Show market buckets with model probability

2026-06-29 22:27:22 +08:00
parent 5404fdef2b
commit bbf2db4da1
3 changed files with 116 additions and 21 deletions
@@ -32,7 +32,7 @@ const SUMMARY_TEXT = {
  region: { en: "Region", zh: "区域" },
  localTime: { en: "Local Time", zh: "当地时间" },
  gaussianMu: { en: "Gaussian μ", zh: "高斯 μ" },
-  probabilityDistribution: { en: "Probability Distribution", zh: "概率分布" },
+  marketMatch: { en: "Market Match", zh: "市场匹配" },
  median: { en: "Median", zh: "模型中位数" },
  spread: { en: "Spread", zh: "分歧范围" },
  empty: { en: "No model summary rows match the current filters.", zh: "当前筛选下没有模型汇总数据。" },
@@ -100,9 +100,11 @@ function FilterToggle({
 function ModelSummaryRowView({
  row,
  nowMs,
+  isEn,
 }: {
  row: ModelSummaryRow;
  nowMs: number | null;
+  isEn: boolean;
 }) {
  return (
    <tr className="group border-b border-slate-100 hover:bg-blue-50/40">
@@ -136,24 +138,26 @@ function ModelSummaryRowView({
        <TemperatureCell value={row.gaussianMu} symbol={row.tempSymbol} emphasis="median" />
      </td>
      <td className="min-w-[420px] max-w-[520px] px-3 py-1.5 text-left">
-        {row.probabilityBuckets.length ? (
+        {row.marketMatches.length ? (
          <div className="flex flex-wrap items-center gap-1">
-            {row.probabilityBuckets.map((bucket) => {
-              const isTopBucket = bucket.key === row.topProbabilityBucketKey;
+            {row.marketMatches.map((match) => {
              return (
-                <span
-                  key={bucket.key}
+                <a
+                  key={match.key}
+                  href={match.marketUrl || undefined}
+                  target={match.marketUrl ? "_blank" : undefined}
+                  rel={match.marketUrl ? "noreferrer" : undefined}
                  className={clsx(
-                    "inline-flex items-center gap-1 rounded border px-1.5 py-0.5 font-mono text-[10px] tabular-nums",
-                    isTopBucket
-                      ? "border-violet-200 bg-violet-50 font-black text-violet-800"
-                      : "border-slate-200 bg-slate-50 font-bold text-violet-700",
+                    "inline-flex items-center gap-1 rounded border px-1.5 py-0.5 font-mono text-[10px] font-bold tabular-nums",
+                    (match.modelProbability ?? 0) >= 0.2
+                      ? "border-emerald-200 bg-emerald-50 text-emerald-800"
+                      : "border-slate-200 bg-slate-50 text-slate-600",
                  )}
-                  title={bucket.label}
+                  title={`${match.label} model ${formatModelSummaryProbability(match.modelProbability)}`}
                >
-                  <span>{bucket.label}</span>
-                  <strong>{formatModelSummaryProbability(bucket.probability)}</strong>
-                </span>
+                  <span className="font-black">{match.label}</span>
+                  <span>{isEn ? "M" : "模"} {formatModelSummaryProbability(match.modelProbability)}</span>
+                </a>
              );
            })}
          </div>
@@ -315,8 +319,8 @@ export function ModelSummaryDashboard({
              <th className="min-w-[92px] px-3 py-2 text-right text-violet-700">
                {copy("gaussianMu", isEn)}
              </th>
-              <th className="min-w-[420px] max-w-[520px] px-3 py-2 text-left text-violet-700">
-                {copy("probabilityDistribution", isEn)}
+              <th className="min-w-[420px] max-w-[520px] px-3 py-2 text-left text-emerald-700">
+                {copy("marketMatch", isEn)}
              </th>
            </tr>
          </thead>
@@ -326,6 +330,7 @@ export function ModelSummaryDashboard({
                key={row.cityKey}
                row={row}
                nowMs={nowMs}
+                isEn={isEn}
              />
            ))}
          </tbody>
@@ -59,6 +59,16 @@ export function runTests() {
        { value: 33, model_probability: 0.31, range: "[32.5~33.5)" },
      ],
      probability_engine: "legacy",
+      all_buckets: [
+        {
+          label: "31.5-32.5°C",
+          model_probability: 0.42,
+        },
+        {
+          label: "33.5-34.5°C",
+          model_probability: 0.08,
+        },
+      ],
    },
    {
      city: "madrid",
@@ -131,6 +141,18 @@ export function runTests() {
      parisRow.topProbabilityBucketKey === "31.5-32.5°C",
    "model summary should map probability buckets and identify the top bucket",
  );
+  assert(parisRow.marketMatches.length === 2, "model summary should keep every Polymarket tradable bucket");
+  assert(
+    parisRow.marketMatches[0].label === "31.5-32.5°C" &&
+      parisRow.marketMatches[0].modelProbability === 0.42 &&
+      parisRow.marketMatches[0].marketUrl === null,
+    "model summary should expose model probability for market-matched buckets without requiring market price",
+  );
+  assert(
+    parisRow.marketMatches[1].label === "33.5-34.5°C" &&
+      parisRow.marketMatches[1].modelProbability === 0.08,
+    "model summary should keep low-probability tradable buckets for manual NO review",
+  );
  assert(formatModelSummaryProbability(null) === "—", "missing probability should render as an em dash");
  assert(formatModelSummaryProbability(0.424) === "42%", "probability buckets should render as rounded percentages");
  assert(formatModelSummaryTemp(null, "°C") === "—", "missing model temperatures should render as an em dash");
@@ -199,10 +221,14 @@ export function runTests() {
      modelSummarySource.includes("hasModelSummaryForecastData") &&
      modelSummarySource.includes("Gaussian μ") &&
      modelSummarySource.includes("高斯 μ") &&
-      modelSummarySource.includes("Probability Distribution") &&
-      modelSummarySource.includes("概率分布") &&
-      modelSummarySource.includes("topProbabilityBucketKey") &&
-      modelSummarySource.includes("probabilityBuckets.map") &&
+      modelSummarySource.includes("Market Match") &&
+      modelSummarySource.includes("市场匹配") &&
+      modelSummarySource.includes("marketMatches.map") &&
+      !modelSummarySource.includes("formatModelSummaryEdge") &&
+      !modelSummarySource.includes("marketProbability") &&
+      !modelSummarySource.includes("edgePercent") &&
+      !modelSummarySource.includes("Probability Distribution") &&
+      !modelSummarySource.includes("概率分布") &&
      !modelSummarySource.includes("probabilityColumns") &&
      modelSummarySource.includes("min-w-[96px]") &&
      modelSummarySource.includes("Local Time") &&
@@ -29,6 +29,13 @@ export type ModelSummaryProbabilityBucket = {
  probability: number;
 };

+export type ModelSummaryMarketMatch = {
+  key: string;
+  label: string;
+  modelProbability: number | null;
+  marketUrl: string | null;
+};
+
 export type ModelSummaryRow = {
  cityKey: string;
  cityName: string;
@@ -47,6 +54,7 @@ export type ModelSummaryRow = {
  gaussianMu: number | null;
  probabilityEngine: string | null;
  topProbabilityBucketKey: string | null;
+  marketMatches: ModelSummaryMarketMatch[];
  searchText: string;
 };

@@ -156,6 +164,54 @@ function weightedProbabilityMu(buckets: ModelSummaryProbabilityBucket[]) {
  return roundToOneDecimal(weightedValue / totalProbability);
 }

+function normalizeProbability(value: unknown) {
+  const numericValue = finiteNumber(value);
+  if (numericValue == null) return null;
+  return numericValue > 1 ? numericValue / 100 : numericValue;
+}
+
+function marketBucketLabel(bucket: Record<string, unknown>, tempSymbol: string) {
+  const textLabel = String(bucket.label || bucket.bucket || bucket.range || "").trim();
+  if (textLabel) return textLabel;
+  const lower = finiteNumber(bucket.lower);
+  const upper = finiteNumber(bucket.upper);
+  if (lower != null && upper != null && upper > lower) {
+    return probabilityBucketLabel(lower, upper, String(bucket.unit || tempSymbol || "°C"));
+  }
+  const value = finiteNumber(bucket.value ?? bucket.temp ?? bucket.temperature);
+  return value == null ? "—" : `${formatBucketBound(value)}${bucket.unit || tempSymbol || "°C"}`;
+}
+
+function buildMarketMatches(row: ScanOpportunityRow): ModelSummaryMarketMatch[] {
+  const marketRow = row as ScanOpportunityRow & {
+    all_buckets?: Array<Record<string, unknown>> | null;
+    top_buckets?: Array<Record<string, unknown>> | null;
+  };
+  const sourceBuckets = (
+    Array.isArray(marketRow.all_buckets) && marketRow.all_buckets.length
+      ? marketRow.all_buckets
+      : Array.isArray(marketRow.top_buckets)
+        ? marketRow.top_buckets
+        : []
+  ) as Array<Record<string, unknown>>;
+  const tempSymbol = row.temp_symbol || "°C";
+
+  return sourceBuckets
+    .map((bucket, index) => {
+      const label = marketBucketLabel(bucket, tempSymbol);
+      const modelProbability = normalizeProbability(bucket.model_probability ?? bucket.probability);
+      return {
+        key: `${label}-${index}`,
+        label,
+        modelProbability,
+        marketUrl: typeof bucket.market_url === "string" ? bucket.market_url : null,
+      };
+    })
+    .sort((a, b) => {
+      return (b.modelProbability ?? -1) - (a.modelProbability ?? -1);
+    });
+}
+
 function normalizeCityKey(row: ScanOpportunityRow, index: number) {
  const rawKey = row.city || row.city_display_name || row.display_name || `row-${index}`;
  return String(rawKey).trim().toLowerCase();
@@ -259,6 +315,13 @@ export function buildModelSummaryRows(
    const probabilitySearchText = probabilityBuckets
      .map((bucket) => `${bucket.label} ${formatModelSummaryProbability(bucket.probability)}`)
      .join(" ");
+    const marketMatches = buildMarketMatches(row);
+    const marketSearchText = marketMatches
+      .map(
+        (match) =>
+          `${match.label} ${formatModelSummaryProbability(match.modelProbability)}`,
+      )
+      .join(" ");

    byCity.set(cityKey, {
      cityKey,
@@ -278,8 +341,9 @@ export function buildModelSummaryRows(
      gaussianMu: weightedProbabilityMu(probabilityBuckets),
      probabilityEngine: row.probability_engine || (probabilityBuckets.length ? "legacy" : null),
      topProbabilityBucketKey: topProbabilityBucket?.key || null,
+      marketMatches,
      searchText:
-        `${cityName} ${row.city || ""} ${region.labelEn} ${region.labelZh} ${modelSearchText} ${probabilitySearchText}`.toLowerCase(),
+        `${cityName} ${row.city || ""} ${region.labelEn} ${region.labelZh} ${modelSearchText} ${probabilitySearchText} ${marketSearchText}`.toLowerCase(),
    });
  });