"""
Quant strategy related Pydantic schemas.
"""
from datetime import date
from decimal import Decimal
from typing import Optional, List, Dict

from pydantic import BaseModel, Field


class FactorWeights(BaseModel):
    """Factor weights for multi-factor strategy."""
    value: Decimal = Field(default=Decimal("0.25"), ge=0, le=1)
    quality: Decimal = Field(default=Decimal("0.25"), ge=0, le=1)
    momentum: Decimal = Field(default=Decimal("0.25"), ge=0, le=1)
    low_vol: Decimal = Field(default=Decimal("0.25"), ge=0, le=1)


class UniverseFilter(BaseModel):
    """Stock universe filtering criteria."""
    markets: List[str] = ["KOSPI", "KOSDAQ"]
    min_market_cap: Optional[int] = None  # in 억원
    max_market_cap: Optional[int] = None
    exclude_stock_types: List[str] = ["spac", "preferred", "reit"]
    exclude_sectors: List[str] = []


class StrategyRequest(BaseModel):
    """Base request for running a strategy."""
    universe: UniverseFilter = UniverseFilter()
    top_n: int = Field(default=30, ge=1, le=100)
    base_date: Optional[date] = None


class MultiFactorRequest(StrategyRequest):
    """Multi-factor strategy request."""
    weights: FactorWeights = FactorWeights()


class QualityRequest(StrategyRequest):
    """Super Quality strategy request."""
    min_fscore: int = Field(default=7, ge=0, le=9)


class ValueMomentumRequest(StrategyRequest):
    """Value-Momentum strategy request."""
    value_weight: Decimal = Field(default=Decimal("0.5"), ge=0, le=1)
    momentum_weight: Decimal = Field(default=Decimal("0.5"), ge=0, le=1)


class StockFactor(BaseModel):
    """Factor scores for a single stock."""
    ticker: str
    name: str
    market: str
    sector_name: Optional[str] = None
    market_cap: Optional[int] = None
    close_price: Optional[Decimal] = None

    # Raw metrics
    per: Optional[Decimal] = None
    pbr: Optional[Decimal] = None
    psr: Optional[Decimal] = None
    pcr: Optional[Decimal] = None
    dividend_yield: Optional[Decimal] = None
    roe: Optional[Decimal] = None

    # Factor scores (z-scores)
    value_score: Optional[Decimal] = None
    quality_score: Optional[Decimal] = None
    momentum_score: Optional[Decimal] = None

    # Composite
    total_score: Optional[Decimal] = None
    rank: Optional[int] = None
    fscore: Optional[int] = None


class StrategyResult(BaseModel):
    """Result from running a strategy."""
    strategy_name: str
    base_date: date
    universe_count: int
    result_count: int
    stocks: List[StockFactor]


class StockInfo(BaseModel):
    """Detailed stock information."""
    ticker: str
    name: str
    market: str
    sector_name: Optional[str] = None
    stock_type: str
    close_price: Optional[Decimal] = None
    market_cap: Optional[int] = None

    # Valuation
    per: Optional[Decimal] = None
    pbr: Optional[Decimal] = None
    psr: Optional[Decimal] = None
    pcr: Optional[Decimal] = None
    dividend_yield: Optional[Decimal] = None

    # Per-share data
    eps: Optional[Decimal] = None
    bps: Optional[Decimal] = None

    base_date: Optional[date] = None

    class Config:
        from_attributes = True


class StockSearchResult(BaseModel):
    """Stock search result."""
    ticker: str
    name: str
    market: str


class PriceData(BaseModel):
    """Price data point."""
    date: date
    open: Decimal
    high: Decimal
    low: Decimal
    close: Decimal
    volume: int

    class Config:
        from_attributes = True