optz

labs/analysis/etf/universe.py

@@ -0,0 +1,53 @@
+"""加标的 vs 加仓位：同一引擎、按比例缩放资金，隔离"标的数量"的影响。
+
+注意：把 universe 缩成 N 只、资金缩到 N/3 只能近似"同时持有更多标的"，
+但它同时缩短了白名单，所以结论按"资金可部署机会数"来读，而不是精确预测。
+
+用法： py -3.14 -B analysis/etf/universe.py
+"""
+
+import sys
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+sys.path.insert(0, str(HERE))
+
+from backtest import (  # noqa: E402
+    MIN_CASH_RATIO, START_CASH, SYMBOLS, SYMBOL_PARAMS, EtfDefaults, EtfSymbolConfig,
+    analyze, fetch_daily, precondition, simulate,
+)
+
+BASE = EtfDefaults()
+FULL = {code: fetch_daily(code) for code in SYMBOLS}
+
+# (白名单, 起始资金, 说明)
+CASES = []
+for size in (1, 2, 3):
+    codes = SYMBOLS[:size]
+    CASES.append((f"白名单 {size} 只（{'+'.join(c[:6] for c in codes)}），资金按 {size}/3 缩放",
+                  codes, START_CASH * size / 3))
+# 同样 20 万资金，但只放 1 只 / 2 只 → 观察"钱多而标的少"是否浪费
+CASES.append(("白名单 1 只，但仍是 20 万资金", SYMBOLS[:1], START_CASH))
+CASES.append(("白名单 2 只，但仍是 20 万资金", SYMBOLS[:2], START_CASH))
+CASES.append(("白名单 3 只，20 万（基准）", SYMBOLS[:3], START_CASH))
+# 每档放大到 5000 股，资金同步放大到 100 万 → 检验"标的不变、仓位变大"的天花板
+CASES.append(("3 只 + buy_shares=5000，资金 100 万", SYMBOLS[:3], 1_000_000.0))
+CASES.append(("3 只 + buy_shares=10000，资金 200 万", SYMBOLS[:3], 2_000_000.0))
+
+print(f"{'场景':52} {'资金':>10} {'权益变动':>10} {'收益率':>7} {'回撤':>6} "
+      f"{'平均占用':>8} {'峰值占用':>8} {'ROI/占用':>8} {'底仓':>4} {'补仓':>4}")
+for label, codes, cash in CASES:
+    data = {c: FULL[c] for c in codes}
+    shares = 5000 if "5000" in label else 10000 if "10000" in label else SYMBOL_PARAMS[codes[0]]["buy_shares"]
+    params = {c: {**SYMBOL_PARAMS[c], "buy_shares": shares, "max_shares": shares * 10} for c in codes}
+    pre = {c: precondition(data[c], EtfSymbolConfig(**params[c]), BASE) for c in codes}
+    result = simulate(data, symbol_params=params, precomputed=pre, start_cash=cash)
+    stats = analyze(result)
+    delta = stats["final_equity"] - cash
+    util = stats["avg_deployed"] / cash * 100
+    max_util = stats["max_deployed"] / cash * 100
+    roi = delta / stats["avg_deployed"] * 100 if stats["avg_deployed"] else 0.0
+    print(f"{label:52} {cash:10.0f} {delta:10.2f} {stats['return_pct']:6.2f}% "
+          f"{stats['max_dd_pct']:5.2f}% {util:7.2f}% {max_util:7.2f}% {roi:7.2f}% "
+          f"{sum(1 for f in result['fills'] if f.kind == 'base'):4} "
+          f"{sum(1 for f in result['fills'] if f.kind == 'add'):4}")