feat: 添加 cloudflare_pages、mysql_d1_backup_scheduler，更新 start_system、ensemble_models 及配置文件

cloudflare_pages/functions/api/accuracy/daily.js

@@ -0,0 +1,101 @@
+function json(data, status = 200) {
+  return new Response(JSON.stringify(data), {
+    status,
+    headers: { "content-type": "application/json; charset=UTF-8" },
+  });
+}
+
+function toInt(v, d) {
+  const n = Number(v);
+  return Number.isFinite(n) && n > 0 ? Math.floor(n) : d;
+}
+
+function ratio(correct, total) {
+  if (!total) return 0;
+  return Math.round((correct / total) * 10000) / 100;
+}
+
+function item(total, correct) {
+  const t = Number(total || 0);
+  const c = Number(correct || 0);
+  return {
+    total: t,
+    correct: c,
+    incorrect: Math.max(0, t - c),
+    accuracy: ratio(c, t),
+  };
+}
+
+export async function onRequestGet(context) {
+  const db = context.env.DB;
+  if (!db) {
+    return json({ success: false, error: "缺少 D1 绑定 DB" }, 500);
+  }
+
+  try {
+    const url = new URL(context.request.url);
+    const month = (url.searchParams.get("month") || new Date().toISOString().slice(0, 7)).trim();
+    const page = toInt(url.searchParams.get("page"), 1);
+    const limit = Math.min(100, toInt(url.searchParams.get("limit"), 10));
+    const offset = (page - 1) * limit;
+
+    const baseWhere = "date LIKE ?";
+    const monthArg = `${month}%`;
+
+    const dataSql = `
+      SELECT
+        date,
+        SUM(CASE WHEN actual_result IS NOT NULL AND ensemble_prediction IS NOT NULL THEN 1 ELSE 0 END) AS ens_total,
+        SUM(CASE WHEN ensemble_correct = 1 THEN 1 ELSE 0 END) AS ens_correct,
+
+        SUM(CASE WHEN actual_result IS NOT NULL AND dlinear_prediction IS NOT NULL THEN 1 ELSE 0 END) AS dl_total,
+        SUM(CASE WHEN dlinear_correct = 1 THEN 1 ELSE 0 END) AS dl_correct,
+
+        SUM(CASE WHEN actual_result IS NOT NULL AND nbeats_prediction IS NOT NULL THEN 1 ELSE 0 END) AS nb_total,
+        SUM(CASE WHEN nbeats_correct = 1 THEN 1 ELSE 0 END) AS nb_correct,
+
+        SUM(CASE WHEN actual_result IS NOT NULL AND ttm_prediction IS NOT NULL THEN 1 ELSE 0 END) AS ttm_total,
+        SUM(CASE WHEN ttm_correct = 1 THEN 1 ELSE 0 END) AS ttm_correct
+      FROM backup_hourly_model_predictions
+      WHERE ${baseWhere}
+      GROUP BY date
+      ORDER BY date DESC
+      LIMIT ? OFFSET ?
+    `;
+
+    const countSql = `
+      SELECT COUNT(*) AS cnt
+      FROM (
+        SELECT date
+        FROM backup_hourly_model_predictions
+        WHERE ${baseWhere}
+        GROUP BY date
+      ) x
+    `;
+
+    const dataResult = await db.prepare(dataSql).bind(monthArg, limit, offset).all();
+    const countResult = await db.prepare(countSql).bind(monthArg).first();
+
+    const rows = (dataResult.results || []).map((r) => ({
+      date: r.date,
+      Ensemble: item(r.ens_total, r.ens_correct),
+      DLinear: item(r.dl_total, r.dl_correct),
+      "N-BEATS": item(r.nb_total, r.nb_correct),
+      "TTM-R2": item(r.ttm_total, r.ttm_correct),
+    }));
+
+    const total = Number(countResult?.cnt || 0);
+    const totalPages = total > 0 ? Math.ceil(total / limit) : 1;
+
+    return json({
+      success: true,
+      data: rows,
+      page,
+      limit,
+      total,
+      total_pages: totalPages,
+    });
+  } catch (error) {
+    return json({ success: false, error: String(error) }, 500);
+  }
+}

cloudflare_pages/functions/api/accuracy/hourly/[date].js

@@ -0,0 +1,67 @@
+function json(data, status = 200) {
+  return new Response(JSON.stringify(data), {
+    status,
+    headers: { "content-type": "application/json; charset=UTF-8" },
+  });
+}
+
+function modelItem(prediction, probability, isCorrect) {
+  if (prediction === null || prediction === undefined) return null;
+  return {
+    prediction: Number(prediction),
+    probability: probability === null || probability === undefined ? null : Number(probability),
+    is_correct: isCorrect === null || isCorrect === undefined ? null : Number(isCorrect) === 1,
+  };
+}
+
+export async function onRequestGet(context) {
+  const db = context.env.DB;
+  if (!db) {
+    return json({ success: false, error: "缺少 D1 绑定 DB" }, 500);
+  }
+
+  try {
+    const date = context.params.date;
+    const sql = `
+      SELECT
+        timestamp,
+        hour,
+        open_price,
+        close_price,
+        actual_result,
+        dlinear_prediction,
+        dlinear_prob,
+        dlinear_correct,
+        nbeats_prediction,
+        nbeats_prob,
+        nbeats_correct,
+        ttm_prediction,
+        ttm_prob,
+        ttm_correct,
+        ensemble_prediction,
+        ensemble_correct
+      FROM backup_hourly_model_predictions
+      WHERE date = ?
+      ORDER BY hour ASC
+    `;
+
+    const result = await db.prepare(sql).bind(date).all();
+    const rows = (result.results || []).map((r) => ({
+      timestamp_cst: r.timestamp,
+      hour: r.hour,
+      open_price: r.open_price === null || r.open_price === undefined ? null : Number(r.open_price),
+      close_price: r.close_price === null || r.close_price === undefined ? null : Number(r.close_price),
+      actual_result: r.actual_result === null || r.actual_result === undefined ? null : Number(r.actual_result),
+      models: {
+        Ensemble: modelItem(r.ensemble_prediction, null, r.ensemble_correct),
+        DLinear: modelItem(r.dlinear_prediction, r.dlinear_prob, r.dlinear_correct),
+        "N-BEATS": modelItem(r.nbeats_prediction, r.nbeats_prob, r.nbeats_correct),
+        "TTM-R2": modelItem(r.ttm_prediction, r.ttm_prob, r.ttm_correct),
+      },
+    }));
+
+    return json({ success: true, date, data: rows });
+  } catch (error) {
+    return json({ success: false, error: String(error) }, 500);
+  }
+}

cloudflare_pages/functions/api/accuracy/monthly.js

@@ -0,0 +1,87 @@
+function json(data, status = 200) {
+  return new Response(JSON.stringify(data), {
+    status,
+    headers: { "content-type": "application/json; charset=UTF-8" },
+  });
+}
+
+function ratio(correct, total) {
+  if (!total) return 0;
+  return Math.round((correct / total) * 10000) / 100;
+}
+
+export async function onRequestGet(context) {
+  const db = context.env.DB;
+  if (!db) {
+    return json({ success: false, error: "缺少 D1 绑定 DB" }, 500);
+  }
+
+  try {
+    const url = new URL(context.request.url);
+    const month = (url.searchParams.get("month") || new Date().toISOString().slice(0, 7)).trim();
+
+    const sql = `
+      SELECT
+        SUM(CASE WHEN actual_result IS NOT NULL AND ensemble_prediction IS NOT NULL THEN 1 ELSE 0 END) AS ens_total,
+        SUM(CASE WHEN ensemble_correct = 1 THEN 1 ELSE 0 END) AS ens_correct,
+
+        SUM(CASE WHEN actual_result IS NOT NULL AND dlinear_prediction IS NOT NULL THEN 1 ELSE 0 END) AS dl_total,
+        SUM(CASE WHEN dlinear_correct = 1 THEN 1 ELSE 0 END) AS dl_correct,
+
+        SUM(CASE WHEN actual_result IS NOT NULL AND nbeats_prediction IS NOT NULL THEN 1 ELSE 0 END) AS nb_total,
+        SUM(CASE WHEN nbeats_correct = 1 THEN 1 ELSE 0 END) AS nb_correct,
+
+        SUM(CASE WHEN actual_result IS NOT NULL AND ttm_prediction IS NOT NULL THEN 1 ELSE 0 END) AS ttm_total,
+        SUM(CASE WHEN ttm_correct = 1 THEN 1 ELSE 0 END) AS ttm_correct
+      FROM backup_hourly_model_predictions
+      WHERE date LIKE ?
+    `;
+
+    const row = await db.prepare(sql).bind(`${month}%`).first();
+
+    const ensTotal = Number(row?.ens_total || 0);
+    const ensCorrect = Number(row?.ens_correct || 0);
+
+    const dlTotal = Number(row?.dl_total || 0);
+    const dlCorrect = Number(row?.dl_correct || 0);
+
+    const nbTotal = Number(row?.nb_total || 0);
+    const nbCorrect = Number(row?.nb_correct || 0);
+
+    const ttmTotal = Number(row?.ttm_total || 0);
+    const ttmCorrect = Number(row?.ttm_correct || 0);
+
+    return json({
+      success: true,
+      month,
+      data: {
+        Ensemble: {
+          total: ensTotal,
+          correct: ensCorrect,
+          incorrect: Math.max(0, ensTotal - ensCorrect),
+          accuracy: ratio(ensCorrect, ensTotal),
+        },
+        DLinear: {
+          total: dlTotal,
+          correct: dlCorrect,
+          incorrect: Math.max(0, dlTotal - dlCorrect),
+          accuracy: ratio(dlCorrect, dlTotal),
+        },
+        "N-BEATS": {
+          total: nbTotal,
+          correct: nbCorrect,
+          incorrect: Math.max(0, nbTotal - nbCorrect),
+          accuracy: ratio(nbCorrect, nbTotal),
+        },
+        "TTM-R2": {
+          total: ttmTotal,
+          correct: ttmCorrect,
+          incorrect: Math.max(0, ttmTotal - ttmCorrect),
+          accuracy: ratio(ttmCorrect, ttmTotal),
+        },
+      },
+    });
+  } catch (error) {
+    return json({ success: false, error: String(error) }, 500);
+  }
+}