#!/usr/bin/env python3
"""Real PEAD test: actual earnings surprises (Nasdaq) x DBEQ prices, leak-free, net of cost.

For each earnings event (ticker, date, surprise%): direction = sign(surprise). Enter at the CLOSE
of the trading day AFTER the announcement (leak-free — announcement + initial reaction already
public), hold N days, signed by surprise. Measure net-of-cost drift, t-stat, long vs short, IS/OOS,
by surprise magnitude, in the small/neglected liquidity band where PEAD survives.
"""
import datetime
import glob
import json
import math
import os
import sys

import numpy as np

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from equity_factor_gate import load, roll  # noqa: E402

LO, HI = 1e6, 50e6
HORIZONS = [1, 3, 5, 10, 20, 40]
EPOCH = datetime.date(1970, 1, 1)


def tstat(x):
    x = np.asarray(x); x = x[np.isfinite(x)]
    return float(x.mean() / (x.std() / math.sqrt(len(x)))) if len(x) > 30 and x.std() > 0 else float("nan")


def main():
    insts, days, close, dvol = load()
    T, N = close.shape
    lc = np.log(close)
    R = np.zeros((T, N)); R[1:] = lc[1:] - lc[:-1]
    advol = roll(np.mean, np.nan_to_num(dvol), 20)
    rt_cost = np.clip(40.0 / np.sqrt(np.maximum(advol, 1.0) / 1e6), 5.0, 80.0) / 1e4
    col = {int(i): k for k, i in enumerate(insts)}
    sym = json.load(open("data/surfer/dbeq_symbology.json"))
    mp = sym.get("result", sym)
    tick2col = {}
    for t, ranges in mp.items():
        if ranges:
            iid = int(ranges[0]["s"])
            if iid in col:
                tick2col[t] = col[iid]

    # load earnings events
    files = glob.glob("data/surfer/earnings/*.json")
    ev_day, ev_col, ev_sgn, ev_mag = [], [], [], []
    matched = 0
    for f in files:
        d = datetime.date.fromisoformat(os.path.basename(f)[:-5])
        epoch = (d - EPOCH).days
        idx = int(np.searchsorted(days, epoch, "left"))
        t_enter = idx + 1                                       # close of day after announcement (leak-free)
        if t_enter >= T - max(HORIZONS):
            continue
        for tk, surp in json.load(open(f)):
            c = tick2col.get(tk)
            if c is None or surp in (None, "", "N/A"):
                continue
            try:
                s = float(str(surp).replace("%", "").replace(",", ""))
            except ValueError:
                continue
            if not np.isfinite(close[t_enter, c]) or not (LO < advol[t_enter, c] < HI):
                continue
            ev_day.append(t_enter); ev_col.append(c); ev_sgn.append(np.sign(s)); ev_mag.append(abs(s)); matched += 1
    ev_day = np.array(ev_day); ev_col = np.array(ev_col); ev_sgn = np.array(ev_sgn); ev_mag = np.array(ev_mag)
    yr = (1970 + days[ev_day] / 365.25).astype(int)
    print(f"PEAD (real surprises) — {len(files)} earnings days loaded, {matched} events matched to DBEQ "
          f"(band ${LO/1e6:.0f}-{HI/1e6:.0f}M, avg cost {1e4*rt_cost[ev_day, ev_col].mean():.0f}bp)")
    if matched < 200:
        print("  (few events — let the fetch finish, then re-run)"); return

    oosm = yr >= 2025
    cost = rt_cost[ev_day, ev_col]
    print(f"\n{'horizon':>8} {'gross%':>7} {'net%':>7} {'t(net)':>7} {'fracpos':>8} | {'LONG net%':>9} {'SHORT net%':>10} | {'OOS net%':>9}")
    for h in HORIZONS:
        fut = lc[ev_day + h, ev_col] - lc[ev_day, ev_col]
        signed = ev_sgn * fut
        net = signed - cost
        up = ev_sgn > 0; dn = ev_sgn < 0
        print(f"{h:>8} {100*np.nanmean(signed):>+7.2f} {100*np.nanmean(net):>+7.2f} {tstat(net):>+7.1f} "
              f"{np.nanmean(net > 0):>8.2f} | {100*np.nanmean(net[up]):>+9.2f} {100*np.nanmean(net[dn]):>+10.2f} | {100*np.nanmean(net[oosm]):>+9.2f}")

    # by surprise magnitude at 20d (PEAD: bigger surprise -> bigger drift)
    h = 20
    fut = lc[ev_day + h, ev_col] - lc[ev_day, ev_col]; net = ev_sgn * fut - cost
    q = np.nanpercentile(ev_mag, [33, 66])
    for lab, m in [("small surprise", ev_mag <= q[0]), ("mid", (ev_mag > q[0]) & (ev_mag <= q[1])), ("large surprise", ev_mag > q[1])]:
        print(f"  20d net, {lab:>14}: {100*np.nanmean(net[m]):+.2f}%  (n={int(m.sum())})")
    print("\nVERDICT: net>0, t(net)>3, positive OOS, long-side works, AND bigger-surprise->bigger-drift = real PEAD.")


if __name__ == "__main__":
    main()