freedakgmail
375 lines
16 KiB
Python
375 lines
16 KiB
Python
#!/usr/bin/env python3
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"""运行多组回测场景并输出对比表（v4.2 真实资金收益率版）。
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v3 基线 vs v4 优化 vs v4.2 延迟确认 全面对比。
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核心改进: 用真实占用资金（而非总周转金额）计算收益率和年化。"""
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import sys
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import os
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import argparse
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from datetime import date, datetime
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from backtest_recommend import (
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get_db_conn, get_codes_with_data, run_backtest, START_DATE,
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MAX_POSITION_AMOUNT, MAX_CONCURRENT_POSITIONS, PRICE_MIN, PRICE_MAX,
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SELL_COOLDOWN_DAYS, MAX_BUYS_PER_DAY,
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)
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def main():
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parser = argparse.ArgumentParser(description='多场景回测对比（v4.2 真实资金收益率）')
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parser.add_argument('--start', type=str, default=None, metavar='YYYY-MM-DD',
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help='回测起始日（默认 2026-01-02）')
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parser.add_argument('-v', '--verbose', action='store_true', help='每个场景输出每日进度')
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parser.add_argument('--quick', type=int, default=None, metavar='N',
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help='仅运行前 N 个场景（快速验证）')
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parser.add_argument('--v3-only', action='store_true', help='仅运行 v3 基线场景')
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parser.add_argument('--v4-only', action='store_true', help='仅运行 v4/v4.2 优化场景')
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args = parser.parse_args()
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start_date = START_DATE
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if args.start:
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try:
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start_date = datetime.strptime(args.start, '%Y-%m-%d').date()
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except ValueError:
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print("错误: --start 格式应为 YYYY-MM-DD")
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return
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conn = get_db_conn()
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end = date.today()
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try:
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codes = get_codes_with_data(conn, end, min_days=30)
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except Exception:
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codes = []
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if not codes:
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print("错误: 无 stock_kline_daily 数据")
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conn.close()
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return
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# ═══ v3 基线 ═══
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v3_scenarios = [
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("v3|仅信号", {}),
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("v3|止盈10+损8", {"take_profit_pct": 10, "stop_loss_pct": 8}),
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]
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# ═══ v4 优化（上轮胜出） ═══
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v4_scenarios = [
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("v4|触发≥2+止盈10+损8", {
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"min_buy_triggered": 2,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4|触发≥2+跟踪6-3+损5", {
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"min_buy_triggered": 2,
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"trailing_start_pct": 6, "trailing_gap_pct": 3,
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"stop_loss_pct": 5,
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}),
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]
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# ═══ v4.1 忽略卖出信号（参考对照） ═══
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v41_scenarios = [
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("v4.1|忽略卖出+止盈10+损8", {
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"ignore_sell_signal": True,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4.1|忽略+跟踪8-3+损5+20天", {
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"ignore_sell_signal": True,
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"trailing_start_pct": 8, "trailing_gap_pct": 3,
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"stop_loss_pct": 5, "max_hold_days": 20,
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}),
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]
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# ═══ v4.2 延迟卖出确认（核心创新） ═══
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v42_scenarios = [
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# ── K: 延迟2天确认 + 各种组合 ──
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("v4.2-K1|延迟2天+止盈10+损8", {
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"sell_confirm_days": 2,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4.2-K2|延迟2天+触发≥2+止盈10+损8", {
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"sell_confirm_days": 2,
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"min_buy_triggered": 2,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4.2-K3|延迟2天+跟踪8-3+损5", {
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"sell_confirm_days": 2,
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"trailing_start_pct": 8, "trailing_gap_pct": 3,
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"stop_loss_pct": 5,
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}),
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("v4.2-K4|延迟2天+跟踪6-3+损5", {
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"sell_confirm_days": 2,
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"trailing_start_pct": 6, "trailing_gap_pct": 3,
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"stop_loss_pct": 5,
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}),
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("v4.2-K5|延迟2天+触发≥2+跟踪8-3+损5", {
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"sell_confirm_days": 2,
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"min_buy_triggered": 2,
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"trailing_start_pct": 8, "trailing_gap_pct": 3,
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"stop_loss_pct": 5,
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}),
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("v4.2-K6|延迟2天+触发≥2+跟踪6-3+损8", {
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"sell_confirm_days": 2,
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"min_buy_triggered": 2,
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"trailing_start_pct": 6, "trailing_gap_pct": 3,
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"stop_loss_pct": 8,
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}),
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# ── L: 延迟3天确认 ──
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("v4.2-L1|延迟3天+止盈10+损8", {
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"sell_confirm_days": 3,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4.2-L2|延迟3天+触发≥2+止盈10+损8", {
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"sell_confirm_days": 3,
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"min_buy_triggered": 2,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4.2-L3|延迟3天+跟踪8-3+损5", {
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"sell_confirm_days": 3,
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"trailing_start_pct": 8, "trailing_gap_pct": 3,
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"stop_loss_pct": 5,
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}),
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("v4.2-L4|延迟3天+触发≥2+跟踪6-3+损5", {
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"sell_confirm_days": 3,
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"min_buy_triggered": 2,
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"trailing_start_pct": 6, "trailing_gap_pct": 3,
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"stop_loss_pct": 5,
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}),
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# ── M: 延迟确认 + 盈利保护 + 超时 ──
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("v4.2-M1|延迟2天+盈保5%+止盈10+损8", {
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"sell_confirm_days": 2,
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"profit_protect_pct": 5,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4.2-M2|延迟2天+盈保5%+触发≥2+止盈10+损8", {
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"sell_confirm_days": 2,
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"profit_protect_pct": 5,
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"min_buy_triggered": 2,
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"take_profit_pct": 10, "stop_loss_pct": 8,
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}),
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("v4.2-M3|延迟3天+跟踪8-3+损5+30天", {
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"sell_confirm_days": 3,
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"trailing_start_pct": 8, "trailing_gap_pct": 3,
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"stop_loss_pct": 5, "max_hold_days": 30,
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}),
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("v4.2-M4|延迟2天+触发≥2+跟踪8-3+损5+30天", {
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"sell_confirm_days": 2,
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"min_buy_triggered": 2,
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"trailing_start_pct": 8, "trailing_gap_pct": 3,
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"stop_loss_pct": 5, "max_hold_days": 30,
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}),
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]
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# 选择场景
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if args.v3_only:
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scenarios = v3_scenarios
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elif args.v4_only:
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scenarios = v4_scenarios + v41_scenarios + v42_scenarios
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else:
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scenarios = v3_scenarios + v4_scenarios + v41_scenarios + v42_scenarios
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if args.quick is not None:
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scenarios = scenarios[: args.quick]
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total = len(scenarios)
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v3_count = len(v3_scenarios) if not args.v4_only else 0
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v4_count = len(v4_scenarios) if not args.v3_only else 0
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v41_count = len(v41_scenarios) if not args.v3_only else 0
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print("=" * 150)
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print(" 多场景回测对比 v4.2（真实资金收益率 + 延迟卖出确认）")
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print("=" * 150)
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print(f" 回测区间 : {start_date} ~ {end}")
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print(f" 场景数 : {total}")
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print(f" 股价区间 : {PRICE_MIN}~{PRICE_MAX} 元 | 单只上限 : ¥{MAX_POSITION_AMOUNT:,}")
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print(f" 每日买入 : 最多 {MAX_BUYS_PER_DAY} 只 | 最大持仓 : {MAX_CONCURRENT_POSITIONS} 只")
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print(f" 冷却期 : {SELL_COOLDOWN_DAYS} 天")
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print(f" ⚠️ 本版使用【真实资金收益率】= 盈亏 / 最大同时占用资金")
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print("=" * 150)
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print()
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rows = []
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for k, (name, kwargs) in enumerate(scenarios, 1):
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print(f"[进度] 场景 {k}/{total}: {name}", flush=True)
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result = run_backtest(
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conn, start_date=start_date, end_date=end,
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verbose=args.verbose, **kwargs
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)
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if not result or not result.get('stats'):
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rows.append((name, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
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continue
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s = result['stats']
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rows.append((
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name,
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s['profit'], # 1: 盈亏
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s.get('capital_pct', 0), # 2: 真实收益率
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s.get('capital_ann_pct', 0), # 3: 真实年化
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s['win_rate'], # 4: 胜率
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s['max_drawdown'], # 5: 最大回撤
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s.get('max_drawdown_pct', 0), # 6: 回撤%
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s['avg_hold_days'], # 7: 平均持仓
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s['profit_factor'], # 8: 盈亏比
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s['trade_count'], # 9: 交易数
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s.get('max_capital', 0), # 10: 最大占用
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s['profit_pct'], # 11: 周转收益率(参考)
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s['annualized_pct'], # 12: 周转年化(参考)
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))
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mc = s.get('max_capital', 0)
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cp = s.get('capital_pct', 0)
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ca = s.get('capital_ann_pct', 0)
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if not args.verbose:
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print(f" → 盈亏 ¥{s['profit']:>+10,.0f} 资金占用 ¥{mc:>8,.0f} "
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f"真实收益 {cp:>+6.1f}% 年化 {ca:>+6.1f}% "
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f"胜率 {s['win_rate']:>5.1f}% 交易 {s['trade_count']} 笔", flush=True)
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conn.close()
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# 找最优（基于真实收益率）
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if rows:
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best_profit_idx = max(range(len(rows)), key=lambda i: rows[i][1])
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best_cap_idx = max(range(len(rows)), key=lambda i: rows[i][2])
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best_ann_idx = max(range(len(rows)), key=lambda i: rows[i][3])
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best_winrate_idx = max(range(len(rows)), key=lambda i: rows[i][4])
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min_dd_idx = min(range(len(rows)), key=lambda i: rows[i][5])
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best_pf_idx = max(range(len(rows)), key=lambda i: rows[i][8])
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else:
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best_profit_idx = best_cap_idx = best_ann_idx = best_winrate_idx = min_dd_idx = best_pf_idx = -1
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# 写入对比表
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out_path = os.path.join(os.path.dirname(__file__), "docs", "backtest_comparison.md")
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os.makedirs(os.path.dirname(out_path), exist_ok=True)
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with open(out_path, "w", encoding="utf-8") as f:
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f.write("# 回测场景对比 v4.2（真实资金收益率版）\n\n")
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f.write(f"回测区间: {start_date} ~ {end}\n\n")
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f.write("> ⚠️ **真实收益率** = 盈亏 / 最大同时占用资金（非总周转金额）\n\n")
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f.write("## 对比结果\n\n")
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f.write("| 场景 | 盈亏(元) | 占用资金 | 真实收益 | 真实年化 | 胜率 | 回撤% | 持仓天 | 盈亏比 | 交易 | 标注 |\n")
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f.write("|------|---------|---------|---------|---------|------|-------|--------|--------|------|------|\n")
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for idx, row_data in enumerate(rows):
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name = row_data[0]
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profit, cap_pct, cap_ann = row_data[1], row_data[2], row_data[3]
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wr, dd, dd_pct = row_data[4], row_data[5], row_data[6]
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hold, pf, n, mc = row_data[7], row_data[8], row_data[9], row_data[10]
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tags = []
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if idx == best_profit_idx:
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tags.append('🏆收益最高')
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if idx == best_ann_idx and idx != best_profit_idx:
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tags.append('📈年化最高')
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if idx == best_cap_idx and idx != best_profit_idx and idx != best_ann_idx:
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tags.append('💰资金效率')
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if idx == best_winrate_idx:
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tags.append('🎯胜率最高')
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if idx == min_dd_idx:
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tags.append('🛡️回撤最小')
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if idx == best_pf_idx and idx != best_profit_idx:
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tags.append('⚖️盈亏比最佳')
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tag_str = ' '.join(tags)
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f.write(f"| {name} | {profit:+,.0f} | ¥{mc:,.0f} | {cap_pct:+.1f}% | {cap_ann:+.1f}% | "
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f"{wr:.1f}% | {dd_pct:.1f}% | {hold:.0f}天 | {pf:.2f} | {n} | {tag_str} |\n")
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f.write("\n")
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# 加说明
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f.write("## 指标说明\n\n")
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f.write("| 指标 | 说明 |\n")
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f.write("|------|------|\n")
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f.write("| 占用资金 | 回测期间最大同时持仓成本 |\n")
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f.write("| 真实收益 | 盈亏 / 最大占用资金 × 100% |\n")
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f.write("| 真实年化 | 按持续期折算年化（复利公式） |\n")
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f.write("| 回撤% | 最大回撤 / 最大占用资金 × 100% |\n")
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f.write("| 盈亏比 | 总盈利金额 / 总亏损金额 |\n")
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f.write("| 延迟N天 | 连续N天推荐卖出才执行卖出 |\n")
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f.write("\n")
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# 控制台表格
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print("\n" + "=" * 160)
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print(" v4.2 整体对比表（★ 真实资金收益率 ★）")
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print("=" * 160)
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header = (f"{'场景':<42} {'盈亏(元)':>10} {'占用资金':>10} {'真实收益':>8} {'真实年化':>8} "
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f"{'胜率':>6} {'回撤%':>7} {'持仓':>6} {'盈亏比':>6} {'交易':>5}")
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print(header)
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print("-" * 160)
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v3_end_idx = v3_count
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v4_end_idx = v3_count + v4_count
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v41_end_idx = v4_end_idx + v41_count
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for idx, row_data in enumerate(rows):
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name = row_data[0]
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profit, cap_pct, cap_ann = row_data[1], row_data[2], row_data[3]
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wr, dd, dd_pct = row_data[4], row_data[5], row_data[6]
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hold, pf, n, mc = row_data[7], row_data[8], row_data[9], row_data[10]
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tags = []
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if idx == best_profit_idx:
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tags.append('🏆')
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if idx == best_ann_idx and idx != best_profit_idx:
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tags.append('📈')
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if idx == best_cap_idx and idx != best_profit_idx and idx != best_ann_idx:
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tags.append('💰')
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if idx == best_winrate_idx:
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tags.append('🎯')
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if idx == min_dd_idx:
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tags.append('🛡️')
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if idx == best_pf_idx and idx != best_profit_idx:
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tags.append('⚖️')
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tag_str = ''.join(tags)
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# 分隔线
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if not args.v3_only and not args.v4_only:
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if idx == v3_end_idx and v3_count > 0:
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print("─" * 160)
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print(f" {'↑ v3 基线 ↓ v4 优化':^148}")
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print("─" * 160)
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if idx == v4_end_idx and v4_count > 0:
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print("─" * 160)
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print(f" {'↑ v4 优化 ↓ v4.1 忽略卖出（参考对照）':^148}")
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print("─" * 160)
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if idx == v41_end_idx and v41_count > 0:
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print("─" * 160)
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print(f" {'↑ v4.1 参考 ↓ v4.2 延迟卖出确认（核心创新）':^148}")
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print("─" * 160)
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print(f"{name:<42} {profit:>+10,.0f} {'¥'+str(int(mc)):>10} {cap_pct:>+7.1f}% {cap_ann:>+7.1f}% "
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f"{wr:>5.1f}% {dd_pct:>6.1f}% {hold:>5.0f}天 {pf:>6.2f} {n:>5} {tag_str}")
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print("=" * 160)
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# 总结
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if rows and len(rows) > 1:
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print("\n 📊 关键发现（★ 基于真实资金收益率 ★）:")
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if best_profit_idx >= 0:
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r = rows[best_profit_idx]
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print(f" 🏆 绝对收益最高: {r[0]} → ¥{r[1]:+,.0f} (真实{r[2]:+.1f}%, 年化{r[3]:+.1f}%)")
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if best_ann_idx >= 0 and best_ann_idx != best_profit_idx:
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r = rows[best_ann_idx]
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print(f" 📈 年化最高: {r[0]} → 真实年化 {r[3]:+.1f}% (占用 ¥{r[10]:,.0f})")
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if best_cap_idx >= 0 and best_cap_idx not in (best_profit_idx, best_ann_idx):
|
||
r = rows[best_cap_idx]
|
||
print(f" 💰 资金效率最高: {r[0]} → 真实收益 {r[2]:+.1f}% (占用 ¥{r[10]:,.0f})")
|
||
if best_winrate_idx >= 0:
|
||
r = rows[best_winrate_idx]
|
||
print(f" 🎯 胜率最高: {r[0]} → {r[4]:.1f}%")
|
||
if best_pf_idx >= 0:
|
||
r = rows[best_pf_idx]
|
||
print(f" ⚖️ 盈亏比最佳: {r[0]} → {r[8]:.2f}")
|
||
if min_dd_idx >= 0:
|
||
r = rows[min_dd_idx]
|
||
print(f" 🛡️ 回撤最小: {r[0]} → {r[6]:.1f}%")
|
||
|
||
# 银行对比
|
||
print("\n 🏦 银行存款利率对比（年化2.5%）:")
|
||
for idx, r in enumerate(rows):
|
||
ann = r[3]
|
||
if ann > 2.5:
|
||
icon = '✅'
|
||
else:
|
||
icon = '❌'
|
||
print(f" {icon} {r[0]:<42} 年化 {ann:>+6.1f}% {'超过银行' if ann > 2.5 else '低于银行'}")
|
||
|
||
print(f"\n场景对比已写入 {out_path}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|