ps
e5d785f511
- add README live-demo badge - tighten engine validation for jump grids and detector indices - replace flaky FastAPI TestClient web tests with direct handler tests - remove unused httpx dev dependency - clean stale frontend comments - re-execute notebooks with clean sequential outputs
207 lines
11 KiB
HTML
207 lines
11 KiB
HTML
<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<title>Adaptive Barrier Monitor</title>
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap">
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<link rel="icon" type="image/svg+xml" href="/static/favicon.svg">
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<script src="https://cdn.plot.ly/plotly-3.0.1.min.js"></script>
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<link rel="stylesheet" href="/static/app.css?v=1" />
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</head>
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<body>
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<header class="site-header">
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<div>
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<p class="eyebrow">Adaptive Barrier Monitor</p>
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<h1>State-dependent sampling near price barriers</h1>
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<p>Monte Carlo demo comparing adaptive sampling with equal-budget and fixed-cadence baselines.</p>
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</div>
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<div id="health" class="health">Loading…</div>
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</header>
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<main class="layout">
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<!-- Left column, row 1: interactive controls. -->
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<section class="controls" id="col-top">
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<div class="card collapsible" id="card-simulation">
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<h2>Simulation parameters</h2>
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<label>Comparison mode
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<select id="mc-comparison-mode">
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<option value="equal_budget">Equal budget</option>
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<option value="fixed_cadence">Fixed cadence</option>
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</select>
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<span class="field-help">Equal budget isolates sample placement. Fixed cadence compares detection and observation cost against an independently chosen interval.</span>
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</label>
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<label id="mc-fixed-cadence-row" hidden>Fixed cadence (minutes)
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<input id="mc-fixed-cadence" type="number" min="0.1" max="39000" step="0.1" value="60" />
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<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>
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</label>
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<label>Annual volatility (σ)
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<div class="range-row">
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<input id="mc-sigma" type="range" min="0.05" max="1.0" step="0.01" value="0.30" />
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<input id="mc-sigma-val" type="number" min="0.05" max="1.0" step="0.01" value="0.30" />
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</div>
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<span class="field-help">Annualised volatility. Higher values produce wider price swings and more barrier events.</span>
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</label>
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<label>Annual drift (μ)
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<input id="mc-mu" type="number" min="-0.5" max="0.5" step="0.01" value="0.07" />
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<span class="field-help">Average annual return. Positive values tilt paths upward; negative values push them toward the lower barrier.</span>
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</label>
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<label>Window (minutes)
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<input id="mc-window" type="number" min="5" max="39000" step="5" value="1950" />
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<span class="field-help">1950 minutes is about five trading days. Longer windows tend to produce more events.</span>
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</label>
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<label>Number of paths
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<input id="mc-paths" type="number" min="1" max="100" step="1" value="20" />
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<span class="field-help">More paths give steadier aggregate results but take longer to render.</span>
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</label>
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<label>Steps per path
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<input id="mc-steps" type="number" min="50" max="2000" step="10" value="500" />
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<span class="field-help">The fine simulation grid used as the reference history. More steps resolve shorter-lived crossings.</span>
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</label>
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<label>Drop threshold (%)
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<input id="mc-threshold" type="number" min="0.1" max="50" step="0.1" value="5" />
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<span class="field-help">Percentage decline from the initial price that defines a lower-barrier event.</span>
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</label>
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<label>Rise threshold (%)
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<input id="mc-rise-threshold" type="number" min="0.1" max="50" step="0.1" value="10" />
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<span class="field-help">Percentage increase from the initial price that defines an upper-barrier event.</span>
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</label>
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<label>Miss budget (ε)
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<div class="range-row">
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<input id="mc-eps" type="range" min="1e-6" max="0.1" step="1e-6" value="0.001" />
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<input id="mc-eps-val" type="number" min="1e-6" max="0.1" step="0.0001" value="0.001" />
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</div>
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<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>
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</label>
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<label>Max interval cap (minutes)
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<input id="mc-dtcap" type="number" min="1" max="1440" step="1" placeholder="auto" />
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<span class="field-help">Leave empty for an automatic cap of window/15. Lower values force denser sampling everywhere.</span>
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</label>
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<label>Detection deadline (grid steps)
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<input id="mc-max-lag" type="number" min="0" max="100" step="1" value="3" />
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<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>
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</label>
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<label>RNG seed
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<input id="mc-seed" type="number" min="0" max="100000" step="1" placeholder="random" />
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<span class="field-help">Leave empty for a new run, or enter a seed to reproduce and share the same paths.</span>
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</label>
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<label>
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<span class="checkbox-label">
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<input id="mc-jumps" type="checkbox" /> Include jumps (Merton jump-diffusion)
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</span>
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<span class="field-help">Adds sudden moves that the diffusion-derived schedule cannot anticipate. Treat this as a model-risk stress test.</span>
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</label>
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<button id="mc-run-btn">Run simulation</button>
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</div>
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</section>
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<!-- Right column: results and simulated paths. -->
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<section class="viewer" id="col-viewer">
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<div class="result-card">
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<div class="result-header">
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<h2 id="result-title">Monte Carlo Sandbox</h2>
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<p id="result-subtitle">Running…</p>
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</div>
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<div class="headline">
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<div class="tile">
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<span class="tile-num" id="hl-adaptive">–</span>
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<span class="tile-lab">events detected — adaptive</span>
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<span class="tile-sub" id="hl-adaptive-sub">↓ lower · ↑ upper</span>
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</div>
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<div class="tile">
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<span class="tile-num" id="hl-fixed">–</span>
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<span class="tile-lab">events detected — fixed</span>
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<span class="tile-sub" id="hl-fixed-sub">↓ lower · ↑ upper</span>
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</div>
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<div class="tile">
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<span class="tile-num" id="hl-samples">–</span>
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<span class="tile-lab">total samples</span>
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<span class="tile-sub" id="hl-samples-sub">matched path by path</span>
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</div>
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<div class="tile">
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<span class="tile-num" id="hl-lag">–</span>
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<span class="tile-lab">mean detection lag</span>
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<span class="tile-sub">adaptive vs fixed · detected events only</span>
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</div>
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</div>
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<p class="result-note" id="comparison-note">
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The adaptive and fixed monitors receive the <strong>same number of samples on every path</strong>.
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The experiment therefore compares sample placement—not computational cost.
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</p>
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<div id="warning-box" class="warning-box" hidden></div>
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<div id="chart-stage" class="chart-stage">
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<div style="padding:3rem; text-align:center; color:var(--muted)">Running simulation…</div>
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</div>
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<details class="advanced-opts method-note" open>
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<summary>How the Monte Carlo comparison works</summary>
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<ol>
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<li><strong>Simulate:</strong> generate independent GBM or Merton jump-diffusion price paths on a fine grid.</li>
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<li><strong>Mark events:</strong> record each path's first lower- and upper-barrier crossing.</li>
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<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>
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<li><strong>Score:</strong> an event is detected when a sampled price remains beyond the barrier within the selected deadline.</li>
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</ol>
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<p>
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<span id="method-conclusion">The headline compares detections and mean lag under an equal sample budget.</span>
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The miss budget <strong>ε</strong> is a local diffusion-model design parameter, not a global guarantee.
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</p>
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</details>
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<details class="json-block">
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<summary>Raw result JSON</summary>
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<pre id="stats-json">{}</pre>
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</details>
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</div>
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</section>
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<!-- Left column, row 2: supporting information. -->
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<section class="controls" id="col-info">
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<div class="card explain">
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<h2>How to read it</h2>
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<dl>
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<dt>Open cyan circles</dt>
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<dd>Adaptive observations. The interval shrinks quadratically as the log-price approaches the nearer barrier.</dd>
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<dt>Grey dots</dt>
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<dd id="fixed-explanation">An equal-budget fixed sampler with the same number of observations on each path, spaced uniformly.</dd>
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<dt>Red × and green ▲</dt>
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<dd>The first lower- and upper-barrier events on the fine reference grid.</dd>
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<dt>Headline</dt>
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<dd id="headline-explanation">Same paths and the same sample budget. Compare how many events each schedule confirms before the deadline.</dd>
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</dl>
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</div>
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<div class="card note">
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<h2>Research demo caveat</h2>
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<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>
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</div>
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<div class="card explain">
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<h2>How this works</h2>
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<p>The app simulates price paths, marks first barrier crossings, and compares two observation schedules on exactly the same paths.</p>
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<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>
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</div>
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<div class="card note">
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<h2>Links</h2>
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<ul class="link-list">
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<li><a href="https://pawelsarkowicz.xyz" target="_blank" rel="noreferrer">pawelsarkowicz.xyz</a></li>
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<li><a href="https://github.com/psark007/adaptive-barrier-monitor" target="_blank" rel="noreferrer">Adaptive Barrier Monitor repo</a></li>
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<li><a href="https://github.com/psark007/adaptive-barrier-monitor/blob/main/LICENSE" target="_blank" rel="noreferrer">License</a></li>
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</ul>
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</div>
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</section>
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</main>
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<footer class="site-footer">
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<span>© Pawel Sarkowicz</span>
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</footer>
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<script src="/static/app.js?v=1"></script>
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</body>
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</html>
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