Touched up notebooks + webapp

This commit is contained in:
2026-07-31 17:05:14 -04:00
commit 8e6c98945b
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/* Theme tokens and page structure intentionally mirror ClimbingBoardGPT. */
:root {
--base00: #1A1B26;
--base01: #16161E;
--base02: #2F3549;
--base03: #444B6A;
--base04: #787C99;
--base05: #A9B1D6;
--base07: #D5D6DB;
--base08: #F7768E;
--base0a: #0DB9D7;
--base0b: #9ECE6A;
--base0c: #B4F9F8;
--base0d: #2AC3DE;
--base0e: #BB9AF7;
--base0f: #F7768E;
--bg: var(--base00);
--off-bg: var(--base01);
--inner-bg: var(--base02);
--fg: var(--base05);
--off-fg: var(--base04);
--muted: var(--base03);
--link: var(--base0d);
--hover: var(--base0c);
--highlight: var(--base0a);
--logo: var(--base0b);
--danger: var(--base08);
--border: rgba(120, 124, 153, 0.3);
--sans: "Inter", sans-serif;
--mono: "Fira Mono", ui-monospace, SFMono-Regular, Menlo, Consolas, "Liberation Mono", monospace;
}
* { box-sizing: border-box; }
body {
margin: 0;
font-family: var(--sans);
font-size: 16px;
line-height: 1.6rem;
background: var(--bg);
color: var(--fg);
}
.site-header {
display: flex;
justify-content: space-between;
align-items: flex-start;
gap: 1.5rem;
max-width: 78rem;
margin: 1rem auto 0;
padding: 0 1rem;
}
.eyebrow {
margin: 0;
color: var(--logo);
font-size: 1rem;
}
.site-header h1 {
margin: 0;
font-size: 1rem;
font-weight: 600;
}
.site-header h1::before { content: none; }
.site-header p {
margin: 0;
color: var(--off-fg);
}
.health {
flex-shrink: 0;
font-size: 0.78rem;
color: var(--highlight);
white-space: nowrap;
border: 1px solid var(--border);
padding: 0.25rem 0.5rem;
background: var(--inner-bg);
}
.layout {
display: grid;
grid-template-columns: 22rem minmax(0, 1fr);
grid-template-rows: auto 1fr;
grid-template-areas:
"col-top col-viewer"
"col-info col-viewer";
gap: 2rem;
padding: 2rem 1rem 1rem;
max-width: 78rem;
margin: 0 auto;
align-items: start;
}
#col-top { grid-area: col-top; }
#col-viewer { grid-area: col-viewer; }
#col-info { grid-area: col-info; }
.controls {
display: flex;
flex-direction: column;
gap: 1rem;
}
.card, .result-card {
background: var(--off-bg);
border: 1px solid var(--border);
padding: 1rem;
}
.card h2, .result-card h2 {
margin: 0 0 1rem;
font-size: 1rem;
font-weight: 600;
color: var(--fg);
}
.card h2::before, .result-card h2::before { content: none; }
label {
display: block;
margin: 0.7rem 0;
font-size: 0.82rem;
color: var(--off-fg);
}
input, select, textarea {
display: block;
width: 100%;
margin-top: 0.28rem;
border: 1px solid var(--border);
padding: 0.6rem 0.7rem;
font: inherit;
color: var(--fg);
background: var(--inner-bg);
}
input:focus, select:focus, textarea:focus {
outline: 2px solid rgba(137, 221, 255, 0.28);
border-color: var(--hover);
}
button {
width: 100%;
border: 1px solid var(--link);
padding: 0.68rem 0.9rem;
margin-top: 0.4rem;
font-weight: 700;
color: var(--bg);
background: var(--link);
cursor: pointer;
font-family: var(--sans);
}
button:hover {
border-color: var(--hover);
background: var(--hover);
}
button:disabled { opacity: 0.55; cursor: not-allowed; }
/* Keep the run control reachable while scrolling through the long parameter card. */
#mc-run-btn {
position: sticky;
bottom: 0.6rem;
z-index: 5;
box-shadow: 0 -2px 10px rgba(0, 0, 0, 0.3);
}
.card.collapsible > h2 {
display: flex;
align-items: center;
cursor: pointer;
user-select: none;
}
.card.collapsible > h2::after {
content: "▾";
font-size: 2rem;
color: var(--muted);
margin-left: auto;
padding-left: 0.5rem;
flex-shrink: 0;
}
.card.collapsible.collapsed > h2::after { content: "▸"; }
.card.collapsible > h2:hover::after { color: var(--off-fg); }
.card.collapsible.collapsed > *:not(h2) { display: none; }
.field-help {
display: block;
margin-top: 0.35rem;
color: var(--muted);
font-size: 0.72rem;
line-height: 1.35;
}
.checkbox-label {
display: flex;
align-items: center;
gap: 0.55rem;
color: var(--fg);
}
.checkbox-label input {
width: auto;
margin: 0;
}
input[type="range"] {
padding: 0;
height: 6px;
-webkit-appearance: none;
appearance: none;
background: var(--inner-bg);
border: 1px solid var(--border);
cursor: pointer;
}
input[type="range"]::-webkit-slider-thumb {
-webkit-appearance: none;
width: 16px;
height: 16px;
background: var(--link);
border-radius: 50%;
cursor: pointer;
}
.range-row {
display: flex;
gap: 0.55rem;
align-items: center;
}
.range-row input[type="range"] { flex: 1; }
.range-row input[type="number"] {
width: 7.5rem;
flex-shrink: 0;
-moz-appearance: textfield;
}
.range-row input[type="number"]::-webkit-inner-spin-button,
.range-row input[type="number"]::-webkit-outer-spin-button {
-webkit-appearance: none;
margin: 0;
}
.note p, .small {
color: var(--off-fg);
font-size: 0.82rem;
line-height: 1.45;
}
.note p:first-of-type { margin-top: 0; }
.note p:last-child { margin-bottom: 0; }
.result-header {
text-align: center;
margin-bottom: 0.85rem;
}
.result-header h2 { margin-bottom: 0.25rem; }
.result-header p {
margin: 0;
color: var(--off-fg);
font-size: 0.84rem;
}
.headline {
display: grid;
grid-template-columns: repeat(4, 1fr);
gap: 0.6rem;
max-width: 820px;
margin: 0 auto 1rem;
}
.tile {
display: flex;
flex-direction: column;
align-items: center;
padding: 0.6rem 0.4rem;
border: 1px solid var(--border);
background: var(--inner-bg);
text-align: center;
}
.tile-num {
font-size: 1.3rem;
font-weight: 700;
color: var(--highlight);
line-height: 1.1;
white-space: nowrap;
}
.tile-lab {
font-size: 0.74rem;
font-weight: 600;
color: var(--fg);
margin-top: 0.2rem;
}
.tile-sub {
font-size: 0.66rem;
color: var(--muted);
margin-top: 0.1rem;
line-height: 1.25;
}
.result-note {
max-width: 760px;
margin: 0 auto 0.85rem;
color: var(--off-fg);
font-size: 0.78rem;
line-height: 1.45;
text-align: center;
}
.chart-stage {
width: 100%;
max-width: 960px;
margin: 0 auto;
min-height: 420px;
border: 1px solid var(--border);
background: var(--bg);
overflow: hidden;
}
.chart-stage .js-plotly-plot { width: 100% !important; }
.warning-box {
margin: 0.7rem auto 0.85rem;
max-width: 760px;
border: 1px solid rgba(255, 203, 107, 0.55);
background: rgba(255, 203, 107, 0.12);
color: var(--highlight);
padding: 0.65rem 0.8rem;
font-size: 0.8rem;
text-align: left;
white-space: pre-line;
}
.advanced-opts {
margin-top: 0.9rem;
}
.advanced-opts summary {
cursor: pointer;
font-size: 0.78rem;
color: var(--muted);
user-select: none;
}
.advanced-opts summary:hover { color: var(--highlight); }
.method-note {
max-width: 900px;
margin-left: auto;
margin-right: auto;
color: var(--off-fg);
font-size: 0.8rem;
line-height: 1.45;
}
.method-note ol {
margin: 0.65rem 0 0.45rem;
padding-left: 1.25rem;
}
.method-note li + li { margin-top: 0.32rem; }
.method-note p { margin: 0.45rem 0 0; }
.method-note strong { color: var(--fg); }
.explain dl { margin: 0; }
.explain dt {
color: var(--highlight);
font-size: 0.78rem;
margin-top: 0.75rem;
}
.explain dt:first-child { margin-top: 0; }
.explain dd {
margin: 0.22rem 0 0;
color: var(--off-fg);
font-size: 0.78rem;
line-height: 1.45;
}
.explain p {
color: var(--off-fg);
font-size: 0.82rem;
line-height: 1.45;
}
.explain p:first-of-type { margin-top: 0; }
.explain p:last-child { margin-bottom: 0; }
.link-list {
margin: 0;
padding-left: 1.1rem;
color: var(--off-fg);
font-size: 0.82rem;
line-height: 1.6;
}
.link-list li::marker {
content: '·\00A0\00A0';
color: var(--muted);
}
.json-block {
margin-top: 1rem;
color: var(--off-fg);
}
.json-block summary {
cursor: pointer;
font-size: 0.78rem;
color: var(--muted);
}
.json-block summary:hover { color: var(--highlight); }
.json-block pre {
overflow: auto;
max-height: 300px;
padding: 1rem;
background: var(--inner-bg);
color: var(--off-fg);
border: 1px solid var(--border);
font-size: 0.76rem;
}
.site-footer {
display: flex;
flex-wrap: wrap;
gap: 0.5rem 1.5rem;
max-width: 78rem;
margin: 0 auto 1.5rem;
padding: 0 1rem;
color: var(--off-fg);
font-size: 0.82rem;
}
.site-footer a, .link-list a {
color: var(--link);
transition: color 0.15s ease;
}
.site-footer a:hover, .link-list a:hover { color: var(--hover); }
@media (max-width: 900px) {
.layout {
grid-template-columns: 1fr;
grid-template-rows: auto auto auto;
grid-template-areas:
"col-top"
"col-viewer"
"col-info";
}
.site-header { flex-direction: column; }
.headline { grid-template-columns: repeat(2, 1fr); }
}
@media (max-width: 520px) {
.headline { grid-template-columns: 1fr; }
.range-row { align-items: stretch; flex-direction: column; }
.range-row input[type="number"] { width: 100%; }
}
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/*
* Browser-side controller for the Adaptive Barrier Monitor demo (Monte Carlo showcase).
*
* Runs a simulation and renders each path with adaptive sampling (cyan) and
* either an equal-budget or independently fixed-cadence baseline (grey).
*/
const state = { lastResult: null };
// ---- helpers ----
function $(id) { return document.getElementById(id); }
async function fetchJson(url, options = {}) {
const resp = await fetch(url, options);
const text = await resp.text();
let payload;
try { payload = text ? JSON.parse(text) : {}; } catch { payload = { detail: text }; }
if (!resp.ok) {
const detail = payload.detail ?? payload;
throw new Error(typeof detail === "string" ? detail : JSON.stringify(detail, null, 2));
}
return payload;
}
function setBusy(button, busy) {
button.disabled = busy;
button.textContent = busy ? "Working…" : (button.dataset.label || button.textContent);
}
function showWarnings(warnings) {
const box = $("warning-box");
const norm = (warnings || []).filter(Boolean).map(String);
if (norm.length === 0) { box.hidden = true; box.textContent = ""; return; }
box.hidden = false;
box.textContent = norm.join("\n");
}
// ---- slider ↔ number sync ----
function bindRange(id) {
const range = $(id);
const num = $(id + "-val");
if (!range || !num) return;
range.addEventListener("input", () => { num.value = parseFloat(range.value); });
num.addEventListener("change", () => { range.value = parseFloat(num.value); });
}
function bindLogRange(id) {
const range = $(id);
const num = $(id + "-val");
if (!range || !num) return;
range.addEventListener("input", () => { num.value = Number(range.value).toExponential(4); });
num.addEventListener("change", () => { range.value = parseFloat(num.value); });
}
function updateComparisonModeCopy() {
const mode = $("mc-comparison-mode").value;
const fixedCadence = mode === "fixed_cadence";
$("mc-fixed-cadence-row").hidden = !fixedCadence;
if (fixedCadence) {
$("fixed-explanation").innerHTML =
'A <strong>fixed-cadence</strong> sampler that observes every chosen number of minutes, independently of the adaptive sample count.';
$("headline-explanation").textContent =
'The schedules use independent sample counts. Compare detections, lag, and total observations to see the qualitycost trade-off.';
$("comparison-note").innerHTML =
'The fixed monitor samples at the selected cadence. The experiment compares <strong>detection quality and observation cost</strong>.';
$("allocation-method").innerHTML =
'<strong>Allocate samples:</strong> adaptive intervals shrink near the closest barrier; the fixed monitor samples at the selected cadence.';
$("method-conclusion").textContent =
'The headline compares detections, mean lag, and total observations under independent schedules.';
} else {
$("fixed-explanation").innerHTML =
'An exactly equal-budget <strong>fixed-rate</strong> sampler: each path receives the same number of observations as its adaptive counterpart, spread uniformly.';
$("headline-explanation").textContent =
'Same paths and exactly the same sample budget. The comparison reports which schedule confirms more lower and upper barrier events before the detection deadline.';
$("comparison-note").innerHTML =
'The adaptive and fixed monitors receive the <strong>same number of samples on every path</strong>. The experiment therefore compares sample placement—not computational cost.';
$("allocation-method").innerHTML =
'<strong>Allocate samples:</strong> adaptive intervals shrink near the closest barrier; the fixed monitor receives the exact same sample count, spaced uniformly.';
$("method-conclusion").textContent =
'The headline compares detections and mean lag under an equal sample budget.';
}
}
// ---- Plotly helpers ----
const PLOTLY_CONFIG = {
displayModeBar: true,
modeBarButtonsToRemove: ["lasso2d", "select2d"],
displaylogo: false,
responsive: true,
};
const PLOTLY_LAYOUT = {
font: { color: "#A9B1D6", family: "Inter, sans-serif" },
paper_bgcolor: "#1A1B26",
plot_bgcolor: "#1A1B26",
xaxis: { gridcolor: "rgba(120,124,153,0.15)", zerolinecolor: "rgba(120,124,153,0.3)" },
yaxis: { gridcolor: "rgba(120,124,153,0.15)", zerolinecolor: "rgba(120,124,153,0.3)" },
margin: { l: 60, r: 30, t: 20, b: 50 },
legend: { font: { color: "#A9B1D6" }, x: 0.01, y: 0.99, bgcolor: "rgba(26,27,38,0.6)" },
};
function renderPlot(data, layoutOverrides = {}) {
Plotly.newPlot("chart-stage", data, { ...PLOTLY_LAYOUT, ...layoutOverrides }, PLOTLY_CONFIG);
}
// ---- Monte Carlo ----
async function runMonteCarlo() {
const button = $("mc-run-btn");
setBusy(button, true);
showWarnings([]);
$("result-subtitle").textContent = "Running simulation…";
try {
const thresholdPct = parseFloat($("mc-threshold").value);
const risePct = parseFloat($("mc-rise-threshold").value);
const dtcapRaw = $("mc-dtcap").value.trim();
const seedRaw = $("mc-seed").value.trim();
const comparisonMode = $("mc-comparison-mode").value;
const payload = {
S0: 100.0,
sigma_annual: parseFloat($("mc-sigma").value),
mu_annual: parseFloat($("mc-mu").value),
window_minutes: parseFloat($("mc-window").value),
n_paths: parseInt($("mc-paths").value),
n_steps: parseInt($("mc-steps").value),
use_jumps: $("mc-jumps").checked,
jump_intensity: 25.0,
jump_mean: -0.02,
jump_sigma: 0.05,
eps: parseFloat($("mc-eps").value),
drop_fraction: thresholdPct / 100.0,
rise_fraction: risePct / 100.0,
dt_cap_minutes: dtcapRaw ? parseFloat(dtcapRaw) : null,
rng_seed: seedRaw ? parseInt(seedRaw) : null,
max_detection_lag_steps: parseInt($("mc-max-lag").value),
comparison_mode: comparisonMode,
fixed_cadence_minutes: parseFloat($("mc-fixed-cadence").value),
};
const r = await fetchJson("/api/simulate", {
method: "POST", headers: { "Content-Type": "application/json" },
body: JSON.stringify(payload),
});
state.lastResult = r;
const colors = ["#2AC3DE", "#BB9AF7", "#9ECE6A", "#0DB9D7", "#B4F9F8",
"#FF9E64", "#7DCFFF", "#C0CAF5", "#73DACA", "#F7768E"];
const t = r.times_minutes;
const t0 = t[0], t1 = t[t.length - 1];
const traces = [];
for (let i = 0; i < r.paths.length; i++) {
const p = r.paths[i];
const c = colors[i % colors.length];
traces.push({
x: t, y: p.prices, type: "scatter", mode: "lines",
line: { color: c, width: 0.8, opacity: 0.5 }, name: `Path ${i + 1}`, showlegend: i < 5,
});
if (p.sample_times.length) {
traces.push({
x: p.sample_times, y: p.sample_prices, type: "scatter", mode: "markers",
marker: { color: "#2AC3DE", size: 6, symbol: "circle-open", opacity: 0.9 },
name: "adaptive", showlegend: i === 0,
});
}
if (p.fixed_sample_times.length) {
traces.push({
x: p.fixed_sample_times, y: p.fixed_sample_prices, type: "scatter", mode: "markers",
marker: { color: "#787C99", size: 3, opacity: 0.5 },
name: r.comparison_mode === "fixed_cadence" ? "fixed cadence" : "fixed equal-budget", showlegend: i === 0,
});
}
}
// Lower barrier (drop)
traces.push({
x: [t0, t1], y: [r.lower_barrier_price, r.lower_barrier_price], type: "scatter", mode: "lines",
line: { color: "#F7768E", width: 2, dash: "dash" }, name: `${thresholdPct}% lower`,
});
// Upper barrier (rise)
traces.push({
x: [t0, t1], y: [r.upper_barrier_price, r.upper_barrier_price], type: "scatter", mode: "lines",
line: { color: "#9ECE6A", width: 2, dash: "dash" }, name: `+${risePct}% upper`,
});
// Lower breach markers (red X)
const bxDown = [], byDown = [];
for (const p of r.paths) {
if (p.lower_breach_idx !== null) { bxDown.push(t[p.lower_breach_idx]); byDown.push(p.prices[p.lower_breach_idx]); }
}
if (bxDown.length) {
traces.push({
x: bxDown, y: byDown, type: "scatter", mode: "markers",
marker: { color: "#F7768E", size: 13, symbol: "x", line: { width: 3 } },
name: `↓ breaches (${bxDown.length})`,
});
}
// Upper breach markers (green triangles)
const bxUp = [], byUp = [];
for (const p of r.paths) {
if (p.upper_breach_idx !== null) { bxUp.push(t[p.upper_breach_idx]); byUp.push(p.prices[p.upper_breach_idx]); }
}
if (bxUp.length) {
traces.push({
x: bxUp, y: byUp, type: "scatter", mode: "markers",
marker: { color: "#9ECE6A", size: 13, symbol: "triangle-up", line: { width: 3 } },
name: `↑ breaches (${bxUp.length})`,
});
}
renderPlot(traces, {
xaxis: { title: "Time (minutes)" },
yaxis: { title: "Price ($)" },
showlegend: r.paths.length <= 10,
});
// Headline tiles — use explicit event counts and method labels.
// A barrier event is the first lower-barrier or upper-barrier crossing on a path.
// One path can therefore contribute up to two events.
const nEvents = r.n_barrier_events ?? r.n_breaches;
const nLowerEvents = r.n_lower_events ?? r.n_lower_breaches;
const nUpperEvents = r.n_upper_events ?? r.n_upper_breaches;
const number = new Intl.NumberFormat();
const directionSummary = (lowerCaught, upperCaught) => {
const lower = nLowerEvents > 0
? `${lowerCaught} of ${nLowerEvents} lower`
: "↓ no lower events";
const upper = nUpperEvents > 0
? `${upperCaught} of ${nUpperEvents} upper`
: "↑ no upper events";
return `${lower} · ${upper}`;
};
$("hl-adaptive").textContent = nEvents > 0
? `${r.adaptive_detections} of ${nEvents}` : "No events";
$("hl-fixed").textContent = nEvents > 0
? `${r.fixed_detections} of ${nEvents}` : "No events";
$("hl-adaptive-sub").textContent = directionSummary(
r.adaptive_lower_detections,
r.adaptive_upper_detections,
);
$("hl-fixed-sub").textContent = directionSummary(
r.fixed_lower_detections,
r.fixed_upper_detections,
);
const equalBudgetMode = r.comparison_mode === "equal_budget";
$("hl-samples").textContent = equalBudgetMode
? `${number.format(r.adaptive_total_samples)} each`
: `A ${number.format(r.adaptive_total_samples)} · F ${number.format(r.fixed_total_samples)}`;
$("hl-samples-sub").textContent = equalBudgetMode
? "same count on every path"
: `adaptive · fixed every ${number.format(r.fixed_cadence_minutes)} min`;
const adaptiveLag = (r.mean_detection_lag !== null && r.mean_detection_lag !== undefined)
? r.mean_detection_lag.toFixed(1) : "";
const fixedLag = (r.mean_fixed_detection_lag !== null && r.mean_fixed_detection_lag !== undefined)
? r.mean_fixed_detection_lag.toFixed(1) : "";
$("hl-lag").textContent = `${adaptiveLag} vs ${fixedLag}`;
const breachedPaths = r.n_paths_with_any_breach ?? r.paths.filter(
(path) => path.lower_breach_idx !== null || path.upper_breach_idx !== null,
).length;
$("result-subtitle").textContent =
`${nEvents} barrier events across ${breachedPaths} of ${r.n_paths} paths` +
` · ${nLowerEvents} lower, ${nUpperEvents} upper` +
` · deadline ≤${r.max_detection_lag_steps} grid steps` +
(r.comparison_mode === "fixed_cadence"
? ` · fixed every ${r.fixed_cadence_minutes} min`
: " · equal budget") +
(r.use_jumps ? " · Merton jumps" : " · pure GBM");
$("stats-json").textContent = JSON.stringify({
threshold_pct: thresholdPct,
rise_pct: risePct,
seed: payload.rng_seed,
n_paths: r.n_paths,
n_paths_with_any_breach: r.n_paths_with_any_breach,
n_barrier_events: r.n_barrier_events ?? r.n_breaches,
n_lower_events: r.n_lower_events ?? r.n_lower_breaches,
n_upper_events: r.n_upper_events ?? r.n_upper_breaches,
adaptive_detections: r.adaptive_detections,
adaptive_lower_detections: r.adaptive_lower_detections,
adaptive_upper_detections: r.adaptive_upper_detections,
fixed_detections: r.fixed_detections,
fixed_lower_detections: r.fixed_lower_detections,
fixed_upper_detections: r.fixed_upper_detections,
adaptive_total_samples: r.adaptive_total_samples,
fixed_total_samples: r.fixed_total_samples,
mean_detection_lag: r.mean_detection_lag,
mean_lower_detection_lag: r.mean_lower_detection_lag,
mean_upper_detection_lag: r.mean_upper_detection_lag,
sigma_annual: r.sigma_annual,
mu_annual: r.mu_annual,
eps: r.eps,
drop_fraction: r.drop_fraction,
rise_fraction: r.rise_fraction,
dt_cap_minutes: r.dt_cap_minutes,
max_detection_lag_steps: r.max_detection_lag_steps,
comparison_mode: r.comparison_mode,
fixed_cadence_minutes: r.fixed_cadence_minutes,
grid_step_minutes: r.grid_step_minutes,
model_scope: r.model_scope,
}, null, 2);
const warnings = [];
if (r.use_jumps) {
warnings.push("Jump stress test: ε is derived from a continuous diffusion and does not bound missed jump events.");
}
if (r.comparison_mode === "fixed_cadence" && r.fixed_cadence_minutes < r.grid_step_minutes) {
warnings.push(`The requested fixed cadence (${r.fixed_cadence_minutes} min) is finer than the simulation grid (${r.grid_step_minutes.toFixed(2)} min), so it is limited to one sample per grid point.`);
}
if (r.n_breaches === 0) {
warnings.push("No barrier events occurred in this run. Increase the horizon/volatility, lower the thresholds, or choose another seed.");
}
showWarnings(warnings);
} catch (err) {
showWarnings([err.message]);
} finally {
setBusy(button, false);
}
}
/** Initialize collapsible cards to match the ClimbingBoardGPT interaction. */
function initCollapsibleCards() {
document.querySelectorAll(".card.collapsible > h2").forEach((heading) => {
const card = heading.parentElement;
heading.addEventListener("click", () => {
card.classList.toggle("collapsed");
});
});
}
// ---- init ----
async function init() {
$("mc-run-btn").dataset.label = "Run simulation";
try {
await fetchJson("/api/health");
$("health").textContent = "ready";
} catch {
$("health").textContent = "offline";
}
bindRange("mc-sigma");
bindLogRange("mc-eps");
updateComparisonModeCopy();
initCollapsibleCards();
$("mc-comparison-mode").addEventListener("change", async () => {
updateComparisonModeCopy();
await runMonteCarlo();
});
$("mc-run-btn").addEventListener("click", runMonteCarlo);
// Enter opens on a compelling scenario immediately.
await runMonteCarlo();
}
init().catch(err => {
$("health").textContent = `Error: ${err.message}`;
showWarnings(["Initialization error: " + err.message]);
console.error(err);
});
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Adaptive Barrier Monitor</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap">
<link rel="icon" type="image/svg+xml" href="/static/favicon.svg">
<script src="https://cdn.plot.ly/plotly-3.0.1.min.js"></script>
<link rel="stylesheet" href="/static/app.css?v=1" />
</head>
<body>
<header class="site-header">
<div>
<p class="eyebrow">Adaptive Barrier Monitor</p>
<h1>State-dependent sampling near price barriers</h1>
<p>Monte Carlo demo comparing adaptive sampling with equal-budget and fixed-cadence baselines.</p>
</div>
<div id="health" class="health">Loading…</div>
</header>
<main class="layout">
<!-- Left column, row 1: interactive controls. -->
<section class="controls" id="col-top">
<div class="card collapsible" id="card-simulation">
<h2>Simulation parameters</h2>
<label>Comparison mode
<select id="mc-comparison-mode">
<option value="equal_budget">Equal budget</option>
<option value="fixed_cadence">Fixed cadence</option>
</select>
<span class="field-help">Equal budget isolates sample placement. Fixed cadence compares detection and observation cost against an independently chosen interval.</span>
</label>
<label id="mc-fixed-cadence-row" hidden>Fixed cadence (minutes)
<input id="mc-fixed-cadence" type="number" min="0.1" max="39000" step="0.1" value="60" />
<span class="field-help">The fixed monitor samples at this interval, plus the start and end of the window. Smaller values use more observations.</span>
</label>
<label>Annual volatility (σ)
<div class="range-row">
<input id="mc-sigma" type="range" min="0.05" max="1.0" step="0.01" value="0.30" />
<input id="mc-sigma-val" type="number" min="0.05" max="1.0" step="0.01" value="0.30" />
</div>
<span class="field-help">Annualised volatility. Higher values produce wider price swings and more barrier events.</span>
</label>
<label>Annual drift (μ)
<input id="mc-mu" type="number" min="-0.5" max="0.5" step="0.01" value="0.07" />
<span class="field-help">Average annual return. Positive values tilt paths upward; negative values push them toward the lower barrier.</span>
</label>
<label>Window (minutes)
<input id="mc-window" type="number" min="5" max="39000" step="5" value="1950" />
<span class="field-help">1950 minutes is about five trading days. Longer windows tend to produce more events.</span>
</label>
<label>Number of paths
<input id="mc-paths" type="number" min="1" max="100" step="1" value="20" />
<span class="field-help">More paths give steadier aggregate results but take longer to render.</span>
</label>
<label>Steps per path
<input id="mc-steps" type="number" min="50" max="2000" step="10" value="500" />
<span class="field-help">The fine simulation grid used as the reference history. More steps resolve shorter-lived crossings.</span>
</label>
<label>Drop threshold (%)
<input id="mc-threshold" type="number" min="0.1" max="50" step="0.1" value="5" />
<span class="field-help">Percentage decline from the initial price that defines a lower-barrier event.</span>
</label>
<label>Rise threshold (%)
<input id="mc-rise-threshold" type="number" min="0.1" max="50" step="0.1" value="10" />
<span class="field-help">Percentage increase from the initial price that defines an upper-barrier event.</span>
</label>
<label>Miss budget (ε)
<div class="range-row">
<input id="mc-eps" type="range" min="1e-6" max="0.1" step="1e-6" value="0.001" />
<input id="mc-eps-val" type="number" min="1e-6" max="0.1" step="0.0001" value="0.001" />
</div>
<span class="field-help">Local diffusion-model design parameter. Lower ε produces denser adaptive sampling; it is not a global guarantee and does not cover jumps.</span>
</label>
<label>Max interval cap (minutes)
<input id="mc-dtcap" type="number" min="1" max="1440" step="1" placeholder="auto" />
<span class="field-help">Leave empty for an automatic cap of window/15. Lower values force denser sampling everywhere.</span>
</label>
<label>Detection deadline (grid steps)
<input id="mc-max-lag" type="number" min="0" max="100" step="1" value="3" />
<span class="field-help">An event counts as detected only if a sample still lies beyond the barrier within this many reference-grid steps.</span>
</label>
<label>RNG seed
<input id="mc-seed" type="number" min="0" max="100000" step="1" placeholder="random" />
<span class="field-help">Leave empty for a new run, or enter a seed to reproduce and share the same paths.</span>
</label>
<label>
<span class="checkbox-label">
<input id="mc-jumps" type="checkbox" /> Include jumps (Merton jump-diffusion)
</span>
<span class="field-help">Adds sudden moves that the diffusion-derived schedule cannot anticipate. Treat this as a model-risk stress test.</span>
</label>
<button id="mc-run-btn">Run simulation</button>
</div>
</section>
<!-- Right column: results and simulated paths. -->
<section class="viewer" id="col-viewer">
<div class="result-card">
<div class="result-header">
<h2 id="result-title">Monte Carlo Sandbox</h2>
<p id="result-subtitle">Running…</p>
</div>
<div class="headline">
<div class="tile">
<span class="tile-num" id="hl-adaptive"></span>
<span class="tile-lab">events detected — adaptive</span>
<span class="tile-sub" id="hl-adaptive-sub">↓ lower · ↑ upper</span>
</div>
<div class="tile">
<span class="tile-num" id="hl-fixed"></span>
<span class="tile-lab">events detected — fixed</span>
<span class="tile-sub" id="hl-fixed-sub">↓ lower · ↑ upper</span>
</div>
<div class="tile">
<span class="tile-num" id="hl-samples"></span>
<span class="tile-lab">total samples</span>
<span class="tile-sub" id="hl-samples-sub">matched path by path</span>
</div>
<div class="tile">
<span class="tile-num" id="hl-lag"></span>
<span class="tile-lab">mean detection lag</span>
<span class="tile-sub">adaptive vs fixed · detected events only</span>
</div>
</div>
<p class="result-note" id="comparison-note">
The adaptive and fixed monitors receive the <strong>same number of samples on every path</strong>.
The experiment therefore compares sample placement—not computational cost.
</p>
<div id="warning-box" class="warning-box" hidden></div>
<div id="chart-stage" class="chart-stage">
<div style="padding:3rem; text-align:center; color:var(--muted)">Running simulation…</div>
</div>
<details class="advanced-opts method-note" open>
<summary>How the Monte Carlo comparison works</summary>
<ol>
<li><strong>Simulate:</strong> generate independent GBM or Merton jump-diffusion price paths on a fine grid.</li>
<li><strong>Mark events:</strong> record each path's first lower- and upper-barrier crossing.</li>
<li id="allocation-method"><strong>Allocate samples:</strong> adaptive intervals shrink near the closest barrier; the fixed monitor receives the same sample count, spaced uniformly.</li>
<li><strong>Score:</strong> an event is detected when a sampled price remains beyond the barrier within the selected deadline.</li>
</ol>
<p>
<span id="method-conclusion">The headline compares detections and mean lag under an equal sample budget.</span>
The miss budget <strong>ε</strong> is a local diffusion-model design parameter, not a global guarantee.
</p>
</details>
<details class="json-block">
<summary>Raw result JSON</summary>
<pre id="stats-json">{}</pre>
</details>
</div>
</section>
<!-- Left column, row 2: supporting information, mirroring ClimbingBoardGPT. -->
<section class="controls" id="col-info">
<div class="card explain">
<h2>How to read it</h2>
<dl>
<dt>Open cyan circles</dt>
<dd>Adaptive observations. The interval shrinks quadratically as the log-price approaches the nearer barrier.</dd>
<dt>Grey dots</dt>
<dd id="fixed-explanation">An equal-budget fixed sampler with the same number of observations on each path, spaced uniformly.</dd>
<dt>Red × and green ▲</dt>
<dd>The first lower- and upper-barrier events on the fine reference grid.</dd>
<dt>Headline</dt>
<dd id="headline-explanation">Same paths and the same sample budget. Compare how many events each schedule confirms before the deadline.</dd>
</dl>
</div>
<div class="card note">
<h2>Research demo caveat</h2>
<p>The adaptive law is derived from a continuous diffusion model. Discrete grids, estimated volatility, latency, and jumps weaken any literal miss-probability guarantee.</p>
</div>
<div class="card explain">
<h2>How this works</h2>
<p>The app simulates price paths, marks first barrier crossings, and compares two observation schedules on exactly the same paths.</p>
<p>Adaptive intervals use a Brownian-bridge proxy and shrink with squared distance to the nearest barrier. Fixed observations are either equal-budget or independently spaced at a chosen cadence.</p>
</div>
<div class="card note">
<h2>Links</h2>
<ul class="link-list">
<li><a href="https://pawelsarkowicz.xyz" target="_blank" rel="noreferrer">pawelsarkowicz.xyz</a></li>
<li><a href="https://github.com/psark007/adaptive-barrier-monitor" target="_blank" rel="noreferrer">Adaptive Barrier Monitor repo</a></li>
<li><a href="https://github.com/psark007/adaptive-barrier-monitor/blob/main/LICENSE" target="_blank" rel="noreferrer">License</a></li>
</ul>
</div>
</section>
</main>
<footer class="site-footer">
<span>© Pawel Sarkowicz</span>
</footer>
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