"""Daily multi-asset strategy families for the panel sweep.

All signals use data through t-1: `panel.shift(1)` everywhere.
"""
from __future__ import annotations

import numpy as np
import pandas as pd


def _vol_scale(panel: pd.DataFrame, target: float, window: int) -> pd.DataFrame:
    ret = panel.pct_change()
    rv = ret.rolling(window).std().shift(1) * np.sqrt(365)
    return (target / rv).clip(upper=1.0).fillna(0.0)


def tsmom_panel(panel: pd.DataFrame, lookback: int, long_only: bool = False,
                vol_target: float | None = None,
                vol_window: int = 60) -> pd.DataFrame:
    c = panel.shift(1)
    mom = c / c.shift(lookback) - 1
    sig = pd.DataFrame(np.where(mom > 0, 1.0, -1.0),
                       index=panel.index, columns=panel.columns)
    if long_only:
        sig = sig.clip(lower=0.0)
    if vol_target is not None:
        sig = sig * _vol_scale(panel, vol_target, vol_window)
    # pre-listing: no data -> flat
    return sig.where(panel.notna(), 0.0).fillna(0.0)


def donchian_panel(panel: pd.DataFrame, window: int, long_only: bool = False,
                   vol_target: float | None = None) -> pd.DataFrame:
    # channel uses highs/lows we don't have on the index panel; approximate
    # with rolling max/min of closes through t-2 (close-based breakout)
    c = panel.shift(1)
    upper = panel.rolling(window).max().shift(2)
    lower = panel.rolling(window).min().shift(2)
    pos = pd.DataFrame(np.nan, index=panel.index, columns=panel.columns)
    pos[c > upper] = 1.0
    if long_only:
        pos[c < lower] = 0.0
    else:
        pos[c < lower] = -1.0
    pos = pos.ffill().fillna(0.0)
    if vol_target is not None:
        pos = pos * _vol_scale(panel, vol_target, 60)
    return pos.where(panel.notna(), 0.0)


def xs_momentum(panel: pd.DataFrame, lookback: int, top_k: int = 2,
                short_bottom: bool = False, rebalance: str = "W") -> pd.DataFrame:
    """Cross-sectional momentum: long top-k by trailing return, optional
    short bottom-k, rebalanced weekly (Mondays) or monthly."""
    c = panel.shift(1)
    mom = c / c.shift(lookback) - 1
    ranks = mom.rank(axis=1, ascending=False)
    n = panel.shape[1]
    tgt = pd.DataFrame(0.0, index=panel.index, columns=panel.columns)
    tgt[ranks <= top_k] = 1.0 / top_k
    if short_bottom:
        tgt[ranks > n - top_k] = -1.0 / top_k
    idx = panel.index
    if rebalance == "W":
        mask = pd.Series(idx.dayofweek == 0, index=idx)
    else:  # monthly: first listed trading day of each month
        mask = idx.to_series().groupby(idx.to_period("M")).transform("min").eq(idx)
    pos = tgt.where(mask, np.nan).ffill().fillna(0.0)
    return pos.where(panel.notna(), 0.0)


def xs_momentum_tsmom_filter(panel: pd.DataFrame, lookback: int, top_k: int = 2,
                             trend_lookback: int = 100,
                             rebalance: str = "W") -> pd.DataFrame:
    """XS momentum gated by per-asset trend: only hold top-k names that are
    also above their own trend_lookback MA."""
    pos = xs_momentum(panel, lookback, top_k, False, rebalance)
    c = panel.shift(1)
    above = c > c.rolling(trend_lookback).mean()
    return pos.where(above, 0.0).fillna(0.0).where(panel.notna(), 0.0)


def buy_and_hold_btc(panel: pd.DataFrame) -> pd.DataFrame:
    pos = pd.DataFrame(0.0, index=panel.index, columns=panel.columns)
    pos["BTC"] = 1.0
    return pos