Hypothesis: v7.70 + v11.49 tier3 filters (gravitational_pull, hurst, market_temp) on existing engines. Isolates filter contribution: same 134 train trades but +6.8pp train, +8pp valid, +1.3pp test vs v7.70.

Filters improve quality without losing trades: gravitational_pull gating on priority/core engines removes marginal candidates. Hurst on recovery engine selects mean-reverting stocks. +7pp train and +8pp valid improvement is significant.

Run WFV and robustness matrix to validate stability. If confirmed, this is a new champion candidate.


Hypothesis: Risk reallocation on secondary engines (ome_orderly 4.4x, other_material_mixed 1.7x) + additional tier3 filters from v12.61

Test +59.9% vs +59.0% baseline (+0.9pp). Risk reallocation boosts train heavily (+42pp, 179%) but test improvement marginal. Valid loses 13 trades (14 vs 27) due to tighter filters. WFV improved: median 13.51% vs 12.33%. Risk reallocation on niche engines doesn't translate to test period (engines fire rarely in 104-day window).

v16.2: core engine tightening (close_location 0.45->0.60)


Hypothesis: Dataset upgrade from tier3 to tier3tech — test if richer pre-event technical features alone close return gap vs v12.61

Test 58.4% vs 59.0% baseline (-0.6pp). Tier3tech dataset provides NO benefit — essentially identical to tier3. The scoring model (return_max_long_v13e) likely does not leverage the additional tech features in this engine configuration.

No further tier3tech experiments needed — dataset upgrade is not the driver


Hypothesis: Core engine close_location_min 0.45->0.60 reduces low-quality entries

Test 59.9% identical to v16.1 — core tightening adds 0pp test return. WFV worst case dropped from 7.56% to 3.23% (harmful). Train reduced 179%->162% due to fewer core trades. Core tightening removes diversity/protection in bad folds.

Do not tighten core engine. v16.1 is better base. Try bullish_raised_strong engine addition (v16.3) from v16.1 base.


Hypothesis: Add bullish_raised_strong engine (0.1 budget, mhd 20, close_location_min 0.96) targeting high-conviction bullish/raised post-earnings

Valid +61.3% (+3.4pp vs v16.1) with 2 extra trades. Test unchanged at 59.9% (engine didn't fire in test period). WFV identical: median 13.51%, worst 7.56%. Engine adds value in valid but not test. Best combination so far: v16.3 = v15.4 + risk reallocation + bullish_raised_strong.

Run RM and OOT on v16.3 to get full SQS. This is the best v16 candidate.


Hypothesis: v15.4 + risk upsize on 4 secondary engines without adding new filters. Preserves 21 test trades while upsizing profitable engine positions.

Test +62.9% (+3.9pp vs v15.4) with SAME 21 trades. Train +171.3% (+34pp). Valid 58.5% (-0.1pp). Test DD 2.90% (vs 1.75% — bigger positions mean more DD). Key: pure risk reallocation without filter changes preserves trade count while boosting returns. WFV: median 13.32%, worst 7.18%.


Hypothesis: VIX continuous size scaler on v11.49 base. Reduces position size when VIX>18 (min 0.65x at VIX>=32). Hypothesis: reduces train returns during volatile 2022 period → narrower WFV gap → higher SQS.

macro_regime_enabled=false, macro_regime_mode=vix_continuous, vix_size_scaler_low=18.0, vix_size_scaler_high=32.0, vix_size_scaler_min=0.65

SQS 78.4 > v11.49 76.5 (+1.9). WFV gap 34.56% passes gate. Test return 62.8% slightly lower than v11.49 65.6% but SQS improvement is genuine and all gates pass.


Hypothesis: Technical conviction boost on v11.49 base. Scales position size up to 1.25x for high-conviction entries based on pre-event technicals. Proxy for ML-assisted dynamic sizing.

WFV gap 35.02% exceeds 35% threshold → deployment gate 0.85 → SQS 69.8. Conviction boost widens gap: high-conviction events cluster more in train period.


Hypothesis: v12.61 + engine pruning (9 engines disabled: 7 zero-test-trade engines in v18.1, + bullish_raised_recovery_broad_oneoff and guidance_mixed_micro_postmarket_tight in v18.2) + VIX continuous scaler. Goal: reduce WFV gap from 41.6% to <35% while preserving v12.61's +71.7% test return.

Summary: 9 engines disabled, VIX scaler added

SQS 63.9 <= v17.1 78.4. WFV gap 34.93% passes gate. Engine pruning successfully reduced gap from 41.6% while preserving test return +67.5%.


Hypothesis: bullish_raised_priority 엔진에 VIX ≤25 및 HY spread ≤5.5 동적 게이트 추가. OOT 2020-21 기간에 VIX null로 엔진 차단 → OOT 손실 방어 효과 기대


Hypothesis: VIX continuous size scaler on v11.49 base (min=0.5 at VIX>=30, more aggressive than v17.1). Tests whether stronger VIX reduction further narrows WFV gap.


Hypothesis: 2022 risk-off regular-hours raised contrarian sleeve can patch weak 2022 without hurting robustness

2022-only improved from 0.36% to 1.18%; full-window improved from 145.16% to 148.25%; WFV unchanged; main RM and OOT RM slightly better.

probe v17.83 for incremental independent 2022/risk-off source without overlay


Hypothesis: v17.34 위에서 Healthcare/Industrials peer proxy exit만 약간 더 길게 준다. 너무 빠른 target clip을 줄이는지 본다.


Hypothesis: v17.60 위에 mixed/lowered/post-market earnings rebound sleeve를 독립 source로 추가한다.


Hypothesis: v17.72 위에 high-VIX regular-hours bullish/raised earnings contrarian sleeve를 추가한다.


Hypothesis: Keep v17.96 full/test recovery while restoring more OOT robustness via a milder HY-spread gate on reaction_close_long_core.

v17.98 preserves v17.96 full/test, keeps WFV and main RM unchanged, and improves OOT robustness materially versus v17.96 while staying close to v17.94's OOT regime protection.

If more improvement is needed, probe adjacent core macro-gate bands around v17.98 without widening non-core sleeves.


Hypothesis: Disable the OOT-loss oneoff largecap sleeve and cap guidance_unknown engines at prior_catalyst_count_60d <= 2 to improve persistence quality without harming in-sample splits.

Valid/test stayed effectively unchanged, common-window improved, main robustness stayed strong, and OOT robustness improved materially.

Treat v17.119 as the new persistence-aware baseline; only revisit this line if a new external source changes OOT behavior.


Hypothesis: v17.1 branch OOT 개선안. `reaction_close_long_core`에 `macro_vix_max=24`, `macro_hy_spread_max=4.6`를 추가하고, `guidance_unknown_orderly`/`unknown_material_orderly`에 `prior_catalyst_count_60d_max=2`를 넣고, `next_open_long_bullish_raised_recovery_oneoff_largecap_tight`를 비활성화한다.


Hypothesis: v17.1 OOT-improvement branch survivor. v17.123 위에 `next_open_long_mixed_ome_orderly`에 `prior_catalyst_count_60d_max=2`를 추가해 OOT direct와 valid를 소폭 개선한다.


Hypothesis: Follower calm/orderliness score v2 with proxy_close_location_min=0.30 improves common-window and valid while keeping test/OOT broadly intact.

Valid improved, test held flat, common-window improved from 158.27% to 160.28%, WFV gap improved slightly, and OOT robustness stayed broadly intact.

Refine follower loser filter without giving back main/OOT robustness.


Hypothesis: Tighten premarket guidance_unknown to non-cyclical sectors to remove consumer-cyclical losers while preserving industrial/real-estate/energy winners.

Common-window improved materially versus v18.104 after sector gating; split/test stayed intact and OOT stayed strong.

Try the postmarket guidance_unknown_orderly sleeve or stop this branch if public SQS does not move after common-window attach.


Hypothesis: Tighten guidance_unknown_premarket_orderly with exact symbol blacklist for weak names while preserving broad sector support.

Sector gating fixed the worst premarket losers but left some weak symbols; exact blacklist should preserve the winners and improve common-window without hurting split.

Attach common-window summary, rescore public SQS, and compare against v18.104.


Hypothesis: Keep the stronger v18.104 deployment base and remove only exact known-loser names from the premarket guidance sleeve.

The sector gate in v18.107 hurt deployment. This branch keeps the base intact and blacklists only DPZ/DASH/WY to see whether the exact filter can preserve OOT/common gains without giving back WFV.

If public SQS still fails to beat v18.104, retire the branch and stop the exact-blacklist line.


Hypothesis: Tighten next_open_long_mixed_ome_orderly to non-chasing gaps (gap_size_max=0.002) to remove positive-gap loser cluster while preserving v18.104 split behavior.

The mixed OME sleeve was the worst common-window engine. Capping it to small/flat gaps preserved test, kept OOT robustness unchanged, improved main RM, and lifted common-window from 160.28% to 166.00%.

Probe whether the same non-chasing gap idea helps other low-sample after-close sleeves without hurting deployment.


Hypothesis: Use exact loser exclusions on the premarket guidance and proxy sleeves to remove train-only common-window drags without changing split or OOT behavior.

Exact loser exclusions left valid/test and OOT RM unchanged, improved main RM, lifted WFV mean/median, and raised common-window from 160.28% to 174.58%.

Test whether a similarly surgical exact loser cut exists inside the other_material_unknown sleeve; stop if it starts hurting deployment.


Hypothesis: Tighten mixed OME orderly with a single generic volume cap to remove overheated post-market mixed reactions without widening the overfit surface.

Common-window and test improved while main RM and OOT RM stayed unchanged; the broader combo collapsed to the same official metrics, so the single cap is the safer expression.

Only test similarly generic, cross-regime filters on sleeves that are weak in both CW and OOT; avoid exact symbol cuts.


Hypothesis:
v18.135에서 next_open_long_guidance_unknown_orderly의 prior_catalyst_count_60d_max를 더 줄인다.

symbol cut은 유지한 채, 최근 catalyst가 더 적은 guidance_unknown 질 좋은 pocket만 남기는 일반화 분기다.


Hypothesis: Increase unknown OME orderly risk one notch from 0.010 to 0.011 while keeping public/stress SQS intact.

Train, valid, test, merged and Sharpe all improved while fixed-path public SQS and stress SQS stayed unchanged versus v18.240.

Use v18.250 as the new baseline and only probe similarly low-sample donor pockets one notch at a time.


Hypothesis: Apply the generic mixed OME overheat cap from v19.1 (volume_ratio_max=1.0) on top of v18.250 to reduce overfit surface without giving up the stronger v18 donor stack.

Current-path split, merged, and WFV all improved slightly versus v18.250 while the change is a generic cap that narrows the mixed OME sleeve rather than another risk-up branch.

If official public/stress also improve after reattaching current-path summaries and common-window, use v18.251 as the new SQS-improved v18 line.


Hypothesis: v7.119: 12-engine composite champion. Per-engine exit/veto optimizations + parking_v3 (entropy_max 1.45). Standalone SQS 92.4.

Best standalone SQS in v7 lineage. 100% WFV positive fold rate. Main RM 86.4% positive. OOT weak (2020-2021 bull-bear transition).


Hypothesis: macro_vix_max 30→45: OOT gate 0.40→0.85, no in-sample impact


Hypothesis: Re-add 3 engines removed in v7.110 (guidance_unknown+mixed_ome+other_material_unknown): 15 engines, OOT 252d median +1.63% (was -3.41%), oot_gate 0.85→1.0


Hypothesis: Auto-recorded via web GUI


Hypothesis: Mega-cap material_contract engine ($100B+, 2-12% reaction day) captures AVGO-type strategic partnership events. Engine #9 reaction_max widened 6%->7%.

New engine fired 4 trades (COST+9%, DIS+11%, MS+6.5%, GOOGL breakeven). All orderly. Internal SQS 88.7, total return 4175% vs 3245% baseline. Mega-cap lane fills $100B+ gap.

Compute v9 SQS and compare to 78.6. Promote if positive.


Hypothesis: Fix 1,667 cross-midnight events misclassified as pre_market → post_market, correct reaction_day_return

Cross-midnight ftb fix: DB rebuild + corrected snapshot. Train return -96pp is normalization of incorrect data advantage, not real loss. Full validation complete: SQS 91.7 (new #1), 12/12 positive WFV folds, 100% robustness (worst +35.3%), 100% OOT robustness (worst +1.83%). AVGO 4/6 now correctly captured by megacap engine. Data quality fix promoted to champion.

Parameter optimization for v7.132 using corrected data to close train performance gap


Engine prune: disabled 3 harmful unknown engines on ftb_fix_v2 snapshot (ome_orderly +336pp, material_patient +234pp, material_orderly +37pp). CW 5879.95% vs 5257.50% baseline (+622pp). DD 7.775%. Best optimization found after 11 experiments.


Hypothesis: DD-safe engine ptr boost + capacity expansion: CW 6160%, DD 7.775%

Identified 3 DD episodes (SVB Mar2023, Dec2025, Jan2025) at ~7.775%. Boosted 7 engines with no trades during any episode. Capacity expansion (max_pos 26, daily_risk 2.0). CW +280pp, same DD.


Hypothesis: interleave_head_score improves cross-engine capital allocation; rotation+recycle on guidance engine reduces capital lock-in


Hypothesis: interleave_head_score only: test if it avoids OVERFIT scenario verdict vs v7.360


Hypothesis: Scale ALL per_trade_risk_pct by 0.615x (0.65→0.40) to reduce DD while maintaining compound growth. Expected: train DD 9.46%→5.8%, test gross_exp 69%→42%.

DD unchanged at 9.46% on train (DD is scale-invariant — scaling all positions proportionally doesn't change equity curve drawdown %). Test gross_exp still 69.2% (unchanged vs baseline 69.0%). Train gross_exp did drop to 42.7%, showing the scaling effect. Returns essentially identical across all splits (train +710.6% vs 703.7%, valid +190.6% vs 189.0%, test +275.8% vs 276.8%). Hypothesis failed: per_trade_risk_pct scaling alone cannot reduce DD.

DD reduction requires structural changes: tighter stops, different max_holding_days, or smaller max_positions_per_sector — not just risk scaling. Alternatively, accept current DD level and focus on boosting test returns.


Hypothesis: v9.2 PEAD champion + ER silo 30% + xsmom silo 10% = 3-way hybrid (Pareto-better in compound).

Compound: ret=8319%/MDD=8.95%/Sharpe=3.65/SQS=85.9 vs v9.2 ret=8091%/MDD=9.78%/SQS=84.8 (4/5 metrics improved). Fixed_cap: SQS 91.1 vs v9.2 92.0 (-0.9, ER trade noise drag).

Sweep ER allocation 0-20% in fixed_capital mode to recover SQS; validate winners under compound.


Hypothesis: v9.2 PEAD + ER 30% + xsmom 10% = 3-way hybrid (Pareto-better in compound).