"""Review-aware factor scoring.

This module is the bridge between daily review results and the next screening
run. Strategy code should score named factors here instead of hard-coding every
``score += N`` forever.
"""

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Any

from app.config.config_loader import get_signal_weights
from app.core.signal_taxonomy import signal_label_for_code


DEFAULT_FACTOR_WEIGHTS = {
    "vp_fly_1h_current": 5.0,
    "volume_consecutive_1h": 4.0,
    "cex_top_gainer_24h": 2.0,
    "volume_divergence_1h": 1.0,
    "static_accum_4h": 5.0,
    "higher_lows_4h": 2.0,
    "compression_surge_4h": 2.0,
    "ignition_1h_current": 4.0,
    "ignition_4h_current": 3.0,
    "ignition_d1_current": 6.0,
    "dynamic_k_1h_bull": 3.0,
    "dynamic_k_d1_bull": 5.0,
    "breakout_pullback_d1": 8.0,
    "breakout_pullback_w1": 8.0,
    "breakout_15m_current": 3.0,
    "pullback_15m_confirm": 2.0,
    "strong_resonance_bypass": 3.0,
    "entry_quality_gate": 2.0,
    "top_trader_long": 1.0,
    "sector_rotation": 2.0,
    "sentiment_resonance": 2.0,
    "dex_volume_spike": 2.0,
    "liquidity_add": 1.5,
    "exchange_outflow": 2.0,
    "whale_accumulation": 2.5,
    "smart_money_buying": 2.5,
    "liquidity_remove_risk": 2.5,
    "exchange_inflow_risk": 2.5,
    "holder_concentration_risk": 2.0,
    "funding_extreme": 2.0,
    "trend_exhaustion": 3.0,
    "false_breakout": 5.0,
    "high_position_reject": 5.0,
    "risk_reward_bad": 2.0,
}

WEIGHT_ALIASES = {
    "vp_fly_1h_current": ("量价齐飞", "1H当前量价齐飞"),
    "volume_consecutive_1h": ("连续3x放量", "连续3x放量(≥3根)", "1H连续放量"),
    "volume_divergence_1h": ("量价背离", "1H量价背离"),
    "static_accum_4h": ("静K蓄力", "4H静K蓄力"),
    "ignition_1h_current": ("静K动K转折", "静K→动K转折", "1H当前起爆点"),
    "ignition_4h_current": ("静K动K转折", "静K→动K转折", "4H当前起爆点"),
    "ignition_d1_current": ("静K动K转折", "静K→动K转折", "日线当前起爆点"),
    "breakout_15m_current": ("15min当前突破",),
    "pullback_15m_confirm": ("15min回踩确认",),
    "top_trader_long": ("大户偏多",),
    "sector_rotation": ("板块联动",),
    "sentiment_resonance": ("舆情共振",),
    "dex_volume_spike": ("DEX 放量", "链上成交放量"),
    "liquidity_add": ("流动性增加",),
    "exchange_outflow": ("交易所流出",),
    "whale_accumulation": ("鲸鱼增持",),
    "smart_money_buying": ("聪明钱买入",),
    "liquidity_remove_risk": ("流动性撤出风险",),
    "exchange_inflow_risk": ("交易所流入风险",),
    "holder_concentration_risk": ("持仓集中风险",),
    "funding_extreme": ("资金费率极端",),
    "trend_exhaustion": ("趋势衰减",),
    "false_breakout": ("假突破",),
    "high_position_reject": ("高位拒绝",),
    "risk_reward_bad": ("盈亏比不合格",),
}


def _safe_float(value: Any, default: float = 0.0) -> float:
    try:
        if value is None or value == "":
            return default
        return float(value)
    except Exception:
        return default


def _clamp(value: float, low: float, high: float) -> float:
    return max(low, min(high, value))


@dataclass
class FactorScorer:
    """Apply review-adjusted factor weights and keep an audit trail."""

    weights: dict[str, Any] = field(default_factory=dict)
    breakdown: list[dict[str, Any]] = field(default_factory=list)

    @classmethod
    def from_runtime(cls) -> "FactorScorer":
        try:
            weights = get_signal_weights() or {}
        except Exception:
            weights = {}
        return cls(weights=weights)

    def _runtime_weight(self, code: str) -> float | None:
        keys = [code, signal_label_for_code(code), *WEIGHT_ALIASES.get(code, ())]
        for key in keys:
            if key in self.weights:
                value = self.weights[key]
                if isinstance(value, dict):
                    value = value.get("weight")
                return max(0.0, _safe_float(value))
        return None

    def delta(self, code: str, base: float, *, evidence: str = "", value: Any = None) -> float:
        """Return the review-aware score delta for one factor.

        ``base`` remains the strategy designer's default score. Once reviews
        have enough samples, ``signal_performance.weight`` becomes a multiplier
        against the factor's canonical baseline. A reviewed weight of 0 means
        the factor is effectively淘汰 for scoring.
        """
        base = _safe_float(base)
        runtime_weight = self._runtime_weight(code)
        baseline = max(0.1, _safe_float(DEFAULT_FACTOR_WEIGHTS.get(code), abs(base) or 1.0))
        if runtime_weight is None:
            adjusted = base
            multiplier = 1.0
            source = "base"
        else:
            multiplier = _clamp(runtime_weight / baseline, 0.0, 1.8)
            adjusted = base * multiplier
            source = "review_weight"
        adjusted = round(adjusted, 3)
        self.breakdown.append(
            {
                "factor_code": code,
                "factor_name": signal_label_for_code(code),
                "base_delta": base,
                "score_delta": adjusted,
                "runtime_weight": runtime_weight,
                "baseline_weight": baseline,
                "multiplier": round(multiplier, 4),
                "source": source,
                "evidence": evidence,
                "value": value,
            }
        )
        return adjusted

    def add_existing(self, code: str, observed_score: float, *, evidence: str = "", value: Any = None, cap: float | None = None) -> float:
        base = _safe_float(observed_score)
        if cap is not None:
            base = min(base, _safe_float(cap, base))
        return self.delta(code, base, evidence=evidence, value=value)

    def summary(self) -> dict[str, Any]:
        total = round(sum(_safe_float(item.get("score_delta")) for item in self.breakdown), 3)
        return {"total_delta": total, "items": self.breakdown}