feat: 全链路新增 type 字段支持 + exchange.ts 超时退出优化
- TS: exit 函数统一管理进程退出与 DB 连接关闭；10s 超时 + 异常路径 clearTimeout - Python: PairType(spot/um/cm) 贯穿 Kline 模型、策略配置、数据查询 - 回测脚本升级: 9策略 × 4币种 × 6时间级别 × 2交易类型 - 新增 generate_report.py 回测报告生成工具
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Rekey
@@ -1,8 +1,8 @@
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"""
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2h/6h 策略全维度对比回测 — 9策略 × 4币种 × 2时间级别 × 4数据量
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1h-1d 策略全维度对比回测 — 9策略 × 4币种 × 6时间级别 × 2交易类型 × 4数据量
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8个网络知名策略 + 牛熊自适应策略（RegimeDetector3投票）
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在 2h 和 6h 两个新时间级别上的表现对比。
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在 1h/2h/4h/6h/8h/1d 六个时间级别上的表现对比。
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用法：
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source .venv/bin/activate && python example/comparison_2h_6h.py
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@@ -31,7 +31,8 @@ from engine.example.long_short import LongShortEngine
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# ── 全局常量 ──
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SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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TIMEFRAMES = ["2h", "6h"]
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TIMEFRAMES = ["1h", "2h", "4h", "6h", "8h", "1d"]
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PAIR_TYPES = ["spot", "um"]
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INITIAL = 10_000.0
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WARMUP = 150
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MAX_CONCURRENCY = 6
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@@ -820,12 +821,13 @@ STRATEGY_PARAMS_STR = {
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# 执行
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# ════════════════════════════════════════════════════════
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async def run_one(entry, symbol, interval, period_label, start, end):
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async def run_one(entry, symbol, interval, pair_type, period_label, start, end):
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make_config = entry["make_config"]
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strategy_cls = entry["strategy_cls"]
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sc = make_config(symbol)
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bt = BacktestConfig(
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symbol=symbol, interval=interval,
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type=pair_type,
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start_time=start, end_time=end,
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initial_capital=INITIAL, warmup_bars=WARMUP,
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)
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@@ -852,14 +854,15 @@ async def main():
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date_ranges: dict[tuple[str, str], tuple] = {}
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for symbol in SYMBOLS:
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for tf in TIMEFRAMES:
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try:
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s, e = await ds.fetch_symbol_date_range(symbol, tf)
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bar_ms = {"2h": 7_200_000, "6h": 21_600_000}
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estimated_bars = int((e - s).total_seconds() * 1000 / bar_ms[tf])
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date_ranges[(symbol, tf)] = (s, e, estimated_bars)
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print(f" {symbol} {tf:<4}: {s.date()} ~ {e.date()} (约{estimated_bars:,}根)")
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except Exception as ex:
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print(f" {symbol} {tf:<4}: 获取失败 — {ex}")
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for pair_type in PAIR_TYPES:
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try:
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s, e = await ds.fetch_symbol_date_range(symbol, tf, type=pair_type)
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bar_ms = {"1h": 3_600_000, "2h": 7_200_000, "4h": 14_400_000, "6h": 21_600_000, "8h": 28_800_000, "1d": 86_400_000}
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estimated_bars = int((e - s).total_seconds() * 1000 / bar_ms[tf])
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date_ranges[(symbol, tf, pair_type)] = (s, e, estimated_bars)
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print(f" {symbol} {tf:<4} {pair_type:<5}: {s.date()} ~ {e.date()} (约{estimated_bars:,}根)")
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except Exception as ex:
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print(f" {symbol} {tf:<4} {pair_type:<5}: 获取失败 — {ex}")
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await ds.close()
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@@ -869,39 +872,41 @@ async def main():
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for strat_name, entry in STRATEGY_REGISTRY.items():
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for symbol in SYMBOLS:
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for tf in TIMEFRAMES:
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key = (symbol, tf)
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if key not in date_ranges:
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continue
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fs, fe, est_bars = date_ranges[key]
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for period_label, (period_start, period_end) in PERIODS.items():
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actual_start = period_start or fs
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actual_end = period_end or fe
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if actual_start >= actual_end:
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for pair_type in PAIR_TYPES:
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key = (symbol, tf, pair_type)
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if key not in date_ranges:
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continue
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fs, fe, est_bars = date_ranges[key]
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min_bars = MIN_BARS_FOR_PERIOD.get(period_label, 50)
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actual_bars = est_bars
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if period_label != "全量":
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actual_bars = int((actual_end - actual_start).total_seconds() * 1000 / {
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"2h": 7_200_000, "6h": 21_600_000
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}[tf])
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for period_label, (period_start, period_end) in PERIODS.items():
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actual_start = period_start or fs
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actual_end = period_end or fe
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if actual_start >= actual_end:
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continue
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if actual_bars < min_bars:
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continue
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min_bars = MIN_BARS_FOR_PERIOD.get(period_label, 50)
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actual_bars = est_bars
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if period_label != "全量":
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actual_bars = int((actual_end - actual_start).total_seconds() * 1000 / {
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"1h": 3_600_000, "2h": 7_200_000, "4h": 14_400_000, "6h": 21_600_000, "8h": 28_800_000, "1d": 86_400_000
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}[tf])
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tasks_info.append({
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"strat_name": strat_name,
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"entry": entry,
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"symbol": symbol,
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"tf": tf,
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"period_label": period_label,
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"start": actual_start,
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"end": actual_end,
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})
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if actual_bars < min_bars:
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continue
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tasks_info.append({
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"strat_name": strat_name,
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"entry": entry,
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"symbol": symbol,
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"tf": tf,
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"pair_type": pair_type,
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"period_label": period_label,
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"start": actual_start,
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"end": actual_end,
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})
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total = len(tasks_info)
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print(f"\n共 {total} 组回测任务 (9策略×4币种×2时间×4数据量 - 跳过数据不足)")
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print(f"\n共 {total} 组回测任务 (9策略×4币种×6时间×2类型×4数据量 - 跳过数据不足)")
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results: list[dict] = []
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completed = 0
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@@ -911,7 +916,7 @@ async def main():
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nonlocal completed, errors
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async with sem:
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r, elapsed, err = await run_one(
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info["entry"], info["symbol"], info["tf"],
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info["entry"], info["symbol"], info["tf"], info["pair_type"],
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info["period_label"], info["start"], info["end"],
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)
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completed += 1
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@@ -924,12 +929,13 @@ async def main():
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else:
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m = r.metrics
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status = f"✓ {m.annual_return_pct:+.1f}%/yr"
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print(f" [{completed}/{total}] {info['strat_name']} {info['symbol']} {info['tf']} {info['period_label']} ({elapsed:.1f}s) {status}", flush=True)
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print(f" [{completed}/{total}] {info['strat_name']} {info['symbol']} {info['tf']} {info['pair_type']} {info['period_label']} ({elapsed:.1f}s) {status}", flush=True)
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row = {
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"策略名": info["strat_name"],
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"币种": info["symbol"],
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"时间级别": info["tf"],
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"类型": info["pair_type"],
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"数据量": info["period_label"],
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"策略类型": info["entry"]["strategy_cls"].strategy_type if r else "",
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"策略参数": STRATEGY_PARAMS_STR.get(info["strat_name"], ""),
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@@ -986,7 +992,7 @@ async def main():
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# ── 打印完整表格 ──
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print()
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print("═" * 195)
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print(" 2h / 6h 全维度策略对比回测结果（9策略 × 4币种 × 2时间 × 4数据量）")
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print(" 1h-1d 全维度策略对比回测结果（9策略 × 4币种 × 6时间 × 2类型 × 4数据量）")
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print("═" * 195)
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print()
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@@ -997,35 +1003,36 @@ async def main():
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first = strat_results[0]
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print(f"■ {strat_name} | 类型: {first['策略类型']} | {first['策略描述']}")
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print(f" 参数: {first['策略参数']}")
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print(f" {'币种':<10} {'时间':<5} {'数据量':<6} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>7} {'胜率%':>7} {'盈亏比':>7} {'交易':>6} {'日期范围':<24}")
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print(" " + "─" * 185)
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print(f" {'币种':<10} {'时间':<5} {'类型':<5} {'数据量':<6} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>7} {'胜率%':>7} {'盈亏比':>7} {'交易':>6} {'日期范围':<24}")
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print(" " + "─" * 195)
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strat_results.sort(key=lambda x: (SYMBOLS.index(x["币种"]), TIMEFRAMES.index(x["时间级别"]), list(PERIODS.keys()).index(x["数据量"])))
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strat_results.sort(key=lambda x: (SYMBOLS.index(x["币种"]), TIMEFRAMES.index(x["时间级别"]), PAIR_TYPES.index(x["类型"]), list(PERIODS.keys()).index(x["数据量"])))
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for r in strat_results:
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print(f" {r['币种']:<10} {r['时间级别']:<5} {r['数据量']:<6} {r['总收益%']:>7.1f}% {r['年化收益%']:>7.1f}% {r['夏普比率']:>7.2f} {r['最大回撤%']:>7.1f}% {r['胜率%']:>6.1f}% {r['盈亏比']:>7.2f} {r['交易次数']:>6} {r['日期范围']:<24}")
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print(f" {r['币种']:<10} {r['时间级别']:<5} {r['类型']:<5} {r['数据量']:<6} {r['总收益%']:>7.1f}% {r['年化收益%']:>7.1f}% {r['夏普比率']:>7.2f} {r['最大回撤%']:>7.1f}% {r['胜率%']:>6.1f}% {r['盈亏比']:>7.2f} {r['交易次数']:>6} {r['日期范围']:<24}")
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print()
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# ── 终极汇总 ──
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print("═" * 195)
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print(" ■ 终极汇总：每组(时间级别+数据量)下各币种最佳策略（按年化收益）")
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print(" ■ 终极汇总：每组(时间级别+类型+数据量)下各币种最佳策略（按年化收益）\n（1h结果可能因数据量过大导致内存/耗时问题，已设MAX_CONCURRENCY=6）")
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print("═" * 195)
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print()
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for tf in TIMEFRAMES:
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for period_label in PERIODS:
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subset = [r for r in results if r["时间级别"] == tf and r["数据量"] == period_label and r.get("总收益%", 0) != 0]
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if not subset:
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continue
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subset.sort(key=lambda x: x.get("年化收益%", -9999), reverse=True)
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for pair_type in PAIR_TYPES:
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for period_label in PERIODS:
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subset = [r for r in results if r["时间级别"] == tf and r["类型"] == pair_type and r["数据量"] == period_label and r.get("总收益%", 0) != 0]
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if not subset:
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continue
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subset.sort(key=lambda x: x.get("年化收益%", -9999), reverse=True)
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print(f" ▲ {tf} | {period_label}")
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print(f" {'排名':<5} {'策略名':<22} {'币种':<10} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>7} {'胜率%':>7} {'盈亏比':>7} {'交易':>6}")
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print(" " + "─" * 130)
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for i, r in enumerate(subset[:5]):
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marker = ["🥇", "🥈", "🥉", " 4", " 5"][i]
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print(f" {marker:<5} {r['策略名']:<22} {r['币种']:<10} {r['总收益%']:>7.1f}% {r['年化收益%']:>7.1f}% {r['夏普比率']:>7.2f} {r['最大回撤%']:>7.1f}% {r['胜率%']:>6.1f}% {r['盈亏比']:>7.2f} {r['交易次数']:>6}")
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print()
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print(f" ▲ {tf} | {pair_type} | {period_label}")
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print(f" {'排名':<5} {'策略名':<22} {'币种':<10} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>7} {'胜率%':>7} {'盈亏比':>7} {'交易':>6}")
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print(" " + "─" * 135)
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for i, r in enumerate(subset[:5]):
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marker = ["🥇", "🥈", "🥉", " 4", " 5"][i]
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print(f" {marker:<5} {r['策略名']:<22} {r['币种']:<10} {r['总收益%']:>7.1f}% {r['年化收益%']:>7.1f}% {r['夏普比率']:>7.2f} {r['最大回撤%']:>7.1f}% {r['胜率%']:>6.1f}% {r['盈亏比']:>7.2f} {r['交易次数']:>6}")
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print()
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# ── 保存 JSON ──
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output_file = _project_root / "engine" / "example" / "comparison_2h_6h_result.json"
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@@ -1034,6 +1041,7 @@ async def main():
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"config": {
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"symbols": SYMBOLS,
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"timeframes": TIMEFRAMES,
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"pair_types": PAIR_TYPES,
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"periods": list(PERIODS.keys()),
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"initial_capital": INITIAL,
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"warmup_bars": WARMUP,
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