diff --git a/app.py b/app.py
index 1f2fb51..53a487c 100644
--- a/app.py
+++ b/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": {
diff --git a/database.py b/database.py
index aa77ba9..65d49e0 100644
--- a/database.py
+++ b/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:
diff --git a/engine/strategies.py b/engine/strategies.py
new file mode 100644
index 0000000..25a833f
--- /dev/null
+++ b/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]
diff --git a/seed_data.py b/seed_data.py
index 09d76dd..7ff48aa 100644
--- a/seed_data.py
+++ b/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：")
diff --git a/static/js/admin.js b/static/js/admin.js
index 0058272..d7e4624 100644
--- a/static/js/admin.js
+++ b/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 {
diff --git a/static/js/strategies.js b/static/js/strategies.js
new file mode 100644
index 0000000..1688fde
--- /dev/null
+++ b/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) =>
+ ``
+ ).join('');
+ $$('#strategyPills .pill').forEach(p => p.onclick = () => {
+ const cfg = strategiesData.find(s => s.key === p.dataset.k);
+ selectStrategy(p.dataset.k, cfg);
+ });
+ selectStrategy(strategiesData[0].key, strategiesData[0]);
+ } catch (e) {
+ $('#strategyPills').innerHTML = '加载失败
';
+ }
+}
+
+function selectStrategy(key, cfg) {
+ curStrategy = key;
+ $$('#strategyPills .pill').forEach(p => p.classList.toggle('active', p.dataset.k === key));
+ if (cfg) renderDesc(cfg);
+ loadMarket(key);
+}
+
+function renderDesc(cfg) {
+ $('#stratDesc').innerHTML = `${cfg.icon} ${cfg.name}（${cfg.params}）：${escapeHtml(cfg.desc)} ${cfg.tags.join(' / ')}`;
+ const s = cfg.summary || {};
+ $('#stratSummary').innerHTML = `
+ 全市场平均收益
${fmtPct(s.avg_return)}
平均夏普
${s.avg_sharpe ?? '--'}
平均胜率
${s.avg_win_rate ?? '--'}%
| 加载中… |
';
+ 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 = '| 暂无回测数据，请到数据管理页重建 |
';
+ } catch (e) {
+ $('#rankTb').innerHTML = '| 加载失败 |
';
+ }
+}
+
+function renderRank() {
+ $('#rankHint').textContent = `（${rankData.length} 只股票）`;
+ $('#rankTb').innerHTML = rankData.map((it, i) => `
+
+ | ${i + 1} |
+ ${it.name} ${it.code} |
+ ${fmtPct(it.total_return)} |
+ ${fmtPct(it.benchmark)} |
+ ${fmtPct(it.excess)} |
+ ${fmtPct(it.max_drawdown)} |
+ ${it.sharpe} |
+ ${it.win_rate}% |
+ ${it.profit_factor} |
+ ${it.trades} |
+
`).join('');
+ // 同步下拉框
+ fillStockSelect();
+}
+
+function fillStockSelect() {
+ const sel = $('#btStock');
+ sel.innerHTML = rankData.map(it => ``).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 = '加载失败
';
+ }
+}
+
+function renderDetail(d) {
+ const m = d.metrics, st = d.stock || {};
+ $('#btHead').innerHTML = `
+
+
+ ${d.stock_name} ${d.code} · ${st.industry || ''} · ${st.board || ''}
+
+
${d.config.icon} ${d.config.name} · 回测 ${m.days} 个交易日
+
策略收益
${fmtPct(m.total_return)}
年化收益
${fmtPct(m.annualized)}
最大回撤
${fmtPct(m.max_drawdown)}
买入持有
${fmtPct(m.benchmark)}
ECharts 加载失败
'; 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 = '该策略在回测期内无交易信号
'; return; }
+ $('#tradeBox').innerHTML = `
+ | 买入日期 | 买入价 | 卖出日期 | 卖出价 | 收益 | 持有(天) | 信号 |
|---|
+ ${trades.map(t => `
+ | ${t.entry_date} | ${t.entry_price} |
+ ${t.exit_date} | ${t.exit_price} |
+ ${fmtPct(t.return)} |
+ ${t.days} |
+ ${escapeHtml(t.reason)} |
+
`).join('')}
+
`;
+}
+
+window.addEventListener('resize', () => chart && chart.resize());
+loadStrategies();
diff --git a/templates/admin.html b/templates/admin.html
index 920337b..e22595b 100644
--- a/templates/admin.html
+++ b/templates/admin.html
@@ -17,6 +17,7 @@
系统维护
+
diff --git a/templates/base.html b/templates/base.html
index 71ab0fb..6270697 100644
--- a/templates/base.html
+++ b/templates/base.html
@@ -30,6 +30,7 @@
📊 仪表盘
🏢 股票池
🎯 荐股中心
+ 📈 量化策略
📰 财经新闻
🏦 机构动向
⚙️ 数据管理
diff --git a/templates/strategies.html b/templates/strategies.html
new file mode 100644
index 0000000..2c4529e
--- /dev/null
+++ b/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 %}
+
+
+
+
+
全市场回测榜
+
+
+
+ | 排名 | 股票 | 策略收益 | 基准 | 超额 |
+ 最大回撤 | 夏普 | 胜率 | 盈亏比 | 交易 |
+
+
+
+
+
+
+
单股回测详情
+
+
+
+
+
+
+
交易明细
+
+
+