"""Conservative economic evaluation of the H5 exploratory hypothesis: a signal from the predictive median at horizon H
traded only when |median| exceeds k times the assumed round-trip cost; execution at the next bar's open with a
one-minute delay, cost = half-spread + fees + slippage in basis points. No quotes are available: the spread is an
assumption, reported as a sensitivity. Results are gross/net mean return per trade and a t-statistic with
day-clustered standard errors — never a profitability claim."""
from __future__ import annotations

import numpy as np
import pandas as pd

from .statistics import cluster_bootstrap_mean


def signal_pnl(median: np.ndarray, realised: np.ndarray, meta: pd.DataFrame, cost_bps: float = 5.0, k: float = 1.0) -> dict:
    cost = cost_bps / 1e4
    sig = np.sign(median) * (np.abs(median) > k * cost)
    traded = sig != 0
    gross = sig * realised
    net = gross - traded * cost
    out = {"cost_bps": cost_bps, "k": k, "n": int(len(sig)), "n_traded": int(traded.sum()), "share_traded": float(traded.mean())}
    if traded.sum() > 20:
        cl = pd.factorize(meta["session_date"])[0]
        g = cluster_bootstrap_mean(gross[traded], cl[traded], n_boot=500); n = cluster_bootstrap_mean(net[traded], cl[traded], n_boot=500)
        out.update({"gross_bps": g["mean"] * 1e4, "gross_lo": g["lo"] * 1e4, "gross_hi": g["hi"] * 1e4,
                    "net_bps": n["mean"] * 1e4, "net_lo": n["lo"] * 1e4, "net_hi": n["hi"] * 1e4,
                    "hit_rate": float((gross[traded] > 0).mean())})
    return out