"""Is the basket uplift just 'helps when TP01 is weak'? Quantify regime-conditionality."""
from __future__ import annotations
import importlib.util, sys
from pathlib import Path
import numpy as np
import pandas as pd

HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(HERE))
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import ortholib as ol
import altlib as al
AGENTS = HERE / "agents"


def _book(path):
    spec = importlib.util.spec_from_file_location(path.stem, path)
    mod = importlib.util.module_from_spec(spec); spec.loader.exec_module(mod)
    return mod.book


def _sh(s):
    r = np.asarray(pd.Series(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 main():
    B = al.tp01_baseline_daily()
    excl = ["12_rebalance_harvest"]
    rows = {}
    for p in sorted(AGENTS.glob("agent_*.py")):
        nm = p.stem.replace("agent_", "")
        if nm in excl: continue
        ev = ol.eval_book(_book(p))
        if ev["daily"].std() > 0:
            rows[nm] = ev["daily"]
    M = pd.concat(rows, axis=1, join="inner").dropna()
    basket = M.mean(axis=1)
    J = pd.concat({"B": B, "C": basket}, axis=1, join="inner").dropna()

    print("Per-year: TP01 own Sharpe  | basket Sharpe | basket ret | blend-uplift")
    print(" (does the basket help SPECIFICALLY in TP01's weak years?)")
    for y in sorted(set(J.index.year)):
        sub = J[J.index.year == y]
        if len(sub) < 20: continue
        tp_sh = _sh(sub["B"]); bk_sh = _sh(sub["C"])
        bk_ret = float((1 + sub["C"]).prod() - 1)
        up = _sh(0.75 * sub["B"] + 0.25 * sub["C"]) - _sh(sub["B"])
        print(f"  {y}: TP01 Sh {tp_sh:+6.2f} | basket Sh {bk_sh:+6.2f} | ret {bk_ret:+7.2%} | uplift {up:+.3f}")

    # correlation: does basket-year-uplift track NEGATIVE TP01-year-Sharpe?
    rows2 = []
    for y in sorted(set(J.index.year)):
        sub = J[J.index.year == y]
        if len(sub) < 20: continue
        rows2.append((_sh(sub["B"]), _sh(0.75*sub["B"]+0.25*sub["C"]) - _sh(sub["B"])))
    arr = np.array(rows2)
    if len(arr) > 2:
        c = np.corrcoef(arr[:,0], arr[:,1])[0,1]
        print(f"\n  corr(TP01 yearly Sharpe, basket yearly uplift) = {c:+.2f}")
        print("  (strongly NEGATIVE => basket only helps when TP01 is weak = regime hedge, not standing alpha)")

    # conditional uplift: split days by whether TP01 trailing-60d return is up or down
    tp_trail = J["B"].rolling(60).sum()
    up_days = J[tp_trail > 0]; dn_days = J[tp_trail <= 0]
    for lbl, d in [("TP01 trailing-60d UP", up_days), ("TP01 trailing-60d DOWN", dn_days)]:
        if len(d) < 30: continue
        u = _sh(0.75*d["B"]+0.25*d["C"]) - _sh(d["B"])
        print(f"  [{lbl}] n={len(d)}  basket Sh {_sh(d['C']):+.2f}  blend-uplift {u:+.3f}")

    # ETH/BTC ratio regime: is the basket net-short ETH (i.e. is it just shorting the bleed)?
    btc, eth = ol.aligned()
    wbs, wes = [], []
    for nm in rows:
        wb, we = _book(AGENTS / f"agent_{nm}.py")(btc, eth)
        wbs.append(np.nan_to_num(np.asarray(wb,float))); wes.append(np.nan_to_num(np.asarray(we,float)))
    wb = np.clip(np.mean(wbs,axis=0),-0.5,0.5); we = np.clip(np.mean(wes,axis=0),-0.5,0.5)
    idx = pd.DatetimeIndex(btc["dt"])
    wbS = pd.Series(wb, index=idx); weS = pd.Series(we, index=idx)
    print("\n  Mean basket leg weights per year (is it structurally short ETH / long BTC?):")
    print("    year   w_btc    w_eth   (positive=long)")
    for y in sorted(set(idx.year)):
        m = idx.year == y
        print(f"    {y}:  {wbS[m].mean():+.3f}  {weS[m].mean():+.3f}")
    print(f"\n  FULL mean: w_btc {wbS.mean():+.3f}  w_eth {weS.mean():+.3f}")
    print("  (a persistent long-BTC/short-ETH tilt = it's a static ETH-bleed short, not RV alpha)")


if __name__ == "__main__":
    main()