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Deploy as a CRON val.\n *\n * Runs on a schedule, sources EVERY input itself, computes the final level set,\n * and writes it to blob storage. The HTTP endpoint (server.ts) then just serves\n * that cached JSON, and the MotiveWave JAR just plots it. No maths anywhere\n * downstream.\n *\n * WHY CRON RATHER THAN COMPUTE-PER-REQUEST:\n * every client would otherwise trigger its own CBOE fetch, Yahoo fetch, SVI fit\n * and PDE solve for byte-identical output. With a Discord community behind it\n * that is N times the work and N times the rate-limit exposure against two\n * third-party endpoints. One compute, many readers.\n *\n * DATA SOURCES (both free, no keys):\n * CBOE delayed chain -> strikes, OI, IV, QQQ spot\n * Yahoo v8 chart NQ=F -> live NQ price, intraday bars, daily history\n *\n * The 15-minute chain delay is deliberate and load-bearing: it keeps this\n * outside OPRA's display and non-display fee schedule. Open interest only\n * updates after clearing anyway, so the delay costs almost nothing. NQ price\n * is the input that must be live, and Yahoo supplies it.\n *\n * DEPLOYMENT: this file exports computeAndStore() for server.ts to call\n * lazily, plus a default handler you can wire to a Cron trigger as a floor.\n *\n * The free Val Town tier caps cron at once per 15 minutes, which is far too\n * stale for a level tool. So the freshness path is LAZY RECOMPUTE: server.ts\n * checks the cache age on each request and refreshes when it exceeds the TTL.\n * That preserves the one-compute-per-minute property (the first caller after\n * expiry does the work, everyone else reads the result) without needing a\n * per-minute cron at all. Keep the 15-minute cron as a floor so a payload\n * exists even during quiet periods.\n */\n\nimport { blob } from \"https://esm.town/v/std/blob/main.ts\";\nimport { fromColumnar, gunzip, type Snapshot } from \"./codec.ts\";\nimport {\n type ChainSnapshot,\n computeLevels,\n DEFAULT_CONFIG,\n forecastSessionSigma,\n type MarketState,\n type OptionQuote,\n} from \"./levels.ts\";\n\nexport const OUT_KEY = \"levels_current\";\nexport const ARCHIVE_PREFIX = \"ladder_\";\n\n/** Sessions of archived ladders to retain. */\nconst ARCHIVE_KEEP = 60;\n\n/**\n * Globex session boundary, 18:00 ET. A session labelled D runs 18:00 ET on D\n * to 18:00 ET on D+1, which is the span the archived ladder is drawn across.\n */\nconst SESSION_BOUNDARY_HOUR = 18;\n\n/** Rolling store of observed (QQQ spot, NQ spot) pairs for the mapping fit. */\nconst MAP_KEY = \"qqq_nq_map\";\nconst MAP_POINTS = 400;\n\n/**\n * The archived ladder for a session is the FIRST one computed after the\n * session opens - i.e. the prediction made at the start, before the session\n * revealed anything.\n *\n * That choice matters for honesty. Archiving a late-session ladder would look\n * far more accurate on the chart, because by 15:00 the running extremes are\n * mostly set and the rungs have collapsed around them. Only the opening ladder\n * is a genuine forecast, and only a genuine forecast is worth scoring.\n */\nfunction sessionKeyET(\n nowSec: number,\n): { key: string; startMs: number; endMs: number } {\n const p = etParts(nowSec);\n const d = new Date(`${p.ymd}T00:00:00Z`);\n // Before 18:00 ET we are still inside the session that opened yesterday.\n if (p.min < SESSION_BOUNDARY_HOUR * 60) d.setUTCDate(d.getUTCDate() - 1);\n const key = d.toISOString().slice(0, 10);\n // Approximate ET offset from the current timestamp rather than hardcoding,\n // so this survives the DST switch without a seasonal edit.\n const offsetMin = Math.round(\n (Date.parse(\n `${p.ymd}T${String(Math.floor(p.min / 60)).padStart(2, \"0\")}:${\n String(p.min % 60).padStart(2, \"0\")\n }:00Z`,\n ) -\n nowSec * 1000) / 60000,\n );\n const startMs =\n Date.parse(\n `${key}T${String(SESSION_BOUNDARY_HOUR).padStart(2, \"0\")}:00:00Z`,\n ) - offsetMin * 60000;\n return { key, startMs, endMs: startMs + 24 * 3600_000 };\n}\nconst MAX_EXPIRIES = 3;\nconst STRIKE_WINDOW = 0.08;\n\n// ---------------------------------------------------------------------------\n// CBOE chain\n// ---------------------------------------------------------------------------\n\nasync function fetchChain(\n prior: Map | null,\n): Promise<\n { spot: number; quotes: OptionQuote[]; expiries: string[]; signed: number }\n> {\n const r = await fetch(\n \"https://cdn.cboe.com/api/global/delayed_quotes/options/QQQ.json\",\n { headers: { \"User-Agent\": \"Mozilla/5.0\" } },\n );\n if (!r.ok) throw new Error(`CBOE HTTP ${r.status}`);\n const j: any = await r.json();\n const spot = Number(j?.data?.current_price ?? j?.data?.close);\n if (!(spot > 0)) throw new Error(\"CBOE: no spot\");\n\n const raw = (j?.data?.options ?? []) as any[];\n const today = new Date().toISOString().slice(0, 10).replace(/-/g, \"\");\n const parse = (o: any) =>\n String(o.option ?? \"\").match(/^([A-Z_]+)(\\d{6})([CP])(\\d{8})$/);\n\n const expiries = [\n ...new Set(\n raw.map((o) => {\n const m = parse(o);\n return m ? `20${m[2]}` : null;\n })\n .filter((e): e is string => !!e && e >= today),\n ),\n ].sort().slice(0, MAX_EXPIRIES);\n const keep = new Set(expiries);\n\n const quotes: OptionQuote[] = [];\n let signed = 0;\n for (const o of raw) {\n const m = parse(o);\n if (!m) continue;\n const expiry = `20${m[2]}`;\n if (!keep.has(expiry)) continue;\n const strike = parseInt(m[4], 10) / 1000;\n if (Math.abs(strike / spot - 1) > STRIKE_WINDOW) continue;\n const iv = Number(o.iv ?? 0), oi = Number(o.open_interest ?? 0);\n if (!(iv > 0) || !(oi > 0)) continue;\n const y = +`20${m[2].slice(0, 2)}`,\n mo = +m[2].slice(2, 4),\n da = +m[2].slice(4, 6);\n const days = Math.max(0, (Date.UTC(y, mo - 1, da) - Date.now()) / 864e5);\n\n const q: OptionQuote = {\n strike,\n right: m[3] as \"C\" | \"P\",\n tau: Math.max(1e-6, days / 365 + (6.5 / 24 / 365) * 0.5),\n iv,\n openInterest: oi,\n };\n\n // Attach day-over-day deltas where we have a prior observation, which is\n // what turns the naive sign prior into an actual inference.\n if (prior) {\n const isoExp = `${expiry.slice(0, 4)}-${expiry.slice(4, 6)}-${\n expiry.slice(6, 8)\n }`;\n const p = prior.get(`${isoExp}|${m[3]}|${strike}`);\n if (p) {\n const mid = (Number(o.bid ?? 0) + Number(o.ask ?? 0)) / 2;\n if (mid > 0 && p.mid > 0) {\n q.deltaOi = oi - p.oi;\n q.deltaPrice = mid - p.mid;\n signed++;\n }\n }\n }\n quotes.push(q);\n }\n if (quotes.length < 40) {\n throw new Error(`CBOE: only ${quotes.length} usable contracts`);\n }\n return { spot, quotes, expiries, signed };\n}\n\n// ---------------------------------------------------------------------------\n// QQQ -> NQ mapping, fitted from observation\n// ---------------------------------------------------------------------------\n\n/**\n * NQ = a + b*QQQ, fitted on recently observed spot pairs.\n *\n * The previous version carried a hardcoded ratio (41.09) plus a basis backed\n * out from the current spot. That was modelling the chain QQQ -> NDX -> NQ,\n * which made sense when NDX was in the pipeline. It no longer is, and we\n * observe both QQQ and NQ directly every minute - so fitting the relationship\n * end to end is strictly better. One empirical fit absorbs the index ratio,\n * the futures carry, dividend accrual and the ETF expense drag together,\n * with no assumption about any of them.\n *\n * Least squares on a rolling window, with a slope sanity band: a bad Yahoo or\n * CBOE tick would otherwise drag the fit and misplace every level at once.\n */\nasync function fitMapping(\n qqqSpot: number,\n nq: number,\n): Promise<{ a: number; b: number; n: number; fitted: boolean }> {\n let pts: Array<[number, number]> = [];\n try {\n const v = await blob.getJSON(MAP_KEY);\n if (Array.isArray(v)) pts = v as Array<[number, number]>;\n } catch { /* first run */ }\n\n if (qqqSpot > 0 && nq > 0) {\n pts.push([qqqSpot, nq]);\n if (pts.length > MAP_POINTS) pts = pts.slice(-MAP_POINTS);\n try {\n await blob.setJSON(MAP_KEY, pts);\n } catch { /* best effort */ }\n }\n\n // Need enough spread in QQQ to identify a slope; a cluster of near-identical\n // spots gives a numerically unstable fit that looks fine until it explodes.\n const xs = pts.map((p) => p[0]);\n const spread = xs.length ? Math.max(...xs) - Math.min(...xs) : 0;\n if (pts.length < 30 || spread < 0.5) {\n return { a: nq - 41.09 * qqqSpot, b: 41.09, n: pts.length, fitted: false };\n }\n\n let sx = 0, sy = 0, sxx = 0, sxy = 0;\n for (const [x, y] of pts) {\n sx += x;\n sy += y;\n sxx += x * x;\n sxy += x * y;\n }\n const n = pts.length;\n const den = n * sxx - sx * sx;\n if (Math.abs(den) < 1e-9) {\n return { a: nq - 41.09 * qqqSpot, b: 41.09, n, fitted: false };\n }\n const b = (n * sxy - sx * sy) / den;\n const a = (sy - b * sx) / n;\n if (!(b > 30 && b < 55)) { // sanity band around the structural ~41\n return { a: nq - 41.09 * qqqSpot, b: 41.09, n, fitted: false };\n }\n return { a, b, n, fitted: true };\n}\n\n// ---------------------------------------------------------------------------\n// Dealer sign inference from our own recorded OI history\n// ---------------------------------------------------------------------------\n\n/**\n * The dOI x dPrice proxy needs YESTERDAY's open interest and option price,\n * which the CBOE delayed feed does not carry. But the recorder has been\n * storing full narrowed chains four times a day - so we diff against the most\n * recent snapshot that is at least a session old.\n *\n * This matters more than any modelling change currently on the table: without\n * it every wall falls back to the naive prior (dealers long calls, short puts)\n * at confidence 0.30, and the naive convention is wrong often enough to invert\n * the sign of the gamma field rather than merely weaken it.\n */\nasync function priorChain(): Promise<\n Map | null\n> {\n try {\n const all = await blob.list(\"chain_QQQ_\");\n if (!all.length) return null;\n const keys = all.map((b: any) => b.key).sort();\n const cutoff = Date.now() - 20 * 3600_000; // at least ~a session back\n let chosen: string | null = null;\n for (let i = keys.length - 1; i >= 0; i--) {\n const m = keys[i].match(/chain_QQQ_(\\d{4}-\\d{2}-\\d{2})T/);\n if (!m) continue;\n if (Date.parse(m[1] + \"T23:59:59Z\") < cutoff + 24 * 3600_000) {\n chosen = keys[i];\n break;\n }\n }\n if (!chosen) chosen = keys[0];\n\n const bytes = await blob.get(chosen);\n if (!bytes) return null;\n const buf = new Uint8Array(await (bytes as any).arrayBuffer?.() ?? bytes);\n const snap = JSON.parse(await gunzip(buf)) as Snapshot;\n if (snap?.v !== 2) return null;\n const map = new Map();\n for (const q of fromColumnar(snap)) {\n map.set(`${q.e}|${q.r}|${q.s}`, { oi: q.oi, mid: (q.b + q.a) / 2 });\n }\n return map;\n } catch {\n return null; // sign inference is an enhancement, never a hard dependency\n }\n}\n\n// ---------------------------------------------------------------------------\n// Yahoo NQ=F\n// ---------------------------------------------------------------------------\n\ninterface Bars {\n t: number[];\n o: number[];\n h: number[];\n l: number[];\n c: number[];\n last: number;\n}\n\nasync function yahoo(interval: string, range: string): Promise {\n const url =\n `https://query1.finance.yahoo.com/v8/finance/chart/NQ%3DF?interval=${interval}&range=${range}`;\n const r = await fetch(url, { headers: { \"User-Agent\": \"Mozilla/5.0\" } });\n if (!r.ok) throw new Error(`Yahoo HTTP ${r.status}`);\n const j: any = await r.json();\n const res = j?.chart?.result?.[0];\n if (!res) throw new Error(\"Yahoo: empty result\");\n const q = res.indicators?.quote?.[0] ?? {};\n const t: number[] = res.timestamp ?? [];\n const keep: number[] = [];\n for (let i = 0; i < t.length; i++) if (q.close?.[i] != null) keep.push(i);\n return {\n t: keep.map((i) => t[i]),\n o: keep.map((i) => q.open[i]),\n h: keep.map((i) => q.high[i]),\n l: keep.map((i) => q.low[i]),\n c: keep.map((i) => q.close[i]),\n last: Number(\n res.meta?.regularMarketPrice ?? q.close[keep[keep.length - 1]],\n ),\n };\n}\n\n/** Realised vol of a bar series, as sum of squared log returns. */\nfunction rv(c: number[]): number {\n let s = 0;\n for (let i = 1; i < c.length; i++) {\n if (c[i] > 0 && c[i - 1] > 0) {\n const r = Math.log(c[i] / c[i - 1]);\n s += r * r;\n }\n }\n return Math.sqrt(s);\n}\n\n/** ET wall-clock parts for a unix second, without pulling in a date library. */\nfunction etParts(sec: number) {\n const d = new Date(sec * 1000);\n const f = new Intl.DateTimeFormat(\"en-US\", {\n timeZone: \"America/New_York\",\n hour12: false,\n year: \"numeric\",\n month: \"2-digit\",\n day: \"2-digit\",\n hour: \"2-digit\",\n minute: \"2-digit\",\n weekday: \"short\",\n }).formatToParts(d);\n const g = (k: string) => f.find((p) => p.type === k)?.value ?? \"\";\n return {\n ymd: `${g(\"year\")}-${g(\"month\")}-${g(\"day\")}`,\n min: +g(\"hour\") * 60 + +g(\"minute\"),\n dow: [\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\"].indexOf(\n g(\"weekday\"),\n ),\n };\n}\n\n// ---------------------------------------------------------------------------\n\nexport interface ComputeResult {\n ok: boolean;\n ms: number;\n error?: string;\n}\n\n/**\n * Fetch everything, compute the level set, write it to blob.\n * Safe to call concurrently: worst case is duplicated work, never corruption,\n * because the write is a single whole-object setJSON.\n */\nexport async function computeAndStore(): Promise {\n const started = Date.now();\n try {\n const prior = await priorChain();\n const [chain, intraday, daily] = await Promise.all([\n fetchChain(prior),\n yahoo(\"5m\", \"5d\"),\n yahoo(\"1d\", \"3mo\"),\n ]);\n\n const nowET = etParts(Math.floor(Date.now() / 1000));\n const RTH_O = 9 * 60 + 30, RTH_C = 16 * 60;\n\n /**\n * Most recent 18:00 ET boundary that actually has bars after it.\n *\n * Over a weekend or holiday the true most-recent boundary can be empty:\n * at 20:00 ET Saturday the last boundary is 18:00 ET Saturday, and Globex\n * does not reopen until Sunday. Stopping there leaves overnightRv at zero,\n * which silently drops the sigma forecast back to EWMA-only and throws\n * away the overnight term - worth ~13% of the OOS sigma error.\n *\n * So: walk back a day at a time until a boundary has real bars behind it.\n * The result is the last COMPLETED overnight session, which is the right\n * thing to condition on when the current one has not started.\n */\n const boundaryAt = (backDays: number) => {\n const probe = Date.now() - backDays * 86400_000;\n const pp = etParts(Math.floor(probe / 1000));\n const b = probe - pp.min * 60_000 + SESSION_BOUNDARY_HOUR * 60 * 60_000;\n return b <= Date.now() ? b : b - 86400_000;\n };\n const countAfter = (ms: number) =>\n intraday.t.reduce((n, t) => n + (t * 1000 >= ms ? 1 : 0), 0);\n\n let boundaryMs = boundaryAt(0);\n let boundaryBackDays = 0;\n for (let back = 0; back < 5 && countAfter(boundaryMs) < 12; back++) {\n boundaryBackDays = back + 1;\n boundaryMs = boundaryAt(back + 1);\n }\n /**\n * The overnight window ends at the NEXT RTH OPEN after the boundary, not\n * boundary + 24h.\n *\n * This matters for consistency with the calibration. The 0.455 weight on\n * overnight realised vol was fitted on the PRE-OPEN Globex portion only\n * (18:00 ET through 09:30 ET). A 24-hour window would fold that session's\n * RTH back in, feeding the model a different quantity from the one the\n * coefficient was estimated on - so the blend would be wrong in a way that\n * looks perfectly healthy in the diagnostics.\n */\n const boundaryEndMs = (() => {\n // Walk forward from the boundary to the first 09:30 ET after it.\n for (let h = 1; h <= 30; h++) {\n const probe = boundaryMs + h * 3600_000;\n const pp = etParts(Math.floor(probe / 1000));\n if (pp.min >= RTH_O) return probe - (pp.min - RTH_O) * 60_000;\n }\n return boundaryMs + 15.5 * 3600_000;\n })();\n\n // Session running high/low from today's RTH bars only.\n let hod: number | null = null, lod: number | null = null;\n let onHi: number | null = null, onLo: number | null = null;\n const overnightClose: number[] = [];\n for (let i = 0; i < intraday.t.length; i++) {\n const p = etParts(intraday.t[i]);\n const isToday = p.ymd === nowET.ymd;\n if (isToday && p.min >= RTH_O && p.min < RTH_C) {\n hod = hod === null ? intraday.h[i] : Math.max(hod, intraday.h[i]);\n lod = lod === null ? intraday.l[i] : Math.min(lod, intraday.l[i]);\n }\n // Overnight = since the MOST RECENT 18:00 ET boundary, by timestamp.\n //\n // The previous form tested \"not today AND after 18:00\", which matches\n // EVERY prior evening in the window, not just the last one. Over a\n // weekend that silently merged three evenings into one \"overnight\" -\n // visible as a 415-point range where a single session is ~150. That\n // inflates overnightRv, which feeds the calibrated sigma blend, so the\n // error propagates into every rung.\n const tms = intraday.t[i] * 1000;\n const sinceBoundary = tms >= boundaryMs && tms < boundaryEndMs;\n if (sinceBoundary) {\n overnightClose.push(intraday.c[i]);\n onHi = onHi === null ? intraday.h[i] : Math.max(onHi, intraday.h[i]);\n onLo = onLo === null ? intraday.l[i] : Math.min(onLo, intraday.l[i]);\n }\n }\n\n // Before RTH opens there is no session extreme yet. Fall back to the\n // Globex extremes so the tool still says something useful overnight,\n // flagged so the client knows which it is looking at.\n const usingOvernightExtremes = hod === null && onHi !== null;\n if (usingOvernightExtremes) {\n hod = onHi;\n lod = onLo;\n }\n\n /**\n * Session fraction, correct outside RTH.\n *\n * The naive clamp of (now - open)/(close - open) breaks overnight: at\n * 22:00 ET it evaluates to 1.92 and clamps to 1, so remaining variance\n * goes to zero, sigma collapses and the ladder degenerates to a point.\n * Since Globex is most of the trading day, that is most of the time.\n *\n * After the close we are forecasting the NEXT RTH session, so the correct\n * value is 0 - the whole session is still ahead. Before the open, likewise.\n */\n const inRth = nowET.min >= RTH_O && nowET.min < RTH_C;\n const sessionFrac = inRth ? (nowET.min - RTH_O) / (RTH_C - RTH_O) : 0;\n const preOpen = !inRth;\n\n // Ex-ante vol inputs. EWMA over daily closes; overnight from tonight's bars.\n const dc = daily.c.slice(-40);\n const dailyRets: number[] = [];\n for (let i = 1; i < dc.length; i++) {\n dailyRets.push(Math.log(dc[i] / dc[i - 1]));\n }\n let ewma = 0, w = 0;\n const hl = 5, decay = Math.pow(0.5, 1 / hl);\n for (let i = dailyRets.length - 1, k = 0; i >= 0; i--, k++) {\n const wt = Math.pow(decay, k);\n ewma += wt * dailyRets[i] * dailyRets[i];\n w += wt;\n }\n ewma = w > 0 ? 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