"""Cycle classification rules (monthly / weekly)."""

from __future__ import annotations

from typing import Any

from crypto_wyckoff.domain_models import WyckoffCycle
from crypto_wyckoff.rules.base import RuleHit, WyckoffRule


def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
    v = ctx.get("features", {}).get(key, default)
    try:
        return float(v) if v is not None else default
    except (TypeError, ValueError):
        return default


class MarkupCycleRule(WyckoffRule):
    rule_id = "cycle_markup"
    category = "cycle"
    timeframes = ("1M", "1w")

    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
        close = _f(context, "close")
        ma20 = _f(context, "ma20")
        ma60 = _f(context, "ma60")
        ma120 = _f(context, "ma120")
        adx = _f(context, "adx")
        slope = _f(context, "ma60_slope")
        if close > ma20 > ma60 and (ma60 >= ma120 or slope > 0) and adx >= 18:
            conf = min(95.0, 55 + adx + (10 if close > ma120 else 0))
            return RuleHit(
                rule_id=self.rule_id,
                cycle=WyckoffCycle.MARKUP.value,
                confidence=conf,
                score=conf,
                reasons=["价格位于均线多头排列", f"ADX={adx:.1f}"],
                metrics={"adx": adx, "slope": slope},
            )
        return None


class MarkdownCycleRule(WyckoffRule):
    rule_id = "cycle_markdown"
    category = "cycle"
    timeframes = ("1M", "1w")

    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
        close = _f(context, "close")
        ma20 = _f(context, "ma20")
        ma60 = _f(context, "ma60")
        ma120 = _f(context, "ma120")
        adx = _f(context, "adx")
        slope = _f(context, "ma60_slope")
        if close < ma20 < ma60 and (ma60 <= ma120 or slope < 0) and adx >= 18:
            conf = min(95.0, 55 + adx + (10 if close < ma120 else 0))
            return RuleHit(
                rule_id=self.rule_id,
                cycle=WyckoffCycle.MARKDOWN.value,
                confidence=conf,
                score=conf,
                reasons=["价格位于均线空头排列", f"ADX={adx:.1f}"],
                metrics={"adx": adx},
            )
        return None


class AccumulationCycleRule(WyckoffRule):
    rule_id = "cycle_accumulation"
    category = "cycle"
    timeframes = ("1M", "1w")

    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
        adx = _f(context, "adx")
        range_pct = _f(context, "range_pct_60")
        close = _f(context, "close")
        ma120 = _f(context, "ma120")
        vol_trend = _f(context, "volume_trend")
        # Range-bound after decline: strictly at/below MA120 (mutually exclusive vs Distribution)
        if adx < 22 and range_pct < 0.28 and close <= ma120:
            conf = 60 + (10 if vol_trend > 0 else 0) + (10 if close < ma120 else 0)
            return RuleHit(
                rule_id=self.rule_id,
                cycle=WyckoffCycle.ACCUMULATION.value,
                confidence=min(90.0, conf),
                score=min(90.0, conf),
                reasons=["低趋势强度区间震荡", "疑似吸筹区间"],
                metrics={"adx": adx, "range_pct_60": range_pct},
            )
        return None


class DistributionCycleRule(WyckoffRule):
    rule_id = "cycle_distribution"
    category = "cycle"
    timeframes = ("1M", "1w")

    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
        adx = _f(context, "adx")
        range_pct = _f(context, "range_pct_60")
        close = _f(context, "close")
        ma120 = _f(context, "ma120")
        vol_trend = _f(context, "volume_trend")
        # Range-bound near highs: strictly above MA120 (mutually exclusive vs Accumulation)
        if adx < 22 and range_pct < 0.28 and close > ma120:
            conf = 60 + (10 if vol_trend < 0 else 0) + (10 if close > ma120 else 0)
            return RuleHit(
                rule_id=self.rule_id,
                cycle=WyckoffCycle.DISTRIBUTION.value,
                confidence=min(90.0, conf),
                score=min(90.0, conf),
                reasons=["高位低趋势震荡", "疑似派发区间"],
                metrics={"adx": adx, "range_pct_60": range_pct},
            )
        return None


def build_rules() -> list[WyckoffRule]:
    # Order: trend cycles first (more decisive), then range cycles
    return [
        MarkupCycleRule(),
        MarkdownCycleRule(),
        AccumulationCycleRule(),
        DistributionCycleRule(),
    ]