feat: walk-forward backtest, US currency, mobile RWD, Gemini news summary

1. Walk-forward backtest: rolling 120-day train window, retrain every 5 days,
no look-ahead bias (replaces single-model backtest)
2. US market: currency auto-switches (NT$/USD) in PredictionPanel, BacktestPanel
3. Mobile RWD: sidebar hidden on mobile, responsive header/prices/tables,
BacktestPanel min-width scroll, PredictionPanel compact layout
4. AI News: /api/stock/{code}/news endpoint (Gemini + Google Search grounding),
NewsPanel component with 30-min cache and refresh button

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

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1;background-color:rgb(55 48 163 / var(--tw-bg-opacity, 1))}.group:hover .group-hover\:text-white{--tw-text-opacity: 1;color:rgb(255 255 255 / var(--tw-text-opacity, 1))}.group:hover .group-hover\:opacity-100{opacity:1}@media (min-width: 640px){.sm\:ml-auto{margin-left:auto}.sm\:block{display:block}.sm\:inline{display:inline}.sm\:w-52{width:13rem}.sm\:flex-none{flex:none}.sm\:grid-cols-2{grid-template-columns:repeat(2,minmax(0,1fr))}.sm\:grid-cols-4{grid-template-columns:repeat(4,minmax(0,1fr))}.sm\:flex-row{flex-direction:row}.sm\:items-center{align-items:center}}@media (min-width: 1024px){.lg\:col-span-1{grid-column:span 1 / span 1}.lg\:col-span-3{grid-column:span 3 / span 3}.lg\:grid-cols-2{grid-template-columns:repeat(2,minmax(0,1fr))}.lg\:grid-cols-3{grid-template-columns:repeat(3,minmax(0,1fr))}.lg\:grid-cols-4{grid-template-columns:repeat(4,minmax(0,1fr))}}@media (min-width: 1280px){.xl\:grid-cols-4{grid-template-columns:repeat(4,minmax(0,1fr))}}

frontend/dist/index.html

@@ -4,8 +4,8 @@
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>台股預測系統</title>
-    <script type="module" crossorigin src="/assets/index-Dc0MjkQM.js"></script>
-    <link rel="stylesheet" crossorigin href="/assets/index-CpU0KnOP.css">
+    <script type="module" crossorigin src="/assets/index-D2_lKS4p.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-CXSOmTiI.css">
   </head>
   <body>
     <div id="root"></div>

frontend/src/App.tsx

@@ -28,6 +28,7 @@ import { Header, Market } from './components/Header'
 import { FavoritesPage, FavoriteItem } from './components/FavoritesPage'
 import { WatchlistPanel } from './components/WatchlistPanel'
 import { BacktestPanel } from './components/BacktestPanel'
+import { NewsPanel } from './components/NewsPanel'
 import { useFirestoreFavorites } from './hooks/useFirestoreFavorites'
 
 // ---------------------------------------------------------------------------
@@ -289,8 +290,8 @@ export default function App() {
           onUpdateNote={updateNote}
         />
       ) : (
-        <div className="max-w-7xl mx-auto px-4 py-6 grid grid-cols-1 lg:grid-cols-4 gap-6">
-          <div className="space-y-4">
+        <div className="max-w-7xl mx-auto px-2 sm:px-4 py-4 sm:py-6 grid grid-cols-1 lg:grid-cols-4 gap-4 sm:gap-6">
+          <div className="hidden lg:block space-y-4">
             <Sidebar
               favorites={favorites}
               onSearch={handleSearch}
@@ -303,11 +304,11 @@ export default function App() {
 
           <main className="lg:col-span-3 space-y-6">
             {stockInfo && (
-              <div className="flex flex-wrap items-center gap-3">
-                <h2 className="text-2xl font-black text-white">
+              <div className="flex flex-wrap items-center gap-2 sm:gap-3">
+                <h2 className="text-xl sm:text-2xl font-black text-white">
                   {stockInfo.stock_no}
                   {stockInfo.name && stockInfo.name !== stockInfo.stock_no && (
-                    <span className="ml-2 text-slate-300 font-semibold text-xl">{stockInfo.name}</span>
+                    <span className="ml-1.5 sm:ml-2 text-slate-300 font-semibold text-lg sm:text-xl">{stockInfo.name}</span>
                   )}
                 </h2>
                 <button
@@ -327,7 +328,7 @@ export default function App() {
                   const currencySymbol = market === 'us' ? 'USD' : 'NT$'
                   return displayPrice != null ? (
                     <div className="ml-auto flex items-center gap-2 flex-wrap justify-end">
-                      <span className={`text-2xl font-bold ${changePct == null ? 'text-slate-200' : isUp ? 'text-green-400' : 'text-red-400'}`}>
+                      <span className={`text-xl sm:text-2xl font-bold ${changePct == null ? 'text-slate-200' : isUp ? 'text-green-400' : 'text-red-400'}`}>
                         {currencySymbol} {displayPrice.toFixed(2)}
                       </span>
                       {changePct != null && (
@@ -399,7 +400,11 @@ export default function App() {
             )}
 
             {prediction && stockInfo && (
-              <PredictionPanel prediction={prediction} stockName={`${stockInfo.stock_no} ${stockInfo.name ?? ''}`} quote={quote} />
+              <PredictionPanel prediction={prediction} stockName={`${stockInfo.stock_no} ${stockInfo.name ?? ''}`} quote={quote} currency={market === 'us' ? 'USD' : market === 'fund' ? 'USD' : 'NT$'} />
+            )}
+
+            {stockInfo && market !== 'fund' && (
+              <NewsPanel stockNo={stockInfo.stock_no} />
             )}
 
             {stockInfo && (
@@ -409,6 +414,7 @@ export default function App() {
                 result={backtest.result}
                 loading={backtest.loading}
                 error={backtest.error}
+                currency={market === 'us' ? 'USD' : market === 'fund' ? 'USD' : 'NT$'}
               />
             )}
 

frontend/src/api/stockApi.ts

@@ -141,6 +141,18 @@ export async function fetchBacktest(
   return res.data
 }
 
+export interface NewsData {
+  stock_no: string
+  summary: string
+  source: string
+  cached: boolean
+}
+
+export async function fetchNews(stockNo: string): Promise<NewsData> {
+  const res = await api.get<NewsData>(`/api/stock/${stockNo}/news`)
+  return res.data
+}
+
 export async function fetchQuote(stockNo: string): Promise<QuoteData> {
   const res = await api.get<QuoteData>(`/api/stock/${stockNo}/quote`)
   return res.data

frontend/src/components/BacktestPanel.tsx

@@ -8,6 +8,7 @@ interface BacktestPanelProps {
   result: BacktestResult | null
   loading: boolean
   error: string | null
+  currency?: string
 }
 
 const StatCard: React.FC<{
@@ -37,7 +38,7 @@ const SignalBadge: React.FC<{ signal: string }> = ({ signal }) => {
 }
 
 export const BacktestPanel: React.FC<BacktestPanelProps> = ({
-  stockNo, onRun, result, loading, error
+  stockNo, onRun, result, loading, error, currency = 'NT$'
 }) => {
   const [months, setMonths] = useState(12)
   const [showAll, setShowAll] = useState(false)
@@ -53,7 +54,7 @@ export const BacktestPanel: React.FC<BacktestPanelProps> = ({
         <div className="flex items-center gap-2">
           <FlaskConical size={18} className="text-indigo-400" />
           <h3 className="font-bold text-white">歷史回測</h3>
-          <span className="text-xs text-slate-500">（以訓練完的模型套用歷史資料，僅供參考）</span>
+          <span className="text-xs text-slate-500">Walk-Forward（滾動訓練，無未來偷看）</span>
         </div>
         <div className="flex items-center gap-2">
           <select
@@ -146,8 +147,8 @@ export const BacktestPanel: React.FC<BacktestPanelProps> = ({
                   {showAll ? '收起' : `顯示全部 ${trades.length} 筆`}
                 </button>
               </div>
-              <div className="overflow-auto max-h-72 rounded-lg border border-slate-800">
-                <table className="w-full text-xs">
+              <div className="overflow-auto max-h-72 rounded-lg border border-slate-800 -mx-1 sm:mx-0">
+                <table className="w-full text-xs min-w-[480px]">
                   <thead className="sticky top-0 bg-slate-800 text-slate-400">
                     <tr>
                       <th className="text-left px-3 py-2">日期</th>

frontend/src/components/Header.tsx

@@ -119,7 +119,7 @@ export const Header: React.FC<HeaderProps> = ({
         {/* Brand */}
         <div className="flex items-center gap-2 mr-2 flex-shrink-0">
           <TrendingUp className="text-blue-400" size={22} />
-          <h1 className="text-lg font-bold tracking-tight text-white whitespace-nowrap hidden sm:block">台股 ML 預測</h1>
+          <h1 className="text-lg font-bold tracking-tight text-white whitespace-nowrap hidden sm:block">ML 股票預測</h1>
         </div>
 
         {/* View nav (分析 / 最愛) */}

frontend/src/components/NewsPanel.tsx

@@ -0,0 +1,70 @@
+import React, { useState, useEffect } from 'react'
+import { Newspaper, Loader2, RefreshCw } from 'lucide-react'
+import { fetchNews, NewsData } from '../api/stockApi'
+
+interface NewsPanelProps {
+  stockNo: string
+}
+
+export const NewsPanel: React.FC<NewsPanelProps> = ({ stockNo }) => {
+  const [news, setNews] = useState<NewsData | null>(null)
+  const [loading, setLoading] = useState(false)
+  const [error, setError] = useState<string | null>(null)
+
+  const load = async () => {
+    setLoading(true)
+    setError(null)
+    try {
+      const data = await fetchNews(stockNo)
+      setNews(data)
+    } catch (err: any) {
+      const msg = err?.response?.data?.detail ?? err?.message ?? '無法取得新聞'
+      setError(msg)
+    } finally {
+      setLoading(false)
+    }
+  }
+
+  useEffect(() => { load() }, [stockNo])
+
+  return (
+    <div className="bg-slate-900 border border-slate-700 rounded-xl p-4 sm:p-6 space-y-3">
+      <div className="flex items-center justify-between">
+        <div className="flex items-center gap-2">
+          <Newspaper size={16} className="text-amber-400" />
+          <h3 className="font-bold text-white text-sm">AI 新聞摘要</h3>
+          <span className="text-xs text-slate-500">Gemini + Google Search</span>
+        </div>
+        <button
+          onClick={load}
+          disabled={loading}
+          className="p-1.5 text-slate-500 hover:text-white rounded-lg hover:bg-slate-800 transition-colors disabled:opacity-50"
+          title="重新整理"
+        >
+          <RefreshCw size={14} className={loading ? 'animate-spin' : ''} />
+        </button>
+      </div>
+
+      {loading && !news && (
+        <div className="flex items-center gap-3 py-6 justify-center text-slate-400">
+          <Loader2 size={20} className="animate-spin text-amber-400" />
+          <span className="text-sm">正在搜尋新聞...</span>
+        </div>
+      )}
+
+      {error && (
+        <div className="text-xs text-slate-500 py-2">{error}</div>
+      )}
+
+      {news && (
+        <div className="text-sm text-slate-300 leading-relaxed whitespace-pre-wrap">
+          {news.summary}
+        </div>
+      )}
+
+      {news?.cached && (
+        <p className="text-xs text-slate-600">快取中（30 分鐘更新）</p>
+      )}
+    </div>
+  )
+}

frontend/src/components/PredictionPanel.tsx

@@ -6,6 +6,7 @@ interface Props {
   prediction: Prediction
   stockName: string
   quote?: QuoteData | null
+  currency?: string
 }
 
 const signalConfig = {
@@ -50,11 +51,12 @@ const confidenceZh = {
   LOW: '低信心',
 }
 
-export const PredictionPanel: React.FC<Props> = ({ prediction, stockName, quote }) => {
+export const PredictionPanel: React.FC<Props> = ({ prediction, stockName, quote, currency = 'NT$' }) => {
   const cfg = signalConfig[prediction.signal]
   const Icon = cfg.icon
   const probPct = Math.round(prediction.signal_probability * 100)
   const changePositive = prediction.predicted_change_pct >= 0
+  const cur = currency
 
   const displayPrice = quote?.price ?? prediction.current_price
   const todayChangePct = quote?.change_pct ?? null
@@ -62,17 +64,17 @@ export const PredictionPanel: React.FC<Props> = ({ prediction, stockName, quote
   const todayUp = todayChangePct != null && todayChangePct >= 0
 
   return (
-    <div className={`rounded-xl border-2 ${cfg.border} ${cfg.bg} p-5 space-y-4`}>
+    <div className={`rounded-xl border-2 ${cfg.border} ${cfg.bg} p-3 sm:p-5 space-y-3 sm:space-y-4`}>
       {/* Header */}
-      <div className="flex items-center justify-between">
+      <div className="flex flex-col sm:flex-row items-start sm:items-center justify-between gap-2">
         <div>
-          <h2 className="text-lg font-bold text-white">{stockName} — ML 預測信號</h2>
+          <h2 className="text-base sm:text-lg font-bold text-white">{stockName} — ML 預測信號</h2>
           <p className="text-xs text-slate-400 mt-0.5">預測期間：未來 5 個交易日</p>
         </div>
-        <div className={`flex items-center gap-2 px-4 py-2 rounded-lg ${cfg.badgeBg} shadow-lg`}>
-          <Icon size={20} className="text-white" />
-          <span className="text-white font-black text-xl tracking-wider">{cfg.label}</span>
-          <span className="text-white/80 text-sm">({cfg.labelZh})</span>
+        <div className={`flex items-center gap-2 px-3 sm:px-4 py-1.5 sm:py-2 rounded-lg ${cfg.badgeBg} shadow-lg`}>
+          <Icon size={18} className="text-white" />
+          <span className="text-white font-black text-lg sm:text-xl tracking-wider">{cfg.label}</span>
+          <span className="text-white/80 text-xs sm:text-sm">({cfg.labelZh})</span>
         </div>
       </div>
 
@@ -81,7 +83,7 @@ export const PredictionPanel: React.FC<Props> = ({ prediction, stockName, quote
         <PriceCard
           label="目前價格"
           labelEn="Current Price"
-          value={`NT$ ${displayPrice.toFixed(2)}`}
+          value={`${cur} ${displayPrice.toFixed(2)}`}
           valueClass={todayChangePct == null ? 'text-white' : todayUp ? 'text-green-400' : 'text-red-400'}
           sub={todayChangePct != null ? (
             <span className={todayUp ? 'text-green-400' : 'text-red-400'}>
@@ -93,7 +95,7 @@ export const PredictionPanel: React.FC<Props> = ({ prediction, stockName, quote
         <PriceCard
           label="預測價格 (5日)"
           labelEn="Predicted Price"
-          value={`NT$ ${prediction.predicted_price.toFixed(2)}`}
+          value={`${cur} ${prediction.predicted_price.toFixed(2)}`}
           sub={
             <span className={changePositive ? 'text-green-400' : 'text-red-400'}>
               {changePositive ? '+' : ''}{prediction.predicted_change_pct.toFixed(2)}%
@@ -104,13 +106,13 @@ export const PredictionPanel: React.FC<Props> = ({ prediction, stockName, quote
         <PriceCard
           label="建議賣出目標"
           labelEn="Sell Target"
-          value={`NT$ ${prediction.sell_target.toFixed(2)}`}
+          value={`${cur} ${prediction.sell_target.toFixed(2)}`}
           valueClass="text-green-300"
         />
         <PriceCard
           label="停損價位"
           labelEn="Stop Loss"
-          value={`NT$ ${prediction.stop_loss.toFixed(2)}`}
+          value={`${cur} ${prediction.stop_loss.toFixed(2)}`}
           valueClass="text-red-300"
         />
       </div>

routers/stock.py

@@ -22,6 +22,7 @@ from data.fetcher import (
 from data.fund_fetcher import search_funds
 from indicators.technical import add_all_indicators
 from services.predictor_service import load_predictor, get_prediction
+from services.news_service import get_stock_news
 
 logger = logging.getLogger(__name__)
 
@@ -208,132 +209,167 @@ async def stock_predict(request: Request, stock_no: str):
         raise HTTPException(status_code=500, detail=str(exc))
 
 
+@router.get("/{stock_no}/news")
+async def stock_news(stock_no: str):
+    """Return AI-generated news summary for a stock (Gemini + Google Search, cached 30 min)."""
+    try:
+        info = get_stock_info(stock_no)
+        name = info.get("name", stock_no)
+    except Exception:
+        name = stock_no
+    result = get_stock_news(stock_no, name)
+    if result is None:
+        raise HTTPException(status_code=503, detail="News service unavailable (GEMINI_API_KEY not set or API error)")
+    return result
+
+
+def _walk_forward_backtest(stock_no: str, months: int) -> dict:
+    """
+    Walk-forward backtest: rolling train window, no look-ahead bias.
+    - Minimum 120 trading days for first training window
+    - Retrain every 5 days on all data up to that point
+    - Predict day i using model trained on data[:i]
+    """
+    from models.predictor import (
+        _build_features, FEATURE_COLUMNS, THRESHOLD_BUY, THRESHOLD_SELL,
+        StockPredictor,
+    )
+    from datetime import datetime
+    from dateutil.relativedelta import relativedelta
+
+    # Fetch extra history for training warmup
+    df = fetch_history(stock_no, months=months + 12)
+    if df.empty:
+        raise HTTPException(status_code=404, detail=f"No data found for {stock_no}")
+
+    df = add_all_indicators(df)
+    feat = _build_features(df)
+
+    MIN_TRAIN = 120  # minimum training window
+    HORIZON = 5
+    RETRAIN_EVERY = 5  # retrain every N prediction days
+
+    cutoff = str((datetime.today() - relativedelta(months=months)).date())
+
+    results = []
+    predictor = None
+    last_train_idx = -RETRAIN_EVERY  # force first train
+
+    for i in range(MIN_TRAIN, len(df) - HORIZON):
+        row_feat = feat.iloc[i]
+        if row_feat[FEATURE_COLUMNS].isna().any():
+            continue
+
+        date_str = str(df["date"].iloc[i])
+        if date_str < cutoff:
+            continue
+
+        # Retrain if needed (every RETRAIN_EVERY prediction days)
+        if i - last_train_idx >= RETRAIN_EVERY or predictor is None:
+            try:
+                predictor = StockPredictor()
+                predictor.train(df.iloc[:i])
+                last_train_idx = i
+            except Exception:
+                continue
+
+        current_close = float(df["close"].iloc[i])
+        future_close = float(df["close"].iloc[i + HORIZON])
+        actual_return = (future_close - current_close) / current_close * 100
+
+        X = row_feat[FEATURE_COLUMNS].values.reshape(1, -1)
+        X_scaled = predictor.scaler.transform(X)
+        buy_prob = float(predictor.classifier.predict_proba(X_scaled)[0][1])
+
+        if buy_prob >= THRESHOLD_BUY:
+            signal = "BUY"
+        elif buy_prob <= THRESHOLD_SELL:
+            signal = "SELL"
+        else:
+            signal = "HOLD"
+
+        correct = (signal == "BUY" and actual_return > 0) or \
+                  (signal == "SELL" and actual_return < 0)
+
+        results.append({
+            "date": date_str,
+            "signal": signal,
+            "buy_prob": round(buy_prob, 3),
+            "close": round(current_close, 2),
+            "future_close": round(future_close, 2),
+            "actual_return_pct": round(actual_return, 2),
+            "correct": correct,
+        })
+
+    buys = [r for r in results if r["signal"] == "BUY"]
+    sells = [r for r in results if r["signal"] == "SELL"]
+    holds = [r for r in results if r["signal"] == "HOLD"]
+
+    def win_rate(lst):
+        return round(sum(1 for r in lst if r["correct"]) / len(lst) * 100, 1) if lst else 0
+
+    def avg_ret(lst):
+        return round(sum(r["actual_return_pct"] for r in lst) / len(lst), 2) if lst else 0
+
+    def max_consecutive(lst, correct_val: bool):
+        max_streak = cur_streak = 0
+        for r in lst:
+            if r["correct"] == correct_val:
+                cur_streak += 1
+                max_streak = max(max_streak, cur_streak)
+            else:
+                cur_streak = 0
+        return max_streak
+
+    buy_returns = [r["actual_return_pct"] for r in buys]
+    non_holds = [r for r in results if r["signal"] != "HOLD"]
+
+    if non_holds:
+        signal_accuracy_overall = round(
+            sum(1 for r in non_holds if r["correct"]) / len(non_holds) * 100, 1
+        )
+    else:
+        signal_accuracy_overall = 0
+
+    return {
+        "stock_no": stock_no,
+        "months": months,
+        "walk_forward": True,
+        "stats": {
+            "total_days": len(results),
+            "buy_signals": len(buys),
+            "sell_signals": len(sells),
+            "hold_signals": len(holds),
+            "buy_win_rate": win_rate(buys),
+            "sell_win_rate": win_rate(sells),
+            "avg_return_on_buy": avg_ret(buys),
+            "avg_return_on_sell": avg_ret(sells),
+            "total_return_if_followed": round(sum(r["actual_return_pct"] for r in buys), 2),
+            "max_consecutive_wins": max_consecutive(non_holds, True),
+            "max_consecutive_losses": max_consecutive(non_holds, False),
+            "best_trade_pct": round(max(buy_returns), 2) if buy_returns else 0,
+            "worst_trade_pct": round(min(buy_returns), 2) if buy_returns else 0,
+            "signal_accuracy_overall": signal_accuracy_overall,
+        },
+        "trades": results,
+    }
+
+
 @router.get("/{stock_no}/backtest")
-@limiter.limit("10/minute")
+@limiter.limit("5/minute")
 async def stock_backtest(
     request: Request,
     stock_no: str,
     months: int = Query(default=12, ge=3, le=24),
 ):
     """
-    Backtest: apply trained model to each historical day and compare
-    signals with actual 5-day forward returns.
-    NOTE: uses the model trained on recent data — treat as indicative only.
+    Walk-forward backtest: rolling training window, no look-ahead bias.
+    Retrains every 5 days using only past data.
     """
+    import asyncio
     try:
-        from models.predictor import _build_features, FEATURE_COLUMNS, THRESHOLD_BUY, THRESHOLD_SELL
-        from datetime import datetime
-        from dateutil.relativedelta import relativedelta
-
-        # Extra months for indicator warmup
-        df = fetch_history(stock_no, months=months + 3)
-        if df.empty:
-            raise HTTPException(status_code=404, detail=f"No data found for {stock_no}")
-
-        df = add_all_indicators(df)
-        import asyncio
-        predictor = await asyncio.to_thread(load_predictor, stock_no, df, raise_http=True)
-        feat = _build_features(df)
-
-        results = []
-        for i in range(len(df) - 5):
-            row_feat = feat.iloc[i]
-            if row_feat[FEATURE_COLUMNS].isna().any():
-                continue
-
-            current_close = float(df["close"].iloc[i])
-            future_close  = float(df["close"].iloc[i + 5])
-            actual_return = (future_close - current_close) / current_close * 100
-            date_str      = str(df["date"].iloc[i])
-
-            X        = row_feat[FEATURE_COLUMNS].values.reshape(1, -1)
-            X_scaled = predictor.scaler.transform(X)
-            buy_prob = float(predictor.classifier.predict_proba(X_scaled)[0][1])
-
-            if buy_prob >= THRESHOLD_BUY:
-                signal = "BUY"
-            elif buy_prob <= THRESHOLD_SELL:
-                signal = "SELL"
-            else:
-                signal = "HOLD"
-
-            correct = (signal == "BUY" and actual_return > 0) or \
-                      (signal == "SELL" and actual_return < 0)
-
-            results.append({
-                "date": date_str,
-                "signal": signal,
-                "buy_prob": round(buy_prob, 3),
-                "close": round(current_close, 2),
-                "future_close": round(future_close, 2),
-                "actual_return_pct": round(actual_return, 2),
-                "correct": correct,
-            })
-
-        # Trim to requested months
-        cutoff = str((datetime.today() - relativedelta(months=months)).date())
-        results = [r for r in results if r["date"] >= cutoff]
-
-        buys  = [r for r in results if r["signal"] == "BUY"]
-        sells = [r for r in results if r["signal"] == "SELL"]
-        holds = [r for r in results if r["signal"] == "HOLD"]
-
-        def win_rate(lst):
-            return round(sum(1 for r in lst if r["correct"]) / len(lst) * 100, 1) if lst else 0
-
-        def avg_ret(lst):
-            return round(sum(r["actual_return_pct"] for r in lst) / len(lst), 2) if lst else 0
-
-        def max_consecutive(lst, correct: bool):
-            """Return longest streak of correct=True (or False) signals."""
-            max_streak = cur_streak = 0
-            for r in lst:
-                if r["correct"] == correct:
-                    cur_streak += 1
-                    max_streak = max(max_streak, cur_streak)
-                else:
-                    cur_streak = 0
-            return max_streak
-
-        # Max / min trade returns (BUY signals only)
-        buy_returns = [r["actual_return_pct"] for r in buys]
-        best_trade_pct  = round(max(buy_returns), 2) if buy_returns else 0
-        worst_trade_pct = round(min(buy_returns), 2) if buy_returns else 0
-
-        # Consecutive win/loss streaks across all non-HOLD signals
-        non_holds = [r for r in results if r["signal"] != "HOLD"]
-        max_consecutive_wins   = max_consecutive(non_holds, correct=True)
-        max_consecutive_losses = max_consecutive(non_holds, correct=False)
-
-        # Overall signal accuracy for non-HOLD signals
-        if non_holds:
-            signal_accuracy_overall = round(
-                sum(1 for r in non_holds if r["correct"]) / len(non_holds) * 100, 1
-            )
-        else:
-            signal_accuracy_overall = 0
-
-        return {
-            "stock_no": stock_no,
-            "months": months,
-            "stats": {
-                "total_days":               len(results),
-                "buy_signals":              len(buys),
-                "sell_signals":             len(sells),
-                "hold_signals":             len(holds),
-                "buy_win_rate":             win_rate(buys),
-                "sell_win_rate":            win_rate(sells),
-                "avg_return_on_buy":        avg_ret(buys),
-                "avg_return_on_sell":       avg_ret(sells),
-                "total_return_if_followed": round(sum(r["actual_return_pct"] for r in buys), 2),
-                "max_consecutive_wins":     max_consecutive_wins,
-                "max_consecutive_losses":   max_consecutive_losses,
-                "best_trade_pct":           best_trade_pct,
-                "worst_trade_pct":          worst_trade_pct,
-                "signal_accuracy_overall":  signal_accuracy_overall,
-            },
-            "trades": results,
-        }
+        result = await asyncio.to_thread(_walk_forward_backtest, stock_no, months)
+        return result
     except HTTPException:
         raise
     except Exception as exc:

services/news_service.py

@@ -0,0 +1,103 @@
+"""
+Stock news summary service — uses Google Gemini API with grounding (web search)
+to fetch and summarize recent news for a given stock.
+
+Requires env var: GEMINI_API_KEY
+"""
+
+import json
+import logging
+import os
+import time
+from typing import Optional
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY", "")
+_GEMINI_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"
+
+# In-memory cache: {stock_no: (result, timestamp)}
+_news_cache: dict = {}
+_NEWS_TTL = 1800  # 30 minutes
+
+
+def get_stock_news(stock_no: str, name: str = "", lang: str = "zh-TW") -> Optional[dict]:
+    """
+    Fetch and summarize recent news for a stock using Gemini with Google Search grounding.
+
+    Returns:
+        {
+            "stock_no": str,
+            "summary": str,        # markdown summary of recent news
+            "source": "gemini",
+            "cached": bool,
+        }
+    or None on failure.
+    """
+    if not GEMINI_API_KEY:
+        return None
+
+    # Check cache
+    now = time.time()
+    if stock_no in _news_cache:
+        cached, ts = _news_cache[stock_no]
+        if now - ts < _NEWS_TTL:
+            return {**cached, "cached": True}
+
+    stock_label = f"{name} ({stock_no})" if name and name != stock_no else stock_no
+
+    prompt = (
+        f"請用繁體中文摘要 {stock_label} 最近一週的重要新聞。\n"
+        f"包含：\n"
+        f"1. 最新股價動態與漲跌原因\n"
+        f"2. 公司重大消息（財報、併購、產品、人事）\n"
+        f"3. 產業趨勢影響\n"
+        f"4. 法人動向（外資、投信）\n\n"
+        f"每則新聞用一行條列，最多 5 則。如果沒有相關新聞，回覆「近期無重大新聞」。\n"
+        f"不要加免責聲明。"
+    )
+
+    payload = {
+        "contents": [{"parts": [{"text": prompt}]}],
+        "tools": [{"google_search": {}}],
+        "generationConfig": {
+            "temperature": 0.3,
+            "maxOutputTokens": 1024,
+        },
+    }
+
+    try:
+        resp = requests.post(
+            _GEMINI_URL,
+            params={"key": GEMINI_API_KEY},
+            json=payload,
+            timeout=30,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+
+        # Extract text from response
+        candidates = data.get("candidates", [])
+        if not candidates:
+            return None
+
+        parts = candidates[0].get("content", {}).get("parts", [])
+        text_parts = [p.get("text", "") for p in parts if "text" in p]
+        summary = "\n".join(text_parts).strip()
+
+        if not summary:
+            return None
+
+        result = {
+            "stock_no": stock_no,
+            "summary": summary,
+            "source": "gemini",
+        }
+        _news_cache[stock_no] = (result, now)
+        return {**result, "cached": False}
+
+    except Exception as exc:
+        logger.warning("Gemini news fetch failed for %s: %s", stock_no, exc)
+        return None