from collections import defaultdict, deque
from typing import Protocol

from app.bars.models import Bar, Timeframe
from app.config import DEFAULT_MAX_BARS_PER_TF


class BarStore(Protocol):
    def put(self, bar: Bar) -> None: ...

    def get(self, tf: Timeframe, limit: int | None = None) -> list[Bar]: ...


class InMemoryBarStore:
    def __init__(self, max_bars_per_tf: int = DEFAULT_MAX_BARS_PER_TF):
        self._bars: dict[Timeframe, deque[Bar]] = defaultdict(
            lambda: deque(maxlen=max_bars_per_tf)
        )

    # How far back from the tail a late bar may still land. A closed minute bar
    # arrives a beat after the ticks that opened the next minute, so it is
    # rarely more than a bucket or two behind.
    LATE_BAR_LOOKBACK = 8

    def put(self, bar: Bar) -> None:
        """Store a bar, replacing the bucket it belongs to.

        Matching only the tail was enough while one closed bar arrived per
        minute. With tick-built bars a minute's authoritative bar shows up
        *after* ticks have already opened the next one, so the exchange's own
        figures were being dropped and the approximation left in place forever.
        """
        bars = self._bars[bar.tf]
        if not bars or bar.t > bars[-1].t:
            bars.append(bar)
            return
        for index in range(len(bars) - 1, max(-1, len(bars) - self.LATE_BAR_LOOKBACK - 1), -1):
            if bars[index].t == bar.t:
                # A provisional bar must never overwrite a settled one: ticks
                # keep arriving for a minute the exchange has already closed.
                if bars[index].closed and not bar.closed:
                    return
                bars[index] = bar
                return
            if bars[index].t < bar.t:
                # Buckets are ordered, so nothing further back can match.
                return

    def get(self, tf: Timeframe, limit: int | None = None) -> list[Bar]:
        bars = list(self._bars[tf])
        return bars[-limit:] if limit is not None else bars

    def counts(self) -> dict[str, int]:
        return {tf.value: len(self._bars[tf]) for tf in Timeframe}