feat: US 3-sleeve portfolio and lowrisk_top20; US portfolio research harness
Register two live strategies from the 2026-09-29 US research: - lowrisk_top20: idiosyncratic vol + low beta, top 20, monthly, buffer 2 (new `low_risk` signal in strategies/robust_momentum.py) - us_three_sleeve: 1/3 each of overnight_recovery_top10, lowrisk_top20 and trend_rider_v7_vt24 in one account (strategies/sleeve_portfolio.py, a mixed stock+ETF strategy). Sleeve shares drift with their returns and reset at a month start once any is >25% off its share. Research (research/lab, S&P 500 PIT, next-open fills): - rotation.py / persistence.py: walk-forward factor weighting (factor momentum, rolling IC, market state) and last-year's-winner selection do not beat a fixed composite - us_sweep.py: holdings count, z/vol weights, dynamic N, tranching and market overlays (Faber SMA, de-risk vol target, statistical jump model, momentum state); engine.py gains zvol weighting and dyn_n - fetch_us_fundamentals.py / fundamentals.py / fund_eval.py: SEC EDGAR point-in-time fundamentals (first-filed values) and factor tests - trend_rider_long.py / us_final.py: 2010-2026 TrendRider curves and the 3-sleeve check (2011-2026 Sharpe 1.21, CAGR 21.9%, MaxDD -23%) Web-UI engine, 2017-09..2026-09, $2/order: us_three_sleeve CAGR 28.9%, Sharpe 1.49, MaxDD -24.8%. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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research/lab/us_final.py
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research/lab/us_final.py
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"""
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Final check of the recommended US portfolio: three sleeves with different
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return sources, equal capital, rebalanced monthly between sleeves.
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momentum : overnight-return momentum + distance from 126d low, top 10, 10-day
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rebalance, hold while rank <= 30 (the live overnight_recovery_top10)
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defensive : sector-neutral quality (GP/A, CFO/A, ROE, accruals, leverage) +
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low risk (idiosyncratic vol, beta), top 20, monthly, buffer 2
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(price-only alternative: low risk alone)
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trend : TrendRider V7 VT24 on leveraged ETFs (live trend_rider_v7_vt24)
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PYTHONPATH=. uv run python -m research.lab.trend_rider_long # once: data/research/tr_long.pkl
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PYTHONPATH=. uv run --with numba python -m research.lab.us_final
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The live version is ``us_three_sleeve`` (strategies/sleeve_portfolio.py).
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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 pickle
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import warnings
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import numpy as np
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import pandas as pd
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from research.lab import fundamentals as fu
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from research.lab.engine import Costs
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from research.lab.factor_scan import RESULTS, load
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from research.lab.report import deflated_sharpe, stats
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from research.lab.rotation import factor_ranks
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from research.lab.us_sweep import _blend, run, sector_rank
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warnings.filterwarnings("ignore")
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SPAN = ("2011-01-03", "2026-09-24")
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LONG = ("2006-09-27", "2026-09-24")
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BLOCKS = [("2006-09-27", "2010-12-31"), ("2011-01-01", "2014-12-31"), ("2015-01-01", "2018-12-31"),
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("2019-01-01", "2022-12-31"), ("2023-01-01", "2026-09-24")]
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def sleeve_scores(p):
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pr = factor_ranks(p, "us")
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F = fu.load(p)
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Q = _blend(*(sector_rank(F[k], p) for k in ("gp_a", "cfo_a", "roe", "accruals", "leverage")))
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low = _blend(pr["ivol"], pr["low_beta"])
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return {
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"momentum": (_blend(pr["overnight_mom"], pr["rec_126"]), dict(top_n=10, buffer=2.0, every=10)),
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"defensive": (_blend(Q, low), dict(top_n=20, buffer=2.0, every=21)),
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"lowrisk": (low, dict(top_n=20, buffer=2.0, every=21)),
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}
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def monthly_blend(R: pd.DataFrame, weights: dict[str, float]) -> pd.Series:
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"""Sleeves drift inside each month and are reset to ``weights`` at month
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ends (the sleeves are separate accounts; only capital moves)."""
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cols = list(weights)
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w0 = np.array([weights[c] for c in cols])
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out = []
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for _, g in R[cols].groupby(R.index.to_period("M")):
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grow = (1 + g).cumprod()
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val = grow.mul(w0, axis=1).sum(axis=1)
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prev = np.r_[1.0, val.values[:-1]]
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out.append(pd.Series(val.values / prev - 1, index=g.index))
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return pd.concat(out)
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def describe(r: pd.Series, blocks=BLOCKS) -> dict:
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s = stats((1 + r).cumprod())
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bl = {f"{a[:4]}-{b[:4]}": stats((1 + r.loc[a:b]).cumprod())["sharpe"]
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for a, b in blocks if len(r.loc[a:b]) > 200}
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yrs = ((1 + r).groupby(r.index.year).prod() - 1)
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return {**{k: float(v) for k, v in s.items()}, "blocks": bl, "yearly": {int(k): float(v) for k, v in yrs.items()}}
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def line(name, d) -> str:
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return (f"{name:44s} cagr {d['cagr']*100:5.1f}% sh {d['sharpe']:.2f} vol {d['vol']*100:4.1f}% "
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f"mdd {d['mdd']*100:5.1f}% calmar {d['calmar']:.2f} worst yr {min(d['yearly'].values())*100:5.1f}% "
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f"blocks [{' '.join(f'{v:.2f}' for v in d['blocks'].values())}]")
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def main() -> None:
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p, _ = load("us")
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pf = p.slice()
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pf.costs = Costs(p.costs.buy, p.costs.sell, None, 2.0, 10_000.0) # + $2 per order on a $10k sleeve
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S = sleeve_scores(p)
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R, Rfee = {}, {}
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for name, (score, kw) in S.items():
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span = LONG if name in ("momentum", "lowrisk") else SPAN
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R[name] = run(p, score.where(p.member), [span], **kw)["_rets"]
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Rfee[name] = run(pf, score.where(p.member), [span], **kw)["_rets"]
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ew = p.close.notna().astype(float).where(p.member)
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R["ew_sp500"] = run(p, ew, [LONG], top_n=10000, every=10)["_rets"]
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with open(os.path.join("data", "research", "tr_long.pkl"), "rb") as fh:
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tr = pickle.load(fh)["equity"]["trend_rider_v7_vt24"]
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R["trend"] = Rfee["trend"] = tr.pct_change().reindex(p.dates).loc["2010-03-01":] # $2/order already
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R["spy"] = p.benchmark.ffill().pct_change().reindex(p.dates)
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R = pd.DataFrame(R).loc[LONG[0]:LONG[1]]
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Rfee = pd.DataFrame(Rfee).loc[LONG[0]:LONG[1]]
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out = {}
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span = R.loc[SPAN[0]:SPAN[1], ["momentum", "defensive", "lowrisk", "trend", "spy"]].fillna(0.0)
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monthly = (1 + span).resample("ME").prod() - 1
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print("monthly return correlation 2011-2026:\n", monthly.corr().round(2).to_string(), "\n")
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print("=== sleeves (10 bp costs; trend includes $2/order)")
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for c in R.columns:
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r = R[c].dropna()
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out[c] = describe(r)
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print(line(f"{c} {r.index[0].year}-", out[c]))
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three = {"momentum": 1 / 3, "defensive": 1 / 3, "trend": 1 / 3}
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three_px = {"momentum": 1 / 3, "lowrisk": 1 / 3, "trend": 1 / 3}
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print("\n=== portfolios 2011-2026, monthly rebalanced between sleeves")
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for label, w, src in [("3 sleeves (defensive = quality+lowrisk)", three, R),
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("3 sleeves (defensive = lowrisk, price only)", three_px, R),
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("3 sleeves, + $2/order on $10k sleeves", three, Rfee),
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("momentum + defensive (no ETF sleeve)", {"momentum": .5, "defensive": .5}, R),
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("momentum + trend", {"momentum": .5, "trend": .5}, R)]:
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r = monthly_blend(src.loc[SPAN[0]:SPAN[1]].fillna(0.0), w)
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out[label] = describe(r)
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print(line(label, out[label]))
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print("\n=== price-only walk-back 2006-2026 (includes 2008; trend sleeve from 2010-03, before that 50/50)")
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rr = R.loc[LONG[0]:LONG[1]]
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pre = monthly_blend(rr.loc[:"2010-02-28", ["momentum", "lowrisk"]].fillna(0.0), {"momentum": .5, "lowrisk": .5})
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post = monthly_blend(rr.loc["2010-03-01":].fillna(0.0), three_px)
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r = pd.concat([pre, post])
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out["walkback price-only"] = describe(r)
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print(line("momentum + lowrisk (+ trend from 2010)", out["walkback price-only"]))
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for c in ("momentum", "lowrisk", "ew_sp500", "spy"):
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print(line(f" {c} 2006-", describe(rr[c].dropna())))
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# deflated Sharpe: every configuration this and the previous round tried
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n = 0
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for f in ("trials_us.csv", "trials_us_sweep.csv"):
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path = os.path.join(RESULTS, f)
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if os.path.exists(path):
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with open(path) as fh:
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n += sum(1 for line in fh if not line.startswith(("combo,", "score,")))
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n += 12 + 60 + 20 # rotation schemes, fund_eval variants, defensive variants
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r = monthly_blend(R.loc[SPAN[0]:SPAN[1]].fillna(0.0), three)
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dsr = deflated_sharpe(r, n)
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out["dsr"] = {"n_trials": n, **{k: float(v) for k, v in dsr.items()}}
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print(f"\nDSR of the 3-sleeve book: {dsr['dsr']:.3f} (Sharpe {dsr['sr_annual']:.2f} vs "
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f"expected max of {n} unskilled trials {dsr['sr0_annual']:.2f})")
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print("\nyearly returns (3 sleeves vs SPY):")
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spy_y = out["spy"]["yearly"]
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for y, v in out["3 sleeves (defensive = quality+lowrisk)"]["yearly"].items():
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print(f" {y}: {v*100:6.1f}% SPY {spy_y.get(y, float('nan'))*100:6.1f}%")
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with open(os.path.join(RESULTS, "us_final.json"), "w") as fh:
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json.dump(out, fh, indent=1)
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R.to_pickle(os.path.join(RESULTS, "us_final_sleeves.pkl"))
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if __name__ == "__main__":
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main()
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