claude
cdd4503164
Two std-vocabulary blueprints (bo_h1 donchian-style H1 breakout; bo_h1_trend adds a post-latch EMA 12/48 regime gate). First real use of the campaign path: process + campaign documents registered and run as executable intent (screen 490d14df.., curves da886931.. with persisted taps). Finding: the gate reshapes the R-distribution per instrument (GER40 flips sign, EURUSD deteriorates); no deployable edge (best P(E[R]<=0)=0.08, generalization floor negative, sign agreement 0/4, all gross of costs). site/: build.py bakes a fully static index.html from the runs/ registry (aura.css embedded verbatim, inline SVG, zero external requests); adversarially verified — all baked figures re-derived from the registry.
64 lines
3.1 KiB
Markdown
64 lines
3.1 KiB
Markdown
# Arc 1 — does a trend regime reshape the H1 breakout's R-distribution?
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**Date:** 2026-07-13
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**Engine:** aura @ `1ebb94c` (release build)
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**Results page:** `site/index.html` (build: `python3 site/build.py`)
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## Hypothesis
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Following the orthogonal confirm/refute research model: a lone signal is not
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expected to be profitable; the question is whether a *conditioner* B reshapes
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the R-distribution of a base signal A. Here A = an H1 donchian-style breakout
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(`blueprints/bo_h1.json`), B = an EMA(12)/EMA(48) trend regime that gates the
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held breakout state post-latch (`blueprints/bo_h1_trend.json`) — a held
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breakout is cut the moment the regime flips against it.
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## Method (campaign path, first real use)
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Everything ran as registered research documents — no ad-hoc verb flags:
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- `research/process-screen.json` — sweep (deflated argmax on sqn_normalized)
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→ 90/30-day rolling walk-forward refit → 1000-resample block-5 R-bootstrap
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pooled over WF OOS trades → cross-instrument generalize (expectancy_r).
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- `research/campaign-arc1.json` (id `490d14df…`) — both variants × 4
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instruments (GER40, US500, EURUSD, XAUUSD) × window 2018-01-01..2026-06-30,
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channel axes {24,48,96}², risk Vol{3, 2.0}, seed 0. 8 cells, 76 s wall.
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- `research/campaign-arc1-curves.json` (id `da886931…`) — both variants at
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default params, taps persisted (equity, exposure, r_equity) for the page.
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No cost block: all R figures are **gross** (net == gross).
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## Findings
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1. **The gate reshapes the R-distribution, instrument-dependently** (pooled
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WF-OOS bootstrap, mean E[R] and P(E[R] ≤ 0)):
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- GER40 **flips sign**: −0.151 (P≤0 = 0.90) → +0.139 (P≤0 = 0.18)
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- US500 improves: +0.193 (0.10) → +0.205 (0.08)
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- EURUSD deteriorates sharply: −0.150 (0.88) → −0.204 (0.99)
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- XAUUSD roughly flat: +0.170 (0.14) → +0.124 (0.15)
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This is the conditional effect the research model looks for: B is not a
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uniform improvement, it is a per-instrument reshaping.
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2. **No deployable edge.** Best cell P(E[R] ≤ 0) ≈ 0.08 (US500 gated) — not
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significant against 8 comparisons; generalization worst-case is deeply
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negative for both variants with sign agreement 0/4; and all of it is
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gross of costs.
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3. **Selection instability.** The full-window IS argmax picks asymmetric
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channels (e.g. hi=96/lo=24) that wander across WF windows — the deflation
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and param-stability records show the screen overfitting the grid, which
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the WF/MC stages correctly deflate.
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## Verdict
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Honest null on deployability; positive on the machinery and on the research
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question's shape: the conditional framing (B reshapes A) is measurable with
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the recorded artifacts alone (MC quantile bands per cell). Arc 2 candidates:
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a costed rerun (constant + vol-slippage cost blocks) to see what survives
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gross-to-net, an orthogonal conditioner (time-of-day / volatility tercile),
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or the same pair on coarser bars (H4).
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## Engine friction
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None. The campaign path (process/campaign documents, three-tier validate,
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`aura campaign run` as executor, persist_taps traces) carried the whole arc
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on its first real use.
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