from pathlib import Path

from pydantic_settings import BaseSettings, SettingsConfigDict

from app.bars.models import Timeframe


TIMEFRAME_WEIGHT = {
    Timeframe.M1: 1,
    Timeframe.M2: 1,
    Timeframe.M5: 1,
    Timeframe.M15: 2,
    Timeframe.M30: 3,
    Timeframe.H1: 4,
    Timeframe.H4: 8,
    Timeframe.D1: 16,
}
MA_WEIGHT_FACTOR = 0.75


class Settings(BaseSettings):
    model_config = SettingsConfigDict(env_file=".env", extra="ignore")

    live_source: str = "yahoo"
    seed_source: str = "yahoo"
    yahoo_symbol: str = "ES=F"
    yahoo_poll_seconds: float = 20
    seed_1h_range: str = "730d"
    seed_1m_range: str = "8d"
    timeframes: str = "1m,2m,5m,15m,30m,1h,4h,1d"
    base_timeframes: str = "1m,30m,1d"
    max_bars_per_tf: int = 5000
    ma_sets__1d: str = "sma10,sma20,sma50,sma100,sma200"
    ma_sets__4h: str = ""
    ma_sets__1h: str = ""
    daily_anchor_et: str = "18:00"
    manual_lines_path: Path = Path("./data/manual_lines.json")
    confluence_min_score: float = 28
    alert_cooldown_seconds: int = 900
    ntfy_topic: str = ""
    ntfy_server: str = "https://ntfy.sh"
    chart_auth_token: str = ""
    replay_file: Path | None = None

    @property
    def enabled_timeframes(self) -> list[Timeframe]:
        return [Timeframe(value.strip()) for value in self.timeframes.split(",") if value.strip()]

    @property
    def ma_sets(self) -> dict[Timeframe, list[tuple[str, int]]]:
        configured = {
            Timeframe.D1: self.ma_sets__1d,
            Timeframe.H4: self.ma_sets__4h,
            Timeframe.H1: self.ma_sets__1h,
        }
        result: dict[Timeframe, list[tuple[str, int]]] = {}
        for tf, value in configured.items():
            definitions = []
            for item in filter(None, (part.strip().lower() for part in value.split(","))):
                kind = "sma" if item.startswith("sma") else "ema" if item.startswith("ema") else ""
                if not kind or not item[len(kind) :].isdigit():
                    raise ValueError(f"Invalid MA definition: {item}")
                definitions.append((kind, int(item[len(kind) :])))
            result[tf] = definitions
        return result