"""ETF 网格策略 vs 同期买入持有（同一区间、同一份日线）。

用法： py -3.14 -B analysis/etf/vs_hold.py
"""

import math
import statistics
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 START_CASH, SYMBOLS, analyze, fetch_daily, simulate  # noqa: E402

data = {code: fetch_daily(code) for code in SYMBOLS}
result = simulate(data)
stats = analyze(result)
curve = result["curve"]
start_day = curve[0][0]
cash0 = result["start_cash"]
grid_final = stats["final_equity"]

# ---- 买入持有：在回测首日按各标的收盘价等权买入，持到期末 ----
bars_by_date = {code: {b["date"]: b for b in data[code]} for code in SYMBOLS}
first_stamp = start_day.strftime("%Y%m%d")
entry = {c: bars_by_date[c][first_stamp]["close"] for c in SYMBOLS}
last_stamp = curve[-1][0].strftime("%Y%m%d")
exit_ = {c: bars_by_date[c][last_stamp]["close"] for c in SYMBOLS}

per_symbol_cash = cash0 / len(SYMBOLS)
hold_shares = {c: per_symbol_cash / entry[c] for c in SYMBOLS}   # 含零股，忽略整手限制
hold_curve = []
for day, _, _, _ in curve:
    stamp = day.strftime("%Y%m%d")
    value = sum(hold_shares[c] * bars_by_date[c].get(stamp, {"close": entry[c]})["close"]
                for c in SYMBOLS)
    hold_curve.append((day, value))
hold_final = hold_curve[-1][1]


def profile(curve_points):
    """从权益曲线算总收益、最大回撤、日波动、夏普与卡玛。"""
    values = [v for _, v in curve_points]
    peak, max_dd = -math.inf, 0.0
    for value in values:
        peak = max(peak, value)
        max_dd = max(max_dd, (peak - value) / peak)
    rets = [values[i] / values[i - 1] - 1 for i in range(1, len(values))]
    mean = statistics.fmean(rets) if rets else 0.0
    sd = statistics.pstdev(rets) if len(rets) > 1 else 0.0
    total = values[-1] / values[0] - 1
    days = len(values)
    annual = (1 + total) ** (252 / days) - 1 if days else 0.0
    return {
        "total_pct": total * 100,
        "annual_pct": annual * 100,
        "max_dd_pct": max_dd * 100,
        "daily_sd_pct": sd * 100,
        "sharpe": (mean / sd * math.sqrt(252)) if sd > 0 else float("nan"),
        "calmar": (total / max_dd) if max_dd > 0 else float("nan"),
    }


grid_curve = [(day, equity) for day, equity, _, _ in curve]

print("=" * 108)
print(f"区间 {start_day} ~ {curve[-1][0]}（{len(curve)} 个交易日），账户 {cash0:,.0f} 元")
print("=" * 108)
print(f"{'策略':26} {'期末权益':>12} {'总收益':>9} {'年化':>8} {'最大回撤':>9} "
      f"{'日波动':>8} {'夏普':>7} {'卡玛':>7}")
for label, points in (("ETF 网格（现配置）", grid_curve), ("等权买入持有", hold_curve)):
    p = profile(points)
    print(f"{label:26} {points[-1][1]:12,.2f} {p['total_pct']:8.2f}% {p['annual_pct']:7.2f}% "
          f"{p['max_dd_pct']:8.2f}% {p['daily_sd_pct']:7.3f}% {p['sharpe']:7.2f} {p['calmar']:7.2f}")

print()
print("== 逐标的买入持有（同一区间）==")
print(f"{'标的':11} {'期初':>8} {'期末':>8} {'涨跌':>9} {'期间最大回撤':>12}")
for code in SYMBOLS:
    bars = [b for b in data[code] if b["date"] >= first_stamp and b["date"] <= last_stamp]
    closes = [b["close"] for b in bars]
    peak, dd = -math.inf, 0.0
    for value in closes:
        peak = max(peak, value)
        dd = max(dd, (peak - value) / peak)
    print(f"{code:11} {closes[0]:8.3f} {closes[-1]:8.3f} "
          f"{(closes[-1] / closes[0] - 1) * 100:8.2f}% {dd * 100:11.2f}%")

print()
print("== 关键口径 ==")
print(f"  网格：平均资金占用 {stats['avg_util_pct']:.2f}%（峰值 {stats['max_deployed'] / cash0 * 100:.2f}%），"
      f"买入名义 {stats['buy_amount']:,.0f}（换手 {stats['turnover_x']:.2f} 倍），"
      f"佣金 {stats['fees']:.2f}")
print(f"  网格：占用部分的收益率（权益变动 ÷ 平均占用）= "
      f"{(grid_final - cash0) / stats['avg_deployed'] * 100:.2f}%")
print(f"  买入持有：资金 100% 占用（从未空仓），无佣金/无交易")
print(f"  网格：完整轮次 {len([f for f in result['fills'] if f.kind == 'exit'])} 次，"
      f"胜率 100%（只在盈利 ≥{stats['params']['min_profit_pct']}% 时才卖）")

# ---- 同风险口径：把网格放大到与买入持有相同回撤，比收益 ----
print()
print("== 同回撤口径（把网格单档股数放大到回撤≈买入持有）==")
from dataclasses import replace  # noqa: E402
from backtest import SYMBOL_PARAMS, SYMBOLS as _SYMS  # noqa: E402

hold_p = profile(hold_curve)
print(f"  目标回撤：买入持有 {hold_p['max_dd_pct']:.2f}%")
print(f"{'放大倍数':>8} {'权益变动':>12} {'总收益':>9} {'最大回撤':>9} {'平均占用':>9} {'夏普':>7} {'卡玛':>7}")
for factor in (1, 4, 10, 20, 35):
    params = {
        c: {**SYMBOL_PARAMS[c],
            "buy_shares": max(100, int(SYMBOL_PARAMS[c]["buy_shares"] * factor)),
            "max_shares": max(1000, int(SYMBOL_PARAMS[c]["max_shares"] * factor))}
        for c in _SYMS
    }
    res = simulate(data, symbol_params=params)
    st = analyze(res)
    pts = [(day, eq) for day, eq, _, _ in res["curve"]]
    p = profile(pts)
    print(f"{factor:8}× {st['final_equity'] - cash0:12,.2f} {p['total_pct']:8.2f}% "
          f"{p['max_dd_pct']:8.2f}% {st['avg_util_pct']:8.2f}% {p['sharpe']:7.2f} {p['calmar']:7.2f}")
print()
print("注：放大是外推（同一段历史、同一批机会等比放大），不是新样本的验证；")
print("    放开股数后实际能否成交/是否滑点恶化，日线回测无法回答。")