Files
signal-platform/docs/research/tr-rank-agreement.md
T
dennisthiessenandClaude Opus 5 ebe9047403 research: survivorship sensitivity and total-return momentum investigation
Three questions answered against snapshot copies, with no production change.

Step 1 — how sensitive is the backtest to the ranking universe? Restoring 85
S&P 500 constituents removed between 2021-06 and 2026-07 moves the same window
from 2.09 Sharpe / 51.6% CAGR to 1.57 / 38.1%. The failed banks never reached
either book (identical best/worst trade R), so this measures universe
recomposition, not survivorship bias. The removals list is curated by hand with
per-symbol sources; only three explicitly confirmed terminal events get marks,
and four unresolved endings are reported as caveats rather than inferred.

Step 2 — does a total-return basis change the ranking? Ordered lists differ on
1009 of 1009 evaluated dates, 4.56 replacements per date. The shift correlates
with trailing implied yield (mean Spearman +0.79), which is strong evidence
consistent with a dividend effect but does not isolate own-yield, since both
the stock and benchmark legs change.

Step 3a — does that reach P&L? A paired replay reusing one shared candidate set
(control reproduces the production row exactly) changes ~30% of entries.
Treatment is -3.7pp CAGR and -1.9pp drawdown, with the entire return gap in
2025. A paired moving-block bootstrap (seed 20260907, 5000 replicates, 21/63
blocks, both reported) puts every interval across zero.

Decision: production stays unchanged. No schema, no ingestion, no wider sweep.
The observed window does not resolve which basis is better, and choosing a
threshold now — with -3.7pp on the table — would be fitting the rule to the
answer.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HAH7vVtn7qbcn1EVheecHQ
2026-09-07 19:29:34 +02:00

5.9 KiB
Raw Blame History

Total-return vs price-return momentum — ranking screen

Run date: 2026-09-07 · Verdict: proceed to Step 3 (pre-registered rule fired) Reports: reports/tr-rank-agreement.json, reports/tr-rank-agreement-mechanism.json Script: scripts/research_tr_rank_agreement.py Cache: backtest_snapshots/tr-rank-cache.sqlite (507 symbols, 630,524 aligned bars)

Question

12-1 momentum is defined on total returns; production computes it on split-adjusted prices, which ignore dividends. Before building the production tr_factor machinery (Step 3 of the plan), does the basis actually change what the gate admits and how it orders?

Deliberately a screen, not a portfolio simulation. A difference here is a reason to measure trades, not evidence that P&L changes.

Configuration

Universe backtest_snapshots/survivorship-a.sqlite, 505 symbols with bars (RHM has a ticker row but no bars in the snapshot, so it is inert in both arms)
Window 2021-06-24 → 2026-07-02, clipped to the snapshot
Decision dates every SPY session; 1,009 had enough history to evaluate
Momentum production compute_residual_12_1_momentum, unchanged; price series against nominal SPY, total-return series against total-return SPY
Volatility leg production compute_realized_vol_6m on the SPLIT series, computed once per date and held fixed in both arms so only momentum varies
Gate production min_momentum_percentile = 80.0, via production _percentiles
Ordering production blend_strategy_rank, symbol as deterministic tie-break, symbols fed in sorted order in both arms
Comparison the full admitted list, never truncated to max_positions
Runtime 146 s with the production function — no NumPy rewrite needed, so no equivalence test was required

Coverage is complete. One Adjustment.SPLIT and one Adjustment.ALL request per symbol; 507 symbols cached, zero dates present in one adjustment but not the other. No symbol with snapshot bars is missing from the cache — the script refuses to run in that case rather than reporting the gap as agreement.

Result

Evaluated dates 1,009
Dates with a different ordered list 1,009 (100%)
Dates with a different admitted set 1,007 (99.8%)
Mean set disagreement, |A △ B| / |A ∪ B| 0.0867
Max set disagreement 0.2143
Mean admitted names per arm 100.09
Mean replacements per date (one out paired with one in) 4.56
Max replacements on a single date 12
Mean symmetric difference (names counted on both sides) 9.13

Per year, every evaluated date differs:

Year Dates Different set Different order
2022 132 132 132
2023 250 250 250
2024 252 252 252
2025 250 248 250
2026 125 125 125

Counted directly from the daily set differences rather than derived from the Jaccard ratio: 4.56 names are replaced on an average date — 4.56 leave and 4.56 arrive, so 9.13 names appear on one side or the other, out of about 100 admitted. A swap moves two names, which makes the symmetric difference twice the replacement count and the two easy to conflate.

Strong evidence of a dividend effect

First changed date, 2022-06-24 — 99 names admitted in each arm, nine swapped:

  • Only on price return: CDNS, MAR, FIX, COST, LIN, AON, STE, CI, AJG
  • Only on total return: EXC, PSX, WY, AMCR, DTE, GIS, FE, BMY, ES

Growth and low-yield names out; utilities, energy, staples and other high payers in. Nine tickers are not evidence, so it was measured (--mechanism 25, saved to reports/tr-rank-agreement-mechanism.json with its sampled dates and per-date values): across 40 sampled dates, the Spearman correlation between a name's trailing 12-month implied yield — from the ALL/SPLIT ratio over the formation window — and its momentum-percentile shift between arms is +0.79 (range +0.71 to +0.85, every sampled date positive).

What that does and does not pin down. The reordering is systematic and directional rather than numerical noise, and it moves with yield. It is not a clean isolation of the stock's own yield: both the stock series and the benchmark leg change between arms, so the measured shift also contains the beta-weighted benchmark yield. Read it as strong evidence consistent with a dividend effect. Isolating own-yield would need a third variant — total-return stock series against a nominal SPY leg — which has not been run.

Verdict

The pre-registered rule was "no ordered-list difference on any evaluated date → stop". Differences occur on every evaluated date, so Step 3 proceeds.

What this does and does not establish:

  • Does: the momentum basis materially changes gate admission and ordering, every day, in a yield-driven direction.
  • Does not: show that realised P&L changes. Production also applies setup qualification, existing holdings, available slots and the rest of the admission path. A name that swaps into the admitted list may never produce a setup. Step 3's paired portfolio replay is what decides whether any of this reaches trades.
  • Worth noting: the tuning recorded as closed in the research log was carried out on a price-return basis throughout. This screen does not invalidate it — it says the input those conclusions were computed on is not the one the factor is defined on.

Reproduce

python scripts/research_tr_rank_agreement.py --fetch          # 1,014 requests, cached
python scripts/research_tr_rank_agreement.py --time-batch 20  # runtime projection
python scripts/research_tr_rank_agreement.py                  # full screen
python scripts/research_tr_rank_agreement.py --mechanism 25   # yield vs percentile shift

The cache makes re-runs free and the screen deterministic; delete backtest_snapshots/tr-rank-cache.sqlite to refetch.