optz

labs/analysis/etf/run.py

@@ -0,0 +1,232 @@
+"""ETF 网格策略回测总报告生成器：一次跑完基准 + 三种成交模型 + 敏感性。
+
+用法：
+    py -3.14 -B analysis/etf/run.py             # 用缓存日线
+    py -3.14 -B analysis/etf/run.py --refresh   # 重新抓日线
+    py -3.14 -B analysis/etf/run.py --ledger    # 额外打印逐笔成交
+
+输出：
+    analysis/etf/results.json   全部结构化结果
+    analysis/etf/run_report.txt 人读的汇总表
+"""
+
+import argparse
+from dataclasses import replace
+from datetime import date, datetime
+import json
+import math
+import statistics
+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, OUT, REPO_DEFAULTS, START_CASH, SYMBOLS, SYMBOL_PARAMS, EtfDefaults,
+    EtfSymbolConfig, analyze, fees, fetch_daily, simulate,
+)
+from analysis import (  # noqa: E402
+    entry_context, exposure, monthly, open_positions, round_trips, scenario_table,
+)
+
+
+def attribute(result, trips, data) -> dict:
+    """按标的拆解盈亏，两种口径都成立且与权益变动对齐。
+
+    ``net_at_cost``：把未了结仓位按**成本**入账（卖出额 − 全部买入额 − 佣金）。
+    ``net_at_market``：加上未实现浮动（期末市值 − 未了结成本）。
+
+    对冲校验：Σ net_at_market = 期末权益 − 期初权益。
+    """
+    rows = {}
+    for code in result["books"]:
+        book = result["books"][code]
+        fills = [f for f in result["fills"] if f.code == code]
+        closed_profit = sum(
+            (f.price * f.volume if f.side == "SELL" else -f.price * f.volume) - f.fee
+            for f in fills
+        )
+        unrealized = _unrealized(book, data, code)
+        rows[code] = {
+            "rounds": sum(1 for t in trips if t.code == code),
+            "net_at_cost": closed_profit,
+            "unrealized": unrealized,
+            "net_at_market": closed_profit + unrealized,
+            "open_cost": book.avg_cost * book.volume,
+            "open_volume": book.volume,
+        }
+    return rows
+
+
+def _unrealized(book, data, code) -> float:
+    """未实现浮动：期末市值 − 未了结仓位成本。"""
+    if book.volume <= 0:
+        return 0.0
+    close = data[code][-1]["close"]
+    return close * book.volume - book.avg_cost * book.volume
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--refresh", action="store_true")
+    parser.add_argument("--ledger", action="store_true")
+    args = parser.parse_args()
+
+    data = {code: fetch_daily(code, args.refresh) for code in SYMBOLS}
+    defaults = REPO_DEFAULTS   # 直接取仓库 _etf.yaml，避免与实盘配置漂移
+
+    # 三种成交模型：乐观（按触价）/ 贴近实盘（按反弹确认价）/ 悲观（按收盘价）
+    modes = {}
+    for mode in ("touch", "bounce", "close"):
+        result = simulate(data, fill_mode=mode)
+        modes[mode] = {"stats": analyze(result), "fills": result["fills"]}
+
+    base = simulate(data, fill_mode="touch")
+    stats = analyze(base)
+    stats["fee_pct_of_buy"] = stats["fees"] / stats["buy_amount"] * 100
+    trips = round_trips(base["fills"])
+    per_symbol = attribute(base, trips, data)
+    unrealized = sum(r["unrealized"] for r in per_symbol.values())
+    equity_delta = base["curve"][-1][1] - base["start_cash"]
+
+    report = {
+        "generated_at": datetime.now().isoformat(timespec="seconds"),
+        "config": {
+            "symbols": {
+                code: SYMBOL_PARAMS[code] for code in SYMBOLS
+            },
+            "defaults": {
+                field: getattr(defaults, field)
+                for field in (
+                    "atr_period", "min_grid_pct", "max_grid_span_pct", "channel_period",
+                    "channel_pct", "rebound_pct", "add_pct", "max_adds", "watch_seconds",
+                    "min_profit_pct", "inner_grids", "min_hold_days", "max_hold_days",
+                    "commission_rate", "min_commission", "max_tick_age_seconds",
+                )
+            },
+            "account": {"start_cash": START_CASH, "min_cash_ratio": MIN_CASH_RATIO},
+        },
+        "period": {
+            "first": base["curve"][0][0].isoformat(),
+            "last": base["curve"][-1][0].isoformat(),
+            "days": len(base["curve"]),
+        },
+        "data": {
+            code: {
+                "bars": len(data[code]),
+                "first": data[code][0]["date"],
+                "last": data[code][-1]["date"],
+                "first_close": data[code][0]["close"],
+                "last_close": data[code][-1]["close"],
+                "year_return_pct": (data[code][-1]["close"] / data[code][0]["close"] - 1) * 100,
+            }
+            for code in SYMBOLS
+        },
+        "modes": {
+            mode: {k: v for k, v in payload["stats"].items() if k not in ("per_symbol", "params")}
+            for mode, payload in modes.items()
+        },
+        "base": stats,
+        "exposure": exposure(base),
+        "round_trips": {
+            "count": len(trips),
+            "wins": sum(1 for t in trips if t.profit > 0),
+            "losses": sum(1 for t in trips if t.profit <= 0),
+            "avg_days": statistics.fmean([t.days for t in trips]) if trips else 0.0,
+            "max_days": max([t.days for t in trips], default=0),
+            "avg_levels": statistics.fmean([t.levels for t in trips]) if trips else 0.0,
+            "max_levels": max([t.levels for t in trips], default=0),
+            "avg_profit": statistics.fmean([t.profit for t in trips]) if trips else 0.0,
+            "best": max([t.profit for t in trips], default=0.0),
+            "worst": min([t.profit for t in trips], default=0.0),
+            "detail": [
+                {
+                    "code": t.code, "opened": t.opened.isoformat(), "closed": t.closed.isoformat(),
+                    "days": t.days, "levels": t.levels, "shares": t.shares,
+                    "buy": round(t.buy_amount, 2), "sell": round(t.sell_amount, 2),
+                    "profit": round(t.profit, 2), "return_pct": round(t.return_pct, 3),
+                    "fees": round(t.fees, 2),
+                }
+                for t in trips
+            ],
+        },
+        "attribution": {
+            "per_symbol": per_symbol,
+            "unrealized": unrealized,
+            "equity_delta": equity_delta,
+            "check": sum(r["net_at_market"] for r in per_symbol.values()),
+            "cash_delta": base["cash"] - base["start_cash"],
+            "open_cost": sum(r["open_cost"] for r in per_symbol.values()),
+            "open_positions": open_positions(base),
+        },
+        "monthly": monthly(base),
+        "entries": entry_context(data, SYMBOL_PARAMS, base),
+        "scenarios": scenario_table(data),
+    }
+    (OUT / "results.json").write_text(
+        json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8"
+    )
+
+    lines = []
+    lines.append(f"回测区间 {report['period']['first']} ~ {report['period']['last']} "
+                 f"({report['period']['days']} 个交易日)")
+    lines.append("")
+    lines.append("== 数据 ==")
+    for code, row in report["data"].items():
+        lines.append(f"  {code} bars={row['bars']} {row['first']}..{row['last']} "
+                     f"区间涨跌={row['year_return_pct']:+.2f}%")
+    lines.append("")
+    lines.append("== 三种成交模型的组合结果 ==")
+    lines.append(f"{'mode':8} {'net':>10} {'ret%':>7} {'maxDD%':>7} {'base':>5} {'add':>4} "
+                 f"{'exit':>5} {'lvl':>4} {'fees':>7} {'util%':>6} {'maxDep':>8}")
+    for mode, row in report["modes"].items():
+        adds = sum(1 for f in modes[mode]["fills"] if f.kind == "add")
+        levels = sum(1 for f in modes[mode]["fills"] if f.kind == "level")
+        lines.append(
+            f"{mode:8} {row['net']:10.2f} {row['return_pct']:7.3f} {row['max_dd_pct']:7.3f} "
+            f"{row['buy_count'] - adds:5} {adds:4} "
+            f"{sum(1 for f in modes[mode]['fills'] if f.kind == 'exit'):5} {levels:4} "
+            f"{row['fees']:7.2f} {row['avg_util_pct']:6.2f} {row['max_deployed']:8.0f}"
+        )
+    lines.append("")
+    lines.append("== 逐标的归因 ==")
+    for code, row in per_symbol.items():
+        lines.append(f"  {code} 轮次={row['rounds']:2} 已了结+未了结成本={row['net_at_cost']:9.2f} "
+                     f"未实现={row['unrealized']:8.2f} 按市价={row['net_at_market']:9.2f} "
+                     f"（未了结 {row['open_volume']} 股，成本 {row['open_cost']:.2f}）")
+    lines.append(f"  按成本口径合计={sum(r['net_at_cost'] for r in per_symbol.values()):.2f}"
+                 f"  ←→  权益变动={equity_delta:.2f}（应相等）")
+    lines.append(f"  按市价口径合计={sum(r['net_at_market'] for r in per_symbol.values()):.2f}"
+                 f"  = 权益变动 {equity_delta:.2f} + 未实现 {unrealized:.2f} - 持仓成本 "
+                 f"{sum(r['open_cost'] for r in per_symbol.values()):.2f}")
+    lines.append("")
+    lines.append("== 敏感性 ==")
+    lines.append(f"{'label':36} {'net':>10} {'ret%':>7} {'maxDD%':>7} {'base':>5} {'add':>4} "
+                 f"{'exit':>5} {'lvl':>4} {'fees':>7} {'util%':>6} {'maxDep':>8}")
+    for row in report["scenarios"]:
+        lines.append(
+            f"{row['label']:36} {row['net']:10.2f} {row['return_pct']:7.3f} {row['max_dd_pct']:7.3f} "
+            f"{row['bases']:5} {row['adds']:4} {row['exits']:5} {row['levels']:4} "
+            f"{row['fees']:7.2f} {row['avg_util_pct']:6.2f} {row['max_deployed']:8.0f}"
+        )
+    lines.append("")
+    lines.append("== 完整轮次明细 ==")
+    for t in report["round_trips"]["detail"]:
+        lines.append(f"  {t['opened']} → {t['closed']} {t['code']} {t['days']:3}天 "
+                     f"档位={t['levels']} 买={t['buy']:9.2f} 卖={t['sell']:9.2f} "
+                     f"净利={t['profit']:8.2f} 收益率={t['return_pct']:6.3f}%")
+    if args.ledger:
+        lines.append("")
+        lines.append("== 逐笔成交（touch 模型）==")
+        for f in base["fills"]:
+            lines.append(f"  {f.day} {f.code} {f.side:4} {f.kind:4} {f.volume:6} "
+                         f"@{f.price:.3f} fee={f.fee:5.2f} {f.note}")
+
+    (OUT / "run_report.txt").write_text("\n".join(lines), encoding="utf-8")
+    print("\n".join(lines))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())