2b:null 2c:["$","title",null,{"children":"Do Stocks Keep Rising After a Big Gap-Up? | DATAVIS SUMMER | Observable"}] 31:T567,tickerOptions = { const normalizeTicker = t => String(t).trim().toUpperCase().replace(".", "-"); const getField = (d, names) => names.map(name => d[name]).find(value => value != null && String(value).trim() !== ""); const companyByTicker = new Map( sp500Companies.map(d => { const symbol = getField(d, ["Symbol", "Ticker", "ticker", "symbol"]); const name = getField(d, ["Name", "Security", "Company", "Company Name"]); const sector = getField(d, ["Sector", "GICS Sector", "gics_sector"]); return [ normalizeTicker(symbol), { name, sector } ]; }) ); const tickers = Array.from( new Set(bucketSummary.map(d => d.ticker).filter(d => d !== "ALL")) ).sort(); return [ { ticker: "ALL", name: "All S&P 500 stocks", sector: "Market-wide summary", label: "All S&P 500 stocks", search: "all s&p 500 stocks market summary" }, ...tickers.map(ticker => { const info = companyByTicker.get(normalizeTicker(ticker)); return { ticker, name: info?.name ?? "Company name unavailable", sector: info?.sector ?? "", label: info?.name ? `${info.name} (${ticker})` : `${ticker}`, search: `${ticker} ${info?.name ?? ""} ${info?.sector ?? ""}`.toLowerCase() }; }) ]; }32:Td45,viewof selectedTicker = { const root = html`

`; root.value = "ALL"; const button = html` `; const panel = html` `; const search = html` `; const list = html`

`; panel.append(search, list); root.append(button, panel); function setSelected(option) { root.value = option.ticker; button.innerHTML = ` ${option.label} ▾ `; panel.style.display = "none"; root.dispatchEvent(new CustomEvent("input", { bubbles: true })); } function renderOptions() { const q = search.value.trim().toLowerCase(); const matches = tickerOptions .filter(d => !q || d.search.includes(q)) .slice(0, 150); list.innerHTML = ""; for (const option of matches) { const row = html` `; row.addEventListener("click", () => setSelected(option)); list.append(row); } if (matches.length === 0) { list.append(html`

No matching companies found.

`); } } button.addEventListener("click", () => { panel.style.display = panel.style.display === "none" ? "block" : "none"; search.value = ""; search.focus(); renderOptions(); }); search.addEventListener("input", renderOptions); document.addEventListener("click", event => { if (!root.contains(event.target)) { panel.style.display = "none"; } }); renderOptions(); return root; }33:Tb59,{ const pct = v => `${(v * 100).toFixed(1)}%`; const num = v => v.toLocaleString("en-US"); const bucketOrder = ["<1%", "1–2%", "2–4%", "4–6%", ">6%"]; const wmean = (rows, col) => { const total = d3.sum(rows, d => d.observations); return d3.sum(rows, d => d[col] * d.observations) / total; }; const tickerBuckets = Array.from( d3.rollup( bucketSummary.filter(d => d.ticker === selectedTicker), rows => ({ ticker: selectedTicker, gap_bucket: rows[0].gap_bucket, observations: d3.sum(rows, d => d.observations), full_gap_fill_rate: wmean(rows, "full_gap_fill_rate"), continuation_rate: wmean(rows, "continuation_rate"), fade_rate: wmean(rows, "fade_rate") }), d => d.gap_bucket ), ([gap_bucket, values]) => values ) .sort((a, b) => bucketOrder.indexOf(a.gap_bucket) - bucketOrder.indexOf(b.gap_bucket)); const tooltipText = d => [ `Ticker: ${d.ticker}`, `Gap-up bucket: ${d.gap_bucket}`, `Historical examples: ${num(d.observations)}`, `Full gap-fill rate: ${pct(d.full_gap_fill_rate)}`, `Continuation rate: ${pct(d.continuation_rate)}`, `Fade rate: ${pct(d.fade_rate)}` ].join("\n"); return Plot.plot({ title: "How often do opening gap-ups fully fill?", subtitle: `${selectedTicker} — gap-up size only; this chart does not include gap-downs`, width: 850, height: 440, marginLeft: 70, marginRight: 35, marginTop: 45, marginBottom: 60, x: { label: "Size of overnight gap-up", domain: bucketOrder }, y: { label: "Full gap-fill rate within gap-up bucket", grid: true, domain: [0, 1], tickFormat: d => `${Math.round(d * 100)}%` }, color: { label: "Full gap-fill rate", scheme: "Blues", domain: [0, 1], legend: true, tickFormat: d => `${Math.round(d * 100)}%` }, marks: [ Plot.ruleY([0.5], { stroke: "black", strokeOpacity: 0.35, strokeDasharray: "4,4" }), Plot.text([{gap_bucket: ">6%", y: 0.5, label: "50% reference"}], { x: "gap_bucket", y: "y", text: "label", dx: 35, dy: -6, fontSize: 11, fill: "black", fillOpacity: 0.65 }), Plot.barY(tickerBuckets, { x: "gap_bucket", y: "full_gap_fill_rate", fill: "full_gap_fill_rate", stroke: "black", strokeOpacity: 0.25, title: tooltipText, tip: true }), Plot.text(tickerBuckets, { x: "gap_bucket", y: d => d.full_gap_fill_rate / 2, text: d => pct(d.full_gap_fill_rate), textAnchor: "middle", fill: d => d.full_gap_fill_rate >= 0.55 ? "white" : "#222", fontSize: 15, fontWeight: "bold" }) ] }); }34:T1421,{ const num = v => v == null || Number.isNaN(v) ? "n/a" : v.toLocaleString("en-US"); const pct = v => v == null || Number.isNaN(v) ? "n/a" : `${v.toFixed(2)}%`; const price = v => v == null || Number.isNaN(v) ? "n/a" : `$${v.toFixed(2)}`; const dateText = d => d instanceof Date ? d3.utcFormat("%Y-%m-%d")(d) : d; if (selectedTicker === "ALL") { return html`

Select one ticker to view candlesticks.

A candlestick chart works best for one company at a time. Showing all 500+ S&P 500 stocks together would be too cluttered to read.

`; } const sourceRows = candlestickDays .filter(d => d.ticker === selectedTicker) .filter(d => d.prev_close > 0 && d.open > 0 && d.high > 0 && d.low > 0 && d.close > 0 ) .sort((a, b) => d3.ascending(a.date_iso, b.date_iso)); const candles = sourceRows.map(d => { const open_rel = 100 * d.open / d.prev_close; const high_rel = 100 * d.high / d.prev_close; const low_rel = 100 * d.low / d.prev_close; const close_rel = 100 * d.close / d.prev_close; const direction = d.close > d.open ? "Finished higher after open" : d.close < d.open ? "Finished lower after open" : "Finished unchanged after open"; return { ...d, date_text: dateText(d.date_iso), open_rel, high_rel, low_rel, close_rel, body_low: Math.min(open_rel, close_rel), body_high: Math.max(open_rel, close_rel), direction }; }); if (candles.length === 0) { return html`

No candlestick examples found for ${selectedTicker}.

`; } const yMin = Math.floor((d3.min(candles, d => d.low_rel) - 5) / 5) * 5; const yMax = Math.ceil((d3.max(candles, d => d.high_rel) + 5) / 5) * 5; const tooltipText = d => [ `Date: ${d.date_text}`, `Ticker: ${d.ticker}`, `Previous close: ${price(d.prev_close)}`, `Open: ${price(d.open)}`, `High: ${price(d.high)}`, `Low: ${price(d.low)}`, `Close: ${price(d.close)}`, `Overnight gap: ${pct(d.gap_pct)}`, `Same-day return after open: ${pct(d.intraday_return_pct)}`, `Volume: ${num(d.volume)}`, `Outcome: ${d.direction}`, `Full gap filled: ${d.full_gap_fill_flag ? "Yes" : "No"}` ].join("\n"); return Plot.plot({ title: "Candlestick view: did the stock finish higher after opening?", subtitle: `${selectedTicker} — selected historical gap-up days, shown chronologically`, width: 950, height: 520, marginLeft: 80, marginRight: 90, marginTop: 55, marginBottom: 85, x: { label: "Date", type: "band", domain: candles.map(d => d.date_text), tickRotate: -35 }, y: { label: "Price move relative to previous close", domain: [yMin, yMax], grid: true, tickFormat: d => `${d >= 100 ? "+" : ""}${(d - 100).toFixed(0)}%` }, color: { legend: true, domain: [ "Finished higher after open", "Finished lower after open", "Finished unchanged after open" ], range: [ "#2ca25f", "#de2d26", "#777777" ] }, marks: [ Plot.ruleY([100], { stroke: "black", strokeOpacity: 0.45, strokeDasharray: "4,4" }), Plot.text( [{ date_text: candles[Math.floor(candles.length * 0.7)].date_text, y: 100, label: "Previous close / 0%" }], { x: "date_text", y: "y", text: "label", dy: -8, fontSize: 11, fill: "black", fillOpacity: 0.7 } ), Plot.ruleX(candles, { x: "date_text", y1: "low_rel", y2: "high_rel", stroke: "direction", strokeWidth: 1.8 }), Plot.barY(candles, { x: "date_text", y1: "body_low", y2: "body_high", fill: "direction", fillOpacity: 0.9, stroke: "black", strokeWidth: 0.7, insetLeft: 7, insetRight: 7 }), Plot.dot( candles, Plot.pointerX({ x: "date_text", y: "close_rel", r: 4, fill: "none", stroke: "black", strokeWidth: 2 }) ), Plot.tip( candles, Plot.pointerX({ x: "date_text", y: "close_rel", title: tooltipText, anchor: "left", dx: 20, dy: 0, lineWidth: 36, fontSize: 12, fill: "white", fillOpacity: 0.97, stroke: "black", strokeWidth: 1 }) ) ] }); }35:Td1e,{ const pct = v => v == null || Number.isNaN(v) ? "n/a" : `${v.toFixed(2)}%`; const num = v => v == null || Number.isNaN(v) ? "n/a" : v.toLocaleString("en-US"); const dateText = d => d instanceof Date ? d3.utcFormat("%Y-%m-%d")(d) : d; const pointRadius = 3; const rightSideThreshold = 68; const filtered = selectedTicker === "ALL" ? scatterSample : scatterSample.filter(d => d.ticker === selectedTicker); const leftTooltipPoints = filtered.filter(d => d.gap_pct <= rightSideThreshold); const rightTooltipPoints = filtered.filter(d => d.gap_pct > rightSideThreshold); const yMin = Math.min( -30, Math.floor((d3.min(filtered, d => d.intraday_return_pct) - 5) / 5) * 5 ); const yMax = Math.max( 60, Math.ceil((d3.max(filtered, d => d.intraday_return_pct) + 18) / 5) * 5 ); const tooltipText = d => [ `Date: ${dateText(d.date_iso)}`, `Ticker: ${d.ticker}`, `Company: ${d.company_name}`, `Sector: ${d.sector}`, `Overnight gap: ${pct(d.gap_pct)}`, `Same-day return: ${pct(d.intraday_return_pct)}`, `Volume: ${num(d.volume)}`, `Outcome: ${d.outcome_label}` ].join("\n"); return Plot.plot({ title: "Raw evidence: individual trading-day gap-ups", subtitle: `${selectedTicker} — each dot is one stock on one trading day`, width: 900, height: 620, marginTop: 45, marginLeft: 75, marginRight: 35, marginBottom: 70, x: { label: "Overnight gap-up size →", grid: true, tickFormat: d => `${d}%` }, y: { label: "Same-day return after opening bell ↑", grid: true, domain: [yMin, yMax], tickFormat: d => `${d}%` }, color: { label: "Same-day return after open", type: "diverging", scheme: "RdYlGn", pivot: 0, domain: [-20, 20], legend: true }, marks: [ Plot.ruleY([0], { stroke: "black", strokeWidth: 1.4, strokeDasharray: "4,4" }), Plot.dot(filtered, { x: "gap_pct", y: "intraday_return_pct", fill: "intraday_return_pct", r: pointRadius, stroke: "black", strokeWidth: 0.35, strokeOpacity: 0.32, fillOpacity: 0.62 }), Plot.dot( filtered, Plot.pointer({ x: "gap_pct", y: "intraday_return_pct", r: pointRadius, fill: "none", stroke: "black", strokeWidth: 2.4 }) ), Plot.tip( leftTooltipPoints, Plot.pointer({ x: "gap_pct", y: "intraday_return_pct", title: tooltipText, anchor: "left", dx: 42, dy: 0, lineWidth: 34, fontSize: 12, fill: "white", fillOpacity: 0.96, stroke: "black", strokeWidth: 1.1 }) ), Plot.tip( rightTooltipPoints, Plot.pointer({ x: "gap_pct", y: "intraday_return_pct", title: tooltipText, anchor: "right", dx: -42, dy: 0, lineWidth: 34, fontSize: 12, fill: "white", fillOpacity: 0.96, stroke: "black", strokeWidth: 1.1 }) ) ] }); }36:T11d5,{ const activeTicker = selectedTicker; const gapOrder = ["<1%", "1–2%", "2–4%", "4–6%", ">6%"]; const volumeOrder = [ "Extremely busy", "Busier than usual", "Typical activity", "Quieter than usual" ]; const volumeLabel = bucket => { const b = String(bucket); if (b.startsWith("<0.75")) return "Quieter than usual"; if (b.startsWith("0.75")) return "Typical activity"; if (b.startsWith("1.25")) return "Busier than usual"; if (b.startsWith(">2")) return "Extremely busy"; return b; }; const pctRate = v => v == null || Number.isNaN(v) ? "n/a" : `${(v * 100).toFixed(1)}%`; const pctMove = v => v == null || Number.isNaN(v) ? "n/a" : `${v.toFixed(2)}%`; const num = v => v == null || Number.isNaN(v) ? "n/a" : v.toLocaleString("en-US"); const wmean = (rows, col) => { const total = d3.sum(rows, d => d.observations); return total ? d3.sum(rows, d => d[col] * d.observations) / total : NaN; }; const rawRows = volumeHeatmap.filter(d => d.ticker === activeTicker); const rolled = Array.from( d3.rollup( rawRows, rows => ({ ticker: activeTicker, gap_bucket: rows[0].gap_bucket, relative_volume_bucket: rows[0].relative_volume_bucket, relative_volume_label: volumeLabel(rows[0].relative_volume_bucket), observations: d3.sum(rows, d => d.observations), mean_intraday_return_pct: wmean(rows, "mean_intraday_return_pct"), median_intraday_return_pct: wmean(rows, "median_intraday_return_pct"), continuation_rate: wmean(rows, "continuation_rate"), fade_rate: wmean(rows, "fade_rate"), full_gap_fill_rate: wmean(rows, "full_gap_fill_rate") }), d => d.gap_bucket, d => volumeLabel(d.relative_volume_bucket) ), ([gap_bucket, volumeMap]) => Array.from(volumeMap, ([relative_volume_label, values]) => values) ).flat(); const lookup = new Map( rolled.map(d => [`${d.gap_bucket}|${d.relative_volume_label}`, d]) ); const heatmapCells = volumeOrder.flatMap(volume => gapOrder.map(gap => lookup.get(`${gap}|${volume}`) ?? { ticker: activeTicker, gap_bucket: gap, relative_volume_label: volume, observations: 0, fade_rate: NaN, continuation_rate: NaN, full_gap_fill_rate: NaN, mean_intraday_return_pct: NaN } ) ); const cellTitle = d => [ `Ticker: ${d.ticker}`, `Gap bucket: ${d.gap_bucket}`, `Trading activity: ${d.relative_volume_label}`, `Historical examples: ${num(d.observations)}`, `Fade rate: ${pctRate(d.fade_rate)}`, `Continuation rate: ${pctRate(d.continuation_rate)}`, `Full gap-fill rate: ${pctRate(d.full_gap_fill_rate)}`, `Average same-day return: ${pctMove(d.mean_intraday_return_pct)}` ].join("\n"); return Plot.plot({ title: "Does trading volume change what happens after a gap-up?", subtitle: `${activeTicker} — cell color shows fade rate by gap size and trading activity`, width: 850, height: 430, marginLeft: 170, marginBottom: 55, marginTop: 45, x: { label: "Size of overnight gap-up", domain: gapOrder }, y: { label: "Trading activity compared with normal", domain: volumeOrder }, color: { label: "Fade rate", scheme: "YlOrRd", domain: [0.35, 0.65], clamp: true, legend: true, tickFormat: d => `${Math.round(d * 100)}%` }, marks: [ Plot.cell(heatmapCells, { x: "gap_bucket", y: "relative_volume_label", fill: "#f2f2f2", stroke: "white", strokeWidth: 2, inset: 1 }), Plot.cell(heatmapCells.filter(d => d.observations > 0), { x: "gap_bucket", y: "relative_volume_label", fill: "fade_rate", stroke: "white", strokeWidth: 2, inset: 1, title: cellTitle, tip: true }), Plot.text(heatmapCells, { x: "gap_bucket", y: "relative_volume_label", text: d => d.observations > 0 ? `${Math.round(d.fade_rate * 100)}%` : "n/a", fill: d => d.observations > 0 && d.fade_rate >= 0.56 ? "white" : "black", fontSize: 13, fontWeight: "bold", title: cellTitle, tip: true }) ] }); }37:T97e,viewof selectedEventIndex = { const root = html`

`; const formatDate = d3.utcFormat("%b %d, %Y"); const formatYear = d3.utcFormat("%Y"); const toDate = d => d instanceof Date ? d : new Date(d); const slider = html` `; const currentLabel = html`

`; const yearEndpoints = html`

Earliest: ${formatYear(toDate(sortedEvents[0].start_date))} Latest: ${formatYear(toDate(sortedEvents[sortedEvents.length - 1].start_date))}

`; const label = html`

`; function render() { const i = +slider.value; const event = sortedEvents[i]; root.value = i; currentLabel.innerHTML = ` Selected event ${i + 1} of ${sortedEvents.length}: ${event.short_label} `; label.innerHTML = `

Event ${i + 1} of ${sortedEvents.length}

${event.short_label}

${formatDate(toDate(event.start_date))} – ${formatDate(toDate(event.end_date))} · ${event.event_type}

${event.plain_english}

`; root.dispatchEvent(new CustomEvent("input", { bubbles: true })); } slider.addEventListener("input", render); root.append( html`

Choose a market event window

`, currentLabel, slider, yearEndpoints, label ); render(); return root; }38:T843,{ const num = v => v == null || Number.isNaN(v) ? "0" : v.toLocaleString("en-US"); const toDate = d => d instanceof Date ? d : new Date(d); const start = toDate(selectedEvent.start_date); const end = d3.utcDay.offset(toDate(selectedEvent.end_date), 1); const rows = scatterSample .filter(d => { const date = d.date_iso instanceof Date ? d.date_iso : new Date(d.date_iso); return ( date >= start && date < end && (selectedTicker === "ALL" || d.ticker === selectedTicker) ); }); const uniqueTickers = new Set(rows.map(d => d.ticker)).size; const topSectors = Array.from( d3.rollup( rows, values => values.length, d => d.sector ), ([sector, count]) => ({ sector, count }) ) .sort((a, b) => d3.descending(a.count, b.count)) .slice(0, 4) .map(d => `${d.sector} (${d.count})`) .join(", "); return html`

${selectedEvent.event_name}

${selectedEvent.start_date} to ${selectedEvent.end_date} · ${selectedEvent.event_type}

${selectedEvent.plain_english}

Why this window matters: ${selectedEvent.why_relevant}

Examples found in sampled gap-up data: ${num(rows.length)} observations across ${num(uniqueTickers)} tickers. ${ topSectors ? html`
Most common sectors: ${topSectors}` : "" }

`; }39:Tdd6,{ const num = v => v == null || Number.isNaN(v) ? "0" : v.toLocaleString("en-US"); const pct = v => v == null || Number.isNaN(v) ? "n/a" : `${v.toFixed(2)}%`; const avgGap = eventRows.length ? d3.mean(eventRows, d => d.gap_pct) : NaN; const avgReturn = eventRows.length ? d3.mean(eventRows, d => d.intraday_return_pct) : NaN; const uniqueTickers = new Set(eventRows.map(d => d.ticker)).size; const continuationCount = eventRows.filter(d => d.intraday_return_pct > 0).length; const fadeCount = eventRows.filter(d => d.intraday_return_pct < 0).length; const unchangedCount = eventRows.filter(d => d.intraday_return_pct === 0).length; const evidenceWarning = eventRows.length < 10 ? html`

Small sample warning: This event window has only ${num(eventRows.length)} sampled observation${eventRows.length === 1 ? "" : "s"}. Treat this as an example view, not a broad statistical conclusion.

` : ""; return html`

How to read this event-window chart

This chart shows gap-up examples that occurred during the selected market event window: ${selectedEvent.short_label}. Each dot represents one stock on one trading day.

The x-axis shows the date within the event window.
The y-axis shows how large the overnight gap-up was. Higher dots opened much further above the previous close.
The color shows what happened after the opening bell: green means the stock kept rising, red means it fell after opening.
Hover over a dot to see the ticker, company, sector, gap size, same-day return, volume, outcome, and VIX market mood.

${num(eventRows.length)}
sampled observations

${num(uniqueTickers)}
unique tickers

${pct(avgGap)}
average overnight gap-up

${pct(avgReturn)}
average same-day return

${num(continuationCount)}
continued rising

${num(fadeCount)}
fell after opening

${evidenceWarning}

`; }3a:Tc6c,{ const pct = v => v == null || Number.isNaN(v) ? "n/a" : `${v.toFixed(2)}%`; const num = v => v == null || Number.isNaN(v) ? "n/a" : v.toLocaleString("en-US"); const fullDate = d3.utcFormat("%Y-%m-%d"); const axisDate = d3.utcFormat("%b %d"); if (eventRows.length === 0) { return html`

No sampled gap-up examples found for this event window.

Try moving the slider to another event window or selecting “ALL” in the ticker dropdown.

`; } const plotRows = eventRows.slice(0, 600); const tooltipText = d => [ `Date: ${fullDate(d.date_obj)}`, `Ticker: ${d.ticker}`, `Company: ${d.company_name}`, `Sector: ${d.sector}`, `Overnight gap: ${pct(d.gap_pct)}`, `Same-day return after open: ${pct(d.intraday_return_pct)}`, `Volume: ${num(d.volume)}`, `Outcome: ${d.outcome_label}`, `VIX mood previous day: ${d.vix_mood_prev_day}` ].join("\n"); const startDate = selectedEvent.start_date instanceof Date ? selectedEvent.start_date : new Date(selectedEvent.start_date); const endDate = selectedEvent.end_date instanceof Date ? selectedEvent.end_date : new Date(selectedEvent.end_date); return Plot.plot({ title: "Event-window evidence: gap-ups during selected market event", subtitle: `${selectedEvent.short_label} — ${ selectedTicker === "ALL" ? "all tickers" : selectedTicker }`, width: 950, height: 540, marginLeft: 75, marginRight: 45, marginTop: 55, marginBottom: 90, x: { label: "Date", type: "utc", domain: [ startDate, d3.utcDay.offset(endDate, 1) ], grid: true, // This is the key fix: always show actual month/day labels tickFormat: axisDate, // Show daily ticks when the event window is short ticks: d3.utcDay.every(1), tickRotate: -35 }, y: { label: "Overnight gap-up size", grid: true, tickFormat: d => `${d}%` }, color: { label: "Same-day return after open", type: "diverging", scheme: "RdYlGn", pivot: 0, domain: [-20, 20], legend: true }, marks: [ Plot.ruleY([0], { stroke: "black", strokeOpacity: 0.4, strokeDasharray: "4,4" }), Plot.dot(plotRows, { x: "date_obj", y: "gap_pct", fill: "intraday_return_pct", r: 4, fillOpacity: 0.72, stroke: "black", strokeWidth: 0.35, strokeOpacity: 0.35, title: tooltipText, tip: true }), Plot.dot( plotRows, Plot.pointer({ x: "date_obj", y: "gap_pct", r: 6, fill: "none", stroke: "black", strokeWidth: 2 }) ) ] }); }3b:T11ae,{ const pct = v => v == null || Number.isNaN(v) ? "n/a" : `${v.toFixed(2)}%`; const num = v => v == null || Number.isNaN(v) ? "n/a" : v.toLocaleString("en-US"); const fullDate = d3.utcFormat("%Y-%m-%d"); const baseRows = horizonGapupWall .filter(d => horizonSector === "All sectors" || d.sector === horizonSector ) .filter(d => selectedTicker === "ALL" || d.ticker === selectedTicker ) .filter(d => { if (horizonTimeMode !== "Selected market event only") return true; const start = selectedEvent.start_date instanceof Date ? selectedEvent.start_date : new Date(selectedEvent.start_date); const end = selectedEvent.end_date instanceof Date ? selectedEvent.end_date : new Date(selectedEvent.end_date); return d.date_obj >= start && d.date_obj <= end; }); if (baseRows.length === 0) { return html`

No sampled gap-up observations found.

Try changing the sector, ticker, or event filter.

`; } const rankedTickers = Array.from( d3.rollup( baseRows, rows => ({ ticker: rows[0].ticker, company_name: rows[0].company_name, sector: rows[0].sector, observations: rows.length, avg_abs_move: d3.mean(rows, d => d.abs_intraday_return_pct), max_gap: d3.max(rows, d => d.gap_pct) }), d => d.ticker ), ([ticker, values]) => values ) .sort((a, b) => d3.descending(a.avg_abs_move, b.avg_abs_move) || d3.descending(a.max_gap, b.max_gap) ) .slice(0, horizonMaxRows) .map(d => d.ticker); const chartRows = baseRows .filter(d => rankedTickers.includes(d.ticker)) .map(d => ({ ...d, row_label: `${d.ticker} · ${d.company_name}` })); const rowOrder = rankedTickers.map(ticker => { const d = chartRows.find(row => row.ticker === ticker); return d ? `${d.ticker} · ${d.company_name}` : ticker; }); const yExtent = Math.max( 10, Math.ceil(d3.max(chartRows, d => Math.abs(d.intraday_return_pct)) / 5) * 5 ); const tooltipText = d => [ `Date: ${fullDate(d.date_obj)}`, `Ticker: ${d.ticker}`, `Company: ${d.company_name}`, `Sector: ${d.sector}`, `Overnight gap-up: ${pct(d.gap_pct)}`, `Same-day return after open: ${pct(d.intraday_return_pct)}`, `Volume: ${num(d.volume)}`, `Outcome: ${d.outcome_label}`, `VIX mood previous day: ${d.vix_mood_prev_day}`, d.event_label ? `Market event: ${d.event_label}` : null ].filter(Boolean).join("\n"); return Plot.plot({ title: "Dense market overview: sampled gap-up behavior over time", subtitle: horizonTimeMode === "Selected market event only" ? `${selectedEvent.short_label} — ${ selectedTicker === "ALL" ? horizonSector : selectedTicker }` : `${selectedTicker === "ALL" ? horizonSector : selectedTicker} — strongest sampled gap-up movers over time`, width: 980, height: 90 + rowOrder.length * 28, marginLeft: 190, marginRight: 40, marginTop: 55, marginBottom: 70, x: { label: "Time →", type: "utc", grid: true }, y: { label: null, domain: [-yExtent, yExtent], ticks: [0], tickFormat: () => "" }, fy: { label: null, domain: rowOrder }, color: { label: "Same-day return after open", type: "diverging", scheme: "RdYlGn", pivot: 0, domain: [-20, 20], legend: true }, r: { range: [2, 7] }, marks: [ Plot.ruleY([0], { stroke: "black", strokeOpacity: 0.18 }), Plot.dot(chartRows, { x: "date_obj", y: "intraday_return_pct", fy: "row_label", fill: "intraday_return_pct", r: "abs_intraday_return_pct", fillOpacity: 0.78, stroke: "black", strokeWidth: 0.35, strokeOpacity: 0.35, title: tooltipText, tip: true }) ] }); 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2f:{"id":"1e3c288b7006af8a","slug":"quanta","title":"Do Stocks Keep Rising After a Big Gap-Up?","thumbnail":"2c49c9c736c002a62594627383596ccaabf3e005d06a5a4f53fdee2f99e84778","access_level":"public","create_time":"$D2026-06-04T09:28:47.906Z","update_time":"$D2026-06-15T23:37:50.068Z","view_time":null,"first_public_time":"$D2026-06-04T14:03:52.962Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"7798254538ffe6ba","login":"dis-2026-summer","name":"DATAVIS 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SUMMER","avatar_url":"https://avatars-next.observableusercontent.com/ca468dc6a7c3135fb5c1b1e7b63a23e1f78da7014b68bc50114f6823acc4bf7b","tier":"pro_2024","role":"editor"},{"type":"individual","id":"e58c0138aff50dcc","login":"jessfar","name":"Jess","avatar_url":"https://avatars-next.observableusercontent.com/cb0e719ce532c7edc28e7e2123b6b2faba43f9e0421096605e2f9501c5248dcb","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Do Stocks Keep Rising After a Big Gap-Up?","cells":[{"id":76,"value":"# Do Stocks Keep Rising After a Big Gap-Up?\n## An interactive investigation of momentum and reversal after the opening bell","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":46,"value":"volume_heatmap_summary1 = await FileAttachment(\"volume_heatmap_summary.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"volume_heatmap_summary1","database":"$undefined"},{"id":48,"value":"largest_gap_days1 = await FileAttachment(\"largest_gap_days@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"largest_gap_days1","database":"$undefined"},{"id":45,"value":"vix_regime_summary1 = await FileAttachment(\"vix_regime_summary@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"vix_regime_summary1","database":"$undefined"},{"id":43,"value":"sp500_ticker_metadata1 = await FileAttachment(\"sp500_ticker_metadata@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"sp500_ticker_metadata1","database":"$undefined"},{"id":62,"value":"bucket_summary = await FileAttachment(\"bucket_summary.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"bucket_summary","database":"$undefined"},{"id":68,"value":"bucketSummary =\n FileAttachment(\"bucket_summary.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"bucketSummary","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"overallBuckets =\n bucketSummary.filter(d => d.ticker === \"ALL\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"sp500Companies = d3.csv(\n \"https://raw.githubusercontent.com/datasets/s-and-p-500-companies/master/data/constituents.csv\",\n d3.autoType\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"**Note:** This chart only shows **gap-ups**, meaning days when a stock opened above its previous closing price. \nThe current summary CSV does not include gap-downs. To compare gap-ups and gap-downs, we would need a signed gap dataset where positive values are gap-ups and negative values are gap-downs.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":115,"value":"candlestickDays = FileAttachment(\"largest_gap_days.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":178,"value":"viewof candlestickMode = Inputs.radio(\n [\"Largest gap-up examples\", \"Most recent gap-up examples\"],\n {\n label: \"Candlestick sample\",\n value: \"Largest gap-up examples\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"ticker = selectedTicker\n\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":132,"value":"scatterSample = FileAttachment(\"scatterplot_sample.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":137,"value":"This scatterplot uses **${scatterSample.length.toLocaleString()} individual historical trading-day observations**.\n\nEach dot represents one stock on one trading day. \nThis is a sampled raw-evidence file, not the full master dataset.\n`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":141,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":143,"value":"**How to read this chart:** \nEach dot is one historical stock-day where the stock opened above its previous closing price.\n\n- Dots **above the zero line** continued rising after the market opened.\n- Dots **below the zero line** faded after the market opened.\n- Greener dots are stronger same-day gains after the open.\n- Redder dots are stronger same-day losses after the open.\n- The vertical dashed lines mark the same gap-size buckets used in the summary charts: **1%, 2%, 4%, and 6%**.\n- Hover over a dot to see the date, ticker, company, gap size, same-day return, volume, and outcome.\n`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":164,"value":"volumeHeatmap = FileAttachment(\"volume_heatmap_summary.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":162,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"**How to read this heatmap:** \nThis chart asks whether a gap-up behaves differently when trading volume is unusual.\n\n- The horizontal axis shows the size of the overnight gap-up.\n- The vertical axis shows whether trading activity was quieter, typical, busier, or extremely busy compared with normal.\n- Each cell shows the **fade rate**: how often the stock fell after opening higher.\n- Darker cells mean the gap-up was more likely to fade during the trading day.\n- Hover over a cell to see the sample size, fade rate, continuation rate, full gap-fill rate, and average same-day return.\n\nCells with small sample sizes should be interpreted cautiously.\n`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":185,"value":"stockClusterFeatures = FileAttachment(\"stock_cluster_features.csv\").csv({typed: true})\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"stockClusterSummary = FileAttachment(\"stock_cluster_summary.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":192,"value":"eventsTimeline = FileAttachment(\"events_timeline.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"sortedEvents = {\n const toDate = d =>\n d instanceof Date\n ? d\n : new Date(d);\n\n return eventsTimeline\n .slice()\n .sort((a, b) => d3.ascending(toDate(a.start_date), toDate(b.start_date)));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":220,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":223,"value":"selectedEvent = sortedEvents[selectedEventIndex]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":225,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":229,"value":"eventRows = {\n const toDate = d =>\n d instanceof Date\n ? d\n : new Date(d);\n\n const start = toDate(selectedEvent.start_date);\n const end = d3.utcDay.offset(toDate(selectedEvent.end_date), 1);\n\n return scatterSample\n .map(d => ({\n ...d,\n date_obj:\n d.date_iso instanceof Date\n ? d.date_iso\n : new Date(d.date_iso)\n }))\n .filter(d =>\n d.date_obj >= start &&\n d.date_obj < end &&\n (selectedTicker === \"ALL\" || d.ticker === selectedTicker)\n )\n .sort((a, b) => d3.ascending(a.date_obj, b.date_obj));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":242,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":231,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"horizonGapupWall = {\n const parseDate = d3.utcParse(\"%Y-%m-%d\");\n\n const rows = await FileAttachment(\"horizon_gapup_wall.csv\").csv({typed: true});\n\n return rows.map(d => ({\n ...d,\n date_obj: d.date instanceof Date ? d.date : parseDate(d.date)\n }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"viewof horizonSector = Inputs.select(\n [\n \"All sectors\",\n ...Array.from(new Set(horizonGapupWall.map(d => d.sector))).sort()\n ],\n {\n label: \"Sector filter\",\n value: \"All sectors\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"viewof horizonTimeMode = Inputs.radio(\n [\"Full available period\", \"Selected market event only\"],\n {\n label: \"Time filter\",\n value: \"Full available period\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":251,"value":"viewof horizonMaxRows = Inputs.range([20, 100], {\n label: \"Number of stock rows\",\n value: 40,\n step: 5\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":257,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":260,"value":"\n**How to read this dense market overview:** \n\nEach row represents one stock. Time moves from left to right.\n\nEach dot is a sampled historical gap-up day for that stock. The dot’s position within the row shows what happened after the opening bell:\n\n- Dots above the center line mean the stock **continued rising after the open**.\n- Dots below the center line mean the stock **fell after the open**.\n- Greener dots are stronger same-day gains.\n- Redder dots are stronger same-day losses.\n- Larger dots represent larger absolute same-day moves.\n\nUse the sector filter, ticker dropdown, and event filter to narrow the wall. This view is meant to be a memorable overview of many stocks at once, not a precise replacement for the individual scatterplot or summary charts.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W3c","stdlib":"1","titling":"auto"}} 30:"$2f:body"