feat etf

labs/analysis/etf/smoke_sectors.py

@@ -0,0 +1,43 @@
+"""新白名单（16 只板块 ETF）的快速回测冒烟：只打印，不覆盖既有 results.json。
+
+用法： py -3.14 -B labs/analysis/etf/smoke_sectors.py [起始资金]
+"""
+
+import sys
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+sys.path.insert(0, str(HERE))
+try:
+    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+except Exception:
+    pass
+
+from backtest import REPO_DEFAULTS, START_CASH, SYMBOLS, SYMBOL_PARAMS, analyze, fetch_daily, simulate  # noqa: E402
+
+CASH = float(sys.argv[1]) if len(sys.argv) > 1 else 600_000.0
+
+data = {code: fetch_daily(code) for code in SYMBOLS}
+print(f"标的 {len(SYMBOLS)} 只，账户 {CASH:,.0f} 元，参数取 py-client/etc/_etf.yaml")
+print(f"{'成交模型':10} {'权益变动':>11} {'收益率':>8} {'最大回撤':>9} {'平均占用':>9} "
+      f"{'峰值占用':>9} {'底仓':>5} {'补仓':>5} {'主出口':>6} {'佣金':>8}")
+for mode in ("touch", "bounce", "close"):
+    result = simulate(data, start_cash=CASH, fill_mode=mode)
+    stats = analyze(result)
+    fills = result["fills"]
+    print(f"{mode:10} {stats['final_equity'] - CASH:11,.2f} {stats['return_pct']:7.2f}% "
+          f"{stats['max_dd_pct']:8.2f}% {stats['avg_deployed'] / CASH * 100:8.2f}% "
+          f"{stats['max_deployed'] / CASH * 100:8.2f}% "
+          f"{sum(1 for f in fills if f.kind == 'base'):5} "
+          f"{sum(1 for f in fills if f.kind == 'add'):5} "
+          f"{sum(1 for f in fills if f.kind == 'exit'):6} {stats['fees']:8.2f}")
+
+# 逐标的参与度：新白名单里谁真的被交易到
+print()
+print("逐标的成交（touch 模型）")
+result = simulate(data, start_cash=CASH, fill_mode="touch")
+for code in SYMBOLS:
+    rows = [f for f in result["fills"] if f.code == code]
+    buys = sum(f.price * f.volume for f in rows if f.side == "BUY")
+    print(f"  {code:11} 成交 {len(rows):3} 笔  买入名义 {buys:10,.0f}  "
+          f"底仓 {sum(1 for f in rows if f.kind == 'base')}  补仓 {sum(1 for f in rows if f.kind == 'add')}")