"""The seasonal screen, pointed at the five instruments it has never seen.

seasonal.py's verdict on the four tick-backed instruments (EURUSD, USDJPY, XAUUSD, SP500) is
in: two directional survivors, both real, both smaller than their spread. This runs the
IDENTICAL statistics - same circular-rotation family-wise null, same max-|t| bar, same
split-half re-measurement - on the five SQX-decoded instruments that share no data path with
the originals: FTSE100, UK100, WTI (two independent feeds), USDCAD. Breadth is the one axis
the seasonal question has not been tested on, and two feeds of the same market (WTI, and
FTSE100/UK100) double as a replication check: a real effect must appear in both.

WHAT IS WEAKER HERE THAN IN seasonal.py, STATED UP FRONT
--------------------------------------------------------
  spread     synthesised, not quoted: breadth.MidBook applies a flat relative spread
             (breadth.SPREAD_BP, rounded UP from measured peers). The move/spread and
             spread/ATR columns are therefore APPROXIMATE and carry no hour-of-day spread
             shape - a session-open spread spike is invisible. A bucket must clear its
             spread by a wide margin before it is worth tick-level follow-up.
  clock      timestamps are whatever the SQX files carry (Dukascopy exports are typically
             UTC, the5ers feeds broker time = UTC+2). Bucket STATISTICS are unaffected -
             only the label's wall-clock meaning is; treat "hour k" as file-time and match
             sessions per feed, not across feeds.
  drift/t    UNAFFECTED by both caveats: computed on mid-price log returns, spread-free.

Run:  python breadth_seasonal.py            (all five, hour/dow/month)
      python breadth_seasonal.py UK100_the5ers ...   (subset, by raw symbol name)
"""
import sys
import numpy as np
import breadth, seasonal

sys.stdout.reconfigure(encoding='utf-8', errors='replace')

if __name__ == '__main__':
    syms = [s for s in sys.argv[1:] if s in breadth.SPREAD_BP] or list(breadth.SPREAD_BP)
    frames = {}
    print("=== loading (M1 -> H1, synthesised book: see module docstring) ===")
    for s in syms:
        try:
            _, frames[s] = breadth.get(s, 'H1')
            print(f"  {breadth.NICE[s]:>8}: {frames[s].n:,} H1 bars")
        except Exception as ex:
            print(f"  {breadth.NICE[s]:>8}: FAILED - {ex}")
    survivors = []
    for kind in ('hour', 'dow', 'month'):
        print(f"\n{'='*100}\n=== {kind.upper()} - five breadth instruments, identical null "
              f"and bar to seasonal.py (clock = file time) ===\n{'='*100}")
        for s, f in frames.items():
            res = seasonal.analyse_frame(f, breadth.NICE[s], kind=kind)
            for k in seasonal.report(res):
                survivors.append((breadth.NICE[s], kind, k))
    print(f"\n{'='*100}")
    if survivors:
        print("family-wise survivors across the whole run (pre split-half):")
        for s, kind, k in survivors:
            print(f"  {s}  {kind}  bucket {k}")
        print("replication check: an effect real in FTSE100 must appear in UK100, and "
              "WTI_d in WTI_5 - same market, independent feeds.")
    else:
        print("no bucket on any of the five instruments clears its family-wise bar.")