import pandas as pd


def build_tradable_mask(
    close: pd.DataFrame,
    volume: pd.DataFrame,
    pit_membership: pd.DataFrame | None,
    min_price: float,
    min_dollar_volume: float,
    min_history_days: int,
    min_valid_volume_days: int,
    liquidity_window: int = 60,
) -> pd.DataFrame:
    """Build a point-in-time tradable universe mask using only lagged inputs."""
    close = close.sort_index()
    volume = volume.reindex(index=close.index, columns=close.columns).sort_index()
    if pit_membership is None:
        pit_mask = pd.DataFrame(True, index=close.index, columns=close.columns)
    else:
        pit_mask = pit_membership.reindex(
            index=close.index,
            columns=close.columns,
            fill_value=False,
        )
        pit_mask = pit_mask.where(pit_mask.notna(), False).astype(bool)

    eligible_close = close.where(pit_mask)
    eligible_volume = volume.where(pit_mask)

    lagged_close = eligible_close.shift(1)
    lagged_volume = eligible_volume.shift(1)
    lagged_dollar_volume = lagged_close * lagged_volume

    price_ok = lagged_close.gt(min_price)
    liquidity_ok = (
        lagged_dollar_volume.rolling(window=liquidity_window, min_periods=1).median().gt(min_dollar_volume)
    )
    history_ok = (
        lagged_close.notna()
        .rolling(window=min_history_days, min_periods=min_history_days)
        .sum()
        .ge(min_history_days)
    )
    valid_volume_ok = (
        lagged_dollar_volume.notna()
        .rolling(window=liquidity_window, min_periods=1)
        .sum()
        .ge(min_valid_volume_days)
    )

    mask = price_ok & liquidity_ok & history_ok & valid_volume_ok
    mask = mask & pit_mask
    return mask.astype(bool)