2 Strong findings · rest null Regime + value-area Crypto perps · FX · Metals · Indices Wave-audited research

GAMS — Cross-Asset
Regime Research

A wave-based forensic backtest of an ex-hedge-fund trader’s “real market edge” framework — One-Time-Framing (OTF) regime detection and Dalton’s value-area rotation rule — across Bybit crypto perps and Dukascopy FX/metals/indices.

2 Strong findings survived 18 waves of audit · rest null
8/8
Assets: VA-fade regime effect
Value-area fades complete more often on non-trending days
6/6
Crypto perps: expectancy edge
Fee-viable reclaim-and-hold resized to daily VA
+0.24R / +0.49R
BTC / ETH full-history edge
Fee-only, 2020/2021–2026
18
Audit waves
Append-only evidence trail, current v16

Verdict

After 18 waves of forensic audit, two findings are Strong: (1) a regime-conditioning effect — value-area fades complete more often on non-trending days, replicated on 8/8 tested assets (crypto, FX, metals, indices); and (2) a fee-and-funding-viable expectancy edge from resizing a reclaim-and-hold rule’s stop against the daily value area, replicated on 6/6 crypto perps (+0.24R BTC, +0.49R ETH full history).

The edge’s yearly expectancy correlates with realized volatility (ρ≈0.76 annual, ≈0.45–0.48 monthly) — low-vol years underperform. A rolling-vol filter/sizing rule improves held-out mean R by +0.03R to +0.11R (permutation p<0.001), though it is a mean shift, not a risk-adjusted one.

Measured execution reality: quoted spread is negligible, but market impact is 1.7–20× the quoted spread depending on order size ($1k–$100k). ETH’s edge survives every tier; BTC’s known 2023 weakness gets modestly worse.

What the framework claims

One-Time-Framing (OTF)

The original framework detects a dominant market regime (trend vs range) in real time. The crypto reversion effect (H1) — short-term mean reversion conditioned on regime — is confirmed on 7/8 tested perps, with TRX the only exception (candidate explanation identified, mechanism search effectively exhausted).

Value-area rotation

Dalton’s rule: price re-entering a value area it just left and being accepted back inside tends to complete a fade. The Strong §3.2 finding: these fades complete more often on non-trending days — 8/8 assets.

Reclaim-and-hold with VA-sized risk

The Strong §3.10 edge: take the reclaim-and-hold setup and size the stop against the previous day’s value-area width instead of a fixed stop. Fee-and-funding-viable on 6/6 crypto perps over full history.

The vol-regime mechanism

Yearly expectancy strongly tracks realized volatility (low-vol years underperform, high-vol outperform, on every symbol tested). At trade level the relationship vanishes (R²≈0.005) — it is a regime-scale phenomenon, not a per-trade signal.

The tested rules

ComponentRuleEvidence
Regime detectionOTF framework: trend vs range classificationBasis for conditioning, not a standalone signal
§3.2 fadeValue-area re-entry + acceptance → fade to opposite edgeCompletes more often on non-trending days — 8/8 assets
§3.10 reclaimReclaim-and-hold, stop = daily value-area width, ~10% stop6/6 crypto perps; +0.24R BTC / +0.49R ETH fee-only
Vol filter (Wave 17)Skip trades below rolling-vol percentile (P10–P50)Held-out +0.03R→+0.11R, permutation p<0.001; pooled threshold beats per-symbol (Wave 18)
SizingVol-weighted capital tilt (optional)+0.548R vs +0.508R unweighted, no participation loss
ExecutionQuoted spread negligible; market impact 1.7–20× spreadETH survives $100k orders; BTC second-order drag

Where the findings hold

FindingMarketsStatus
§3.2 VA-fade regime conditioning8/8 — crypto, FX, metals, indicesStrong
§3.10 reclaim-and-hold edge6/6 crypto perps (BTC, ETH, LINK, STX, XMR, ALT)Strong
H1 OTF crypto reversion7/8 crypto perps confirmedStrong
H2 timeframe-scale flipPooled crypto held out at 18–36mo; Gold leg infeasibleExploratory
H5 combination checkBTC/ETH confirmed nullNull

Headline numbers

MeasureResultNote
§3.10 expectancy (fee-only, full history)+0.24R BTC / +0.49R ETH2020/2021–2026, 10% stop, daily-VA risk unit
Vol filter improvement (held-out)+0.03R → +0.11RMonotonic with aggressiveness; p=0.0006 (P30), p=0.0033 (P50)
Monthly vol-expectancy correlationρ≈0.45–0.48R²≈0.22–0.24, p<10⁻¹⁸ across 349 symbol-months
Trade-level vol-expectancyR²≈0.005Regime-scale, not trade-scale
Market impact vs quoted spread1.7–20×$1k→$100k order tiers; ETH survives all
BTC 2023 dipnot anomalousp=0.317 block-bootstrap; driven by low-vol regime, not a fixable bug
Per-symbol vol thresholds were tested (Wave 18) and rejected — the natural fix for the pooled-threshold caveats makes the Sharpe-like statistic worse at every level and doesn't close the retention imbalance (84.3→74.8 pts at P30). The pooled threshold is the better-supported design. A proposed fix is a hypothesis until tested.

How it was tested

Wave-based audit

STRATEGY_AUDIT.md rewritten each wave (currently v16); WAVE*_FINDINGS.md is the append-only evidence trail. No result is trusted beyond N=1–2 until checked across the full tested set.

Data

Bybit klines (last-traded price), funding and OI for 7 perps; Dukascopy for FX/metals/indices; Bybit L2 order-book archive for execution-cost measurement.

Significance

Block bootstrap and circular-permutation tests (e.g. BTC 2023 dip: p=0.317), held-out absolute-date cutoffs, permutation tests on filter rules (20,000 draws).

Execution realism

Wave 15 measured quoted spread (negligible); Wave 18 built a book-walking simulator to measure market impact at $1k/$10k/$100k tiers.

Run it yourself

# read the current state of belief first
cat STRATEGY_AUDIT.md   # v16 — supersedes everything else
# reproduce a specific wave's analysis (e.g. H1 / H5 / H6)
cd backtest && pip install -r requirements.txt
python scripts/run_h1.py && python scripts/run_h5.py && python scripts/run_h6.py
python scripts/regime_classifier.py && python scripts/volume_profile.py
# public dashboard
open docs/index.html   # GitHub Pages: docs/