_step_pct: float,
) -> Dict[str, float]:
    """Prefer contexts that stay confident without collapsing into a near-flat path."""
    p50_path = np.asarray(analysis_bundle.get("p50", []), dtype=float)
    responsiveness = _forecast_path_responsiveness(p50_path, last_ohlc4)
    step_abs_mean_pct = float(responsiveness["step_abs_mean_pct"])
    range_pct = float(responsiveness["range_pct"])
    step_floor = max(0.015, recent_abs_step_pct * 0.18)
    range_floor = max(0.05, recent_abs_step_pct * 0.70)
    step_bonus = min(step_abs_mean_pct / step_floor, 1.0) * 14.0
    range_bonus = min(range_pct / range_floor, 1.0) * 10.0
    flat_penalty = (
        6.0
        if step_abs_mean_pct < (step_floor * 0.55) and range_pct < (range_floor * 0.55)
        else 0.0
    )
    confidence = float(analysis_bundle.get("confidence", 0.0))
    score = confidence + step_bonus + range_bonus - flat_penalty
    return {
        "score": round(score, 4),
        "confidence": round(confidence, 4),
        "step_floor": round(step_floor, 4),
        "range_floor": round(range_floor, 4),
        **responsiveness,
    }


def _calc_vote_gauge(buy: int, sell: int, neutral: int) -> float:
    """Equal-weight PTKT vote gauge from Buy / Sell / Neutral counts."""
    buy_f = float(max(0, buy))
    sell_f = float(max(0, sell))
    neutral_f = float(max(0, neutral))
    denominator = buy_f + sell_f + neutral_f
    if denominator <= 0:
        return 50.0
    raw_gauge = 50.0 + 50.0 * ((buy_f - sell_f) / denominator)
    return max(0.0, min(100.0, raw_gauge))


def _calc_adx_trend_activation(adx_value: Optional[float] = None) -> float:
    """Sigmoid ADX factor that activates PTKT votes only when trend strength is present."""
    adx_numeric = float(adx_value) if adx_value is not None else 0.0
    if math.isnan(adx_numeric):
        adx_numeric = 0.0
    adx_numeric = max(0.0, adx_numeric)
    exponent = -((adx_numeric - 20.0) / 8.0)
    return float(_clamp(1.0 / (1.0 + math.exp(exponent)), 0.0, 1.0))


def _calc_vote_participation_factor(buy: int, sell: int, neutral: int) -> float:
    """Reward PTKT gauge only when enough signals are directional instead of neutral."""
    buy_f = float(max(0, buy))
    sell_f = float(max(0, sell))
    neutral_f = float(max(0, neutral))
    denominator = buy_f + sell_f + neutral_f
    if denominator <= 0:
        return 0.0
    return float(_clamp((buy_f + sell_f) / denominator, 0.0, 1.0))


def _calc_adx_weighted_technical_gauge(
    buy: int,
    sell: int,
    neutral: int,
    adx_value: Optional[float] = None,
) -> float:
    """Final PTKT gauge using the sigmoid ADX-weighted vote formula."""
    buy_f = float(max(0, buy))
    sell_f = float(max(0, sell))
    neutral_f = float(max(0, neutral))
    denominator = buy_f + sell_f + neutral_f
    if denominator <= 0:
        return 50.0

    trend_activation = _calc_adx_trend_activation(adx_value)
    participation_factor = _calc_vote_participation_factor(buy, sell, neutral)
    vote_balance = (buy_f - sell_f) / denominator
    raw_gauge = 50.0 + (50.0 * vote_balance * participation_factor * trend_activation)
    return float(_clamp(raw_gauge, 0.0, 100.0))


def _vote_direction_from_counts(buy: int, sell: int) -> int:
    if buy > sell:
        return 1
    if sell > buy:
        return -1
    return 0


def _candle_ohlc4(candle: Dict[str, Any]) -> Optional[float]:
    try:
        open_value = float(candle["open"])
        high_value = float(candle["high"])
        low_value = float(candle["low"])
        close_value = float(candle["close"])
    except (KeyError, TypeError, ValueError):
        return None
    values = (open_value, high_value, low_value, close_value)
    if not all(math.isfinite(value) for value in values):
        return None
    return sum(values) / 4.0


def _performance_action(change_pct: Optional[float], *, included_in_vote: bool) -> str:
    if not included_in_vote or change_pct is None:
        return "N/A"
    if change_pct > PERFORMANCE_NEUTRAL_EPSILON_PCT:
        return "Mua"
    if change_pct < -PERFORMANCE_NEUTRAL_EPSILON_PCT:
        return "Bán"
    return "Trung lập"


def _get_performance_vote_weight(lookback: Any) -> float:
    try:
        return float(PERFORMANCE_VOTE_WEIGHTS.get(int(lookback), 1.0))
    except (TypeError, ValueError):
        return 1.0


def _build_performance_rows(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    latest_ohlc4 = _candle_ohlc4(data[-1]) if data else None
    rows: List[Dict[str, Any]] = []

    for lookback in PERFORMANCE_LOOKBACKS:
        reference_index = len(data) - 1 - lookback
        reference_ohlc4 = _candle_ohlc4(data[reference_index]) if reference_index >= 0 else None
        included_in_vote = (
            latest_ohlc4 is not None
            and reference_ohlc4 is not None
            and abs(reference_ohlc4) > 1e-12
        )
        change_pct = (
            ((latest_ohlc4 / reference_ohlc4) - 1.0) * 100.0
            if included_in_vote and latest_ohlc4 is not None and reference_ohlc4 is not None
            else None
        )
        rows.append(
            {
                "key": f"performance_{lookback}",
                "name": f"Hiệu suất {lookback} nến",
                "lookback": lookback,
                "current_ohlc4": latest_ohlc4,
                "reference_ohlc4": reference_ohlc4,
                "change_pct": change_pct,
                "display_value": (
                    f"{float(latest_ohlc4):.4f} vs {float(reference_ohlc4):.4f} ({float(change_pct):+.2f}%)"
                    if included_in_vote and change_pct is not None
                    else "N/A"
                ),
                "action": _performance_action(change_pct, included_in_vote=included_in_vote),
                "included_in_vote": included_in_vote,
            }
        )
    return rows


def _calc_osc_score(osc_data: list, interval: str = "1h") -> dict:
    """Summarize oscillator votes for the PTKT classification table."""
    buy = sell = neutral = 0
    buy_weight = sell_weight = neutral_weight = 0.0

    for item in osc_data:
        key = item.get("key") or _extract_osc_key(item["name"])
        w = OSC_WEIGHTS.get(key, 1.0)
        act = str(item.get("action", "")).strip().lower()
        if act == "mua":
            buy += 1
            buy_weight += w
        elif act == "bán":
            sell += 1
            sell_weight += w
        else:
            neutral += 1
            neutral_weight += w

    return {
        "signal": _gauge_to_signal(_calc_vote_gauge(buy, sell, neutral), interval),
        "buy": buy, "sell": sell, "neutral": neutral,
        "buy_weight": round(buy_weight, 2),
        "sell_weight": round(sell_weight, 2),
        "neutral_weight": round(neutral_weight, 2),
        "formula_version": TECHNICAL_SCORING_VERSION,
    }

def _calc_ma_score(ma_data: list, closes: np.ndarray, interval: str = "1h") -> dict:
    """Summarize moving-average votes for the PTKT classification table."""
    buy = sell = neutral = 0
    buy_weight = sell_weight = neutral_weight = 0.0

    for item in ma_data:
        w = _get_ma_weight(item.get("key") or item["name"])
        act = str(item.get("action", "")).strip().lower()
        if act == "mua":
            buy += 1
            buy_weight += w
        elif act == "bán":
            sell += 1
            sell_weight += w
        else:
            neutral += 1
            neutral_weight += w

    ema50 = _ema(closes, 50)[-1] if len(closes) >= 50 else float("nan")
    ema200 = _ema(closes, 200)[-1] if len(closes) >= 200 else float("nan")
    
    return {
        "signal": _gauge_to_signal(_calc_vote_gauge(buy, sell, neutral), interval),
        "buy": buy, "sell": sell, "neutral": neutral,
        "buy_weight": round(buy_weight, 2),
        "sell_weight": round(sell_weight, 2),
        "neutral_weight": round(neutral_weight, 2),
        "structure_bonus": 0.0,
        "golden_cross": bool(not any(math.isnan(x) for x in (ema50, ema200)) and ema50 > ema200),
        "death_cross": bool(not any(math.isnan(x) for x in (ema50, ema200)) and ema50 < ema200),
        "formula_version": TECHNICAL_SCORING_VERSION,
    }


def _calc_performance_score(performance_data: list, interval: str = "1h") -> dict:
    """Summarize OHLC4 performance votes across configured lookback candles."""
    buy = sell = neutral = 0
    buy_weight = sell_weight = neutral_weight = 0.0
    available = missing = 0

    for item in performance_data:
        if not bool(item.get("included_in_vote")):
            missing += 1
            continue
        available += 1
        vote_weight = _get_performance_vote_weight(item.get("lookback"))
        act = str(item.get("action", "")).strip().lower()
        if act == "mua":
            buy += int(vote_weight)
            buy_weight += vote_weight
        elif act == "bán":
            sell += int(vote_weight)
            sell_weight += vote_weight
        else:
            neutral += int(vote_weight)
            neutral_weight += vote_weight

    return {
        "signal": _gauge_to_signal(_calc_vote_gauge(buy, sell, neutral), interval),
        "buy": buy,
        "sell": sell,
        "neutral": neutral,
        "buy_weight": round(buy_weight, 2),
        "sell_weight": round(sell_weight, 2),
        "neutral_weight": round(neutral_weight, 2),
        "available": available,
        "missing": missing,
        "lookbacks": list(PERFORMANCE_LOOKBACKS),
        "formula_version": TECHNICAL_SCORING_VERSION,
    }

def _calc_ai_forecast_score(
    blended: dict,
    forecast_rows: List[Dict[str, Any]],
    last_close: float,
    indicators: dict,
    horizon: int,
    interval: str,
    model_reference_price: Optional[float] = None,
) -> dict:
    """MODULE 3: Score one AI forecast from the full path and band discipline."""
    atr_pct = max(float(indicators.get("atr", {}).get("pct") or 0.0), 0.1)

    p50_path = np.array(blended.get("p50", []), dtype=float)
    p10_path = np.array(blended.get("p10", []), dtype=float)
    p90_path = np.array(blended.get("p90", []), dtype=float)

    if not len(p50_path):
        p50_path = np.array([last_close], dtype=float)
    if not len(p10_path):
        p10_path = np.array([last_close], dtype=float)
    if not len(p90_path):
        p90_path = np.array([last_close], dtype=float)

    series_reference_price = float(model_reference_price or last_close)
    path_metrics = _forecast_path_metrics(p50_path, series_reference_price)
    final_ret_pct = path_metrics["final_return_pct"]
    weighted_ret_pct = path_metrics["weighted_return_pct"]
    market_return_pct = _pct(float(p50_path[-1]), last_close) if len(p50_path) else 0.0
    path_consistency = _clamp(path_metrics["path_consistency"] / 100.0, 0.0, 1.0)
    monotonicity = _clamp(path_metrics["monotonicity"] / 100.0, 0.0, 1.0)
    adverse_excursion_pct = path_metrics["max_adverse_excursion_pct"]
    band_profile = _forecast_band_profile(p10_path, p50_path, p90_path)

    direction_vote_threshold = max(atr_pct * 0.04, 0.04)

    def _direction_vote(value_pct: float) -> int:
        if abs(value_pct) < direction_vote_threshold:
            return 0
        return 1 if value_pct > 0 else -1

    direction_votes = [
        _direction_vote(weighted_ret_pct),
        _direction_vote(final_ret_pct),
        _direction_vote(market_return_pct),
    ]
    bullish_votes = sum(1 for vote in direction_votes if vote > 0)
    bearish_votes = sum(1 for vote in direction_votes if vote < 0)
    direction_vote_balance = (
        (bullish_votes - bearish_votes) / max(1, len(direction_votes))
    )
    direction_bias_sign = 0.0
    if direction_vote_balance > 0:
        direction_bias_sign = 1.0
    elif direction_vote_balance < 0:
        direction_bias_sign = -1.0
    elif weighted_ret_pct > 0:
        direction_bias_sign = 1.0
    elif weighted_ret_pct < 0:
        direction_bias_sign = -1.0

    avg_band_tightness = math.exp(
        -float(band_profile["avg_band_pct"]) / max(atr_pct * 3.0, 0.75)
    )
    end_band_tightness = math.exp(
        -float(band_profile["end_band_pct"]) / max(atr_pct * 3.4, 0.85)
    )
    band_stability_score = math.exp(
        -(
            (float(band_profile["band_stability_pct"]) / max(atr_pct * 1.8, 0.35))
            + (float(band_profile["band_step_change_pct"]) / max(atr_pct * 1.2, 0.25))
        )
        * 0.75
    )
    adverse_control_score = math.exp(
        -adverse_excursion_pct / max(atr_pct * 2.5, 0.8)
    )
    certainty_score = (
        avg_band_tightness * 0.38 +
        end_band_tightness * 0.16 +
        band_stability_score * 0.16 +
        path_consistency * 0.15 +
        monotonicity * 0.10 +
        adverse_control_score * 0.05
    )
    certainty_score = _clamp(certainty_score, 0.08, 0.98)

    directional_edge_pct = (weighted_ret_pct * 0.72) + (final_ret_pct * 0.28)
    direction_norm = np.tanh(directional_edge_pct / max(atr_pct * 1.20, 0.28))
    weighted_move_in_atr = abs(weighted_ret_pct) / max(atr_pct, 0.1)
    final_move_in_atr = abs(final_ret_pct) / max(atr_pct, 0.1)
    path_move_score = np.tanh(((weighted_move_in_atr * 0.65) + (final_move_in_atr * 0.35)) / 1.7)
    path_quality = 0.48 + 0.30 * path_consistency + 0.22 * monotonicity
    certainty_factor = 0.72 + 0.28 * certainty_score
    direction_consensus = _clamp(
        (abs(direction_vote_balance) * 0.42) +
        (path_consistency * 0.36) +
        (monotonicity * 0.22),
        0.0,
        1.0,
    )
    direction_core_push = (
        direction_norm
        * (18.0 + 16.0 * path_move_score)
        * (0.60 + 0.40 * path_quality)
    )
    micro_consensus_push = 0.0
    if direction_bias_sign != 0.0 and abs(direction_norm) < 0.18 and abs(direction_vote_balance) >= 0.66:
        remaining_room = (0.18 - abs(direction_norm)) / 0.18
        micro_consensus_push = (
            direction_bias_sign
            * (2.4 + 5.6 * direction_consensus)
            * remaining_room
            * (0.44 + 0.56 * certainty_score)
        )

    direction_gauge = max(
        8.0,
        min(92.0, 50.0 + direction_core_push + micro_consensus_push),
    )
    directional_push = (direction_gauge - 50.0) * certainty_factor
    conviction_gauge = max(8.0, min(92.0, 50.0 + directional_push))
    gauge = direction_gauge

    confidence_pct = _clamp((certainty_score * 0.65 + path_quality * 0.35) * 100.0, 12.0, 98.0)
    direction_label = "bullish" if gauge >= 58 else "bearish" if gauge <= 42 else "neutral"

    return {
        "gauge": round(gauge, 1),
        "direction_gauge": round(direction_gauge, 1),
        "conviction_gauge": round(conviction_gauge, 1),
        "normalized_score": round(_gauge_to_normalized_score(gauge), 4),
        "confidence_pct": round(confidence_pct, 1),
        "forecast_return_pct": round(market_return_pct, 2),
        "model_reference_return_pct": round(final_ret_pct, 2),
        "weighted_return_pct": round(weighted_ret_pct, 2),
        "direction": direction_label,
        "direction_consensus": round(direction_consensus * 100.0, 1),
        "direction_strength_pct": round(abs(direction_gauge - 50.0) * 2.0, 1),
        "magnitude_vs_atr": round(weighted_move_in_atr, 2),
        "band_uncertainty_pct": round(float(band_profile["avg_band_pct"]), 2),
        "band_uncertainty_end_pct": round(float(band_profile["end_band_pct"]), 2),
        "band_stability_pct": round(float(band_profile["band_stability_pct"]), 2),
        "certainty": round(certainty_score * 100.0, 1),
        "path_consistency": round(path_consistency * 100.0, 1),
        "monotonicity": round(monotonicity * 100.0, 1),
        "max_adverse_excursion_pct": round(adverse_excursion_pct, 2),
        "path_metrics": path_metrics,
        "signal": _gauge_to_signal(gauge, interval),
        "formula": "full_path_direction_gauge",
        "formula_version": AI_SCORING_VERSION,
    }

def _calc_technical_score_v2(
    osc_score: dict,
    ma_score: dict,
    performance_score: dict,
    interval: str = "1h",
    adx_value: Optional[float] = None,
) -> dict:
    """Gauge PTKT follows the sigmoid ADX-weighted vote formula on total technical votes."""
    buy = osc_score["buy"] + ma_score["buy"] + performance_score["buy"]
    sell = osc_score["sell"] + ma_score["sell"] + performance_score["sell"]
    neutral = osc_score["neutral"] + ma_score["neutral"] + performance_score["neutral"]
    adx_numeric = float(adx_value) if adx_value is not None else 0.0
    if math.isnan(adx_numeric):
        adx_numeric = 0.0
    adx_numeric = max(0.0, adx_numeric)
    trend_activation = _calc_adx_trend_activation(adx_numeric)
    vote_participation = _calc_vote_participation_factor(buy, sell, neutral)
    final_gauge = _calc_adx_weighted_technical_gauge(buy, sell, neutral, adx_numeric)
    osc_dir = _vote_direction_from_counts(int(osc_score["buy"]), int(osc_score["sell"]))
    ma_dir = _vote_direction_from_counts(int(ma_score["buy"]), int(ma_score["sell"]))
    performance_dir = _vote_direction_from_counts(
        int(performance_score["buy"]),
        int(performance_score["sell"]),
    )
    active_group_directions = [
        direction
        for direction in (osc_dir, ma_dir, performance_dir)
        if direction != 0
    ]

    return {
        "gauge": round(final_gauge, 1),
        "normalized_score": round(_gauge_to_normalized_score(final_gauge), 4),
        "signal": _gauge_to_signal(final_gauge, interval),
        "buy": buy,
        "sell": sell,
        "neutral": neutral,
        "buy_weight": round(
            osc_score.get("buy_weight", 0.0)
            + ma_score.get("buy_weight", 0.0)
            + performance_score.get("buy_weight", 0.0),
            2,
        ),
        "sell_weight": round(
            osc_score.get("sell_weight", 0.0)
            + ma_score.get("sell_weight", 0.0)
            + performance_score.get("sell_weight", 0.0),
            2,
        ),
        "neutral_weight": round(
            osc_score.get("neutral_weight", 0.0)
            + ma_score.get("neutral_weight", 0.0)
            + performance_score.get("neutral_weight", 0.0),
            2,
        ),
        "adx_period": ADX_GAUGE_PERIOD,
        "trend_strength_adx": round(adx_numeric, 2),
        "trend_activation": round(trend_activation, 4),
        "trend_multiplier": round(trend_activation, 4),
        "vote_participation": round(vote_participation, 4),
        "alignment": len(active_group_directions) >= 2 and len(set(active_group_directions)) == 1,
        "performance_available": int(performance_score.get("available", 0)),
        "performance_missing": int(performance_score.get("missing", 0)),
        "components": {
            "formula": "max(0, min(100, 50 + 50 * ((buy - sell) / total_votes) * ((buy + sell) / total_votes) * (1 / (1 + exp(-(adx10 - 20) / 8)))))",
            "formula_version": TECHNICAL_SCORING_VERSION,
            "categories": {
                "oscillators": osc_score.get("signal", "Trung lập"),
                "moving_averages": ma_score.get("signal", "Trung lập"),
            },
        }
    }

def _calc_technical_score_v2(
    osc_score: dict,
    ma_score: dict,
    performance_score: dict,
    interval: str = "1h",
    adx_value: Optional[float] = None,
) -> dict:
    """Gauge PTKT follows the sigmoid ADX-weighted vote formula on total technical votes."""
    buy = osc_score["buy"] + ma_score["buy"] + performance_score["buy"]
    sell = osc_score["sell"] + ma_score["sell"] + performance_score["sell"]
    neutral = osc_score["neutral"] + ma_score["neutral"] + performance_score["neutral"]
    adx_numeric = float(adx_value) if adx_value is not None else 0.0
    if math.isnan(adx_numeric):
        adx_numeric = 0.0
    adx_numeric = max(0.0, adx_numeric)
    trend_activation = _calc_adx_trend_activation(adx_numeric)
    vote_participation = _calc_vote_participation_factor(buy, sell, neutral)
    final_gauge = _calc_adx_weighted_technical_gauge(buy, sell, neutral, adx_numeric)
    osc_dir = _vote_direction_from_counts(int(osc_score["buy"]), int(osc_score["sell"]))
    ma_dir = _vote_direction_from_counts(int(ma_score["buy"]), int(ma_score["sell"]))
    performance_dir = _vote_direction_from_counts(
        int(performance_score["buy"]),
        int(performance_score["sell"]),
    )
    active_group_directions = [
        direction
        for direction in (osc_dir, ma_dir, performance_dir)
        if direction != 0
    ]

    return {
        "gauge": round(final_gauge, 1),
        "normalized_score": round(_gauge_to_normalized_score(final_gauge), 4),
        "signal": _gauge_to_signal(final_gauge, interval),
        "buy": buy,
        "sell": sell,
        "neutral": neutral,
        "buy_weight": round(
            osc_score.get("buy_weight", 0.0)
            + ma_score.get("buy_weight", 0.0)
            + performance_score.get("buy_weight", 0.0),
            2,
        ),
        "sell_weight": round(
            osc_score.get("sell_weight", 0.0)
            + ma_score.get("sell_weight", 0.0)
            + performance_score.get("sell_weight", 0.0),
            2,
        ),
        "neutral_weight": round(
            osc_score.get("neutral_weight", 0.0)
            + ma_score.get("neutral_weight", 0.0)
            + performance_score.get("neutral_weight", 0.0),
            2,
        ),
        "adx_period": ADX_GAUGE_PERIOD,
        "trend_strength_adx": round(adx_numeric, 2),
        "trend_activation": round(trend_activation, 4),
        "trend_multiplier": round(trend_activation, 4),
        "vote_participation": round(vote_participation, 4),
        "alignment": len(active_group_directions) >= 2 and len(set(active_group_directions)) == 1,
        "performance_available": int(performance_score.get("available", 0)),
        "performance_missing": int(performance_score.get("missing", 0)),
        "components": {
            "formula": "max(0, min(100, 50 + 50 * ((buy - sell) / total_votes) * ((buy + sell) / total_votes) * (1 / (1 + exp(-(adx10 - 20) / 8)))))",
            "formula_version": TECHNICAL_SCORING_VERSION,
            "categories": {
                "oscillators": osc_score.get("signal", "Trung lập"),
                "moving_averages": ma_score.get("signal", "Trung lập"),
                "performance": performance_score.get("signal", "Trung lập"),
            },
        }
    }


def _calc_summary_score_v2(tech_score: dict, ai_score: dict, interval: str = "1h") -> dict:
    """Final decision score = arithmetic mean of technical and AI gauges."""
    tech_gauge = float(tech_score["gauge"])
    ai_gauge = float(ai_score["gauge"])
    tech_weight = 0.50
    ai_weight = 0.50
    final_gauge = max(5.0, min(95.0, (ai_gauge * ai_weight) + (tech_gauge * tech_weight)))
    tech_delta = tech_gauge - 50.0
    ai_delta = ai_gauge - 50.0
    tech_dir = 0 if abs(tech_delta) < 2.0 else (1 if tech_delta > 0 else -1)
    ai_dir = 0 if abs(ai_delta) < 2.0 else (1 if ai_delta > 0 else -1)
    dist = abs(final_gauge - 50.0)
    conviction = "R?t m?nh" if dist >= 25 else "M?nh" if dist >= 15 else "Trung b?nh" if dist >= 8 else "Y?u"

    bias = "neutral"
    if final_gauge >= 58:
        bias = "bullish"
    elif final_gauge <= 42:
        bias = "bearish"

    return {
        "gauge": round(final_gauge, 1),
        "normalized_score": round(_gauge_to_normalized_score(final_gauge), 4),
        "signal": _gauge_to_signal(final_gauge, interval),
        "conviction": conviction,
        "bias": bias,
        "agreement": tech_dir != 0 and tech_dir == ai_dir,
        "buy": tech_score["buy"],
        "sell": tech_score["sell"],
        "neutral": tech_score["neutral"],
        "buy_weight": tech_score.get("buy_weight", 0.0),
        "sell_weight": tech_score.get("sell_weight", 0.0),
        "neutral_weight": tech_score.get("neutral_weight", 0.0),
        "components": {
            "technical": round(tech_gauge, 1),
            "ai_forecast": round(ai_gauge, 1),
            "ai_weight": round(ai_weight, 2),
            "technical_weight": round(tech_weight, 2),
            "certainty_pct": round(float(ai_score.get("certainty", 0.0)), 1),
            "formula": "(technical_gauge + ai_gauge) / 2",
            "formula_version": AI_SCORING_VERSION,
        }
    }


def _build_dashboard_payload(
    last_close: float,
    forecast_rows: List[Dict[str, Any]],
    technical_score: Dict[str, Any],
    ai_score: Dict[str, Any],
    summary: Dict[str, Any],
) -> Dict[str, Any]:
    """Single source of truth for hero gauges consumed by the frontend."""
    forecast_end = last_close
    if forecast_rows:
        forecast_end = float(forecast_rows[-1].get("p50") or last_close)

    return {
        "technical": {
            "gauge": technical_score["gauge"],
            "normalized_score": technical_score["normalized_score"],
            "signal": technical_score["signal"],
            "buy": technical_score["buy"],
            "sell": technical_score["sell"],
            "neutral": technical_score["neutral"],
            "buy_weight": technical_score.get("buy_weight", 0.0),
            "sell_weight": technical_score.get("sell_weight", 0.0),
            "neutral_weight": technical_score.get("neutral_weight", 0.0),
        },
        "ai": {
            "gauge": ai_score["gauge"],
            "direction_gauge": ai_score.get("direction_gauge", ai_score["gauge"]),
            "conviction_gauge": ai_score.get("conviction_gauge", ai_score["gauge"]),
            "normalized_score": ai_score["normalized_score"],
            "signal": ai_score["signal"],
            "forecast_return_pct": ai_score["forecast_return_pct"],
            "weighted_return_pct": ai_score["weighted_return_pct"],
            "confidence_pct": ai_score["confidence_pct"],
            "certainty": ai_score["certainty"],
            "direction_consensus": ai_score.get("direction_consensus", 0.0),
            "direction_strength_pct": ai_score.get("direction_strength_pct", 0.0),
            "path_consistency": ai_score["path_consistency"],
            "monotonicity": ai_score.get("monotonicity", 50.0),
            "max_adverse_excursion_pct": ai_score.get("max_adverse_excursion_pct", 0.0),
            "current_price": round(float(last_close), 6),
            "forecast_price": round(float(forecast_end), 6),
        },
        "summary": {
            "gauge": summary["gauge"],
            "normalized_score": summary["normalized_score"],
            "signal": summary["signal"],
            "conviction": summary["conviction"],
            "bias": summary["bias"],
            "agreement": summary.get("agreement", False),
            "buy_weight": summary.get("buy_weight", 0.0),
            "sell_weight": summary.get("sell_weight", 0.0),
            "neutral_weight": summary.get("neutral_weight", 0.0),
            "components": summary.get("components", {}),
        },
    }


def _normalize_presentation_card_payload(
    card: Optional[Dict[str, Any]],
    *,
    prefer_direction_gauge: bool = False,
) -> Dict[str, Any]:
    source = dict(card or {})
    gauge_candidates: List[Any] = []
    if prefer_direction_gauge:
        gauge_candidates.append(source.get("direction_gauge"))
    gauge_candidates.append(source.get("gauge"))

    gauge_value = 50.0
    for candidate in gauge_candidates:
        try:
            numeric = float(candidate)
        except (TypeError, ValueError):
            continue
        if math.isfinite(numeric):
            gauge_value = numeric
            break

    source["gauge"] = round(float(gauge_value), 1)
    signal = source.get("signal")
    if not isinstance(signal, str) or not signal.strip():
        source["signal"] = "--"
    return source


def _build_ai_aggregation_message(
    requested_count: int,
    ready_count: int,
    failed_model_keys: List[str],
    complete: bool,
    *,
    ai_available: bool,
    summary_available: bool,
) -> str:
    if requested_count <= 0:
        if ai_available and summary_available:
            return "AI forecast san sang"
        return "Can chay du bao AI de tong hop"
    if failed_model_keys:
        failed_labels = ", ".join(str(model_key) for model_key in failed_model_keys)
        return f"Dang tong hop {ready_count}/{requested_count} model - loi: {failed_labels}"
    if not complete:
        return f"Dang tong hop {ready_count}/{requested_count} model AI"
    if requested_count == 1 and ai_available and summary_available:
        return "AI forecast san sang"
    return f"Trung binh cong {requested_count} model AI"


def _build_analysis_presentation(
    technical_score: Optional[Dict[str, Any]],
    ai_score: Optional[Dict[str, Any]],
    summary: Optional[Dict[str, Any]],
    *,
    requested_models: Optional[Dict[str, bool]] = None,
    active_model_keys: Optional[List[str]] = None,
    failed_model_keys: Optional[List[str]] = None,
    pending_model_keys: Optional[List[str]] = None,
    message: Optional[str] = None,
) -> Dict[str, Any]:
    technical_card = _normalize_presentation_card_payload(technical_score)
    ai_card = _normalize_presentation_card_payload(
        ai_score,
        prefer_direction_gauge=True,
    )
    summary_card = _normalize_presentation_card_payload(summary)

    requested_payload = (
        {model_key: bool(enabled) for model_key, enabled in requested_models.items()}
        if isinstance(requested_models, dict)
        else {}
    )
    enabled_keys = [
        model_key for model_key, enabled in requested_payload.items() if enabled
    ]
    ready_keys = list(active_model_keys or [])
    failed_keys = list(failed_model_keys or [])
    if pending_model_keys is None:
        pending_keys = [
            model_key
            for model_key in enabled_keys
            if model_key not in ready_keys and model_key not in failed_keys
        ]
    else:
        pending_keys = list(pending_model_keys)

    ai_available = isinstance(ai_score, dict) and bool(ai_score)
    summary_available = isinstance(summary, dict) and bool(summary)

    if enabled_keys:
        requested_count = len(enabled_keys)
    elif ai_available or summary_available:
        requested_count = max(1, len(ready_keys) + len(failed_keys) + len(pending_keys))
    else:
        requested_count = 0

    ready_count = len(ready_keys) if ready_keys else (1 if ai_available and summary_available else 0)
    finalized = not pending_keys
    complete = bool(
        ai_available
        and summary_available
        and not failed_keys
        and not pending_keys
        and (
            requested_count == 0
            or ready_count >= requested_count
        )
    )
    commit_ready = bool(finalized and ready_count > 0)
    ai_ready = commit_ready and ai_available
    summary_ready = commit_ready and summary_available
    combo_active = bool(
        ai_ready
        and summary_ready
        and (
            summary_card.get("agreement")
            or (
                abs(float(technical_card.get("gauge", 50.0)) - 50.0) > 6.0
                and abs(float(ai_card.get("gauge", 50.0)) - 50.0) > 6.0
                and math.copysign(1.0, float(technical_card.get("gauge", 50.0)) - 50.0)
                == math.copysign(1.0, float(ai_card.get("gauge", 50.0)) - 50.0)
            )
        )
    )
    message_text = message or _build_ai_aggregation_message(
        requested_count,
        ready_count,
        failed_keys,
        complete,
        ai_available=ai_available,
        summary_available=summary_available,
    )

    if ai_ready:
        ai_state = "ready"
    elif failed_keys and ready_count <= 0:
        ai_state = "error"
    elif requested_count > 0:
        ai_state = "loading"
    else:
        ai_state = "idle"

    if summary_ready:
        summary_state = "ready"
    elif failed_keys and ready_count <= 0:
        summary_state = "error"
    elif requested_count > 0:
        summary_state = "loading"
    else:
        summary_state = "idle"

    return {
        "version": ANALYSIS_PRESENTATION_VERSION,
        "technical": technical_card,
        "ai": ai_card,
        "summary": summary_card,
        "aggregation": {
            "requested": requested_payload,
            "enabled_keys": enabled_keys,
            "ready_keys": ready_keys,
            "failed_keys": failed_keys,
            "pending_keys": pending_keys,
            "complete": complete,
            "finalized": finalized,
            "commit_ready": commit_ready,
            "ready_count": ready_count,
            "requested_count": requested_count,
        },
        "card_states": {
            "technical": "ready" if technical_score else "idle",
            "ai": ai_state,
            "summary": summary_state,
        },
        "ai_ready": ai_ready,
        "summary_ready": summary_ready,
        "combo_active": combo_active,
        "message": message_text,
    }

def _build_trade_analysis(
    symbol: str, interval: str, data: List[Dict[str, Any]], indicators: Dict[str, Any],
    forecast_rows: List[Dict[str, Any]], confidence: float, source: str,
    blended: Optional[Dict[str, Any]] = None,
    forecast_reference_price: Optional[float] = None,
    include_ai: bool = True,
    active_model_keys: Optional[List[str]] = None,
    failed_model_keys: Optional[List[str]] = None,
    requested_models: Optional[Dict[str, bool]] = None,
) -> Dict[str, Any]:
    """
    TradingView-style technical analysis dashboard (v6.1 Rework).
    PTKT is one vote-only gauge across oscillator + performance + moving-average rows,
    while AI keeps full-path scoring.
    """
    if not data or len(data) < 30:
        return {
            "oscillators": {
                "signal": "Trung lập",
                "buy": 0,
                "sell": 0,
                "neutral": 0,
                "buy_weight": 0.0,
                "sell_weight": 0.0,
                "neutral_weight": 0.0,
                "data": [],
            },
            "performance": {
                "signal": "Trung lập",
                "buy": 0,
                "sell": 0,
                "neutral": 0,
                "buy_weight": 0.0,
                "sell_weight": 0.0,
                "neutral_weight": 0.0,
                "available": 0,
                "missing": len(PERFORMANCE_LOOKBACKS),
                "lookbacks": list(PERFORMANCE_LOOKBACKS),
                "data": [],
            },
            "moving_averages": {
                "signal": "Trung lập",
                "buy": 0,
                "sell": 0,
                "neutral": 0,
                "buy_weight": 0.0,
                "sell_weight": 0.0,
                "neutral_weight": 0.0,
                "golden_cross": False,
                "death_cross": False,
                "data": [],
            },
            "summary": {"signal": "Trung lập", "buy": 0, "sell": 0, "neutral": 0},
        }

    closes = np.array([float(d["close"]) for d in data], dtype=float)
    highs  = np.array([float(d["high"])  for d in data], dtype=float)
    lows   = np.array([float(d["low"])   for d in data], dtype=float)
    last_close = closes[-1]
    model_reference_price = float(forecast_reference_price or last_close)

    def _lv(arr):
        return _latest_finite_value(arr)

    def _fmt(value: Optional[float]) -> str:
        return _format_analysis_value(value)

    ema50_arr = _ema(closes, 50)
    ema50_current = _lv(ema50_arr)
    ema200_current = _lv(indicators["ema"].get("ema200"))
    ema50_prev = (
        float(ema50_arr[-6])
        if len(ema50_arr) >= 6 and not math.isnan(float(ema50_arr[-6]))
        else (ema50_current or last_close)
    )

    regime, regime_label = _classify_regime(indicators, last_close, _safe(indicators.get("atr", {}).get("pct"), 1.0))

    # ── 1. Oscillators ──
    osc_data = []
    atr_scale = max(float(indicators.get("atr", {}).get("value") or last_close * 0.01), max(last_close * 0.0015, 1e-6))
    def _add_osc(label, val, action_name, key=None, **kw):
        if isinstance(val, (np.ndarray, pd.Series, list)): v = _lv(val)
        else: v = float(val) if val is not None else None
        osc_key = key or action_name
        osc_kwargs = {
            "last_close": last_close,
            "ema50": ema50_current or last_close,
            "ema200": ema200_current or last_close,
            "ema50_prev": ema50_prev,
            "atr": atr_scale,
            **kw,
        }
        act = _osc_action(action_name, v if v is not None else 0, **osc_kwargs)
        osc_data.append({
            "key": osc_key,
            "name": label,
            "value": v,
            "display_value": _fmt(v),
            "action": act,
        })

    stoch_price_k, stoch_price_d = _stoch_kd(highs, lows, closes, 14, 3, 3)
    stoch_rsi_k, stoch_rsi_d = _stoch_rsi(closes, 14, 14, 3, 3)
    demarker14 = _demarker(highs, lows, 14)
    _add_osc("Chỉ số Sức mạnh tương đối (14)", _rsi(closes, 14), "rsi", key="rsi")
    _add_osc("Stochastic %K (14, 3, 3)", stoch_price_k, "stoch", key="stoch")
    _add_osc("Chỉ số Kênh hàng hóa (20)", _cci(highs, lows, closes, 20), "cci", key="cci")
    adx_vals = _adx(highs, lows, closes, ADX_GAUGE_PERIOD)
    _add_osc(
        f"Chỉ số Định hướng Trung bình ({ADX_GAUGE_PERIOD})",
        adx_vals[0],
        "adx",
        key="adx",
        plus_di=_lv(adx_vals[1]) or 0,
        minus_di=_lv(adx_vals[2]) or 0,
    )
    ao_vals = _awesome_oscillator(highs, lows)
    _add_osc("Chỉ số Dao động AO", ao_vals, "ao", key="ao", epsilon=atr_scale * 0.02)
    _add_osc("Xung lượng (10)", _momentum(closes, 10), "momentum", key="momentum", scale=atr_scale * 1.15)
    macd_vals = _macd(closes, 12, 26, 9)
    _add_osc("Cấp độ MACD (12, 26)", macd_vals[0], "macd", key="macd", epsilon=atr_scale * 0.02)
    _add_osc("Đường RSI Nhanh (3, 3, 14, 14)", stoch_rsi_k, "stoch_rsi", key="stoch_rsi")
    _add_osc("Vùng Phần trăm Williams (14)", _williams_r(highs, lows, closes, 14), "williams", key="williams")
    bull_power_vals, bear_power_vals = _bull_bear_power(highs, lows, closes, 13)
    bull_power = _lv(bull_power_vals)
    bear_power = _lv(bear_power_vals)
    osc_data.append(
        {
            "key": "bbp",
            "name": "Sức Mạnh Giá Lên và Giá Xuống",
            "value": {"bull": bull_power, "bear": bear_power},
            "display_value": f"{_fmt(bull_power)} / {_fmt(bear_power)}",
            "action": _osc_action(
                "bbp",
                bull_power if bull_power is not None else 0.0,
                atr=atr_scale,
                bull_power=bull_power,
                bear_power=bear_power,
                epsilon=atr_scale * 0.02,
            ),
        }
    )
    _add_osc("Dao động Ultimate (7, 14, 28)", _ultimate_oscillator(highs, lows, closes, 7, 14, 28), "ultimate", key="ultimate")
    _add_osc("Tốc độ biến động ROC (12)", _roc(closes, 12), "roc", key="roc")
    _add_osc("TRIX (18)", _trix(closes, 18), "trix", key="trix")
    _add_osc("PPO (12, 26)", _ppo(closes, 12, 26), "ppo", key="ppo")
    _add_osc("CMO (14)", _cmo(closes, 14), "cmo", key="cmo")
    _add_osc("DPO (20)", _dpo(closes, 20), "dpo", key="dpo", scale=atr_scale * 0.75)
    _add_osc("Aroon Oscillator (25)", _aroon_oscillator(highs, lows, 25), "aroon", key="aroon")
    _add_osc("TSI (25, 13)", _tsi(closes, 25, 13), "tsi", key="tsi")
    _add_osc("DeMarker (14)", demarker14, "demarker", key="demarker")

    osc_score = _calc_osc_score(osc_data, interval)

    # ── 2. Moving Averages ──
    ma_data = []
    ema_periods = sorted({period for pair in EMA_PAIR_SEQUENCE for period in pair})
    ema_map: Dict[int, np.ndarray] = {
        period: closes.copy() if period == 1 else _ema(closes, period)
        for period in ema_periods
    }

    def _add_ema_pair_row(fast_period: int, slow_period: int) -> None:
        fast_arr = ema_map[fast_period]
        slow_arr = ema_map[slow_period]
        fast_value = _lv(fast_arr)
        slow_value = _lv(slow_arr)
        pair_key = f"ema_{fast_period}_{slow_period}"
        act = _ema_pair_action(fast_value, slow_value, pair_key)
        ma_data.append(
            {
                "key": pair_key,
                "name": f"EMA{fast_period} / EMA{slow_period}",
                "display_value": f"{_fmt(fast_value)} / {_fmt(slow_value)}",
                "value": f"{fast_value if fast_value is not None else '—'} / {slow_value if slow_value is not None else '—'}",
                "action": act,
                "fast": fast_value,
                "slow": slow_value,
            }
        )

    for fast_period, slow_period in EMA_PAIR_SEQUENCE:
        _add_ema_pair_row(fast_period, slow_period)

    ichimoku_base = _latest_finite_value(_ichimoku_base(highs, lows, 26))
    ma_data.append(
        {
            "key": "ichimoku_base_26",
            "name": "Giá / Ichimoku Base (26)",
            "display_value": f"{_fmt(last_close)} / {_fmt(ichimoku_base)}",
            "value": f"{last_close if last_close is not None else '—'} / {ichimoku_base if ichimoku_base is not None else '—'}",
            "action": _price_vs_level_action(last_close, ichimoku_base, neutral_band_pct=0.08),
            "fast": last_close,
            "slow": ichimoku_base,
        }
    )

    ma_score = _calc_ma_score(ma_data, closes, interval)
    performance_data = _build_performance_rows(data)
    performance_score = _calc_performance_score(performance_data, interval)
    technical_score = _calc_technical_score_v2(
        osc_score,
        ma_score,
        performance_score,
        interval,
        adx_value=_lv(adx_vals[0]),
    )

    support_resistance = _calc_price_action_levels(data, last_close, interval)

    if not include_ai:
        presentation = _build_analysis_presentation(
            technical_score,
            None,
            None,
            message="Can chay du bao AI de tong hop",
        )
        return {
            "style": "tradingview",
            "regime": {"key": regime, "label": regime_label},
            "dashboard": {
                "technical": {
                    "gauge": technical_score["gauge"],
                    "normalized_score": technical_score["normalized_score"],
                    "signal": technical_score["signal"],
                    "buy": technical_score["buy"],
                    "sell": technical_score["sell"],
                    "neutral": technical_score["neutral"],
                    "buy_weight": technical_score.get("buy_weight", 0.0),
                    "sell_weight": technical_score.get("sell_weight", 0.0),
                    "neutral_weight": technical_score.get("neutral_weight", 0.0),
                },
                "ai": None,
                "summary": None,
            },
            "summary": None,
            "presentation": presentation,
            "technicals": technical_score,
            "oscillators": {
                "signal": osc_score["signal"],
                "buy": osc_score["buy"],
                "sell": osc_score["sell"],
                "neutral": osc_score["neutral"],
                "buy_weight": osc_score.get("buy_weight", 0.0),
                "sell_weight": osc_score.get("sell_weight", 0.0),
                "neutral_weight": osc_score.get("neutral_weight", 0.0),
                "data": osc_data
            },
            "performance": {
                "signal": performance_score["signal"],
                "buy": performance_score["buy"],
                "sell": performance_score["sell"],
                "neutral": performance_score["neutral"],
                "buy_weight": performance_score.get("buy_weight", 0.0),
                "sell_weight": performance_score.get("sell_weight", 0.0),
                "neutral_weight": performance_score.get("neutral_weight", 0.0),
                "available": performance_score.get("available", 0),
                "missing": performance_score.get("missing", 0),
                "lookbacks": performance_score.get("lookbacks", list(PERFORMANCE_LOOKBACKS)),
                "data": performance_data,
            },
            "moving_averages": {
                "signal": ma_score["signal"],
                "buy": ma_score["buy"],
                "sell": ma_score["sell"],
                "neutral": ma_score["neutral"],
                "buy_weight": ma_score.get("buy_weight", 0.0),
                "sell_weight": ma_score.get("sell_weight", 0.0),
                "neutral_weight": ma_score.get("neutral_weight", 0.0),
                "golden_cross": ma_score["golden_cross"],
                "death_cross": ma_score["death_cross"],
                "data": ma_data
            },
            "ai_gauge": None,
            "support_resistance": support_resistance,
            "pivot_points": support_resistance,
        }

    # ── 3. AI Forecast Gauge ──
    if not blended:
        # Fallback to simple blended if no forecast available
        blended = {
            "p50": [last_close * (1 + (confidence-50)/1000)],
            "p10": [last_close * 0.98], "p90": [last_close * 1.02],
            "agreement": True, "scale": 1.0
        }
    
    ai_score = _calc_ai_forecast_score(
        blended,
        forecast_rows,
        last_close,
        indicators,
        len(forecast_rows) or 10,
        interval,
        model_reference_price=model_reference_price,
    )

    # ── 4. Summary ──
    summary = _calc_summary_score_v2(technical_score, ai_score, interval)
    dashboard = _build_dashboard_payload(last_close, forecast_rows, technical_score, ai_score, summary)
    presentation = _build_analysis_presentation(
        technical_score,
        ai_score,
        summary,
        active_model_keys=active_model_keys or ["combined"],
        failed_model_keys=failed_model_keys,
        requested_models=requested_models,
    )

    return {
        "style": "tradingview",
        "regime": {"key": regime, "label": regime_label},
        "dashboard": dashboard,
        "summary": summary,
        "presentation": presentation,
        "technicals": technical_score,
        "oscillators": {
            "signal": osc_score["signal"],
            "buy": osc_score["buy"],
            "sell": osc_score["sell"],
            "neutral": osc_score["neutral"],
            "buy_weight": osc_score.get("buy_weight", 0.0),
            "sell_weight": osc_score.get("sell_weight", 0.0),
            "neutral_weight": osc_score.get("neutral_weight", 0.0),
            "data": osc_data
        },
        "performance": {
            "signal": performance_score["signal"],
            "buy": performance_score["buy"],
            "sell": performance_score["sell"],
            "neutral": performance_score["neutral"],
            "buy_weight": performance_score.get("buy_weight", 0.0),
            "sell_weight": performance_score.get("sell_weight", 0.0),
            "neutral_weight": performance_score.get("neutral_weight", 0.0),
            "available": performance_score.get("available", 0),
            "missing": performance_score.get("missing", 0),
            "lookbacks": performance_score.get("lookbacks", list(PERFORMANCE_LOOKBACKS)),
            "data": performance_data,
        },
        "moving_averages": {
            "signal": ma_score["signal"],
            "buy": ma_score["buy"],
            "sell": ma_score["sell"],
            "neutral": ma_score["neutral"],
            "buy_weight": ma_score.get("buy_weight", 0.0),
            "sell_weight": ma_score.get("sell_weight", 0.0),
            "neutral_weight": ma_score.get("neutral_weight", 0.0),
            "golden_cross": ma_score["golden_cross"],
            "death_cross": ma_score["death_cross"],
            "data": ma_data
        },
        "ai_gauge": ai_score,
        "support_resistance": support_resistance,
        "pivot_points": support_resistance,
    }