| """
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| Is the START of the month special for metals, or is it just drift?
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|
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| Every trading-day-of-month position gets the same treatment: the 2-day window
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| entered at the close of TDOM k-1 and exited at the close of TDOM k+1. If the
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| first-2-days window is merely riding the 2016-26 metals rally, every position
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| earns about the drift and TDOM 1-2 is not special. If it is a genuine turn-of-
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| month effect, it should rank at or near the top of ~20 positions in BOTH IS and
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| OOS, and beat the drift baseline by a margin a permutation test respects.
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| """
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|
|
| from __future__ import annotations
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|
|
| import sys
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| import numpy as np
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|
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| sys.path.insert(0, __file__.rsplit("\\", 1)[0] if "\\" in __file__ else ".")
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| import fx_screen as fs # noqa: E402
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|
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| SPLIT = np.datetime64("2016-01-01")
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|
|
|
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| def tdom(ts):
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| """1-based trading-day-of-month for each bar."""
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| mon = ts.astype("datetime64[D]").astype("datetime64[M]")
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| out = np.ones(len(ts), int)
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| for i in range(1, len(ts)):
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| out[i] = 1 if mon[i] != mon[i - 1] else out[i - 1] + 1
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| return out
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|
|
|
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| def profile(sym, width=2):
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| d = fs.load(sym, "D1")
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| ts, c = d["ts"], d["c"]
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| k = tdom(ts)
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| r = np.full(len(c), np.nan)
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| r[width:] = (c[width:] - c[:-width]) / c[:-width] # window ENDING at bar i
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| end_k = k # TDOM of the exit bar
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| res = {}
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| for pos in range(width, 21): # exit on TDOM pos -> window TDOM pos-1..pos
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| m = (end_k == pos) & np.isfinite(r)
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| res[pos] = (r[m & (ts < SPLIT)], r[m & (ts >= SPLIT)])
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| return res, np.nanmean(r[ts < SPLIT]), np.nanmean(r[ts >= SPLIT])
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|
|
|
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| if __name__ == "__main__":
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| rng = np.random.default_rng(0)
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| for sym in sys.argv[1:] or ["XAUUSD", "XAGUSD", "XPTUSD", "XPDUSD", "XAUEUR"]:
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| res, dis, doos = profile(sym)
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| is_m = {p: v[0].mean() * 1e4 for p, v in res.items()}
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| oo_m = {p: v[1].mean() * 1e4 for p, v in res.items()}
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| rank_is = sorted(is_m, key=is_m.get, reverse=True).index(2) + 1
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| rank_oo = sorted(oo_m, key=oo_m.get, reverse=True).index(2) + 1
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| #--- permutation: how often does a RANDOM set of month-windows of the same
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| #--- size beat the TDOM-1..2 excess over drift, OOS
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| oos_all = np.concatenate([v[1] for v in res.values()])
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| tom = res[2][1]
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| exc = tom.mean() - oos_all.mean()
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| perm = np.mean([rng.choice(oos_all, len(tom), replace=False).mean() - oos_all.mean() >= exc
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| for _ in range(5000)])
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| print(f"{sym}: TDOM1-2 window IS {is_m[2]:+6.1f} bp (rank {rank_is}/19, all-window mean {dis * 1e4:+.1f})"
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| f" OOS {oo_m[2]:+6.1f} bp (rank {rank_oo}/19, all-window mean {doos * 1e4:+.1f})"
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| f" OOS excess {exc * 1e4:+.1f} bp, permutation p = {perm:.4f}")
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| top = sorted(oo_m.items(), key=lambda x: -x[1])[:4]
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| print(f" OOS top windows (exit TDOM: bp): {[(p, round(v, 1)) for p, v in top]}")
|
6192393511