from pathlib import Path

from pydantic_settings import BaseSettings, SettingsConfigDict

from app.bars.models import Timeframe


DEFAULT_MAX_BARS_PER_TF = 5000

TIMEFRAME_WEIGHT = {
    Timeframe.M1: 1,
    Timeframe.M2: 1,
    Timeframe.M5: 1,
    Timeframe.M15: 2,
    Timeframe.M30: 3,
    Timeframe.H1: 4,
    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_30m_range: str = "60d"
    seed_1m_range: str = "8d"
    timeframes: str = "1m,5m,15m,30m,1h,1d"
    base_timeframes: str = "1m,30m,1d"
    max_bars_per_tf: int = DEFAULT_MAX_BARS_PER_TF
    # Rollback gate for source-timeframe trendline geometry. When false, both
    # server pricing and browser rendering use the previous 1m/displayed grid.
    trendline_source_geometry: bool = True
    ma_sets__1d: str = "sma10,sma20,sma50,sma100,sma200"
    daily_anchor_et: str = "18:00"
    manual_lines_path: Path = Path("./data/manual_lines.json")

    # Schwab. Empty until the app's keys are issued; nothing reads them while
    # live_source is yahoo. The token lives under data/ so it lands on the
    # Coolify persistent volume — a rebuild would otherwise log you out, and
    # re-authenticating is an interactive browser flow.
    schwab_api_key: str = ""
    schwab_app_secret: str = ""
    schwab_callback_url: str = "https://chart.amow.com/api/qt"
    schwab_token_path: Path = Path("./data/.schwab_token.json")
    schwab_symbol: str = "/ES"
    # Seconds between forming-bar emissions built from LEVEL_ONE_FUTURES ticks.
    # Set negative to drop the Level 1 subscription and take closed minute bars
    # only. Measured in regular hours, /ES supplies a price-changing trade far
    # faster than this, so the value is the update rate: at 1.0 the throttle was
    # the limiter and the chart felt sluggish.
    schwab_tick_seconds: float = 0.25
    confluence_min_score: float = 28
    # Four hours, chosen from the sweep in scripts/calibrate_alerts.py. Suppression is
    # per price zone, so an unrelated zone still alerts immediately; this only
    # governs how often the *same* area repeats itself.
    alert_cooldown_seconds: int = 14400
    # On the persistent volume in production: suppression has to outlive a
    # deploy or every push re-fires every zone that currently qualifies.
    alert_state_path: Path = Path("./data/alert_state.json")
    events_path: Path = Path("./data/events.json")
    user_prefs_path: Path = Path("./data/user_prefs.json")
    ntfy_topic: str = ""
    ntfy_server: str = "https://ntfy.sh"
    chart_auth_token: str = ""
    chart_password: str = ""
    # Zone used for times a person reads rather than a machine: the timestamp in
    # an alert push. The container runs UTC; this is where you are.
    alert_timezone: str = "America/Chicago"
    replay_file: Path | None = None

    @property
    def live_symbol(self) -> str:
        """What the live source calls the instrument.

        Yahoo wants ES=F, Schwab wants /ES. Seeding always uses the Yahoo
        symbol, because Yahoo is always the source of history.
        """
        return self.schwab_symbol if self.live_source == "schwab" else self.yahoo_symbol

    @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}
        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