145 lines
5.8 KiB
Python
145 lines
5.8 KiB
Python
"""为什么收益率低 / 改进方案量化：同一天数据、同一策略逻辑，只改参数与下单规模。
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用法： py -3.14 -B analysis/etf/sweep.py
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"""
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from dataclasses import replace
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import statistics
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import sys
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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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START_CASH, SYMBOLS, SYMBOL_PARAMS, EtfDefaults, EtfSymbolConfig, analyze,
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fetch_daily, precondition, simulate,
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)
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data = {code: fetch_daily(code) for code in SYMBOLS}
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BASE = EtfDefaults()
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_CACHE: dict[tuple, dict] = {}
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def pre_for(defaults, params):
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"""按"影响指标计算"的参数缓存 precondition 结果。
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注意：``channel_pct`` / ``atr_period`` / ``channel_period`` / ``min_grid_pct`` 与逐标的
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``atr_multiplier`` 会改变指标，必须进缓存键；``add_pct`` / ``min_profit_pct`` 只影响
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下单判定，不进键。
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"""
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key = (
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defaults.channel_pct, defaults.atr_period, defaults.channel_period,
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defaults.min_grid_pct,
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tuple(sorted((c, params[c]["atr_multiplier"]) for c in params)),
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)
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if key not in _CACHE:
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_CACHE[key] = {
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c: precondition(data[c], EtfSymbolConfig(**params[c]), defaults) for c in data
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}
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return _CACHE[key]
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def run(label, *, defaults=None, params=None, sizer=None, fill_mode="touch"):
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defaults = defaults or BASE
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params = params or SYMBOL_PARAMS
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result = simulate(data, defaults=defaults, symbol_params=params, sizer=sizer,
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fill_mode=fill_mode, precomputed=pre_for(defaults, params))
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stats = analyze(result)
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adds = sum(1 for f in result["fills"] if f.kind == "add")
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bases = sum(1 for f in result["fills"] if f.kind == "base")
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trips = [
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(f.price * f.volume if f.side == "SELL" else -f.price * f.volume) - f.fee
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for f in result["fills"]
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]
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return {
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"label": label,
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"equity": stats["final_equity"] - result["start_cash"],
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"ret_pct": stats["return_pct"],
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"dd_pct": stats["max_dd_pct"],
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"bases": bases,
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"adds": adds,
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"exits": sum(1 for f in result["fills"] if f.kind == "exit"),
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"roi_on_deployed_pct": (
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(stats["final_equity"] - result["start_cash"]) / stats["avg_deployed"] * 100
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if stats["avg_deployed"] else 0.0
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),
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"util_pct": stats["avg_util_pct"],
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"max_util_pct": stats["max_deployed"] / result["start_cash"] * 100,
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"fees": stats["fees"],
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}
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def show(rows):
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print(f"{'场景':46} {'权益变动':>10} {'收益率':>7} {'回撤':>6} {'底仓':>4} {'补仓':>4} "
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f"{'占用ROI':>8} {'平均占用':>8} {'峰值占用':>8}")
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for r in rows:
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print(f"{r['label'][:46]:46} {r['equity']:10.2f} {r['ret_pct']:6.2f}% {r['dd_pct']:5.2f}% "
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f"{r['bases']:4} {r['adds']:4} {r['roi_on_deployed_pct']:7.2f}% "
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f"{r['util_pct']:7.2f}% {r['max_util_pct']:7.2f}%")
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def sized(shares):
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"""把逐标的 buy_shares / max_shares 同步放大，保持 10 档容量不变。"""
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return {c: {**SYMBOL_PARAMS[c], "buy_shares": shares, "max_shares": shares * 10}
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for c in SYMBOLS}
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print("=" * 132)
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print("A. 只放大单档规模（其余参数一律不动）")
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print("=" * 132)
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show([run(f"buy_shares={n}（现值 1000）", params=sized(n)) for n in (1000, 2000, 5000, 10000, 20000)])
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print()
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print("=" * 132)
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print("B. 按账户资金比例下单（sizer，替代固定股数；每档 = 现金的 x%）")
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print("=" * 132)
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rows = []
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for pct in (0.02, 0.05, 0.10, 0.20):
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sizer = (lambda p: (lambda equity, price: int(equity * p / price)))(pct)
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rows.append(run(f"每档 = 现金 {pct:.0%}", params=sized(20000), sizer=sizer))
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show(rows)
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print()
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print("=" * 132)
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print("C. 入场/补仓/止盈参数（规模固定 buy_shares=2000）")
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print("=" * 132)
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P2 = sized(2000)
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show([
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run("基准参数（add 3% / profit 1% / channel 15%）", params=P2),
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run("add_pct=2%", defaults=replace(BASE, add_pct=2.0), params=P2),
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run("add_pct=1.5%", defaults=replace(BASE, add_pct=1.5), params=P2),
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run("add_pct=1.0%", defaults=replace(BASE, add_pct=1.0), params=P2),
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run("min_profit_pct=0.6%", defaults=replace(BASE, min_profit_pct=0.6), params=P2),
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run("min_profit_pct=3%", defaults=replace(BASE, min_profit_pct=3.0), params=P2),
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run("channel_pct=10%", defaults=replace(BASE, channel_pct=10.0), params=P2),
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run("channel_pct=30%", defaults=replace(BASE, channel_pct=30.0), params=P2),
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run("add 1.5% + profit 0.6%", defaults=replace(BASE, add_pct=1.5, min_profit_pct=0.6), params=P2),
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run("add 1.5% + profit 0.6% + channel 30%", defaults=replace(BASE, add_pct=1.5, min_profit_pct=0.6, channel_pct=30.0), params=P2),
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])
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print()
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print("=" * 132)
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print("D. 组合方案（把 A/B/C 的结论叠起来）")
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print("=" * 132)
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best = []
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for shares in (2000, 5000, 10000):
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for add_pct, profit in ((1.5, 0.6), (1.5, 1.0), (2.0, 0.6), (1.0, 0.6)):
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label = f"buy={shares} add={add_pct}% profit={profit}%"
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best.append(run(label, defaults=replace(BASE, add_pct=add_pct, min_profit_pct=profit),
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params=sized(shares)))
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best.sort(key=lambda r: -r["equity"])
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show(best[:10])
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print()
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print("=" * 132)
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print("E. 现金利用率天花板：每档 = 现金 10%，同时放开通道与补仓（看能否把 20 万用起来）")
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print("=" * 132)
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rows = []
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for add_pct, profit, channel in ((3.0, 1.0, 15.0), (1.5, 0.6, 30.0), (1.0, 0.6, 30.0), (1.0, 0.5, 40.0)):
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sizer = lambda equity, price: int(equity * 0.10 / price)
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rows.append(run(f"add={add_pct}% profit={profit}% channel={channel}% 每档10%现金",
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defaults=replace(BASE, add_pct=add_pct, min_profit_pct=profit, channel_pct=channel),
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params=sized(20000), sizer=sizer))
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show(rows)
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