[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_102":103,"_104":105},"routes/embed.x.$handle.$name.$",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":-5},"handle","jfkll","name","ivmodule","path","levels.ts","project",{"_22":45,"_11":12,"_33":46,"_47":-5,"_48":42,"_49":50,"_51":50,"_52":53,"_54":-5,"_55":56,"_57":58,"_59":32,"_60":-5,"_61":-5,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":-5,"_69":-5,"_70":71,"_72":71,"_73":74,"_75":74,"_76":74,"_77":71,"_78":71,"_79":80,"_81":82,"_83":84,"_85":42},"file",{"_22":23,"_11":14,"_24":25,"_26":-5,"_27":28,"_29":30,"_31":32,"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":-5,"_13":44},"permalink","https://www.val.town/x/jfkll/ivmodule/code/levels.ts","extension","id","9d5bec64-6eca-4b39-8fb9-63aa5b02c688","fileId","4fc39b2a-9e72-11f1-827f-1607ee4eb77e","parentId","branchId","9501ca55-9d64-4f95-91aa-9fa7ca96c333","branchVersion",56,"latest",true,"createdAt",["D",1787482907306],"version",3,"fileType","script","content","/**\n * levels.ts — the Phase 0 pure function.\n *\n * computeLevels(chain, market, config) -> LevelSet\n *\n * No I/O. No clock reads. No randomness. Given identical inputs it returns\n * identical output, which is what makes historical replay and calibration\n * possible at all.\n *\n * Design notes that differ from the report, because building it exposed better\n * choices:\n *\n * 1. sigma_eff = sigma0 / (1 + lambda*Ghat) rather than / (1 - lambda*G).\n * The report's form has a pole at lambda*G -> 1. This form is monotone,\n * bounded and needs no ad-hoc clamp to stay physical.\n *\n * 2. Gamma enters BOTH as an OU reversion rate theta(S) AND as a local vol\n * modifier. These are the same mechanism seen from two angles: theta\n * captures the directional pull toward the gamma centroid, sigma_eff\n * captures the reduction in diffusion. Using only one under-reads the\n * effect; the OU-bands framing needs theta, the report's framing needs\n * sigma_eff, and the truth needs both.\n *\n * 3. The ladder is produced by inverting a monotone P(touch) curve rather\n * than by placing bands at fixed sigma multiples. That is what makes the\n * rungs mean what they claim to mean.\n */\n\nimport {\n bsGamma,\n bsVanna,\n expectedRange,\n firstPassageProb,\n reflectionTouchProb,\n} from \"./numerics.ts\";\nimport {\n detectIvWalls,\n dupireLocalVol,\n fitSvi,\n type IvWall,\n type SmilePoint,\n sviIv,\n type SviParams,\n} from \"./surface.ts\";\n\n// ---------------------------------------------------------------------------\n// Inputs\n// ---------------------------------------------------------------------------\n\nexport interface OptionQuote {\n strike: number;\n right: \"C\" | \"P\";\n /** Expiry in YEARS from the snapshot instant. 0DTE at 14:00 ET ~ 0.0008. */\n tau: number;\n /** This strike's own IV. Never substitute ATM IV — it misprices the wings. */\n iv: number;\n openInterest: number;\n /** Change in OI vs prior session close. Used for the cheap sign proxy. */\n deltaOi?: number;\n /** Change in this contract's price vs prior close. Used for the sign proxy. */\n deltaPrice?: number;\n}\n\nexport interface ChainSnapshot {\n /** \"NDX\" | \"QQQ\" | \"NQ\" — used for notional normalisation. */\n symbol: string;\n /** Spot of THIS book's underlying (NDX index, QQQ share price, NQ future). */\n underlying: number;\n /** Contract multiplier: 100 for NDX/QQQ, 20 for NQ. */\n multiplier: number;\n /** Multiply this book's price axis by this to reach NQ points. */\n toNqScale: number;\n /** Additive basis applied AFTER scaling, in NQ points. Carry, not a ratio. */\n toNqBasis: number;\n quotes: OptionQuote[];\n}\n\nexport interface MarketState {\n /** Current NQ price. */\n nqSpot: number;\n /** Session running high in NQ points, or null pre-open. */\n runningHigh: number | null;\n /** Session running low in NQ points, or null pre-open. */\n runningLow: number | null;\n /** Fraction of the RTH session elapsed, 0 = 09:30 ET, 1 = 16:00 ET. */\n sessionFrac: number;\n /** ATM 0DTE implied vol, annualised. Drives the baseline diffusion. */\n atmIv: number;\n /**\n * Optional measured-vol path. When BOTH are supplied, the engine uses\n * forecastSessionSigma() instead of atmIv, because the measured blend\n * out-predicts implied vol for realised session range (13% better OOS RMSE\n * than EWMA alone; IV carries a variance risk premium that biases it high).\n * Units: session log-return sigma, NOT annualised.\n */\n ewmaPastRthRv?: number;\n overnightRv?: number;\n /** JS day-of-week (0=Sun..6=Sat) for the DOW sigma correction. */\n dayOfWeek?: number;\n /**\n * Volatility stress: sigma_exante / rolling_median_sigma - 1, clipped [0,2].\n * Drives calibration shrinkage. 0 if unknown (calibration applied in full).\n */\n stress?: number;\n}\n\nexport interface EngineConfig {\n /** Gamma -> OU reversion coupling. Calibrate; do not assume. */\n lambdaTheta: number;\n /** Gamma -> local vol coupling. Calibrate; do not assume. */\n lambdaSigma: number;\n /** Baseline OU reversion with zero dealer gamma. Usually small. */\n thetaBaseline: number;\n /** Weight on the dOI x dPrice sign proxy vs the structural prior, 0..1. */\n signProxyWeight: number;\n /** Ladder rungs to emit, as P(touch before close). */\n ladderRungs: number[];\n /**\n * \"ladder\" = full 4-rung-per-side set.\n * \"quad\" = 4 zones total: primary HOD/LOD plus one expansion zone per side,\n * with the expansion probability lift-adjusted for continuation.\n */\n zoneMode: \"ladder\" | \"quad\";\n /** quad mode: P(touch) for the primary zones. */\n primaryRung: number;\n /** quad mode: P(touch) for the expansion zones, before the lift. */\n expansionRung: number;\n /** Space grid nodes. 241 is ample; cost is linear. */\n gridNodes: number;\n /** Backward time steps. */\n timeSteps: number;\n /** Grid half-width in units of remaining-session sigma. */\n gridWidthSigmas: number;\n /** Isotonic calibration map as (raw, calibrated) knots. Identity if empty. */\n calibration: Array<[number, number]>;\n /** Apply the measured fat-tail correction to touch probabilities. */\n useFatTailAdjust: boolean;\n /**\n * Shrinks the calibration map toward identity as stress rises:\n * w = 1/(1 + k*stress), final = w*calibrated + (1-w)*raw\n * where stress = sigma_exante / rolling_median_sigma - 1, clipped to [0,2].\n * Worth +0.54% OOS Brier over a plain global map. Marginal. Set 0 to disable.\n */\n stressShrinkK: number;\n /** Use the fitted SVI smile for gamma instead of raw quoted IVs. */\n useSviForGamma: boolean;\n /**\n * Use Dupire local vol from the surface for sigma_eff when >=2 expiries are\n * available. When this succeeds, lambdaSigma is NOT used — sigma_eff is READ\n * from the market rather than fitted, removing a free parameter.\n */\n useDupireLocalVol: boolean;\n /** |z| threshold for the IV-wall curvature detector. */\n ivWallZ: number;\n /** Minimum |residual| in vol points for an IV wall. */\n ivWallMinResidual: number;\n}\n\n/**\n * Fat-tail correction, MEASURED on 2,985 NQ sessions.\n *\n * The Gaussian first-passage law materially under-predicts touch at distance.\n * Realised vs an UPPER BOUND on the Gaussian two-sided rate (4*Phi(-z), which\n * over-counts by ignoring the overlap of the two barriers):\n *\n * z=1.5 Gaussian <=26.7% realised 28.2%\n * z=2.0 Gaussian <= 9.1% realised 13.6%\n * z=2.5 Gaussian <= 2.5% realised 6.3%\n * z=3.0 Gaussian <= 0.5% realised 3.2%\n *\n * Realised exceeds even the upper bound from z=2 out, so this is not an\n * artefact of the approximation. It survives switching to a fast EWMA vol\n * estimator, so it is not a slow-estimator artefact either. Excess kurtosis of\n * daily returns is 23.5.\n *\n * Interpretation: genuine jumps. Applied as a distance-dependent inflation of\n * the tail. This is a stopgap - the principled fix is the compound-Poisson\n * jump term, which needs the event calendar to condition intensity on.\n */\nexport function fatTailAdjust(p: number, z: number): number {\n if (!(z > 1.2)) return p;\n const excess = Math.min(0.06, 0.018 * (z - 1.2));\n return Math.min(1, p + excess);\n}\n\n/**\n * MEASURED gamma coupling. Fitted on 1,987 QQQ sessions, 2018-02 to 2026-01,\n * 22,067 forward 30-minute observations.\n *\n * log(rv^2) = a + b*log(expected_var) + TOD fixed effects + d*log(atm_iv)\n * + e*log(prior_rv^2) + c*Ghat\n *\n * c = -0.1044 95% CI [-0.1168, -0.0944]\n *\n * Since rv ~ exp(c*Ghat/2) and sigma_eff = sigma_0/(1 + lambdaSigma*Ghat)\n * ~ sigma_0*exp(-lambdaSigma*Ghat) for small lambda, the mapping is\n * lambdaSigma = -c/2 = 0.052.\n *\n * An OOS sweep on ladder Brier score puts the optimum around 0.10-0.20 - flat\n * across that range, so 0.10 is chosen as the conservative end of the plateau\n * rather than the peak. Note what the sweep says about the demo's illustrative\n * 0.45: it makes the ladder WORSE than no gamma at all (-1.70% OOS). Guessing\n * this parameter would have been actively harmful.\n *\n * HOW MUCH IS IT WORTH: pooled OOS ladder Brier 0.15134 -> 0.15007, about\n * +0.84%. Real, positive in 6 of 7 walk-forward years, and small. Layer 0\n * carries the skill (0.151 vs 0.245 climatology, ~38%); gamma refines it.\n *\n * WHY lambdaTheta STAYS ZERO: a symmetric placebo run on the same panel found\n * gamma predicts PAST variance about twice as strongly as future variance\n * (backward c = -0.208 vs forward -0.105, P(forward stronger) = 0.000). So\n * gamma is predominantly a contemporaneous measure of vol conditions with a\n * real but secondary forecasting component. That supports scaling the\n * DIFFUSION, which is a variance statement. It does not support a directional\n * DRIFT toward the gamma centroid, which would be a much stronger claim than\n * the evidence carries.\n */\n/**\n * MEASURED CONTINUATION LIFT.\n *\n * At the moment price breaks a zone, does it extend further than a fresh\n * diffusion forecast from that point predicts? Measured on 8,550 break events\n * across 1,579 sessions (primary z=1.0, expansion z=1.8):\n *\n * all breaks lift 1.222 CI [1.151, 1.290]\n * UPSIDE breaks lift 1.064 CI [0.971, 1.166] NOT significant\n * DOWNSIDE breaks lift 1.340 CI [1.242, 1.434]\n * long gamma lift 1.120 CI [1.033, 1.235]\n * short gamma lift 1.313 CI [1.214, 1.407]\n *\n * Continuation is real but overwhelmingly a DOWNSIDE phenomenon - the leverage\n * effect. Downside breaks trigger genuine selling; upside breaks grind, and\n * their confidence interval crosses 1.\n *\n * ON THE OBVIOUS TEST THAT DOES NOT WORK: asking \"given price touched the\n * primary, how often did it reach expansion\" is tautological. Nested barriers\n * mean reaching the far level implies reaching the near one, so the answer is\n * P(exp)/P(prim) by set inclusion - always, in every regime. The numbers above\n * condition on the MOMENT of the break and re-forecast from there, which is\n * the only version that carries information.\n */\nexport const CONTINUATION_LIFT = { up: 1.064, down: 1.340 };\nexport const CONTINUATION_LIFT_SHORT_GAMMA = { up: 1.15, down: 1.45 };\n\nexport const DEFAULT_CONFIG: EngineConfig = {\n lambdaTheta: 0.0, // measured evidence does not support a drift term\n lambdaSigma: 0.10, // MEASURED (see above); demo's 0.45 was illustrative\n thetaBaseline: 0.0,\n signProxyWeight: 0.6,\n ladderRungs: [0.75, 0.55, 0.35, 0.20],\n zoneMode: \"quad\",\n primaryRung: 0.60,\n expansionRung: 0.25,\n gridNodes: 241,\n timeSteps: 60,\n gridWidthSigmas: 3.2,\n /**\n * MEASURED isotonic calibration map, P_touch space.\n *\n * Fitted on 47,995 observations across 3,004 NQ sessions (2014-2026), eight\n * intraday checkpoints per session, both sides. Validated under purged\n * walk-forward CV (train on all prior years, test on the next).\n *\n * WHAT IT IS WORTH: pooled OOS Brier 0.1507 raw -> 0.1488 calibrated, about\n * +1.3%. Small. The raw model already scores 0.1507 against a climatology\n * baseline of 0.2391, so the MODEL carries the skill (~37%) and calibration\n * is polish. Do not expect a calibration map to rescue a weak model.\n *\n * PROVISIONAL: these knots were fitted with sigma driven by EWMA realised\n * vol, because that is what 12 years of OHLCV supports. In production sigma\n * comes from 0DTE ATM implied vol, a different input distribution. The map\n * must be refitted once the recorder has accumulated enough chain history.\n * Until then treat this as a sensible prior, not a finished calibration.\n */\n calibration: [\n [0.02, 0.0],\n [0.08, 0.087],\n [0.14, 0.132],\n [0.2, 0.171],\n [0.26, 0.222],\n [0.32, 0.254],\n [0.38, 0.32],\n [0.44, 0.343],\n [0.5, 0.403],\n [0.56, 0.462],\n [0.62, 0.543],\n [0.68, 0.624],\n [0.74, 0.663],\n [0.8, 0.728],\n [0.86, 0.84],\n [0.92, 0.913],\n [0.98, 0.988],\n ],\n useFatTailAdjust: true,\n stressShrinkK: 1.0,\n useSviForGamma: true,\n useDupireLocalVol: true,\n ivWallZ: 2.0,\n ivWallMinResidual: 0.0015,\n};\n\n// ---------------------------------------------------------------------------\n// Outputs\n// ---------------------------------------------------------------------------\n\nexport type Regime = \"suppressive\" | \"neutral\" | \"amplifying\";\nexport type Stance = \"fade\" | \"extreme_in\" | \"breakout\";\n\nexport interface Level {\n nq: number;\n side: \"upper\" | \"lower\";\n /** The rung this level represents. */\n rung: number;\n pTouch: number;\n pTouchNull: number;\n /** Distance in units of remaining-session sigma. All thresholds live here. */\n z: number;\n /** Nearest gamma wall within half a rung spacing, if any. */\n nearestWall: Wall | null;\n /** Nearest IV wall within half a rung spacing, if any. */\n nearestIvWall: IvWallNq | null;\n /** quad mode: which zone this is. */\n role?: \"primary\" | \"expansion\";\n /**\n * Expansion zones only: P(reach this | the primary on this side breaks),\n * after the measured continuation lift. THIS is the number to act on once a\n * break happens - not the unconditional touch probability.\n */\n pReachGivenBreak?: number;\n stanceWeights: Record;\n}\n\nexport interface Wall {\n nq: number;\n /** Dealer $gamma per 1% move, in NQ-point-normalised units. */\n gammaNotional: number;\n /** Positive = dealers long gamma here (suppressive). */\n sign: number;\n /** 0..1 confidence in the sign inference. */\n signConfidence: number;\n source: string;\n}\n\n/** An IV wall mapped onto the NQ price axis. */\nexport interface IvWallNq {\n nq: number;\n book: string;\n expiry: number;\n residual: number;\n z: number;\n kind: \"vol_premium\" | \"vol_discount\";\n}\n\nexport interface LevelSet {\n asofSessionFrac: number;\n nqSpot: number;\n /** Remaining-session sigma in NQ points. The unit everything is measured in. */\n sessionSigmaNq: number;\n expectedRangeNq: number;\n regime: {\n state: Regime;\n /** tanh-squashed. SATURATES at +/-1: see gammaSaturated. */\n netGammaNormalised: number;\n /**\n * True when |netGammaNormalised| > 0.99, i.e. the tanh has saturated and\n * the normalised value has stopped carrying magnitude information. The\n * regime call is still valid (the sign is right) but the depth proxy needs\n * rescaling before lambdaTheta can be calibrated meaningfully.\n */\n gammaSaturated: boolean;\n /** Raw net dealer $gamma per 1% move at spot, pre-normalisation. */\n netGammaRaw: number;\n hhi: number;\n gammaCentroidNq: number | null;\n };\n running: {\n pHighSurvives: number | null;\n pLowSurvives: number | null;\n pHighSurvivesNull: number | null;\n pLowSurvivesNull: number | null;\n };\n walls: Wall[];\n ivWalls: IvWallNq[];\n /**\n * Where a gamma wall and an IV wall sit on the same price. Gamma walls come\n * from OPEN INTEREST (positioning, backward-looking, updates after clearing);\n * IV walls come from PRICES (forward-looking, updates every quote). Agreement\n * is a strong level. Disagreement is itself information.\n */\n confluence: Array<\n { nq: number; gammaSign: number; ivKind: string; agree: boolean }\n >;\n levels: Level[];\n diagnostics: {\n contractsUsed: number;\n contractsDropped: number;\n booksMerged: string[];\n layer0Only: boolean;\n surfacesFitted: number;\n sviRmse: number | null;\n localVolSource: \"dupire\" | \"lambda_sigma\" | \"none\";\n };\n}\n\n// ---------------------------------------------------------------------------\n// Intraday variance profile\n// ---------------------------------------------------------------------------\n\n/**\n * MEASURED intraday variance profile for NQ RTH.\n *\n * 26 x 15-minute buckets, 08:30-15:00 CT, normalised to mean 1.0.\n * Estimated from 3,005 clean RTH sessions, 2014-01-02 to 2026-01-30,\n * roll-adjusted (Panama) 1-minute bars.\n *\n * This REPLACES a parametric guess that was materially wrong in both\n * directions: it understated the opening bucket by ~40% and overstated the\n * closing hour by ~35%. Since the ladder's width is driven by remaining\n * variance, that guess made the ladder too tight at the open and too wide\n * into the close - errors in opposite directions at the two times of day\n * when levels matter most.\n *\n * Note bucket 18 (13:00-13:15 CT = 14:00-14:15 ET). The local bump is the\n * FOMC statement release imprinting itself on a 12-year average. It is a\n * calendar effect, not a time-of-day effect, and on non-FOMC days it should\n * be suppressed. Left in for now because removing it needs an event calendar.\n */\nexport const EMPIRICAL_VAR_PROFILE = [\n 2.7173,\n 1.9100,\n 1.9269,\n 1.4734,\n 1.3247,\n 1.0526,\n 1.0029,\n 0.8668,\n 0.8311,\n 0.7520,\n 0.7205,\n 0.6339,\n 0.6279,\n 0.5752,\n 0.6522,\n 0.6320,\n 0.6341,\n 0.6046,\n 0.9821,\n 0.7359,\n 0.8010,\n 0.7707,\n 0.8865,\n 0.8171,\n 0.9166,\n 1.1522,\n];\n\n/**\n * Instantaneous variance multiplier at session fraction u, linearly\n * interpolated between bucket centres.\n */\nexport function varianceMultiplier(u: number): number {\n const c = Math.max(0, Math.min(1, u));\n const n = EMPIRICAL_VAR_PROFILE.length;\n const x = c * n - 0.5;\n if (x <= 0) return EMPIRICAL_VAR_PROFILE[0];\n if (x >= n - 1) return EMPIRICAL_VAR_PROFILE[n - 1];\n const i = Math.floor(x);\n const w = x - i;\n return EMPIRICAL_VAR_PROFILE[i] * (1 - w) + EMPIRICAL_VAR_PROFILE[i + 1] * w;\n}\n\n/** Remaining variance fraction of a full session, from u to close. */\nexport function remainingVarianceFraction(u: number, nQuad = 64): number {\n const a = Math.max(0, Math.min(1, u));\n if (a >= 1) return 0;\n let acc = 0;\n const h = (1 - a) / nQuad;\n for (let i = 0; i < nQuad; i++) {\n const m0 = varianceMultiplier(a + i * h);\n const m1 = varianceMultiplier(a + (i + 1) * h);\n acc += 0.5 * (m0 + m1) * h;\n }\n return acc;\n}\n\n// ---------------------------------------------------------------------------\n// Session volatility forecast\n// ---------------------------------------------------------------------------\n\n/**\n * MEASURED session-sigma forecast.\n *\n * Sigma sets the width of every rung, so it is the single highest-leverage\n * input in the engine. Fitted on 3,004 NQ sessions (2014-2026):\n *\n * log(RTH_rv) = -0.030 + 0.509*log(EWMA_past_RTH_rv) + 0.455*log(overnight_rv)\n *\n * in-sample R2 0.711 (EWMA alone) -> 0.788 (+ overnight)\n * OOS RMSE 0.3095 -> 0.2692 (13% better)\n *\n * The overnight session carries almost as much weight as five days of RTH\n * history (0.455 vs 0.509). That is a large effect and it is free - the\n * information is sitting in the Globex tape every morning before the open.\n *\n * NOTE ON IMPLIED VOL: once the recorder has chain history, ATM IV should join\n * this blend and the weights be refitted three-way. IV is forward-looking and\n * should carry real weight, but it also embeds a variance risk premium, so it\n * is a biased estimator of realised vol and cannot simply replace this.\n */\n/**\n * MEASURED day-of-week correction to the sigma forecast. Index 0 = Monday.\n *\n * The blend above is BIASED by weekday, strongly and significantly:\n *\n * Mon x0.9016 (t = -8.28) Thu x1.0365 (t = +3.58)\n * Tue x1.0046 (t = +0.45) Fri x0.9838 (t = -1.58)\n * Wed x1.0688 (t = +6.30)\n *\n * WHY Monday is the big one: the Sunday-evening-to-Monday-morning Globex\n * session carries weekend gap risk, so its realised vol runs ~9% hot. But that\n * excess is largely weekend news being repriced BEFORE the open - it does not\n * carry into Monday's RTH session. The regression applies one uniform weight to\n * overnight vol, so on Mondays it over-forecasts by ~10%.\n *\n * This mattered beyond the sigma itself: uncorrected, it made Monday's\n * normalised range look 7.9% below average, when the raw range is only 3.7%\n * below. Roughly half of an apparent \"quiet Monday\" effect was this bug.\n *\n * Worth +1.92% OOS RMSE on the log-sigma error. Small, but free and unbiased.\n */\nexport const DOW_SIGMA_CORRECTION = [0.9016, 1.0046, 1.0688, 1.0365, 0.9838];\n\nexport function forecastSessionSigma(\n /** EWMA (halflife ~5 sessions) of past RTH realised vol, log-return units. */\n ewmaPastRth: number,\n /** Realised vol of the overnight Globex session just ended, same units. */\n overnightRv: number,\n /** JS day-of-week, 0=Sunday..6=Saturday. Omit to skip the correction. */\n jsDayOfWeek?: number,\n): number {\n let base: number;\n if (!(ewmaPastRth > 0)) base = overnightRv > 0 ? overnightRv : 0;\n else if (!(overnightRv > 0)) base = ewmaPastRth;\n else {base = Math.exp(\n -0.030 + 0.509 * Math.log(ewmaPastRth) + 0.455 * Math.log(overnightRv),\n );}\n\n if (jsDayOfWeek === undefined) return base;\n const i = jsDayOfWeek - 1; // Mon(1) -> 0 ... Fri(5) -> 4\n if (i < 0 || i > 4) return base; // weekend: no correction\n return base * DOW_SIGMA_CORRECTION[i];\n}\n\n/**\n * NEGATIVE RESULT: monthly OPEX gamma is NOT detectable in NQ price data.\n *\n * OPEX is a calendar-determined spike in dealer gamma, so if gamma suppressed\n * excursion we should see it without any options data. Once day-of-week is\n * controlled (OPEX day is always a Friday, so it must be compared to other\n * Fridays), over 3,003 sessions:\n *\n * OPEX day vs other Fridays -3.90% t = -1.20 (not significant)\n * OPEX week Mon-Fri -3.58% t = -1.94 (marginal)\n * week before OPEX -3.54% t = -1.42 (not significant)\n *\n * The sign is right - suppression - but the magnitude is small and the\n * evidence weak. Worse for the thesis, the era split runs BACKWARDS: the\n * effect is largest in 2014-2018 (-8.3%) and smallest in 2022-2026 (-2.9%),\n * the 0DTE era when gamma effects should be strongest. None are significant\n * and the per-era samples are 35-59 sessions, so this is suggestive at best.\n *\n * For calibration: this is a WEAK PRIOR on lambdaTheta. It does not refute the\n * gamma thesis - OPEX gamma is a blunt monthly proxy for a continuous\n * intraday field, and NQ is enormous relative to any options book - but it\n * argues against expecting a large effect. Set expectations accordingly when\n * the chain history arrives.\n */\nexport const OPEX_GAMMA_EFFECT_IS_WEAK = true;\n\n/**\n * NEGATIVE RESULT, recorded so it does not get re-proposed.\n *\n * Overnight high/low are NOT attractors for the RTH extreme. Measured over the\n * same 3,004 sessions, hit rate vs a chance baseline at matched tolerance:\n *\n * tol 0.05 sigma HOD near ON high 7.8% chance 7.4%\n * tol 0.10 sigma 14.9% chance 14.8%\n * tol 0.20 sigma 28.8% chance 29.6%\n *\n * Indistinguishable from chance, and slightly BELOW it at the widest band.\n * Supporting evidence: RTH takes out the overnight high 64.8% of sessions and\n * the overnight low 57.9%, and only 4.0% of sessions stay entirely inside the\n * overnight range. The overnight range contains nothing.\n *\n * The overnight session is therefore useful for its VOLATILITY (above), not\n * for its levels. No ON-high/ON-low levels are emitted.\n */\nexport const OVERNIGHT_LEVELS_ARE_NOT_PREDICTIVE = true;\n\n// ---------------------------------------------------------------------------\n// Sign inference\n// ---------------------------------------------------------------------------\n\n/**\n * Under $100/mo there is no exchange open-close data and no OPRA trade+quote\n * feed, so per-trade side classification is off the table. This is the best\n * available EOD proxy:\n *\n * OI rose AND contract price rose -> customers opened longs -> dealers SHORT\n * OI rose AND contract price fell -> customers opened shorts -> dealers LONG\n * OI fell -> closing; weak signal, decay toward prior\n *\n * Noisy per strike, but it aggregates. Blended with a structural prior that\n * encodes the persistent call-overwriting population (dealers long calls).\n */\nfunction inferDealerSign(\n q: OptionQuote,\n proxyWeight: number,\n): { sign: number; conf: number } {\n // Structural prior: dealers long calls (overwriting supply), short puts\n // (protection demand). This is a prior, not a fact.\n const prior = q.right === \"C\" ? +1 : -1;\n\n if (\n q.deltaOi === undefined || q.deltaPrice === undefined || q.openInterest <= 0\n ) {\n return { sign: prior, conf: 0.3 };\n }\n\n const turnover = Math.min(\n 1,\n Math.abs(q.deltaOi) / Math.max(1, q.openInterest),\n );\n if (q.deltaOi <= 0 || turnover < 0.02) {\n return { sign: prior, conf: 0.3 + 0.2 * turnover };\n }\n\n const proxy = q.deltaPrice > 0 ? -1 : +1; // customer bought -> dealer short\n const blended = proxyWeight * proxy * turnover + (1 - proxyWeight) * prior;\n const sign = blended >= 0 ? +1 : -1;\n const conf = Math.min(0.9, 0.3 + 0.6 * turnover * proxyWeight);\n return { sign, conf };\n}\n\n// ---------------------------------------------------------------------------\n// Gamma field\n// ---------------------------------------------------------------------------\n\n// ---------------------------------------------------------------------------\n// Surface layer\n// ---------------------------------------------------------------------------\n\nexport interface BookSurface {\n book: string;\n expiry: number; // tau in years\n fit: SviParams;\n ivWalls: IvWall[];\n forward: number; // this book's forward for that expiry\n toNqScale: number;\n toNqBasis: number;\n}\n\n/**\n * Fit one SVI smile per (book, expiry) and detect IV walls against it.\n *\n * Weighting by open interest matters: a 2-lot quote at a far strike carries\n * the same IV as a 50,000-lot ATM quote but a fraction of the information.\n * Unweighted fits get dragged around by junk at the wings.\n */\nexport function buildSurfaces(\n books: ChainSnapshot[],\n cfg: EngineConfig,\n): BookSurface[] {\n const out: BookSurface[] = [];\n\n for (const book of books) {\n const byTau = new Map();\n for (const q of book.quotes) {\n if (!(q.tau > 0) || !(q.iv > 0) || !(q.openInterest > 0)) continue;\n const key = Math.round(q.tau * 3650000) / 3650000;\n const arr = byTau.get(key);\n if (arr) arr.push(q);\n else byTau.set(key, [q]);\n }\n\n for (const [tau, quotes] of byTau) {\n const F = book.underlying;\n\n // OTM-only smile: use puts below the forward and calls above. ITM quotes\n // are wide and their IVs are noisy, and including both rights at the same\n // strike double-counts the same information at different quality.\n const pts: SmilePoint[] = [];\n for (const q of quotes) {\n const otm = (q.right === \"P\" && q.strike <= F) ||\n (q.right === \"C\" && q.strike > F);\n if (!otm) continue;\n const k = Math.log(q.strike / F);\n if (!Number.isFinite(k) || Math.abs(k) > 0.30) continue;\n pts.push({\n k,\n iv: q.iv,\n weight: Math.max(1, q.openInterest),\n strike: q.strike,\n });\n }\n if (pts.length < 8) continue;\n\n const fit = fitSvi(pts, tau);\n if (!fit) continue;\n\n out.push({\n book: book.symbol,\n expiry: tau,\n fit,\n ivWalls: detectIvWalls(pts, fit, cfg.ivWallZ, cfg.ivWallMinResidual),\n forward: F,\n toNqScale: book.toNqScale,\n toNqBasis: book.toNqBasis,\n });\n }\n }\n\n return out.sort((a, b) => a.expiry - b.expiry);\n}\n\n/** Look up the fitted IV for a strike, falling back to the quoted value. */\nfunction surfaceIv(\n surfaces: BookSurface[],\n bookSym: string,\n tau: number,\n strike: number,\n forward: number,\n fallback: number,\n): number {\n for (const s of surfaces) {\n if (s.book !== bookSym) continue;\n if (Math.abs(s.expiry - tau) > 1e-9) continue;\n const iv = sviIv(s.fit, Math.log(strike / forward));\n return iv > 0.001 && iv < 5 ? iv : fallback;\n }\n return fallback;\n}\n\ninterface StrikeGamma {\n nq: number;\n signedGammaAt: (nqPrice: number) => number;\n peakNotional: number;\n sign: number;\n signConfidence: number;\n source: string;\n}\n\nfunction buildStrikeGammas(\n books: ChainSnapshot[],\n cfg: EngineConfig,\n surfaces: BookSurface[],\n): { strikes: StrikeGamma[]; used: number; dropped: number } {\n const strikes: StrikeGamma[] = [];\n let used = 0;\n let dropped = 0;\n\n for (const book of books) {\n for (const q of book.quotes) {\n if (!(q.iv > 0) || !(q.tau > 0) || !(q.openInterest > 0)) {\n dropped++;\n continue;\n }\n used++;\n\n const { sign, conf } = inferDealerSign(q, cfg.signProxyWeight);\n const strikeNq = q.strike * book.toNqScale + book.toNqBasis;\n\n // Use the FITTED smile IV, not the raw quote. Quoted IVs are noisy and\n // occasionally absent; the fit smooths them while preserving skew. This\n // is the strike-own-IV requirement done properly rather than by\n // substituting a single ATM number across the chain.\n const ivUse = cfg.useSviForGamma\n ? surfaceIv(\n surfaces,\n book.symbol,\n q.tau,\n q.strike,\n book.underlying,\n q.iv,\n )\n : q.iv;\n\n // Notional normalisation: dollar gamma per 1% move, then expressed on a\n // common NQ-point axis. Raw OI is NOT comparable across these books —\n // an NDX contract carries roughly 41x the notional of a QQQ contract.\n const notionalScale = book.multiplier * q.openInterest * book.toNqScale;\n\n const fn = (nqPrice: number) => {\n const bookSpot = (nqPrice - book.toNqBasis) / book.toNqScale;\n if (!(bookSpot > 0)) return 0;\n const g = bsGamma({\n S: bookSpot,\n K: q.strike,\n tau: q.tau,\n sigma: ivUse,\n });\n // dollar gamma per 1% move\n return sign * g * bookSpot * bookSpot * 0.01 * notionalScale;\n };\n\n const peak = Math.abs(fn(strikeNq));\n\n strikes.push({\n nq: strikeNq,\n signedGammaAt: fn,\n peakNotional: peak,\n sign,\n signConfidence: conf,\n source: `${book.symbol}_${q.right}${q.strike}`,\n });\n }\n }\n\n return { strikes, used, dropped };\n}\n\n// ---------------------------------------------------------------------------\n// Calibration map\n// ---------------------------------------------------------------------------\n\nfunction applyCalibration(p: number, knots: Array<[number, number]>): number {\n if (knots.length < 2) return p;\n if (p <= knots[0][0]) return knots[0][1];\n const last = knots[knots.length - 1];\n if (p >= last[0]) return last[1];\n for (let i = 1; i < knots.length; i++) {\n const [x0, y0] = knots[i - 1];\n const [x1, y1] = knots[i];\n if (p <= x1) {\n const w = (p - x0) / Math.max(1e-12, x1 - x0);\n return y0 + w * (y1 - y0);\n }\n }\n return p;\n}\n\n// ---------------------------------------------------------------------------\n// Stance weighting\n// ---------------------------------------------------------------------------\n\nfunction stanceWeights(\n regime: Regime,\n signConf: number,\n): Record {\n const base: Record> = {\n suppressive: { fade: 0.70, extreme_in: 0.25, breakout: 0.05 },\n neutral: { fade: 0.30, extreme_in: 0.50, breakout: 0.20 },\n // Fade weight is exactly ZERO here, not merely small. Fading a level while\n // dealer hedging amplifies moves is where the drawdowns come from.\n amplifying: { fade: 0.00, extreme_in: 0.30, breakout: 0.70 },\n };\n const w = { ...base[regime] };\n // Low sign confidence pulls everything toward the neutral book.\n const k = Math.max(0, Math.min(1, (signConf - 0.3) / 0.5));\n const n = base.neutral;\n return {\n fade: k * w.fade + (1 - k) * n.fade,\n extreme_in: k * w.extreme_in + (1 - k) * n.extreme_in,\n breakout: k * w.breakout + (1 - k) * n.breakout,\n };\n}\n\n// ---------------------------------------------------------------------------\n// Main entry point\n// ---------------------------------------------------------------------------\n\nexport function computeLevels(\n books: ChainSnapshot[],\n mkt: MarketState,\n cfg: EngineConfig = DEFAULT_CONFIG,\n): LevelSet {\n const S0 = mkt.nqSpot;\n\n // --- remaining-session sigma, variance-time scaled -----------------------\n const SESSION_YEARS = 6.5 / 24 / 365;\n const remFrac = remainingVarianceFraction(mkt.sessionFrac);\n const sessionVarYears = SESSION_YEARS * remFrac;\n // Prefer the measured blend when both realised-vol inputs are present;\n // fall back to implied otherwise.\n const measured =\n mkt.ewmaPastRthRv !== undefined && mkt.overnightRv !== undefined\n ? forecastSessionSigma(mkt.ewmaPastRthRv, mkt.overnightRv, mkt.dayOfWeek)\n : null;\n const sigmaLog = measured !== null\n ? measured * Math.sqrt(Math.max(1e-12, remFrac))\n : mkt.atmIv * Math.sqrt(Math.max(1e-12, sessionVarYears));\n const sessionSigmaNq = sigmaLog * S0;\n\n // --- surface layer -------------------------------------------------------\n const surfaces = buildSurfaces(books, cfg);\n\n // Prefer the INDEX book for the volatility surface: it is the direct\n // underlying, so its smile describes NQ dynamics without a ratio conversion\n // in the middle. Fall back to whichever book fitted the most points.\n const indexSurfaces = surfaces.filter((s) => s.toNqScale === 1);\n const surfPool = indexSurfaces.length >= 2 ? indexSurfaces : surfaces;\n\n // --- gamma field ---------------------------------------------------------\n const { strikes, used, dropped } = buildStrikeGammas(books, cfg, surfaces);\n\n const gammaAt = (nqPrice: number): number => {\n let acc = 0;\n for (const s of strikes) acc += s.signedGammaAt(nqPrice);\n return acc;\n };\n\n const netGammaSpot = gammaAt(S0);\n\n // Normalise gamma to a dimensionless scale so lambda is portable across\n // regimes and notional levels. Scale = median |peak gamma| across strikes\n // within the tradeable window, which is a crude depth proxy. With a real\n // depth measure from the Rithmic book this should be replaced.\n const nearby = strikes\n .filter((s) => Math.abs(s.nq - S0) < 2.0 * sessionSigmaNq)\n .map((s) => s.peakNotional)\n .sort((a, b) => a - b);\n const depthProxy = nearby.length\n ? Math.max(\n 1e-9,\n nearby[Math.floor(nearby.length / 2)] * Math.max(4, nearby.length) * 0.25,\n )\n : 1e-9;\n\n const gHat = (nqPrice: number) => Math.tanh(gammaAt(nqPrice) / depthProxy);\n\n // --- walls ---------------------------------------------------------------\n const wallMap = new Map();\n for (const s of strikes) {\n if (Math.abs(s.nq - S0) > 3.0 * sessionSigmaNq) continue;\n const key = Math.round(s.nq * 4) / 4; // NQ tick\n const prev = wallMap.get(key);\n const contrib = s.sign * s.peakNotional;\n if (prev) {\n prev.gammaNotional += contrib;\n prev.signConfidence = Math.max(prev.signConfidence, s.signConfidence);\n } else {\n wallMap.set(key, {\n nq: key,\n gammaNotional: contrib,\n sign: s.sign,\n signConfidence: s.signConfidence,\n source: s.source,\n });\n }\n }\n const walls = [...wallMap.values()]\n .map((w) => ({ ...w, sign: w.gammaNotional >= 0 ? 1 : -1 }))\n .sort((a, b) => Math.abs(b.gammaNotional) - Math.abs(a.gammaNotional))\n .slice(0, 12);\n\n // --- IV walls, mapped onto the NQ axis -----------------------------------\n const ivWalls: IvWallNq[] = [];\n for (const surf of surfaces) {\n for (const w of surf.ivWalls) {\n const nq = w.strike * surf.toNqScale + surf.toNqBasis;\n if (Math.abs(nq - S0) > 3.0 * sessionSigmaNq) continue;\n ivWalls.push({\n nq: Math.round(nq * 4) / 4,\n book: surf.book,\n expiry: surf.expiry,\n residual: w.residual,\n z: w.z,\n kind: w.kind,\n });\n }\n }\n ivWalls.sort((a, b) => Math.abs(b.residual) - Math.abs(a.residual));\n\n // --- concentration (HHI) -------------------------------------------------\n const window = strikes.filter((s) =>\n Math.abs(s.nq - S0) < 1.5 * sessionSigmaNq\n );\n const total = window.reduce((a, s) => a + s.peakNotional, 0);\n const hhi = total > 0\n ? window.reduce((a, s) => a + Math.pow(s.peakNotional / total, 2), 0)\n : 0;\n\n // --- gamma centroid: the OU anchor --------------------------------------\n let wsum = 0;\n let wacc = 0;\n for (const s of window) {\n const w = s.peakNotional * (s.sign > 0 ? 1 : 0); // only long-gamma pins\n wsum += w;\n wacc += w * s.nq;\n }\n const gammaCentroid = wsum > 0 ? wacc / wsum : null;\n\n // --- regime --------------------------------------------------------------\n const netGammaNorm = Math.tanh(netGammaSpot / depthProxy);\n let state: Regime;\n if (netGammaNorm > 0.15 && hhi > 0.06) state = \"suppressive\";\n else if (netGammaNorm < -0.15) state = \"amplifying\";\n else state = \"neutral\";\n\n const layer0Only = cfg.lambdaTheta === 0 && cfg.lambdaSigma === 0;\n\n // --- PDE grid ------------------------------------------------------------\n const halfWidth = cfg.gridWidthSigmas * sigmaLog;\n const n = cfg.gridNodes;\n const x = new Float64Array(n);\n for (let i = 0; i < n; i++) x[i] = -halfWidth + (2 * halfWidth * i) / (n - 1);\n const spotIdx = Math.round((n - 1) / 2);\n\n const anchorLog = gammaCentroid !== null ? Math.log(gammaCentroid / S0) : 0;\n const tauClock = 1; // work in normalised session time; vol already carries scale\n\n // PRECOMPUTED FIELDS.\n //\n // The gamma field is a function of price only, so it is constant across time\n // steps and across barrier solves. Evaluating it inside the PDE inner loop\n // costs O(nodes x steps x barriers x strikes) — measured at 15 SECONDS on a\n // 155-contract chain, which would blow any serverless budget. Precomputing\n // on the grid makes it O(nodes x strikes), a ~1000x reduction.\n const gHatGrid = new Float64Array(n);\n const thetaGrid = new Float64Array(n);\n const volScaleGrid = new Float64Array(n);\n for (let i = 0; i < n; i++) {\n const g = layer0Only ? 0 : gHat(S0 * Math.exp(x[i]));\n gHatGrid[i] = g;\n thetaGrid[i] = layer0Only ? 0 : cfg.thetaBaseline + cfg.lambdaTheta * g;\n // Monotone, bounded, no pole. Long gamma (g>0) suppresses diffusion.\n volScaleGrid[i] = layer0Only ? 1 : 1 / (1 + cfg.lambdaSigma * g);\n }\n\n // Time-step vol multipliers also precompute: they depend only on tIdx.\n const timeVolMult = new Float64Array(cfg.timeSteps + 1);\n for (let s = 0; s <= cfg.timeSteps; s++) {\n const u = mkt.sessionFrac + (1 - mkt.sessionFrac) * (s / cfg.timeSteps);\n const instVar = varianceMultiplier(u) / Math.max(1e-9, remFrac);\n timeVolMult[s] = Math.sqrt(Math.max(1e-9, instVar));\n }\n\n // Dupire local vol, if the surface supports it. When this works, sigma_eff\n // is READ FROM THE MARKET rather than fitted, and lambdaSigma goes unused.\n // One fewer free parameter is one fewer thing to overfit.\n let localVolSource: \"dupire\" | \"lambda_sigma\" | \"none\" = layer0Only\n ? \"none\"\n : \"lambda_sigma\";\n const dupireGrid = new Float64Array(n);\n if (!layer0Only && cfg.useDupireLocalVol && surfPool.length >= 2) {\n const near = surfPool[0];\n const far = surfPool[1];\n let good = 0;\n for (let i = 0; i < n; i++) {\n const lv = dupireLocalVol(near.fit, far.fit, x[i]);\n if (lv !== null && lv > 0.01 && lv < 3.0) {\n dupireGrid[i] = lv;\n good++;\n }\n }\n // Require most of the grid to resolve. A patchy local vol surface is worse\n // than none: it would silently mix two different vol models across price.\n if (good > 0.8 * n) {\n localVolSource = \"dupire\";\n // Fill any gaps by nearest-neighbour so the PDE sees a continuous field.\n for (let i = 0; i < n; i++) {\n if (dupireGrid[i] > 0) continue;\n let j = i;\n while (j >= 0 && dupireGrid[j] <= 0) j--;\n let k2 = i;\n while (k2 < n && dupireGrid[k2] <= 0) k2++;\n dupireGrid[i] = j >= 0 ? dupireGrid[j] : (k2 < n ? dupireGrid[k2] : 0);\n }\n } else if (!layer0Only) {\n localVolSource = \"lambda_sigma\";\n }\n }\n\n // Dupire returns an ANNUALISED vol; convert to the same remaining-session\n // log-sigma units the rest of the solver works in.\n const dupireScale = new Float64Array(n);\n if (localVolSource === \"dupire\") {\n const atmIdxDup = spotIdx;\n const ref = dupireGrid[atmIdxDup] > 0 ? dupireGrid[atmIdxDup] : mkt.atmIv;\n for (let i = 0; i < n; i++) {\n dupireScale[i] = ref > 0\n ? dupireGrid[i] / ref\n : 1;\n }\n }\n\n const dxGrid = x[1] - x[0];\n const idxOf = (xv: number) => {\n let i = Math.round((xv - x[0]) / dxGrid);\n return i < 0 ? 0 : i > n - 1 ? n - 1 : i;\n };\n\n const driftFn = (xv: number, _tIdx: number) => {\n if (layer0Only) return 0;\n const i = idxOf(xv);\n return thetaGrid[i] * (anchorLog - xv);\n };\n\n const volFn = (xv: number, tIdx: number) => {\n const base = sigmaLog * timeVolMult[tIdx];\n if (layer0Only) return base;\n const i = idxOf(xv);\n // Market-read local vol wins over the fitted lambda_sigma path when it is\n // available and clean, because it needs no calibration at all.\n if (localVolSource === \"dupire\") return base * dupireScale[i];\n return base * volScaleGrid[i];\n };\n\n const touchProbAtIdx = (bIdx: number, upper: boolean): number => {\n if (bIdx === spotIdx) return 1;\n const raw = firstPassageProb({\n x,\n spotIdx,\n drift: driftFn,\n vol: volFn,\n nSteps: cfg.timeSteps,\n tau: tauClock,\n barrierIdx: bIdx,\n upper,\n });\n const z = Math.abs(x[bIdx]) / Math.max(1e-12, sigmaLog);\n const adj = cfg.useFatTailAdjust ? fatTailAdjust(raw, z) : raw;\n const cal = applyCalibration(adj, cfg.calibration);\n // Shrink toward the raw model as stress rises. Measured: the calibration\n // map is fitted mostly on calm sessions and actively HURT in 2018, 2020\n // and 2022 (the 48-63%-stressed years) when applied at full strength.\n const stress = Math.max(0, Math.min(2, mkt.stress ?? 0));\n const w = 1 / (1 + cfg.stressShrinkK * stress);\n return w * cal + (1 - w) * adj;\n };\n\n // --- probability curve, then invert to get the ladder --------------------\n const buildSide = (upper: boolean): Level[] => {\n const idxs: number[] = [];\n const step = 3;\n if (upper) {\n for (let i = spotIdx + step; i < n; i += step) idxs.push(i);\n } else {\n for (let i = spotIdx - step; i >= 0; i -= step) idxs.push(i);\n }\n\n const curve = idxs.map((i) => ({\n idx: i,\n nq: S0 * Math.exp(x[i]),\n p: touchProbAtIdx(i, upper),\n }));\n\n // Enforce monotonicity (guards against tiny CN wiggle near the barrier).\n for (let i = 1; i < curve.length; i++) {\n if (curve[i].p > curve[i - 1].p) curve[i].p = curve[i - 1].p;\n }\n\n const out: Level[] = [];\n for (const rung of cfg.ladderRungs) {\n let found: number | null = null;\n for (let i = 1; i < curve.length; i++) {\n const a = curve[i - 1];\n const b = curve[i];\n if (a.p >= rung && b.p <= rung) {\n const w = (a.p - rung) / Math.max(1e-12, a.p - b.p);\n found = a.nq + w * (b.nq - a.nq);\n break;\n }\n }\n if (found === null) continue;\n\n const dist = Math.abs(found - S0);\n const z = dist / Math.max(1e-9, sessionSigmaNq);\n const nullP = reflectionTouchProb(Math.log(1 + dist / S0), sigmaLog, 1);\n\n const spacing = 0.35 * sessionSigmaNq;\n let nearestWall: Wall | null = null;\n let bestD = Infinity;\n for (const w of walls) {\n const d = Math.abs(w.nq - found);\n if (d < spacing && d < bestD) {\n bestD = d;\n nearestWall = w;\n }\n }\n\n let nearestIvWall: IvWallNq | null = null;\n let bestIvD = Infinity;\n for (const w of ivWalls) {\n const d = Math.abs(w.nq - found);\n if (d < spacing && d < bestIvD) {\n bestIvD = d;\n nearestIvWall = w;\n }\n }\n\n out.push({\n nq: Math.round(found * 4) / 4,\n side: upper ? \"upper\" : \"lower\",\n rung,\n pTouch: rung,\n pTouchNull: nullP,\n z,\n nearestWall,\n nearestIvWall,\n stanceWeights: stanceWeights(\n state,\n nearestWall ? nearestWall.signConfidence : 0.3,\n ),\n });\n }\n return out;\n };\n\n let levels = [...buildSide(true), ...buildSide(false)];\n\n // --- quad mode: 4 zones instead of 8 -------------------------------------\n if (cfg.zoneMode === \"quad\") {\n const shortGamma = netGammaNorm < 0;\n const lift = shortGamma ? CONTINUATION_LIFT_SHORT_GAMMA : CONTINUATION_LIFT;\n const picked: Level[] = [];\n for (const side of [\"upper\", \"lower\"] as const) {\n const mine = levels.filter((l) => l.side === side);\n if (!mine.length) continue;\n const near = (target: number) =>\n mine.reduce((a, b) =>\n Math.abs(a.rung - target) < Math.abs(b.rung - target) ? a : b\n );\n const prim = { ...near(cfg.primaryRung), role: \"primary\" as const };\n const expn = { ...near(cfg.expansionRung), role: \"expansion\" as const };\n if (Math.abs(expn.nq - prim.nq) < 1e-6) {\n picked.push(prim);\n continue;\n }\n // Conditional reach, lift-adjusted. The ratio is the diffusion baseline;\n // the lift is what the break itself adds on top of it.\n const k = side === \"upper\" ? lift.up : lift.down;\n expn.pReachGivenBreak = Math.min(\n 0.99,\n (expn.pTouch / Math.max(1e-6, prim.pTouch)) * k,\n );\n picked.push(prim, expn);\n }\n levels = picked;\n }\n\n // --- survival of the running extremes ------------------------------------\n const survives = (\n extreme: number | null,\n upper: boolean,\n ): [number | null, number | null] => {\n if (extreme === null) return [null, null];\n const logB = Math.log(extreme / S0);\n if ((upper && logB <= 0) || (!upper && logB >= 0)) return [0, 0];\n let bIdx = Math.round(((logB + halfWidth) / (2 * halfWidth)) * (n - 1));\n bIdx = Math.max(0, Math.min(n - 1, bIdx));\n const p = 1 - touchProbAtIdx(bIdx, upper);\n const pNull = 1 - reflectionTouchProb(Math.abs(logB), sigmaLog, 1);\n return [p, pNull];\n };\n\n const [pHigh, pHighNull] = survives(mkt.runningHigh, true);\n const [pLow, pLowNull] = survives(mkt.runningLow, false);\n\n // --- confluence between the two independent wall families ----------------\n //\n // \"Agree\" is not merely co-location. A long-gamma strike (dealers suppress\n // moves) lining up with a vol_discount strike (market prices LESS vol there)\n // is two independent sources saying the same thing: this is a pin. A\n // long-gamma strike sitting on a vol_premium is a contradiction worth seeing\n // rather than averaging away.\n const confluence: LevelSet[\"confluence\"] = [];\n const tol = 0.12 * sessionSigmaNq;\n for (const gw of walls) {\n for (const iw of ivWalls) {\n if (Math.abs(gw.nq - iw.nq) > tol) continue;\n const agree = (gw.sign > 0 && iw.kind === \"vol_discount\") ||\n (gw.sign < 0 && iw.kind === \"vol_premium\");\n confluence.push({\n nq: gw.nq,\n gammaSign: gw.sign,\n ivKind: iw.kind,\n agree,\n });\n break;\n }\n }\n\n return {\n asofSessionFrac: mkt.sessionFrac,\n nqSpot: S0,\n sessionSigmaNq,\n expectedRangeNq: expectedRange(sigmaLog, 1) * S0,\n regime: {\n state,\n netGammaNormalised: netGammaNorm,\n gammaSaturated: Math.abs(netGammaNorm) > 0.99,\n netGammaRaw: netGammaSpot,\n hhi,\n gammaCentroidNq: gammaCentroid,\n },\n running: {\n pHighSurvives: pHigh,\n pLowSurvives: pLow,\n pHighSurvivesNull: pHighNull,\n pLowSurvivesNull: pLowNull,\n },\n walls,\n ivWalls: ivWalls.slice(0, 12),\n confluence,\n levels,\n diagnostics: {\n contractsUsed: used,\n contractsDropped: dropped,\n booksMerged: books.map((b) => b.symbol),\n layer0Only,\n surfacesFitted: surfaces.length,\n sviRmse: surfaces.length\n ? surfaces.reduce((a, s2) => a + s2.fit.rmse, 0) / surfaces.length\n : null,\n localVolSource,\n },\n };\n}","deleted",false,"label",[14],"2119d995-ca04-498c-aa95-f7a73f7c0d10",["D",1787415250732],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","","imageUrl","userId","edb0ee8b-6eb5-4033-8065-92fa86e65418","pinnedFileId","0ca60dd2-9e72-11f1-83cf-1607ee4eb77e","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","019f98c4-b792-72f1-83e7-7d98b5b52c8e","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_9":10,"_101":53},"org",{"_9":10,"_96":53,"_97":53,"_55":56,"_98":99,"_100":42},"branch",{"_35":86,"_11":87,"_22":28,"_88":45,"_89":-5,"_90":-5,"_91":-5,"_92":-5,"_93":-5,"_94":12,"_95":10},"isOwned",63,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","displayName","avatar","tier","free","canRestrictVals","profileImageUrl","ctx",{"_79":-5,"_135":136,"_137":138,"_139":140,"_141":42,"_142":42,"_143":42,"_144":32,"_145":-5,"_146":-7,"_147":-7,"_148":63,"_149":42,"_150":42,"_151":-5,"_152":42,"_153":42,"_154":42,"_155":-5},"ENV",{"_106":107,"_108":109,"_110":87,"_111":112,"_113":114,"_115":116,"_117":118,"_119":120,"_121":122,"_123":124,"_125":126,"_127":128,"_129":130,"_131":132,"_133":134},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town","orgs",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]