Tokyo Sector-Peer Idiosyncratic Down-Shock Rebound: buy a Japanese large cap (banks / trading houses / autos / insurers) at the next TSE open after a session in which its beta-adjusted return fell >=2 sigma below its own sector peer basket, fixed 5-session open-to-open clock (JPEQ 1-DAY, 23-name pre-registered panel, long/flat, unlevered, same-date entries capped)

Original Research Lead hypothesis (not seeded). It is a breadth test of the peer-relative down-shock template that lesson L266 says is worth testing on JPEQ. That template was promoted on iteration 1 on ASX gold miners (288 trades, PF 1.19, DD 8.8%) and on TSX miners (337 trades, PF 1.27). It also worked as a BTC residual on Bybit and Hyperliquid perps. JPEQ is almost unused (7 of 5,011 experiments), and no Japanese sector-relative rule has been tested. The one JPEQ failure (US SMH shock diffusion into Tokyo chip names) used a cross-market raw signal with a short arm; this design drops both. MECHANISM: a single-name drop that its close sector peers do NOT share is most often liquidity-driven rather than news-driven. Examples are a block sale, a fund rebalance, a foreign-investor de-risking of one name, or a stale-news overreaction. Japanese large caps have well-documented short-horizon reversal, and the peer residual removes the sector/macro component that tends to persist (a BOJ rate move hits every bank together; that should not trigger). UNIVERSE (fixed in advance; all names in config/jp_equities_universe.txt with daily bars from 2018-01-04; four sector clusters, each name's peer basket is the equal-weight mean of the OTHER names in its cluster): BANKS/BROKERS 8306 MUFG, 8316 SMFG, 8411 Mizuho, 8308 Resona, 8309 Sumitomo Mitsui Trust, 8604 Nomura, 8601 Daiwa, 8591 ORIX; TRADING HOUSES 8058 Mitsubishi Corp, 8001 Itochu, 8031 Mitsui & Co, 8053 Sumitomo Corp, 8002 Marubeni, 8015 Toyota Tsusho; AUTOS/PARTS 7203 Toyota, 7267 Honda, 7269 Suzuki, 5108 Bridgestone, 6902 Denso; NON-LIFE/LIFE INSURERS 8766 Tokio Marine, 8725 MS&AD, 8630 Sompo, 8750 Dai-ichi Life. All 23 have been continuously listed over the whole history, so there is no delisting survivorship inside the window. The selection itself is today's top caps (a known bias, stated in risk_factors), and the trigger is residual to peers that share that bias. SIGNAL (computed at each session close, causal): r_i = log close-to-close return; peer_t = mean r of the other cluster members; beta_i = rolling 120-session OLS beta of r_i on peer, known as of the PREVIOUS session; resid_i = r_i - beta_i * peer_t; z_i = resid_i / std(resid_i over the previous 60 sessions, excluding today). EXECUTION: session venue, so a decision at the close fills at the NEXT session's open (next_open_fill, supported); the exit is also a market order decided at the close of the 4th session after entry, filling at the 5th session's open (open-to-open 5-session clock). No stops are needed for risk control: route (b), signal/time exit with capped unlevered sizing. PRE-REGISTERED EVENT COUNT, measured with the frozen baseline rule on these exact 23 names over 2018-01 to 2026-10 (gross, before costs, before concurrency caps): 1,192 qualifying name-events on 737 distinct trigger sessions. Mean 5-session open-to-open return +0.65%, median +0.46%, hit rate 55%. By year: 2018 -0.43% (52), 2019 -0.16% (140), 2020 +0.03% (172), 2021 +0.96%, 2022 +1.00%, 2023 -0.08%, 2024 +1.99%, 2025 +1.03%, 2026 +1.01%. Early years are weak, so the analyst should expect losing years. This measurement only sizes the evidence; it is not a performance claim. After the concurrency caps, roughly 900-1,000 trades on roughly 650 distinct entry dates are expected. Per lesson L238, the honest sample is the distinct entry dates (~650), not the per-name trade count. COSTS: JPEQ has zero commission and no transaction tax; the platform models spread+impact, about 0.05-0.10% round trip on these mega caps. Arithmetic: 0.65% gross minus ~0.10% gives ~0.55% net per trade, 5.5x the round trip. Accounts and metrics are in JPY (USD view in fx_report). Board lot is 100 shares: size with self.lot_size() and round down. Daily price limits are enforced by the venue.