min_rr = 2.0 was hand-set in Admin (2026-06-24) and never swept — the gate
ablation only tested the floor on-vs-off, never its level. It was the last
un-swept knob in the live gate.
Swept against portfolio Sharpe under the real exit, with a parity self-check
(reproduces_production_gate: the row at the live floor must rebuild production's
exact 1,089-setup qualified set — it does).
min_rr qualified in-sample Sh/CAGR OOS Sh/CAGR (entries >= 2024-07)
0.0 6636 1.98 / 58.5% 2.02 / 66.2%
1.2 3897 1.34 / 33.9% 1.12 / 28.8%
1.5 3127 1.20 / 29.6% 1.12 / 28.8%
1.75 1974 1.64 / 44.5% 1.15 / 27.4%
2.0 (live) 1089 2.04 / 50.4% 2.78 / 73.3%
2.25 577 1.64 / 31.8% 1.71 / 31.9%
2.5 286 1.67 / 29.0% 0.68 / 8.7%
KEEP 2.0. It is the optimum in both windows, and a peak that reproduces in data
it was never fitted to is real evidence. But treat it as fragile: unlike the ATR
trail (a plateau), this is a spike with a trough beside it — +/-0.25 costs ~0.4
Sharpe in-sample and ~1.6 out-of-sample — and the curve is bimodal (floor-off is
good, 1.2-1.75 is bad, 2.0 is good). The hand-set value landed on the peak by
luck, not by tuning. Do not nudge it.
Worth knowing: turning the floor OFF entirely is the second-best row in both
windows, with substantially higher CAGR (58.5% / 66.2%) and more trades. If CAGR
ever outranks Sharpe here, "no R:R floor" is a live option — and it would sever
the gate's last dependency on the weak S/R detector.
Also fixes a metric artifact in the holdout harness. The train book's equity curve
ran to the end of the data while its entries stopped at the split, so it sat in
flat cash for two years and deflated its own CAGR/Sharpe (reported 0.95 / 14.6%;
actually 1.31 / 29.6%). _simulate_portfolio now truncates the calendar to
hold_days after the last entry when end_date is set — it only triggers on the
holdout train window, so no other number moves. The clear-air OOS verdict is
unaffected: it rests on the test row, whose entries and curve both start at the
split and were always clean. Both holdout reports regenerated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The README opened with "find the path of least resistance, key S/R zones, and
asymmetric R:R setups" — a description of a strategy we do not run. What we run
is a long-only cross-sectional momentum book with a trailing exit. The S/R
engine, the composite score, sentiment and fundamentals are screening and
display; none has a measured edge.
- Rewrites the intro/philosophy around the real strategy, and says plainly what
is NOT the edge.
- Adds a mermaid decision graph, universe -> qualified -> ranked -> opened ->
closed, with the real exit distribution on the terminal nodes: initial stop 45%,
trailing stop 31%, max hold 24%, S/R target 0%. Validated against the mermaid
parser, not eyeballed.
- Documents that the R:R and touch-probability are GATE INPUTS, not forecasts of
the trade — the single easiest way to misread this app.
- Adds win rate, best/worst R and the exit-reason split to the production
baseline table.
- New docs/research/README.md: every strategy tested, the result, the decision,
and why we stay with the current one. 12 rejected ideas (take-profit exits,
clear-air gate relaxation, EV gate, regime overlay, inverse-vol sizing, shorts,
standalone vol, FIP, ...), the confirmed tuning knobs, the open leads, and the
method rules we learned the hard way (nested lookbacks are not out-of-sample; a
rising win rate is a warning, not a win).
- Documents the research flags and the holdout harness, and warns that the
portfolio_monitor lookbacks are nested windows, NOT a holdout.
- Notes the snapshot must copy paper_% settings or it silently diverges from prod.
All baseline numbers re-verified against reports/backtest-20260711-prod-baseline.json
(506 tickers, 1,089 qualified, CAGR 50.4%, +413.8% vs SPY +95.7%, DD -21.4%,
Sharpe 2.04, 320 trades, 15.3d avg hold, and all five promotion contenders). No
corrections were needed — the numbers were right, the framing was not.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Investigated whether our support/resistance detection follows best practice
and whether we actually use it that way. Three findings, all backed by runs
against the prod snapshot and written up in docs/research/sr-levels-and-exits.md:
- The S/R target must NOT become an exit. Honoring it as a take-profit on top
of the 3x ATR trail drops Sharpe 2.04 -> 1.47 and halves CAGR. Win rate rises
(37.5% -> 40.0%), which is the tell: it truncates the right tail where
momentum's edge lives.
- The clear-air fallback (synthesize a 3xATR target where no resistance exists,
so 52-week-high breakouts stop being vetoed) looked strictly better in-sample
(Sharpe 2.04 -> 2.07, CAGR 50.4% -> 62.3%, DD 21.4% -> 20.1%) but FAILED a
real out-of-sample holdout: on entries after 2024-07-01 it is worse on Sharpe
(2.78 -> 2.45) and Calmar, better only on raw CAGR. Not shipped.
- The detector itself is weak vs best practice (POC/VAH/VAL computed then
discarded, HVN = any above-mean bin, 1.48x volume double-counting, "touch"
counts pass-throughs, no round numbers), but its only causal path to P&L is
the entry gate. Fix it for the displayed levels, not for returns.
Method note: nested lookback windows are NOT out-of-sample. The in-sample result
was clean, large, and consistent across five windows, and still did not survive
a proper entry-date split.
All research paths are off by default and the default report is unchanged:
BACKTEST_RESEARCH_EXITS=1 take-profit exit rows
BACKTEST_ATR_TARGET_FALLBACK=k synthetic k*ATR target when S/R offers none
BACKTEST_FALLBACK_CLEAR_AIR_ONLY=1 restrict that to genuinely clear air
BACKTEST_HOLDOUT_SPLIT=YYYY-MM-DD train/test split by entry date
Also fixes two reproducibility holes found while reconciling our local baseline
against the live report:
- create_backtest_snapshot.py now copies paper_% settings. The production
monitor row replays the runtime exit policy via get_exit_policy(); without
those keys a snapshot silently falls back to code defaults, so a live-tuned
exit would never be reflected.
- Migration 020 drops activation_min_expected_value and
activation_min_target_probability. Both are orphans of the June EV-gate
redesign, read by no code path, but prod carries min_target_probability = 50.0
which implies a probability floor that is not enforced (the real floor is the
20% constant in qualification.py).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
dennisthiessen
