"""Benchmarks: buy-and-hold, cash, simple momentum, equal-risk (constant vol target)."""

from __future__ import annotations

import pandas as pd

from ktrade.strategy.base import ParamSpec, Strategy, StrategyMeta, log_returns, vol_target_scale


class BuyAndHold(Strategy):
    meta = StrategyMeta(name="buy_and_hold", label="Buy & hold (benchmark)", family="benchmark", hypothesis="Fully invested from the first bar.", experimental=False, min_bars=2, params=[])

    def features(self, df: pd.DataFrame) -> pd.DataFrame:
        return pd.DataFrame({"ts": df["ts"].values, "close": df["close"].values}, index=df.index)

    def target(self, feats: pd.DataFrame) -> pd.Series:
        return pd.Series(1.0, index=feats.index)


class Cash(Strategy):
    meta = StrategyMeta(name="cash", label="Cash (benchmark)", family="benchmark", hypothesis="Never invested; zero return, zero risk (ignores interest).", experimental=False, min_bars=1, params=[])

    def features(self, df: pd.DataFrame) -> pd.DataFrame:
        return pd.DataFrame({"ts": df["ts"].values, "close": df["close"].values}, index=df.index)

    def target(self, feats: pd.DataFrame) -> pd.Series:
        return pd.Series(0.0, index=feats.index)


class SimpleMomentum(Strategy):
    meta = StrategyMeta(
        name="simple_momentum", label="Simple momentum (benchmark)", family="benchmark",
        hypothesis="Long when the trailing N-bar return is positive, otherwise cash. No vol scaling.", experimental=False, min_bars=30,
        params=[ParamSpec(name="lookback", type="int", default=24, min=2, max=2000)],
    )

    def features(self, df: pd.DataFrame) -> pd.DataFrame:
        f = pd.DataFrame({"ts": df["ts"].values, "close": df["close"].values}, index=df.index)
        f["mom"] = df["close"] / df["close"].shift(self.params["lookback"]) - 1
        return f

    def target(self, feats: pd.DataFrame) -> pd.Series:
        return (feats["mom"] > 0).astype(float).where(feats["mom"].notna(), 0.0)


class EqualRisk(Strategy):
    meta = StrategyMeta(
        name="equal_risk", label="Constant-vol long (benchmark)", family="benchmark",
        hypothesis="Always long, sized to a constant annualized volatility target (inverse-vol).", experimental=False, min_bars=60,
        params=[ParamSpec(name="vol_lookback", type="int", default=48, min=5, max=1000), ParamSpec(name="target_vol", type="float", default=0.40, min=0.01, max=3.0)],
    )

    def features(self, df: pd.DataFrame) -> pd.DataFrame:
        f = pd.DataFrame({"ts": df["ts"].values, "close": df["close"].values}, index=df.index)
        f["ret"] = log_returns(df["close"])
        bars_per_year = 365 * 24 * 60 / float(df.attrs.get("interval_min", 60))
        f["vol_scale"] = vol_target_scale(f["ret"], self.params["vol_lookback"], self.params["target_vol"], bars_per_year)
        return f

    def target(self, feats: pd.DataFrame) -> pd.Series:
        return feats["vol_scale"].fillna(0.0)