"""
Strategy optimizer schemas.
"""
from datetime import date
from decimal import Decimal
from typing import Annotated, Any, Dict, List, Optional

from pydantic import BaseModel, Field, PlainSerializer

FloatDecimal = Annotated[
    Decimal,
    PlainSerializer(lambda v: float(v), return_type=float, when_used="json"),
]

# --- Default parameter grids per strategy type ---

KJB_DEFAULT_GRID: Dict[str, List[Any]] = {
    "stop_loss_pct": [0.03, 0.05, 0.07],
    "target1_pct": [0.05, 0.07, 0.10],
    "rs_lookback": [10, 20, 30],
}

MULTI_FACTOR_DEFAULT_GRID: Dict[str, List[Any]] = {
    "weights.value": [0.15, 0.25, 0.35],
    "weights.quality": [0.15, 0.25, 0.35],
    "weights.momentum": [0.15, 0.25, 0.35],
}

QUALITY_DEFAULT_GRID: Dict[str, List[Any]] = {
    "min_fscore": [5, 6, 7, 8],
}

VALUE_MOMENTUM_DEFAULT_GRID: Dict[str, List[Any]] = {
    "value_weight": [0.3, 0.4, 0.5, 0.6, 0.7],
    "momentum_weight": [0.3, 0.4, 0.5, 0.6, 0.7],
}

DEFAULT_GRIDS: Dict[str, Dict[str, List[Any]]] = {
    "kjb": KJB_DEFAULT_GRID,
    "multi_factor": MULTI_FACTOR_DEFAULT_GRID,
    "quality": QUALITY_DEFAULT_GRID,
    "value_momentum": VALUE_MOMENTUM_DEFAULT_GRID,
}

STRATEGY_TYPES = ["kjb", "multi_factor", "quality", "value_momentum"]


class OptimizeRequest(BaseModel):
    strategy_type: str = Field(
        ...,
        description="Strategy type: kjb, multi_factor, quality, value_momentum",
    )
    param_grid: Optional[Dict[str, List[Any]]] = Field(
        default=None,
        description="Parameter grid. If None, uses default preset for the strategy type.",
    )
    start_date: date
    end_date: date
    initial_capital: Decimal = Field(default=Decimal("100000000"), gt=0)
    commission_rate: Decimal = Field(default=Decimal("0.00015"), ge=0, le=1)
    slippage_rate: Decimal = Field(default=Decimal("0.001"), ge=0, le=1)
    benchmark: str = Field(default="KOSPI")
    top_n: int = Field(default=30, ge=1, le=100)
    rank_by: str = Field(
        default="sharpe_ratio",
        description="Metric to rank results by: sharpe_ratio, cagr, total_return, mdd",
    )


class OptimizeResultItem(BaseModel):
    rank: int
    params: Dict[str, Any]
    total_return: FloatDecimal
    cagr: FloatDecimal
    mdd: FloatDecimal
    sharpe_ratio: FloatDecimal
    volatility: FloatDecimal
    benchmark_return: FloatDecimal
    excess_return: FloatDecimal


class OptimizeResponse(BaseModel):
    strategy_type: str
    total_combinations: int
    results: List[OptimizeResultItem]
    best_params: Dict[str, Any]