#!/usr/bin/env python3
"""FUSION backtest: regime-adaptive QQQ <-> crisis-alpha rotation (the synthesis of all the ideas).

QQQ core whose weight is driven by edge-decay-TRUST (trailing risk-adjusted health, EMA-smoothed,
resurrection-capable) — NOT vol-normalized (so it keeps QQQ's CAGR in calm regimes). When QQQ's trust
decays (sustained downtrend/crisis) it ROTATES into the crisis-alpha sleeve (trend / diversified book,
which RISES in crises) instead of to cash; resurrects back to QQQ as it heals. Plus a light drawdown
circuit-breaker as a tail backstop. Honest test: vs QQQ / book / static GD, full + IS/OOS split +
turnover (the whipsaw cost). Verdict: fusion must beat static GD on CAGR-per-drawdown (Calmar), esp OOS.
"""
import datetime
import json
import math
import os
import sys
import urllib.request

import numpy as np

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from multistrat_paper import book_series  # noqa: E402


def yh(s):
    r = json.loads(urllib.request.urlopen(urllib.request.Request(
        f"https://query1.finance.yahoo.com/v8/finance/chart/{s}?interval=1d&range=25y",
        headers={"User-Agent": "Mozilla/5.0"}), timeout=30).read())["chart"]["result"][0]
    a = r["indicators"].get("adjclose", [{}])[0].get("adjclose") or r["indicators"]["quote"][0]["close"]
    return {datetime.datetime.utcfromtimestamp(t).strftime("%Y-%m-%d"): float(c) for t, c in zip(r["timestamp"], a) if c}


def edge_decay_trust(ret, win=126, ema=0.94):
    """trailing-Sharpe health -> theta in [0.1, 1], EMA-smoothed, resurrection-capable."""
    T = len(ret); th = 1.0; out = np.ones(T)
    for t in range(win, T):
        seg = ret[t - win:t]; sr = seg.mean() / (seg.std() + 1e-9) * math.sqrt(252)
        target = float(np.clip((sr + 0.5) / 1.0, 0.10, 1.0))
        th = ema * th + (1 - ema) * target; out[t] = th
    return out


def main():
    syms = ["QQQ", "SPY", "IEF", "GLD", "DBC"]
    data = {s: yh(s) for s in syms}
    common = sorted(set.intersection(*[set(data[s]) for s in syms])); T = len(common)
    yrs = (datetime.date.fromisoformat(common[-1]) - datetime.date.fromisoformat(common[0])).days / 365.25
    P = {s: np.array([data[s][d] for d in common]) for s in syms}
    R = {s: np.concatenate([[0], P[s][1:] / P[s][:-1] - 1]) for s in syms}
    qqq = R["QQQ"]
    R4 = np.column_stack([R[s] for s in ["SPY", "IEF", "GLD", "DBC"]]); P4 = np.column_stack([P[s] for s in ["SPY", "IEF", "GLD", "DBC"]])
    # trend sleeve (12-1 TS-mom, equal-risk = managed-futures proxy) + diversified book
    lc = np.log(P4); sg = np.zeros((T, 4)); sg[252:] = np.sign(lc[252:] - lc[:-252])
    vol = np.full((T, 4), np.nan)
    for t in range(63, T):
        vol[t] = R4[t - 63:t].std(0)
    iv = 1 / np.where(vol > 0, vol, np.nan); w = np.nan_to_num(sg * iv); g = np.abs(w).sum(1, keepdims=True); g[g == 0] = 1; w = w / g
    trend = np.zeros(T); trend[1:] = np.sum(w[:-1] * R4[1:], axis=1)
    book, _, _ = book_series(np.column_stack([R4, trend]))

    # FUSION: w_qqq = trust(QQQ); rotate (1-w) into the crisis-alpha sleeve; + tail drawdown-CB
    theta = edge_decay_trust(qqq)
    def fusion(sleeve, dd_deadband=0.15):
        wq = theta.copy(); ws = 1 - wq
        r = np.zeros(T); eq = pk = 1.0; turn = 0.0
        for t in range(252, T - 1):
            base = wq[t] * qqq[t + 1] + ws[t] * sleeve[t + 1]
            dd = eq / pk - 1
            cb = float(np.clip(1 - 3 * max(0.0, -dd - dd_deadband), 0.5, 1.0))   # tail backstop only (>15% dd)
            r[t + 1] = cb * base; eq *= (1 + r[t + 1]); pk = max(pk, eq)
            turn += abs(wq[t] - wq[t - 1])
        return r, turn / yrs   # annualized one-way turnover of the QQQ leg
    fus_trend, turn_t = fusion(trend)
    fus_book, turn_b = fusion(book)

    static_gd = 0.7 * qqq + 0.3 * book

    def stats(r, lo=0, hi=None):
        seg = r[lo:hi] if hi else r[lo:]; seg = np.nan_to_num(seg)
        n = (seg != 0).sum(); y = n / 252 if n else 1
        eq = np.cumprod(1 + seg); cagr = eq[-1] ** (1 / y) - 1 if y > 0 else 0
        v = seg.std() * math.sqrt(252); dd = float((eq / np.maximum.accumulate(eq) - 1).min())
        sh = (seg.mean() * 252 - 0.03) / v if v > 0 else 0
        calmar = cagr / abs(dd) if dd < 0 else 0
        return cagr, v, sh, dd, calmar
    sp = int(0.6 * T)
    print(f"FUSION BACKTEST — {common[0]}..{common[-1]} ({yrs:.0f}y), OOS split @ {common[sp]}")
    print(f"  {'strategy':>26} | seg | CAGR  | maxDD | Sharpe* | Calmar | turn/yr")
    cands = [("QQQ buy-hold", qqq, 0), ("book (diversified)", book, 0), ("static GD 70/30", static_gd, 0),
             ("FUSION QQQ<->trend", fus_trend, turn_t), ("FUSION QQQ<->book", fus_book, turn_b)]
    for nm, r, turn in cands:
        for seg, lab in [((0, None), "ALL"), ((0, sp), "IS "), ((sp, None), "OOS")]:
            c, v, sh, dd, cal = stats(r, seg[0], seg[1])
            tt = f"{turn:.1f}x" if (lab == "ALL" and turn) else ""
            print(f"  {nm if lab=='ALL' else '':>26} | {lab} | {100*c:>4.1f}% | {100*dd:>+4.0f}% | {sh:>+5.2f}  | {cal:>5.2f}  | {tt}")
    print("  *Sharpe excess over financing. Calmar = CAGR/|maxDD| (higher=better frontier).")
    print("  VERDICT: FUSION beats static GD on OOS Calmar = regime-rotation shifts the frontier out (worth it).")
    print("           If not = whipsaw eats it; keep the simpler static GD.")


if __name__ == "__main__":
    main()