v1.2.0: 新增量化策略模块(6主流策略全市场回测+单股净值曲线/交易明细)，策略: 双均线/MACD/RSI/布林带/动量/N日突破

app.py

@@ -50,6 +50,11 @@ def page_institutions():
     return render_template("institutions.html", service=SERVICE_NAME, is_mock=IS_MOCK)
 
 
+@app.route("/strategies")
+def page_strategies():
+    return render_template("strategies.html", service=SERVICE_NAME, is_mock=IS_MOCK)
+
+
 @app.route("/admin")
 def page_admin():
     return render_template("admin.html", service=SERVICE_NAME, is_mock=IS_MOCK)
@@ -479,11 +484,70 @@ def api_holdings_moves():
     return jsonify({"quarter": latest, "increase": inc, "decrease": dec})
 
 
+# ------------------------------------------------------------------ 量化策略
+@app.route("/api/strategies")
+def api_strategies():
+    from engine.strategies import STRATEGIES, strategy_summary
+    items = []
+    for key, cfg in STRATEGIES.items():
+        items.append({"key": key, "name": cfg["name"], "icon": cfg["icon"],
+                      "params": cfg["params"], "desc": cfg["desc"], "tags": cfg["tags"],
+                      "summary": strategy_summary(key)})
+    return jsonify({"items": items})
+
+
+@app.route("/api/backtest/market")
+def api_backtest_market():
+    from engine.strategies import market_rank
+    key = request.args.get("strategy", "ma_cross")
+    return jsonify({"items": market_rank(key)})
+
+
+@app.route("/api/backtest")
+def api_backtest():
+    """单股×单策略回测详情（从预计算表读取）"""
+    from engine.strategies import STRATEGIES
+    import json as _json
+    key = request.args.get("strategy", "ma_cross")
+    code = request.args.get("code", "")
+    if key not in STRATEGIES or not code:
+        return jsonify({"error": "参数错误"}), 400
+    row = query_one("SELECT * FROM strategy_backtests WHERE strategy=? AND code=?", (key, code))
+    if not row:
+        return jsonify({"error": "回测数据不存在，请先在数据管理页重建"}), 404
+    stock = query_one("SELECT name, industry, board FROM stocks WHERE code=?", (code,))
+    return jsonify({
+        "strategy": key,
+        "config": STRATEGIES[key],
+        "code": code, "stock_name": row["stock_name"], "stock": stock,
+        "metrics": _json.loads(row["metrics"]),
+        "equity": _json.loads(row["equity"]),
+        "trades": _json.loads(row["trades"]),
+        "run_at": row["run_at"],
+    })
+
+
+@app.route("/api/backtest/rebuild", methods=["POST"])
+def api_backtest_rebuild():
+    import threading
+
+    def run():
+        from engine.strategies import build_all
+        try:
+            build_all()
+        except Exception as e:
+            log.error("backtest rebuild fail: %s", e)
+
+    threading.Thread(target=run, daemon=True).start()
+    return jsonify({"ok": True, "msg": "全市场回测重建已启动"})
+
+
 # ------------------------------------------------------------------ 数据管理
 @app.route("/api/admin/stats")
 def api_admin_stats():
     tables = ("stocks", "stock_daily", "news", "institutions", "inst_ratings",
-              "fund_holdings", "watchlist", "analysis_cache", "market_index")
+              "fund_holdings", "watchlist", "analysis_cache", "analysis_history",
+              "strategy_backtests", "market_index")
     return jsonify({
         "tables": {t: table_count(t) for t in tables},
         "vector": {

database.py

@@ -111,6 +111,17 @@ CREATE TABLE IF NOT EXISTS analysis_history (
 );
 CREATE INDEX IF NOT EXISTS idx_history_code ON analysis_history(code);
 
+CREATE TABLE IF NOT EXISTS strategy_backtests (
+  strategy TEXT NOT NULL,
+  code TEXT NOT NULL,
+  stock_name TEXT DEFAULT '',
+  metrics TEXT DEFAULT '{}',       -- JSON：收益/回撤/夏普/胜率等
+  equity TEXT DEFAULT '[]',       -- JSON：[{date,value,bh}, ...] 净值曲线
+  trades TEXT DEFAULT '[]',       -- JSON：交易明细
+  run_at TEXT DEFAULT (datetime('now','localtime')),
+  PRIMARY KEY (strategy, code)
+);
+
 CREATE TABLE IF NOT EXISTS market_index (
   date TEXT PRIMARY KEY,
   sh REAL DEFAULT 0,   -- 上证指数（点）
@@ -177,7 +188,7 @@ def wipe_all():
     """清空业务表（保留结构）+ 重置自增序列，用于重灌数据"""
     for t in ("stock_daily", "inst_ratings", "fund_holdings", "news",
               "institutions", "stocks", "watchlist", "analysis_cache", "analysis_history",
-              "market_index"):
+              "market_index", "strategy_backtests"):
         with db() as conn:
             conn.execute(f'DELETE FROM "{t}"')
     with db() as conn:

engine/strategies.py

@@ -0,0 +1,425 @@
+# -*- coding: utf-8 -*-
+"""
+量化策略引擎：主流策略信号生成 + 回测框架
+- 6 个主流策略：双均线 / MACD / RSI / 布林带 / 动量 / N日突破
+- 回测规则：收盘产生信号，次日开盘成交（全仓多头，避免未来函数）
+- 指标：总收益 / 年化 / 最大回撤 / 夏普 / 胜率 / 盈亏比 / 交易次数
+- 结果持久化到 strategy_backtests 表，支持全市场批量回测
+
+扩展新策略：在 STRATEGIES 注册 name/desc + 实现信号生成函数即可
+"""
+import json
+import math
+import statistics
+
+from database import executemany, query_one
+
+# ===================================================================== 指标序列
+def ma_series(closes, n):
+    out = []
+    s = 0.0
+    for i, c in enumerate(closes):
+        s += c
+        if i >= n:
+            s -= closes[i - n]
+        out.append(s / n if i >= n - 1 else None)
+    return out
+
+
+def ema_series(vals, n):
+    out = []
+    k = 2 / (n + 1)
+    e = vals[0] if vals else 0
+    for i, v in enumerate(vals):
+        e = v if i == 0 else v * k + e * (1 - k)
+        out.append(e)
+    return out
+
+
+def macd_series(closes):
+    ema12 = ema_series(closes, 12)
+    ema26 = ema_series(closes, 26)
+    dif = [a - b for a, b in zip(ema12, ema26)]
+    dea = ema_series(dif, 9)
+    return dif, dea
+
+
+def rsi_series(closes, n=14):
+    out = [None] * len(closes)
+    if len(closes) <= n:
+        return out
+    gains, losses = [], []
+    for i in range(1, len(closes)):
+        chg = closes[i] - closes[i - 1]
+        gains.append(max(chg, 0))
+        losses.append(max(-chg, 0))
+    avg_g = sum(gains[:n]) / n
+    avg_l = sum(losses[:n]) / n
+    for i in range(n, len(gains)):
+        avg_g = (avg_g * (n - 1) + gains[i]) / n
+        avg_l = (avg_l * (n - 1) + losses[i]) / n
+        rs = 100 if avg_l == 0 else avg_g / avg_l
+        out[i + 1] = 100 - 100 / (1 + rs)
+    out[n] = 100 if avg_l == 0 else 100 - 100 / (1 + avg_g / max(avg_l, 1e-9))
+    return out
+
+
+def boll_series(closes, n=20, k=2.0):
+    mid, upper, lower = [], [], []
+    for i in range(len(closes)):
+        if i >= n - 1:
+            seg = closes[i - n + 1:i + 1]
+            m = sum(seg) / n
+            sd = statistics.pstdev(seg)
+            mid.append(m); upper.append(m + k * sd); lower.append(m - k * sd)
+        else:
+            mid.append(None); upper.append(None); lower.append(None)
+    return mid, upper, lower
+
+
+# ===================================================================== 策略注册
+STRATEGIES = {
+    "ma_cross": {
+        "name": "双均线金叉",
+        "icon": "📐",
+        "params": "MA5 / MA20",
+        "desc": "短均线MA5上穿长均线MA20买入（金叉），下穿卖出（死叉）。经典趋势跟踪策略。",
+        "tags": ["趋势"],
+    },
+    "macd": {
+        "name": "MACD 金叉",
+        "icon": "🟢",
+        "params": "12/26/9",
+        "desc": "DIF 上穿 DEA 买入，下穿卖出。捕捉中线趋势拐点，过滤震荡噪音。",
+        "tags": ["趋势", "动量"],
+    },
+    "rsi_rev": {
+        "name": "RSI 超买超卖",
+        "icon": "🔄",
+        "params": "RSI(14) 30/70",
+        "desc": "RSI 低于 30 超卖买入、高于 70 超买卖出。均值回归型反转策略。",
+        "tags": ["反转"],
+    },
+    "boll": {
+        "name": "布林带回归",
+        "icon": "📦",
+        "params": "20日 / 2σ",
+        "desc": "价格跌破下轨买入、突破上轨卖出，赌价格向中轨回归。震荡市表现佳。",
+        "tags": ["回归"],
+    },
+    "momentum": {
+        "name": "20日动量",
+        "icon": "🚀",
+        "params": "20日涨幅 / MA20",
+        "desc": "20日涨幅超阈值且站上MA20买入，跌破MA20卖出。顺势强者恒强。",
+        "tags": ["动量"],
+    },
+    "breakout": {
+        "name": "N日新高突破",
+        "icon": "🧗",
+        "params": "20日高低点",
+        "desc": "收盘突破20日新高买入，跌破20日新低卖出。海龟式突破策略。",
+        "tags": ["突破"],
+    },
+}
+
+
+def _signals(bars, key):
+    """生成信号列表 [{date, action:'buy'/'sell', close, reason}]"""
+    closes = [b["close"] for b in bars]
+    n = len(bars)
+    sigs = []
+
+    if key == "ma_cross":
+        ma5, ma20 = ma_series(closes, 5), ma_series(closes, 20)
+        prev_state = None
+        for i in range(n):
+            if ma5[i] is None or ma20[i] is None:
+                continue
+            state = ma5[i] > ma20[i]
+            if prev_state is not None and state != prev_state:
+                if state:
+                    sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
+                                 "reason": f"MA5({ma5[i]:.2f})上穿MA20({ma20[i]:.2f})金叉"})
+                else:
+                    sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
+                                 "reason": f"MA5({ma5[i]:.2f})下穿MA20({ma20[i]:.2f})死叉"})
+            prev_state = state
+
+    elif key == "macd":
+        dif, dea = macd_series(closes)
+        prev = None
+        for i in range(n):
+            if dif[i] is None or dea[i] is None:
+                continue
+            state = dif[i] > dea[i]
+            if prev is not None and state != prev:
+                if state:
+                    sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
+                                 "reason": f"MACD金叉 DIF({dif[i]:.3f})上穿DEA({dea[i]:.3f})"})
+                else:
+                    sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
+                                 "reason": f"MACD死叉 DIF({dif[i]:.3f})下穿DEA({dea[i]:.3f})"})
+            prev = state
+
+    elif key == "rsi_rev":
+        rsi = rsi_series(closes)
+        for i in range(1, n):
+            if rsi[i] is None or rsi[i - 1] is None:
+                continue
+            if rsi[i - 1] >= 30 and rsi[i] < 30:
+                sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
+                             "reason": f"RSI({rsi[i]:.1f})下穿30超卖"})
+            elif rsi[i - 1] <= 70 and rsi[i] > 70:
+                sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
+                             "reason": f"RSI({rsi[i]:.1f})上穿70超买"})
+
+    elif key == "boll":
+        mid, upper, lower = boll_series(closes)
+        prev_state = None
+        for i in range(n):
+            if lower[i] is None:
+                continue
+            if closes[i] < lower[i]:
+                state = "buy"
+            elif closes[i] > upper[i]:
+                state = "sell"
+            else:
+                state = prev_state
+            if state != prev_state and state is not None:
+                if state == "buy":
+                    sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
+                                 "reason": f"收盘跌破下轨({lower[i]:.2f})"})
+                else:
+                    sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
+                                 "reason": f"收盘突破上轨({upper[i]:.2f})"})
+            prev_state = state
+
+    elif key == "momentum":
+        ma20 = ma_series(closes, 20)
+        prev_state = None
+        for i in range(n):
+            if i < 20 or ma20[i] is None:
+                continue
+            mom = closes[i] / closes[i - 20] - 1
+            state = "buy" if (mom > 0.03 and closes[i] > ma20[i]) else ("sell" if closes[i] < ma20[i] else prev_state)
+            if state != prev_state and state is not None:
+                if state == "buy":
+                    sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
+                                 "reason": f"20日动量{mom*100:+.1f}%且站上MA20"})
+                else:
+                    sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
+                                 "reason": "跌破MA20止盈/止损"})
+            prev_state = state
+
+    elif key == "breakout":
+        N = 20
+        for i in range(N, n):
+            window = closes[i - N:i]
+            if closes[i] > max(window) and closes[i] > closes[i - 1]:
+                sigs.append({"date": bars[i]["date"], "action": "buy", "close": closes[i],
+                             "reason": f"突破{N}日新高({max(window):.2f})"})
+            elif closes[i] < min(window):
+                sigs.append({"date": bars[i]["date"], "action": "sell", "close": closes[i],
+                             "reason": f"跌破{N}日新低({min(window):.2f})"})
+
+    return sigs
+
+
+# ===================================================================== 回测
+def backtest(bars, key):
+    """全仓多头回测。收盘信号 → 次日开盘成交。返回 metrics/equity/trades"""
+    signals = _signals(bars, key)
+    n = len(bars)
+    opens = [b["open"] for b in bars]
+    closes = [b["close"] for b in bars]
+    dates = [b["date"] for b in bars]
+
+    # 信号按日期归类（同一天可能多个 buy/sell，取最后一个有效方向）
+    by_day = {}
+    for s in signals:
+        by_day[s["date"]] = s
+
+    position = 0.0          # 持股数量（按买入价格折算）
+    cash = 1.0              # 初始资金=1
+    trades = []
+    entry = None
+    equity = []
+    trade_idx = 0
+
+    for i in range(n):
+        d = dates[i]
+        # 当日开盘执行前一日收盘信号
+        sig = by_day.get(d)
+        exec_price = opens[i]
+        if sig and sig["action"] == "buy" and position == 0:
+            position = cash / exec_price
+            cash = 0.0
+            entry = {"date": d, "price": exec_price, "reason": sig["reason"]}
+        elif sig and sig["action"] == "sell" and position > 0:
+            cash = position * exec_price
+            position = 0.0
+            if entry:
+                ret = (exec_price / entry["price"] - 1)
+                trades.append({"entry_date": entry["date"], "entry_price": round(entry["price"], 3),
+                               "exit_date": d, "exit_price": round(exec_price, 3),
+                               "return": round(ret * 100, 2),
+                               "days": _day_diff(dates, entry["date"], d),
+                               "reason": entry["reason"]})
+                entry = None
+
+        value = cash + position * closes[i]
+        equity.append({"date": d, "value": round(value, 4)})
+
+    # 期末仍持仓则平仓（按最后收盘）
+    if position > 0 and entry:
+        last = closes[-1]
+        cash = position * last
+        ret = last / entry["price"] - 1
+        trades.append({"entry_date": entry["date"], "entry_price": round(entry["price"], 3),
+                       "exit_date": dates[-1], "exit_price": round(last, 3),
+                       "return": round(ret * 100, 2), "days": _day_diff(dates, entry["date"], dates[-1]),
+                       "reason": entry["reason"] + "（期末平仓）"})
+        position = 0.0
+        equity[-1]["value"] = round(cash, 4)
+
+    metrics = _calc_metrics(equity, trades, dates, bars)
+    # 买入持有基准
+    bh = bars[0]["close"]
+    for e in equity:
+        e["bh"] = round(bars[n - 1]["close"] / bh, 4) if bh else 1.0
+    return {"metrics": metrics, "equity": equity, "trades": trades}
+
+
+def _day_diff(dates, start, end):
+    try:
+        from datetime import date
+        ds = date.fromisoformat(start)
+        de = date.fromisoformat(end)
+        return (de - ds).days
+    except Exception:
+        return 0
+
+
+def _calc_metrics(equity, trades, dates, bars):
+    n = len(equity)
+    final = equity[-1]["value"] if equity else 1.0
+    total = final - 1
+    years = n / 252.0
+    ann = (final ** (1 / years) - 1) if (years > 0 and final > 0) else 0
+    # 最大回撤
+    peak, mdd = equity[0]["value"], 0.0
+    for e in equity:
+        peak = max(peak, e["value"])
+        mdd = min(mdd, (e["value"] - peak) / peak)
+    # 日收益 → 夏普
+    rets = []
+    for i in range(1, n):
+        prev = equity[i - 1]["value"]
+        if prev > 0:
+            rets.append(equity[i]["value"] / prev - 1)
+    sharpe = 0.0
+    if rets and statistics.stdev(rets) > 0:
+        sharpe = statistics.mean(rets) / statistics.stdev(rets) * math.sqrt(252)
+    # 交易统计
+    n_tr = len(trades)
+    wins = [t for t in trades if t["return"] > 0]
+    losses = [t for t in trades if t["return"] <= 0]
+    win_rate = len(wins) / n_tr if n_tr else 0.0
+    gp = sum(t["return"] for t in wins)
+    gl = abs(sum(t["return"] for t in losses))
+    pf = (gp / gl) if gl > 0 else (gp if gp > 0 else 0)
+    avg_hold = sum(t["days"] for t in trades) / n_tr if n_tr else 0
+    # 基准（买入持有）
+    bh_ret = bars[-1]["close"] / bars[0]["close"] - 1
+    return {
+        "total_return": round(total * 100, 2),
+        "annualized": round(ann * 100, 2),
+        "max_drawdown": round(mdd * 100, 2),
+        "sharpe": round(sharpe, 2),
+        "win_rate": round(win_rate * 100, 1),
+        "profit_factor": round(pf, 2),
+        "trades": n_tr,
+        "avg_hold_days": round(avg_hold, 1),
+        "benchmark": round(bh_ret * 100, 2),
+        "excess": round((total - bh_ret) * 100, 2),
+        "days": n,
+    }
+
+
+# ===================================================================== 批量回测
+def run_one(code, name, bars, key):
+    """单只股票单策略回测，返回入库行"""
+    res = backtest(bars, key)
+    return {
+        "strategy": key, "code": code, "stock_name": name,
+        "metrics": json.dumps(res["metrics"], ensure_ascii=False),
+        "equity": json.dumps(res["equity"], ensure_ascii=False),
+        "trades": json.dumps(res["trades"], ensure_ascii=False),
+    }
+
+
+def build_all(progress=None):
+    """全市场 × 全策略批量回测（覆盖写入 strategy_backtests）"""
+    from database import query, executemany
+    stocks = query("SELECT code, name FROM stocks")
+    rows = []
+    for si, s in enumerate(stocks):
+        bars = query("SELECT date, open, high, low, close, volume FROM stock_daily "
+                     "WHERE code=? ORDER BY date ASC", (s["code"],))
+        if len(bars) < 30:
+            continue
+        for key in STRATEGIES:
+            rows.append(run_one(s["code"], s["name"], bars, key))
+        if progress:
+            progress(si + 1, len(stocks))
+    executemany(
+        "INSERT OR REPLACE INTO strategy_backtests(strategy, code, stock_name, metrics, equity, trades, run_at) "
+        "VALUES(?,?,?,?,?,?,datetime('now','localtime'))",
+        [(r["strategy"], r["code"], r["stock_name"], r["metrics"], r["equity"], r["trades"]) for r in rows])
+    return len(rows)
+
+
+def strategy_summary(key):
+    """某策略的全市场统计（用于列表页头部）"""
+    from database import query
+    rows = query("SELECT metrics FROM strategy_backtests WHERE strategy=?", (key,))
+    if not rows:
+        return {}
+    best = None
+    sums = {"total": 0, "sharpe": 0, "win": 0, "n": 0}
+    for r in rows:
+        m = json.loads(r["metrics"])
+        sums["total"] += m["total_return"]
+        sums["sharpe"] += m["sharpe"]
+        sums["win"] += m["win_rate"]
+        sums["n"] += 1
+        if best is None or m["total_return"] > best["metrics"]["total_return"]:
+            best = {"code": None, "metrics": m}
+    # 顺便找最优个股（第二遍，轻量）
+    bcode, bname, bret = None, None, -1e9
+    rows2 = query("SELECT code, stock_name, metrics FROM strategy_backtests WHERE strategy=?", (key,))
+    for r in rows2:
+        m = json.loads(r["metrics"])
+        if m["total_return"] > bret:
+            bret, bcode, bname = m["total_return"], r["code"], r["stock_name"]
+    nn = sums["n"]
+    return {
+        "avg_return": round(sums["total"] / nn, 2) if nn else 0,
+        "avg_sharpe": round(sums["sharpe"] / nn, 2) if nn else 0,
+        "avg_win_rate": round(sums["win"] / nn, 1) if nn else 0,
+        "n": nn,
+        "best_code": bcode, "best_name": bname, "best_return": round(bret, 2) if bcode else None,
+    }
+
+
+def market_rank(key, limit=100):
+    """某策略全市场收益榜"""
+    from database import query
+    rows = query("SELECT code, stock_name, metrics FROM strategy_backtests WHERE strategy=? ORDER BY run_at DESC", (key,))
+    items = []
+    for r in rows:
+        m = json.loads(r["metrics"])
+        items.append({"code": r["code"], "name": r["stock_name"], **m})
+    items.sort(key=lambda x: x["total_return"], reverse=True)
+    return items[:limit]

seed_data.py

@@ -404,6 +404,7 @@ def build_vectors(news, stocks):
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--skip-vector", action="store_true", help="跳过向量索引重建")
+    parser.add_argument("--no-strategies", action="store_true", help="跳过量化策略回测")
     args = parser.parse_args()
 
     print(">>> 初始化数据库 ...")
@@ -461,6 +462,14 @@ def main():
     else:
         print("    (跳过)")
 
+    print(">>> 量化策略全市场回测 ...")
+    if not args.no_strategies:
+        from engine.strategies import build_all, STRATEGIES
+        cnt = build_all()
+        print(f"    回测记录 {cnt} 条（{len(STRATEGIES)} 策略 × 全市场）")
+    else:
+        print("    (跳过)")
+
     from database import table_count
     print("=" * 50)
     print("数据库统计：")

static/js/admin.js

@@ -2,7 +2,8 @@
 const tableNames = {
   stocks: '股票', stock_daily: '日线行情', news: '财经新闻', institutions: '机构',
   inst_ratings: '机构评级', fund_holdings: '基金持仓', watchlist: '自选股',
-  analysis_cache: '研报缓存', market_index: '市场指数'
+  analysis_cache: '研报缓存', analysis_history: 'AI分析历史',
+  strategy_backtests: '策略回测', market_index: '市场指数'
 };
 
 async function refreshStats() {
@@ -30,6 +31,15 @@ async function refreshStats() {
   }
 }
 
+async function rebuildBt() {
+  if (!confirm('将重新跑全市场 6 策略 × 全部股票回测（几秒完成），确定？')) return;
+  try {
+    await api('/api/backtest/rebuild', { method: 'POST' });
+    toast('回测重建已启动');
+    setTimeout(refreshStats, 3000);
+  } catch (e) { toast('启动失败'); }
+}
+
 async function reseed() {
   if (!confirm('将清空全部业务数据并重新生成（含向量索引重建，需 1-3 分钟），确定继续？')) return;
   try {

static/js/strategies.js

@@ -0,0 +1,148 @@
+/* 量化策略回测页 */
+let curStrategy = 'ma_cross';
+let rankData = [];
+let strategiesData = [];
+let chart = null;
+
+async function loadStrategies() {
+  try {
+    const d = await api('/api/strategies');
+    strategiesData = d.items || [];
+    $('#strategyPills').innerHTML = strategiesData.map((s, i) =>
+      `<button class="pill ${i === 0 ? 'active' : ''}" data-k="${s.key}">${s.icon} ${s.name} <span style="color:var(--muted);font-size:11px">${s.params}</span></button>`
+    ).join('');
+    $$('#strategyPills .pill').forEach(p => p.onclick = () => {
+      const cfg = strategiesData.find(s => s.key === p.dataset.k);
+      selectStrategy(p.dataset.k, cfg);
+    });
+    selectStrategy(strategiesData[0].key, strategiesData[0]);
+  } catch (e) {
+    $('#strategyPills').innerHTML = '<div class="empty">加载失败</div>';
+  }
+}
+
+function selectStrategy(key, cfg) {
+  curStrategy = key;
+  $$('#strategyPills .pill').forEach(p => p.classList.toggle('active', p.dataset.k === key));
+  if (cfg) renderDesc(cfg);
+  loadMarket(key);
+}
+
+function renderDesc(cfg) {
+  $('#stratDesc').innerHTML = `<b style="color:var(--text)">${cfg.icon} ${cfg.name}（${cfg.params}）</b>：${escapeHtml(cfg.desc)} <span class="tag">${cfg.tags.join(' / ')}</span>`;
+  const s = cfg.summary || {};
+  $('#stratSummary').innerHTML = `
+    <div class="kpi"><div class="kpi-label">全市场平均收益</div><div class="kpi-value num ${s.avg_return >= 0 ? 'up' : 'down'}">${fmtPct(s.avg_return)}</div></div>
+    <div class="kpi"><div class="kpi-label">平均夏普</div><div class="kpi-value num">${s.avg_sharpe ?? '--'}</div></div>
+    <div class="kpi"><div class="kpi-label">平均胜率</div><div class="kpi-value num">${s.avg_win_rate ?? '--'}%</div></div>
+    <div class="kpi"><div class="kpi-label">覆盖股票</div><div class="kpi-value num">${s.n ?? '--'}</div></div>
+    <div class="kpi"><div class="kpi-label">最优标的</div><div class="kpi-value" style="font-size:15px">${s.best_name ? `<a href="/stock/${s.best_code}" target="_blank">${s.best_name}</a> <span class="num up">${fmtPct(s.best_return)}</span>` : '--'}</div></div>`;
+}
+
+async function loadMarket(key) {
+  $('#rankTb').innerHTML = '<tr><td colspan="10" class="loading">加载中…</td></tr>';
+  try {
+    const d = await api('/api/backtest/market?strategy=' + key);
+    rankData = d.items || [];
+    renderRank();
+    // 默认选中第一名股票
+    if (rankData.length) loadDetail(rankData[0].code);
+    else $('#rankTb').innerHTML = '<tr><td colspan="10" class="empty">暂无回测数据，请到数据管理页重建</td></tr>';
+  } catch (e) {
+    $('#rankTb').innerHTML = '<tr><td colspan="10" class="empty">加载失败</td></tr>';
+  }
+}
+
+function renderRank() {
+  $('#rankHint').textContent = `（${rankData.length} 只股票）`;
+  $('#rankTb').innerHTML = rankData.map((it, i) => `
+    <tr class="row-link" onclick="loadDetail('${it.code}')">
+      <td class="num">${i + 1}</td>
+      <td><b>${it.name}</b><div style="color:var(--muted);font-size:12px">${it.code}</div></td>
+      <td class="num ${pctClass(it.total_return)}"><b>${fmtPct(it.total_return)}</b></td>
+      <td class="num ${pctClass(it.benchmark)}">${fmtPct(it.benchmark)}</td>
+      <td class="num ${pctClass(it.excess)}">${fmtPct(it.excess)}</td>
+      <td class="num down">${fmtPct(it.max_drawdown)}</td>
+      <td class="num ${it.sharpe >= 0 ? 'up' : 'down'}">${it.sharpe}</td>
+      <td class="num">${it.win_rate}%</td>
+      <td class="num">${it.profit_factor}</td>
+      <td class="num">${it.trades}</td>
+    </tr>`).join('');
+  // 同步下拉框
+  fillStockSelect();
+}
+
+function fillStockSelect() {
+  const sel = $('#btStock');
+  sel.innerHTML = rankData.map(it => `<option value="${it.code}">${it.name}（${it.code}）</option>`).join('');
+  sel.onchange = () => loadDetail(sel.value);
+}
+
+async function loadDetail(code) {
+  const sel = $('#btStock');
+  if (sel.value !== code) sel.value = code;
+  try {
+    const d = await api(`/api/backtest?strategy=${curStrategy}&code=${code}`);
+    renderDetail(d);
+  } catch (e) {
+    $('#btHead').innerHTML = '<div class="empty">加载失败</div>';
+  }
+}
+
+function renderDetail(d) {
+  const m = d.metrics, st = d.stock || {};
+  $('#btHead').innerHTML = `
+    <div class="flex between wrap">
+      <div>
+        <b style="font-size:16px">${d.stock_name}</b> <span style="color:var(--muted)">${d.code} · ${st.industry || ''} · ${st.board || ''}</span>
+      </div>
+      <span style="color:var(--muted);font-size:12px">${d.config.icon} ${d.config.name} · 回测 ${m.days} 个交易日</span>
+    </div>`;
+  $('#btMetrics').innerHTML = `
+    <div class="kpi"><div class="kpi-label">策略收益</div><div class="kpi-value num ${pctClass(m.total_return)}">${fmtPct(m.total_return)}</div></div>
+    <div class="kpi"><div class="kpi-label">年化收益</div><div class="kpi-value num ${pctClass(m.annualized)}">${fmtPct(m.annualized)}</div></div>
+    <div class="kpi"><div class="kpi-label">最大回撤</div><div class="kpi-value num down">${fmtPct(m.max_drawdown)}</div></div>
+    <div class="kpi"><div class="kpi-label">夏普比率</div><div class="kpi-value num ${m.sharpe >= 0 ? 'up' : 'down'}">${m.sharpe}</div></div>
+    <div class="kpi"><div class="kpi-label">胜率</div><div class="kpi-value num">${m.win_rate}%</div></div>
+    <div class="kpi"><div class="kpi-label">盈亏比</div><div class="kpi-value num">${m.profit_factor}</div></div>
+    <div class="kpi"><div class="kpi-label">交易次数</div><div class="kpi-value num">${m.trades}</div></div>
+    <div class="kpi"><div class="kpi-label">买入持有</div><div class="kpi-value num ${pctClass(m.benchmark)}">${fmtPct(m.benchmark)}</div></div>
+    <div class="kpi"><div class="kpi-label">超额收益</div><div class="kpi-value num ${pctClass(m.excess)}">${fmtPct(m.excess)}</div></div>`;
+  renderEquity(d.equity);
+  renderTrades(d.trades);
+}
+
+function renderEquity(equity) {
+  const el = $('#equityChart');
+  if (!window.echarts) { el.innerHTML = '<div class="empty">ECharts 加载失败</div>'; return; }
+  if (!chart) chart = echarts.init(el);
+  chart.setOption({
+    backgroundColor: 'transparent', animation: false,
+    tooltip: { trigger: 'axis', valueFormatter: v => (v * 100).toFixed(1) + '%' },
+    legend: { data: ['策略净值', '买入持有'], textStyle: { color: '#9da7b3' }, top: 0 },
+    grid: { left: 50, right: 14, top: 28, bottom: 24 },
+    xAxis: { type: 'category', data: equity.map(e => e.date), axisLabel: { color: '#6e7681' }, axisLine: { lineStyle: { color: '#262d3a' } } },
+    yAxis: { type: 'value', name: '净值', scale: true, splitLine: { lineStyle: { color: '#1f2630' } }, axisLabel: { color: '#6e7681', formatter: v => (v * 100).toFixed(0) + '%' } },
+    series: [
+      { name: '策略净值', type: 'line', data: equity.map(e => e.value), showSymbol: false, lineStyle: { width: 2, color: '#f59e0b' }, areaStyle: { color: 'rgba(245,158,11,.08)' } },
+      { name: '买入持有', type: 'line', data: equity.map(e => e.bh), showSymbol: false, lineStyle: { width: 1.5, color: '#3b82f6', type: 'dashed' } },
+    ],
+  }, true);
+}
+
+function renderTrades(trades) {
+  if (!trades.length) { $('#tradeBox').innerHTML = '<div class="empty">该策略在回测期内无交易信号</div>'; return; }
+  $('#tradeBox').innerHTML = `<table>
+    <tr><th>买入日期</th><th>买入价</th><th>卖出日期</th><th>卖出价</th><th>收益</th><th>持有(天)</th><th>信号</th></tr>
+    ${trades.map(t => `<tr>
+      <td>${t.entry_date}</td><td class="num">${t.entry_price}</td>
+      <td>${t.exit_date}</td><td class="num">${t.exit_price}</td>
+      <td class="num ${pctClass(t.return)}"><b>${fmtPct(t.return)}</b></td>
+      <td class="num">${t.days}</td>
+      <td style="max-width:200px;white-space:normal;font-size:12px;color:var(--text2)">${escapeHtml(t.reason)}</td>
+    </tr>`).join('')}
+  </table>`;
+}
+
+window.addEventListener('resize', () => chart && chart.resize());
+loadStrategies();

templates/admin.html

@@ -17,6 +17,7 @@
   <div class="card-title"><span class="bar" style="background:var(--gold)"></span>系统维护</div>
   <div class="flex wrap">
     <button class="btn btn-primary" onclick="reseed()">♻️ 一键重灌数据（含向量重建）</button>
+    <button class="btn" onclick="rebuildBt()">📈 重建量化回测</button>
     <button class="btn" onclick="health()">🔌 依赖连通性检查</button>
     <button class="btn" onclick="refreshStats()">🔄 刷新统计</button>
   </div>

templates/base.html

@@ -30,6 +30,7 @@
       <a href="/" class="nav-item" data-nav="/">📊 仪表盘</a>
       <a href="/stocks" class="nav-item" data-nav="/stocks">🏢 股票池</a>
       <a href="/recommend" class="nav-item" data-nav="/recommend">🎯 荐股中心</a>
+      <a href="/strategies" class="nav-item" data-nav="/strategies">📈 量化策略</a>
       <a href="/news" class="nav-item" data-nav="/news">📰 财经新闻</a>
       <a href="/institutions" class="nav-item" data-nav="/institutions">🏦 机构动向</a>
       <a href="/admin" class="nav-item" data-nav="/admin">⚙️ 数据管理</a>

templates/strategies.html

@@ -0,0 +1,41 @@
+{% extends "base.html" %}
+{% block title %}量化策略{% endblock %}
+{% block page_title %}量化策略回测{% endblock %}
+{% block content %}
+<div class="card">
+  <div class="pill-bar" id="strategyPills">
+    <div class="loading">加载策略中…</div>
+  </div>
+  <div id="stratDesc" class="mb8" style="color:var(--text2);font-size:13px"></div>
+  <div class="kpi-grid" id="stratSummary" style="margin-bottom:4px"></div>
+</div>
+
+<div class="grid grid-2-1 mt16">
+  <div class="card">
+    <div class="card-title"><span class="bar"></span>全市场回测榜 <span id="rankHint" style="font-weight:400;color:var(--muted);font-size:12px"></span></div>
+    <div style="overflow:auto;max-height:560px">
+      <table>
+        <thead><tr>
+          <th>排名</th><th>股票</th><th>策略收益</th><th>基准</th><th>超额</th>
+          <th>最大回撤</th><th>夏普</th><th>胜率</th><th>盈亏比</th><th>交易</th>
+        </tr></thead>
+        <tbody id="rankTb"></tbody>
+      </table>
+    </div>
+  </div>
+  <div class="card">
+    <div class="card-title"><span class="bar" style="background:var(--gold)"></span>单股回测详情</div>
+    <div class="input-group mb8">
+      <select class="input" id="btStock" style="flex:1"></select>
+    </div>
+    <div id="btHead"></div>
+    <div id="equityChart" style="height:220px"></div>
+    <div id="btMetrics" class="kpi-grid" style="margin-top:12px"></div>
+    <div class="card-title mt16" style="font-size:14px"><span class="bar" style="background:var(--cyan)"></span>交易明细</div>
+    <div id="tradeBox" style="overflow:auto;max-height:220px"></div>
+  </div>
+</div>
+{% endblock %}
+{% block scripts %}
+<script src="{{ url_for('static', filename='js/strategies.js') }}"></script>
+{% endblock %}