105 lines
5.0 KiB
Python
105 lines
5.0 KiB
Python
"""旧配置 vs 新配置（etc/_etf.yaml）对照，并给出新配置下的资金需求与规模敏感性。
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用法： py -3.14 -B analysis/etf/compare.py
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"""
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import sys
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from dataclasses import replace
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from pathlib import Path
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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from backtest import ( # noqa: E402
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REPO_DEFAULTS, SYMBOLS, SYMBOL_PARAMS, EtfSymbolConfig, analyze, fetch_daily,
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precondition, simulate,
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)
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data = {code: fetch_daily(code) for code in SYMBOLS}
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# 旧配置（本报告第一版的 etc/_etf.yaml：三只都是 1000 股 / 10000 股上限）
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OLD_PARAMS = {
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"588000.SH": dict(is_t0=False, buy_shares=1000, max_shares=10000, atr_multiplier=0.5, inner_step=0.9),
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"510300.SH": dict(is_t0=False, buy_shares=1000, max_shares=10000, atr_multiplier=1.0, inner_step=0.7),
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"518880.SH": dict(is_t0=True, buy_shares=1000, max_shares=10000, atr_multiplier=1.0, inner_step=0.8),
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}
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NEW_PARAMS = SYMBOL_PARAMS
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def total_rung_notional(params, cash_scale=None):
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"""三只各一档的名义金额（按区间最低价估）与铺满 10 档的总需求。"""
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one = sum(p["buy_shares"] * min(b["low"] for b in data[c]) for c, p in params.items())
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return one, one * 10
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def run(params, cash):
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pre = {c: precondition(data[c], EtfSymbolConfig(**params[c]), REPO_DEFAULTS) for c in data}
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result = simulate(data, symbol_params=params, precomputed=pre, start_cash=cash)
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stats = analyze(result)
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return result, stats
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print("=" * 120)
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print("A. 两版配置的资金需求（按各标的区间最低价估算）")
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print("=" * 120)
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for label, params in (("旧（1000 股/档）", OLD_PARAMS), ("新（10000/4000/2000 股/档）", NEW_PARAMS)):
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one, full = total_rung_notional(params)
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detail = " ".join(
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f"{c[:6]}={p['buy_shares']}股×{min(b['low'] for b in data[c]):.2f}≈{p['buy_shares'] * min(b['low'] for b in data[c]):,.0f}"
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for c, p in params.items()
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)
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print(f"{label:28} 三只各 1 档 ≈ {one:>10,.0f} 铺满 10 档 ≈ {full:>10,.0f}")
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print(f"{'':28} {detail}")
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print()
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print("=" * 120)
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print("B. 旧 vs 新：同一资金 50 万，同一天数据、同一套逻辑")
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print("=" * 120)
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print(f"{'配置':24} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'轮次':>5} {'胜率':>6} "
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f"{'单轮均利':>8} {'平均占用':>8} {'峰值占用':>8} {'佣金':>7} {'名义周转':>8}")
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for label, params in (("旧（1000 股/档）", OLD_PARAMS), ("新（现 _etf.yaml）", NEW_PARAMS)):
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result, stats = run(params, 500_000.0)
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trips = [f for f in result["fills"] if f.kind == "exit"]
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delta = stats["final_equity"] - 500_000.0
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util = stats["avg_deployed"] / 500_000 * 100
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max_util = stats["max_deployed"] / 500_000 * 100
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print(f"{label:24} {delta:10.2f} {stats['return_pct']:6.2f}% {stats['max_dd_pct']:7.2f}% "
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f"{len(trips):5} {'':>6} {'':>8} {util:7.2f}% {max_util:7.2f}% {stats['fees']:7.2f} "
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f"{stats['buy_amount'] / 500_000:7.2f}x")
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print(f"{'':24} 已了结盈亏={stats['net']:>10.2f} 买入名义={stats['buy_amount']:>10.2f} "
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f"占用ROI={delta / stats['avg_deployed'] * 100 if stats['avg_deployed'] else 0:.2f}%")
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print()
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print("=" * 120)
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print("C. 新配置：绝对盈亏与账户规模无关（按固定股数下单），但收益率会摊薄")
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print("=" * 120)
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print(f"{'起始资金':>10} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'平均占用':>8} {'峰值占用':>8} {'底仓':>4} {'补仓':>4}")
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for cash in (200_000, 300_000, 500_000, 800_000, 1_200_000):
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result, stats = run(NEW_PARAMS, cash)
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print(f"{cash:10,.0f} {stats['final_equity'] - cash:10.2f} {stats['return_pct']:6.2f}% "
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f"{stats['max_dd_pct']:7.2f}% {stats['avg_deployed'] / cash * 100:7.2f}% "
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f"{stats['max_deployed'] / cash * 100:7.2f}% "
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f"{sum(1 for f in result['fills'] if f.kind == 'base'):4} "
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f"{sum(1 for f in result['fills'] if f.kind == 'add'):4}")
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print()
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print("=" * 120)
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print("D. 新配置下再放大/缩小单档股数（资金 50 万不变）")
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print("=" * 120)
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print(f"{'场景':26} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'平均占用':>8} {'峰值占用':>8} {'占用ROI':>8} {'收益/回撤':>8}")
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for factor in (0.25, 0.5, 1.0, 2.0, 4.0):
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params = {
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c: {**NEW_PARAMS[c],
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"buy_shares": max(100, int(NEW_PARAMS[c]["buy_shares"] * factor)),
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"max_shares": max(1000, int(NEW_PARAMS[c]["max_shares"] * factor))}
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for c in SYMBOLS
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}
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result, stats = run(params, 500_000.0)
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delta = stats["final_equity"] - 500_000.0
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roi = delta / stats["avg_deployed"] * 100 if stats["avg_deployed"] else 0.0
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ratio = stats["return_pct"] / stats["max_dd_pct"] if stats["max_dd_pct"] > 0.01 else 0.0
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label = "基准（现 _etf.yaml）" if factor == 1.0 else f"buy_shares×{factor}"
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print(f"{label:26} {delta:10.2f} {stats['return_pct']:6.2f}% {stats['max_dd_pct']:7.2f}% "
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f"{stats['avg_deployed'] / 500_000 * 100:7.2f}% {stats['max_deployed'] / 500_000 * 100:7.2f}% "
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f"{roi:7.2f}% {ratio:8.2f}")
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