Gahow Wang
4899bb9702
- overnight_book: swapping the all-day momentum sleeve for the overnight-only one lifts the three-sleeve book from Sharpe 0.98 to 1.38 (halves 1.16/1.64) and MaxDD from -37% to -17% at 1 bp a side; at 4 bp the gain is gone. - overnight_book_mf: at the leverage for 50%/yr, a DBMF sleeve leaves the levered MaxDD unchanged (~-36%) and needs more leverage; no CTA sleeve. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
196 lines
9.0 KiB
Python
196 lines
9.0 KiB
Python
"""
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Does the live three-sleeve book get better when its momentum sleeve holds
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only overnight (research/lab/overnight_only.py), and how much leverage would
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the book need for 50% a year?
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PYTHONPATH=. uv run python -m research.lab.overnight_only # once: daily.pkl
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PYTHONPATH=. uv run python -m research.lab.overnight_book
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Streams (daily, costs inside):
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momentum overnight_recovery_top10 held all day (stream_portfolio streams.pkl)
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overnight the same score's top 10 held close -> open only, 1 bp a side,
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cash idle during the day earns nothing (overnight_only daily.pkl,
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sleeve_score)
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overnight_12_1 overnight momentum alone, same rules (reported only)
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lowrisk, trend_v7 (trend_rider_v7_vt24, the live sleeve), trend (V10): streams.pkl
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Book: separate sleeves with equal base shares, drift, a month-start reset
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when any share is > 25% off, 10 bp on capital moved
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(managed_futures_book.band_blend). Sharpe is excess over the 3-month T-bill.
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Leverage (reported only): r_L = L x r - (L - 1) x (T-bill + 1%) / 252, the
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L at which 2006-10..2026-09 CAGR reaches 50%, and MaxDD / worst year at it.
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All of 2006-2026 was inspected before, and the overnight result was seen
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before this rule was written. This is a consistency gate for the forward test
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(overnight_paper.py), not out-of-sample evidence. The rule is written to
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prereg.json before the run; the script refuses to overwrite results.json.
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Result (2026-10-09): CONTINUE. Both candidates pass B1 and B2.
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book (1x) CAGR Sharpe halves MaxDD worst yr L for 50% MaxDD at L
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three_sleeve_v7 (baseline) 18.9% 0.98 0.70 / 1.33 -37.2% -22.3% 3.58 -86.5%
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three_sleeve_overnight 22.3% 1.38 1.16 / 1.64 -16.5% -7.0% 2.35 -36.7%
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two_sleeve_overnight_trend 29.4% 1.39 1.12 / 1.69 -20.7% -13.8% 1.73 -34.8%
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three_sleeve_overnight_12_1 24.9% 1.48 1.36 / 1.64 -15.7% -7.0% 2.05 -30.6% (reported)
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- Why it works: the overnight sleeve drops the all-day sleeve's -69% MaxDD
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and 2008's -55% to -31% and -7%, at monthly corr 0.17 with trend_v7
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(all-day momentum: 0.32). 2008: book +3% vs -22%.
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- Cost sensitivity (reported after the run), three_sleeve_overnight Sharpe
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by cost per side:
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1 bp 1.38
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2 bp 1.26
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3 bp 1.13
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4 bp 1.01 (halves 0.78 / 1.29), about the baseline.
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The auction cost measured by the paper test decides how much of the gain
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is real.
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- Boundary: TrendRider before 2010 is synthetic 3x ETFs. The overnight
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sleeve's idle daytime cash earns nothing here. Leverage above 2x
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overnight needs portfolio margin or futures.
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"""
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from __future__ import annotations
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import json
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import os
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import numpy as np
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import pandas as pd
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from research.lab.factor_scan import RESULTS
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from research.lab.levered_book import stats, tbill
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from research.lab.managed_futures_book import band_blend
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OUT = os.path.join(RESULTS, "overnight_book")
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STREAMS = os.path.join(RESULTS, "stream_portfolio", "streams.pkl")
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OVERNIGHT = os.path.join(RESULTS, "overnight_only", "daily.pkl")
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SPAN = ("2006-10-02", "2026-09-24")
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HALVES = [("2006-10-02", "2016-12-30"), ("2017-01-03", "2026-09-24")]
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BORROW_SPREAD = 0.01
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BASELINE = ["momentum", "lowrisk", "trend_v7"]
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CANDIDATES = {
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"three_sleeve_overnight": ["overnight", "lowrisk", "trend_v7"],
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"two_sleeve_overnight_trend": ["overnight", "trend_v7"],
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}
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REPORTED = {
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"three_sleeve_v7": BASELINE,
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"three_sleeve_overnight_v10": ["overnight", "lowrisk", "trend"],
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"three_sleeve_overnight_12_1": ["overnight_12_1", "lowrisk", "trend_v7"],
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"overnight_plus_momentum_lowrisk_trend": ["overnight", "momentum", "lowrisk", "trend_v7"],
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}
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PREREG = {
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"question": "Does replacing the all-day momentum sleeve with its overnight-only version raise the "
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"three-sleeve book's Sharpe in both halves without a deeper drawdown?",
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"span": SPAN, "halves": HALVES, "baseline": {"three_sleeve_v7": BASELINE},
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"candidates": CANDIDATES, "reported_only": REPORTED,
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"blend": "managed_futures_book.band_blend (equal shares, 25% band, month-start reset, 10 bp)",
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"gates_per_candidate": ["B1 excess Sharpe >= baseline + 0.15 in each half",
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"B2 full-span MaxDD >= max(baseline MaxDD, -30%)"],
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"decision": {"CONTINUE": "a candidate passes B1-B2 -> keep the overnight paper test running; after >= 20 "
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"paper nights compare realized vs print returns, then decide on a live sleeve swap",
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"STOP": "no candidate passes -> the overnight sleeve stays a paper experiment only"},
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"reported": "leverage L for 50%/yr CAGR (borrow at T-bill + 1%), MaxDD and worst year at L; yearly returns",
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"data_status": "all periods previously inspected; overnight result seen before this rule",
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}
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def summary(r: pd.Series, rf: pd.Series) -> dict:
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r = r.loc[SPAN[0]:SPAN[1]]
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d = {k: float(v) for k, v in stats(r, rf).items()}
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d["halves"] = [float(stats(r.loc[a:b], rf)["sharpe"]) for a, b in HALVES]
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return d
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def levered(r: pd.Series, rf: pd.Series, L: float) -> pd.Series:
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return L * r - (L - 1) * (rf.reindex(r.index) + BORROW_SPREAD) / 252
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def leverage_for(r: pd.Series, rf: pd.Series, target: float = 0.50) -> float:
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lo, hi = 1.0, 6.0
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if stats(levered(r, rf, hi), rf)["cagr"] < target:
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return float("nan")
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for _ in range(50):
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mid = (lo + hi) / 2
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if stats(levered(r, rf, mid), rf)["cagr"] < target:
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lo = mid
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else:
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hi = mid
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return hi
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def main() -> None:
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os.makedirs(OUT, exist_ok=True)
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res_path = os.path.join(OUT, "results.json")
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if os.path.exists(res_path):
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raise SystemExit(f"{res_path} exists; this study does not overwrite results")
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with open(os.path.join(OUT, "prereg.json"), "w") as fh:
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json.dump(PREREG, fh, indent=2)
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R = pd.read_pickle(STREAMS)
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O = pd.read_pickle(OVERNIGHT)
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R["overnight"] = O["sleeve_score"].reindex(R.index)
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R["overnight_12_1"] = O["overnight_12_1"].reindex(R.index)
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R = R.loc[SPAN[0]:SPAN[1]]
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rf = tbill(R.index)
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fmt = lambda k, d: (f"{k:40s} cagr {d['cagr']*100:5.1f}% xsh {d['sharpe']:5.2f} vol {d['vol']*100:4.1f}% "
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f"mdd {d['mdd']*100:6.1f}% worst yr {d['worst_yr']*100:6.1f}% halves "
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f"{d['halves'][0]:5.2f} {d['halves'][1]:5.2f}")
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res = {"streams": {}, "books": {}, "leverage_for_50": {}}
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print("=== streams")
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for c in ["momentum", "overnight", "overnight_12_1", "lowrisk", "trend_v7", "trend"]:
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res["streams"][c] = summary(R[c].dropna(), rf)
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print(fmt(c, res["streams"][c]))
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B = {}
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print("=== books (1x)")
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for name, cols in {**REPORTED, **CANDIDATES}.items():
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B[name], _ = band_blend(R[cols], rf)
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res["books"][name] = summary(B[name], rf)
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print(fmt(name, res["books"][name]))
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print("=== leverage for 50%/yr (borrow at T-bill + 1%)")
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for name, r in B.items():
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L = leverage_for(r, rf)
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d = {"L": L}
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if np.isfinite(L):
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s = stats(levered(r, rf, L), rf)
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d.update({"mdd": float(s["mdd"]), "worst_yr": float(s["worst_yr"]), "vol": float(s["vol"]),
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"halves_cagr": [float(stats(levered(r.loc[a:b], rf, L), rf)["cagr"]) for a, b in HALVES]})
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print(f" {name:40s} L {L:4.2f} vol {d['vol']*100:4.1f}% mdd {d['mdd']*100:6.1f}% worst yr "
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f"{d['worst_yr']*100:6.1f}% halves cagr {d['halves_cagr'][0]*100:5.1f}% {d['halves_cagr'][1]*100:5.1f}%")
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res["leverage_for_50"][name] = d
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m = (1 + R[["momentum", "overnight", "lowrisk", "trend_v7"]].fillna(0.0)).resample("ME").prod() - 1
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res["monthly_corr"] = m.corr().round(3).to_dict()
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print("monthly corr:\n", m.corr().round(2).to_string())
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yr = pd.DataFrame({k: (1 + B[k]).groupby(B[k].index.year).prod() - 1
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for k in ["three_sleeve_v7", *CANDIDATES]})
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res["yearly"] = {k: {int(y): float(v) for y, v in yr[k].items()} for k in yr}
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print("yearly %:\n", (yr * 100).round(1).to_string())
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base = res["books"]["three_sleeve_v7"]
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gates = {}
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for k in CANDIDATES:
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d = res["books"][k]
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gates[k] = {"B1": all(d["halves"][i] >= base["halves"][i] + 0.15 for i in range(2)),
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"B2": d["mdd"] >= max(base["mdd"], -0.30)}
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passing = [k for k, g in gates.items() if all(g.values())]
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decision = "CONTINUE" if passing else "STOP"
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res["verdict"] = {"gates": gates, "passing": passing, "decision": decision}
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print(f"gates {gates}\n-> {decision} {passing}")
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with open(res_path, "w") as fh:
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json.dump(res, fh, indent=2)
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pd.DataFrame(B).to_pickle(os.path.join(OUT, "books.pkl"))
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from research.lab.trials import append_trials
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append_trials(os.path.join(RESULTS, "trials_us.jsonl"),
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[{"name": f"overnight_book:{k}", **{x: v for x, v in d.items() if x != "halves"}}
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for k, d in res["books"].items()],
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{"study": "overnight_book", "role": "preregistered consistency gate"})
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if __name__ == "__main__":
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main()
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