"""verify_intra — adversarial gauntlet on the intraday orthogonal combo, the SAME tests
that killed the ortho relative-value wave. Does the low-turnover intraday combo survive?
  1. in-sample (pre-2025) standalone Sharpe + per-cut uplift (is it pre-2025 real or 2025-only?)
  2. WALK-FORWARD selection (pick orthogonal positive-uplift signals on PAST data, test forward)
  3. drop-one-mechanism (carried by one signal?)
  4. fee stress to 0.30% RT
"""
from __future__ import annotations
import importlib.util, sys
from pathlib import Path
import numpy as np, pandas as pd
HERE = Path(__file__).resolve().parent
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al  # noqa: E402
AG = HERE/"agents"
ORTHO = ["agent_05_open_drive", "agent_09_prevday_range_breakout", "agent_06_vol_event_revert_15m",
         "agent_07_volume_spike_revert", "agent_08_gap_fill"]


def _t(name):
    p = AG/f"{name}.py"; s = importlib.util.spec_from_file_location(name, p); m = importlib.util.module_from_spec(s); s.loader.exec_module(m); return m.target


def _sh(s):
    r = np.asarray(s.dropna().values, float); return float(np.mean(r)/np.std(r)*np.sqrt(365.25)) if len(r) > 2 and np.std(r) > 0 else 0.0


def _u(c, B, cut="2018-01-01", end=None, w=0.25):
    J = pd.concat({"B": B, "C": c}, axis=1, join="inner").dropna(); J = J[J.index >= pd.Timestamp(cut, tz="UTC")]
    if end: J = J[J.index < pd.Timestamp(end, tz="UTC")]
    return _sh((1-w)*J["B"]+w*J["C"]) - _sh(J["B"]) if len(J) > 30 else float("nan")


def daily(name, fee=al.FEE_SIDE):
    tf = "15m" if "_15m" in name else "1h"
    return al.candidate_daily(_t(name), tf=tf, fee_side=fee)


def main():
    B = al.tp01_baseline_daily()
    dl = {n: daily(n) for n in ORTHO}
    M = pd.concat(dl, axis=1, join="inner").dropna()
    combo = M.mean(axis=1)
    H = pd.Timestamp("2025-01-01", tz="UTC")
    ci = combo[combo.index < H]
    print(f"\n  COMBO standalone Sharpe full {_sh(combo):.2f}  PRE-2025 {_sh(ci):.2f}  corrTP {pd.concat({'b':B,'c':combo},axis=1,join='inner').dropna().corr().iloc[0,1]:.2f}")
    print(f"  per-cut uplift: " + "  ".join(f"{c[:4]} {_u(combo,B,c):+.2f}" for c in ["2021-01-01","2022-01-01","2023-01-01","2024-01-01","2025-01-01"]))
    # pre-2025-only uplift (exclude the suspect window entirely)
    pre = pd.concat({"B": B, "C": combo}, axis=1, join="inner").dropna(); pre = pre[pre.index < H]
    print(f"  PRE-2025 ONLY uplift (2018->2025): {_sh(0.75*pre['B']+0.25*pre['C'])-_sh(pre['B']):+.3f}")

    print("\n  WALK-FORWARD SELECTION (pick orthogonal +uplift signals on PAST only, test fwd):")
    ALL = sorted(p.stem for p in AG.glob("agent_*.py"))
    dlall = {}
    for n in ALL:
        try: dlall[n] = daily(n)
        except Exception: pass
    for sel_end in ["2023-01-01", "2024-01-01"]:
        picks = []
        for n, d in dlall.items():
            up = _u(d, B, "2018-01-01", sel_end)
            cc = pd.concat({"b": B, "c": d}, axis=1, join="inner").dropna()
            cc = cc[cc.index < pd.Timestamp(sel_end, tz="UTC")]
            corr = abs(cc.corr().iloc[0, 1]) if len(cc) > 30 else 1
            if not np.isnan(up) and up > 0.05 and corr < 0.4:
                picks.append(n)
        if picks:
            cb = pd.concat({n: dlall[n] for n in picks}, axis=1, join="inner").dropna().mean(axis=1)
            print(f"   select<{sel_end}: {len(picks)} picks {[p.replace('agent_','')[:12] for p in picks]}")
            print(f"     -> FORWARD uplift {sel_end}->now: {_u(cb, B, sel_end):+.3f}")
        else:
            print(f"   select<{sel_end}: no qualifying picks")

    print("\n  DROP-ONE-MECHANISM (full & pre-2025 uplift):")
    for drop in ORTHO:
        keep = [n for n in ORTHO if n != drop]
        cb = M[keep].mean(axis=1)
        pr = pd.concat({"B": B, "C": cb}, axis=1, join="inner").dropna(); pr = pr[pr.index < H]
        print(f"   -{drop.replace('agent_',''):<26} full {_u(cb,B):+.3f}  pre2025 {_sh(0.75*pr['B']+0.25*pr['C'])-_sh(pr['B']):+.3f}")

    print("\n  FEE STRESS (combo):")
    for fee in [0.0005, 0.001, 0.0015]:
        cb = pd.concat({n: daily(n, fee) for n in ORTHO}, axis=1, join="inner").dropna().mean(axis=1)
        print(f"   {2*fee*100:.2f}%RT: standalone Sh {_sh(cb):.2f}  uplift_full {_u(cb,B):+.3f}")


if __name__ == "__main__":
    main()