from datetime import date, datetime

import json

from sqlalchemy import Date, DateTime, Float, ForeignKey, Index, String, Text
from sqlalchemy.orm import Mapped, mapped_column, relationship

from app.database import Base


class TradeSetup(Base):
    __tablename__ = "trade_setups"
    __table_args__ = (Index("ix_trade_setups_ticker_rr", "ticker_id", "rr_ratio"),)

    id: Mapped[int] = mapped_column(primary_key=True)
    ticker_id: Mapped[int] = mapped_column(
        ForeignKey("tickers.id", ondelete="CASCADE"), nullable=False
    )
    direction: Mapped[str] = mapped_column(String(10), nullable=False)
    entry_price: Mapped[float] = mapped_column(Float, nullable=False)
    stop_loss: Mapped[float] = mapped_column(Float, nullable=False)
    target: Mapped[float] = mapped_column(Float, nullable=False)
    rr_ratio: Mapped[float] = mapped_column(Float, nullable=False)
    composite_score: Mapped[float] = mapped_column(Float, nullable=False)
    detected_at: Mapped[datetime] = mapped_column(
        DateTime(timezone=True), nullable=False
    )

    confidence_score: Mapped[float | None] = mapped_column(Float, nullable=True)
    # Ticker's activation momentum percentile across the universe at detection
    # time. Since July 2026 this is residual 12-1 momentum when benchmark data is
    # available, with raw 12-1 as a fallback.
    momentum_percentile: Mapped[float | None] = mapped_column(Float, nullable=True)
    # Production ordering score. July 2026 promotion: residual momentum remains
    # the gate, while this rank blends residual momentum with realized volatility.
    strategy_rank: Mapped[float | None] = mapped_column(Float, nullable=True)
    volatility_percentile: Mapped[float | None] = mapped_column(Float, nullable=True)
    targets_json: Mapped[str | None] = mapped_column(Text, nullable=True)
    conflict_flags_json: Mapped[str | None] = mapped_column(Text, nullable=True)
    recommended_action: Mapped[str | None] = mapped_column(String(20), nullable=True)
    reasoning: Mapped[str | None] = mapped_column(Text, nullable=True)
    risk_level: Mapped[str | None] = mapped_column(String(10), nullable=True)
    actual_outcome: Mapped[str | None] = mapped_column(String(20), nullable=True)
    evaluated_at: Mapped[datetime | None] = mapped_column(
        DateTime(timezone=True), nullable=True
    )
    outcome_date: Mapped[date | None] = mapped_column(Date, nullable=True)

    ticker = relationship("Ticker", back_populates="trade_setups")

    @property
    def targets(self) -> list[dict]:
        if not self.targets_json:
            return []
        try:
            parsed = json.loads(self.targets_json)
        except (TypeError, ValueError):
            return []
        return parsed if isinstance(parsed, list) else []

    @property
    def conflict_flags(self) -> list[str]:
        if not self.conflict_flags_json:
            return []
        try:
            parsed = json.loads(self.conflict_flags_json)
        except (TypeError, ValueError):
            return []
        if not isinstance(parsed, list):
            return []
        return [str(item) for item in parsed]
