"""
Exposure overlays: series in [0, 1] read on signal dates by engine.simulate
(``exposure=``). The un-invested part earns the panel's cash return.
"""

from __future__ import annotations

import numpy as np
import pandas as pd

from research.lab.engine import simulate


def ew_index(p) -> pd.Series:
    """Equal-weight index of PIT members (daily rebalanced)."""
    r = p.close.pct_change(fill_method=None).where(p.member.shift(1, fill_value=False))
    return (1 + r.mean(axis=1).fillna(0)).cumprod()


def trend(level: pd.Series, ma: int = 200, floor: float = 0.0) -> pd.Series:
    """1 when the index is above its moving average, else ``floor``."""
    above = level > level.rolling(ma, min_periods=ma).mean()
    return above.astype(float).where(above, floor)


def breadth(p, ma: int = 200, lo: float = 0.4, hi: float = 0.6) -> pd.Series:
    """Share of members above their own MA mapped linearly from lo→0 to hi→1."""
    c = p.close.ffill()
    above = (c > c.rolling(ma, min_periods=ma).mean()).where(p.member)
    share = above.mean(axis=1)
    return ((share - lo) / (hi - lo)).clip(0, 1)


def drawdown_brake(level: pd.Series, dd: float = 0.15, ma: int = 50) -> pd.Series:
    """Cut to zero when the index is > dd below its 1y high AND below its MA."""
    high = level.rolling(252, min_periods=60).max()
    bad = (level / high - 1 < -dd) & (level < level.rolling(ma, min_periods=ma).mean())
    return (~bad).astype(float)


def vol_target(p, score, target: float, lookback: int = 60, cap: float = 1.0,
               base_kwargs: dict | None = None, exposure: pd.Series | None = None) -> pd.Series:
    """Scale exposure by target / realized vol of the unscaled strategy.

    Two passes: run the strategy fully invested, measure its trailing
    daily-return vol, then size the next rebalance; any given ``exposure``
    (e.g. a trend filter) multiplies the result.
    """
    kw = dict(base_kwargs or {})
    base = simulate(p, score, **kw)
    rv = base.returns.rolling(lookback, min_periods=lookback // 2).std() * np.sqrt(252)
    scale = (target / rv).clip(upper=cap).fillna(cap)
    if exposure is not None:
        scale = scale * exposure.reindex(scale.index).ffill().fillna(1.0)
    return scale