From edc50e880920dd8ce5701052c8a53bb9a86f4483 Mon Sep 17 00:00:00 2001 From: Rekey Date: Sat, 13 Jun 2026 19:30:25 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=96=B0=E5=A2=9E2h/6h=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E6=A1=86=E6=9E=B6=E6=94=AF=E6=8C=81=EF=BC=8C=E7=AD=96?= =?UTF-8?q?=E7=95=A5=E9=87=8D=E6=9E=84=E4=B8=BA=E5=A2=9E=E9=87=8F=E6=8C=87?= =?UTF-8?q?=E6=A0=87=E8=AE=A1=E7=AE=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 数据层: build_aggregates_sql 新增 2h/6h 聚合视图，默认起始时间调整为 2017-05 - 模型层: KlineInterval 类型扩展 2h/6h，DataService 新增对应表名和毫秒映射 - 指标层: 新增 incremental.py 增量指标模块 (EmaInc/AtrInc/RsiInc/BbInc)，O(1) per bar - 策略重构: long_short.py 和 regime_all.py 从批量 ema/atr 迁移至增量指标，避免每 bar 重复全量计算 - regime 探测器: RegimeDetector3 改为增量 EMA200，detect() 接口简化 - 回测扩展: regime_timeframe_comparison 从 4h/1d 扩展至 2h/4h/6h/1d - 新增示例: multi_strategy_report, vol_break_compare/periods, intraday_explore, top3_trades 等分析脚本 --- data/run/build_aggregates_sql.ts | 4 +- engine/backtest/TIMEFRAME_COMPARISON_2H_6H.md | 136 + engine/common/models.py | 2 +- engine/data/service.py | 4 + engine/example/analyze_comparison.py | 226 + engine/example/full_comparison.py | 974 + engine/example/full_comparison_result.json | 14623 + engine/example/intraday_explore.py | 563 + engine/example/long_short.py | 21 +- engine/example/multi_strategy_report.py | 393 + engine/example/regime_all.py | 58 +- engine/example/regime_intraday.py | 122 + engine/example/regime_timeframe_comparison.py | 6 +- engine/example/top3_trades.py | 96 + engine/example/vol_break_compare.py | 189 + engine/example/vol_break_periods.py | 212 + engine/example/web_strategies.py | 701 + engine/example/web_strategies_result.json | 466001 +++++++++++++++ engine/indicators/__init__.py | 3 + engine/indicators/incremental.py | 244 + 20 files changed, 484544 insertions(+), 34 deletions(-) create mode 100644 engine/backtest/TIMEFRAME_COMPARISON_2H_6H.md create mode 100644 engine/example/analyze_comparison.py create mode 100644 engine/example/full_comparison.py create mode 100644 engine/example/full_comparison_result.json create mode 100644 engine/example/intraday_explore.py create mode 100644 engine/example/multi_strategy_report.py create mode 100644 engine/example/regime_intraday.py create mode 100644 engine/example/top3_trades.py create mode 100644 engine/example/vol_break_compare.py create mode 100644 engine/example/vol_break_periods.py create mode 100644 engine/example/web_strategies.py create mode 100644 engine/example/web_strategies_result.json create mode 100644 engine/indicators/incremental.py diff --git a/data/run/build_aggregates_sql.ts b/data/run/build_aggregates_sql.ts index f946a45..fb806c6 100644 --- a/data/run/build_aggregates_sql.ts +++ b/data/run/build_aggregates_sql.ts @@ -52,13 +52,15 @@ const AGGREGATE_VIEWS = [ "klines_15m", "klines_30m", "klines_1h", + "klines_2h", "klines_4h", + "klines_6h", "klines_1d", "klines_1w", ] as const; /** 默认起始年月 */ -const DEFAULT_START = { year: 2017, month: 8 }; // 2018-09 +const DEFAULT_START = { year: 2017, month: 5 }; // 2018-09 /** 默认结束年月 */ const DEFAULT_END = { year: 2026, month: 6 }; // 2019-01 diff --git a/engine/backtest/TIMEFRAME_COMPARISON_2H_6H.md b/engine/backtest/TIMEFRAME_COMPARISON_2H_6H.md new file mode 100644 index 0000000..b30b95c --- /dev/null +++ b/engine/backtest/TIMEFRAME_COMPARISON_2H_6H.md @@ -0,0 +1,136 @@ +# 牛熊自适应策略 — 多时间级别回测对比 + +> 生成时间：2026-06-12 12:09 + +## 一、全量数据（所有可用历史） + +### 2h 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | 数据范围 | +|------|------|------|--------|------|------|------|------|------|------|---------|---------|---------| +| BTCUSDT | 2h | 全量 | +166.4% | +11.8% | 0.50 | -66.9% | 374 | 34.0% | 1.21 | +22365 | +5117 | 2017-08-17~2026-06-11 | +| ETHUSDT | 2h | 全量 | -2.9% | -0.3% | 0.16 | -71.6% | 329 | 32.2% | 1.06 | +19399 | -14483 | 2017-08-17~2026-06-11 | +| BNBUSDT | 2h | 全量 | +343.0% | +19.0% | 0.61 | -65.3% | 267 | 32.6% | 1.27 | +39949 | +5623 | 2017-11-06~2026-06-11 | +| SOLUSDT | 2h | 全量 | -68.1% | -17.9% | -0.40 | -74.0% | 163 | 31.3% | 0.79 | -246 | -5504 | 2020-08-11~2026-06-11 | + +### 4h 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | 数据范围 | +|------|------|------|--------|------|------|------|------|------|------|---------|---------|---------| +| BTCUSDT | 4h | 全量 | +493.5% | +22.7% | 0.80 | -34.1% | 208 | 36.5% | 1.53 | +41513 | +14936 | 2017-08-17~2026-06-11 | +| ETHUSDT | 4h | 全量 | +831.8% | +29.2% | 0.88 | -48.9% | 162 | 38.9% | 1.57 | +60985 | +31285 | 2017-08-17~2026-06-11 | +| BNBUSDT | 4h | 全量 | +3282.2% | +51.4% | 1.15 | -38.0% | 150 | 36.7% | 1.60 | +403316 | -40234 | 2017-11-06~2026-06-11 | +| SOLUSDT | 4h | 全量 | +34.9% | +5.4% | 0.32 | -59.6% | 78 | 33.3% | 1.13 | +7744 | -3164 | 2020-08-11~2026-06-11 | + +### 6h 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | 数据范围 | +|------|------|------|--------|------|------|------|------|------|------|---------|---------|---------| +| BTCUSDT | 6h | 全量 | +317.6% | +18.0% | 0.67 | -43.3% | 122 | 35.2% | 1.37 | +50431 | -14466 | 2017-08-17~2026-06-11 | +| ETHUSDT | 6h | 全量 | +231.4% | +14.9% | 0.58 | -54.9% | 98 | 38.8% | 1.33 | +19350 | +7040 | 2017-08-17~2026-06-11 | +| BNBUSDT | 6h | 全量 | +495.5% | +23.6% | 0.78 | -41.8% | 96 | 34.4% | 1.60 | +53465 | +212 | 2017-11-06~2026-06-11 | +| SOLUSDT | 6h | 全量 | +107.0% | +13.7% | 0.51 | -47.6% | 55 | 36.4% | 1.35 | +8969 | +2611 | 2020-08-11~2026-06-11 | + +### 1d 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | 数据范围 | +|------|------|------|--------|------|------|------|------|------|------|---------|---------|---------| +| BTCUSDT | 1d | 全量 | +689.4% | +28.9% | 1.07 | -30.5% | 28 | 46.4% | 2.88 | +40825 | +28909 | 2017-08-17~2026-06-11 | +| ETHUSDT | 1d | 全量 | +216.7% | +15.2% | 0.69 | -39.2% | 20 | 40.0% | 2.64 | +4360 | +17454 | 2017-08-17~2026-06-11 | +| BNBUSDT | 1d | 全量 | +63.5% | +6.4% | 0.36 | -40.7% | 18 | 38.9% | 1.53 | +3220 | +3247 | 2017-11-06~2026-06-11 | +| SOLUSDT | 1d | 全量 | +247.1% | +27.3% | 0.90 | -60.5% | 15 | 33.3% | 1.64 | +9206 | +15504 | 2020-08-11~2026-06-11 | + +## 二、近两年（2024.06 — 2026.06） + +### 2h 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | +|------|------|------|--------|------|------|------|------|------|------|---------|---------| +| BTCUSDT | 2h | 近2年 | +34.5% | +16.2% | 0.81 | -20.3% | 80 | 36.2% | 1.50 | +4390 | +76 | +| ETHUSDT | 2h | 近2年 | -50.2% | -29.8% | -1.17 | -53.4% | 82 | 22.0% | 0.57 | +1094 | -5591 | +| BNBUSDT | 2h | 近2年 | +1.1% | +0.5% | 0.12 | -24.6% | 56 | 28.6% | 1.10 | -1680 | +2341 | +| SOLUSDT | 2h | 近2年 | -23.1% | -12.4% | -0.52 | -34.1% | 49 | 26.5% | 0.78 | +2474 | -4307 | + +### 4h 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | +|------|------|------|--------|------|------|------|------|------|------|---------|---------| +| BTCUSDT | 4h | 近2年 | +28.5% | +14.0% | 0.70 | -17.6% | 49 | 40.8% | 1.50 | +3072 | +391 | +| ETHUSDT | 4h | 近2年 | +13.1% | +6.7% | 0.38 | -20.9% | 38 | 42.1% | 1.26 | +1860 | -119 | +| BNBUSDT | 4h | 近2年 | -4.2% | -2.2% | -0.00 | -25.6% | 35 | 31.4% | 0.98 | +675 | -760 | +| SOLUSDT | 4h | 近2年 | +0.8% | +0.4% | 0.20 | -39.7% | 21 | 23.8% | 1.04 | +4083 | -3750 | + +### 6h 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | +|------|------|------|--------|------|------|------|------|------|------|---------|---------| +| BTCUSDT | 6h | 近2年 | +6.3% | +3.3% | 0.26 | -30.4% | 25 | 36.0% | 1.16 | +3304 | -2368 | +| ETHUSDT | 6h | 近2年 | -14.1% | -7.9% | -0.25 | -26.9% | 23 | 34.8% | 0.74 | -608 | -577 | +| BNBUSDT | 6h | 近2年 | +11.0% | +5.8% | 0.37 | -21.3% | 23 | 30.4% | 1.34 | +2925 | -1544 | +| SOLUSDT | 6h | 近2年 | +16.1% | +8.4% | 0.45 | -19.9% | 13 | 46.2% | 1.44 | +641 | +1134 | + +### 1d 周期 + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | 多头P&L | 空头P&L | +|------|------|------|--------|------|------|------|------|------|------|---------|---------| +| BTCUSDT | 1d | 近2年 | +42.8% | +30.4% | 1.40 | -15.4% | 5 | 60.0% | 5.38 | +148 | +4327 | +| ETHUSDT | 1d | 近2年 | +57.4% | +40.2% | 1.76 | -7.6% | 2 | 100.0% | 5903.01 | +0 | +5903 | +| BNBUSDT | 1d | 近2年 | -8.0% | -6.0% | -1.04 | -9.6% | 3 | 33.3% | 0.27 | -928 | +251 | +| SOLUSDT | 1d | 近2年 | +22.0% | +16.0% | 1.08 | -12.0% | 2 | 50.0% | 9.85 | +0 | +2403 | + +--- + +## 三、全维度汇总 + +| 币种 | 周期 | 范围 | 总收益% | 夏普 | 回撤% | 交易 | 胜率% | 盈亏比 | +|------|------|------|--------|------|------|------|------|------| +| BNBUSDT | 1d | 全量 | +63.5% | 0.36 | -40.7% | 18 | 38.9% | 1.53 | +| BNBUSDT | 2h | 全量 | +343.0% | 0.61 | -65.3% | 267 | 32.6% | 1.27 | +| BNBUSDT | 4h | 全量 | +3282.2% | 1.15 | -38.0% | 150 | 36.7% | 1.60 | +| BNBUSDT | 6h | 全量 | +495.5% | 0.78 | -41.8% | 96 | 34.4% | 1.60 | +| BNBUSDT | 1d | 近2年 | -8.0% | -1.04 | -9.6% | 3 | 33.3% | 0.27 | +| BNBUSDT | 2h | 近2年 | +1.1% | 0.12 | -24.6% | 56 | 28.6% | 1.10 | +| BNBUSDT | 4h | 近2年 | -4.2% | -0.00 | -25.6% | 35 | 31.4% | 0.98 | +| BNBUSDT | 6h | 近2年 | +11.0% | 0.37 | -21.3% | 23 | 30.4% | 1.34 | +| BTCUSDT | 1d | 全量 | +689.4% | 1.07 | -30.5% | 28 | 46.4% | 2.88 | +| BTCUSDT | 2h | 全量 | +166.4% | 0.50 | -66.9% | 374 | 34.0% | 1.21 | +| BTCUSDT | 4h | 全量 | +493.5% | 0.80 | -34.1% | 208 | 36.5% | 1.53 | +| BTCUSDT | 6h | 全量 | +317.6% | 0.67 | -43.3% | 122 | 35.2% | 1.37 | +| BTCUSDT | 1d | 近2年 | +42.8% | 1.40 | -15.4% | 5 | 60.0% | 5.38 | +| BTCUSDT | 2h | 近2年 | +34.5% | 0.81 | -20.3% | 80 | 36.2% | 1.50 | +| BTCUSDT | 4h | 近2年 | +28.5% | 0.70 | -17.6% | 49 | 40.8% | 1.50 | +| BTCUSDT | 6h | 近2年 | +6.3% | 0.26 | -30.4% | 25 | 36.0% | 1.16 | +| ETHUSDT | 1d | 全量 | +216.7% | 0.69 | -39.2% | 20 | 40.0% | 2.64 | +| ETHUSDT | 2h | 全量 | -2.9% | 0.16 | -71.6% | 329 | 32.2% | 1.06 | +| ETHUSDT | 4h | 全量 | +831.8% | 0.88 | -48.9% | 162 | 38.9% | 1.57 | +| ETHUSDT | 6h | 全量 | +231.4% | 0.58 | -54.9% | 98 | 38.8% | 1.33 | +| ETHUSDT | 1d | 近2年 | +57.4% | 1.76 | -7.6% | 2 | 100.0% | 5903.01 | +| ETHUSDT | 2h | 近2年 | -50.2% | -1.17 | -53.4% | 82 | 22.0% | 0.57 | +| ETHUSDT | 4h | 近2年 | +13.1% | 0.38 | -20.9% | 38 | 42.1% | 1.26 | +| ETHUSDT | 6h | 近2年 | -14.1% | -0.25 | -26.9% | 23 | 34.8% | 0.74 | +| SOLUSDT | 1d | 全量 | +247.1% | 0.90 | -60.5% | 15 | 33.3% | 1.64 | +| SOLUSDT | 2h | 全量 | -68.1% | -0.40 | -74.0% | 163 | 31.3% | 0.79 | +| SOLUSDT | 4h | 全量 | +34.9% | 0.32 | -59.6% | 78 | 33.3% | 1.13 | +| SOLUSDT | 6h | 全量 | +107.0% | 0.51 | -47.6% | 55 | 36.4% | 1.35 | +| SOLUSDT | 1d | 近2年 | +22.0% | 1.08 | -12.0% | 2 | 50.0% | 9.85 | +| SOLUSDT | 2h | 近2年 | -23.1% | -0.52 | -34.1% | 49 | 26.5% | 0.78 | +| SOLUSDT | 4h | 近2年 | +0.8% | 0.20 | -39.7% | 21 | 23.8% | 1.04 | +| SOLUSDT | 6h | 近2年 | +16.1% | 0.45 | -19.9% | 13 | 46.2% | 1.44 | + +## 四、各币种最佳组合（按夏普排序） + +| 币种 | 周期 | 范围 | 总收益% | 年化% | 夏普 | 回撤% | 交易 | 胜率% | +|------|------|------|--------|------|------|------|------|------| +| BTCUSDT | **1d** | 近2年 | +42.8% | +30.4% | 1.40 | -15.4% | 5 | 60.0% | +| ETHUSDT | **1d** | 近2年 | +57.4% | +40.2% | 1.76 | -7.6% | 2 | 100.0% | +| BNBUSDT | **4h** | 全量 | +3282.2% | +51.4% | 1.15 | -38.0% | 150 | 36.7% | +| SOLUSDT | **1d** | 近2年 | +22.0% | +16.0% | 1.08 | -12.0% | 2 | 50.0% | + +--- + +## 五、结论 + +- **4h vs 1d**：低波动大市值币种(BTC)偏向日线(1d)，高波动币种(ETH/BNB)偏向4h +- **全量 vs 近两年**：近两年市场环境与长周期统计可能有显著差异，需结合当前市场结构选择周期 +- **交易频率**：1d 交易数约为 4h 的 1/5 ~ 1/10，适合低频策略 + diff --git a/engine/common/models.py b/engine/common/models.py index f5640fb..b52bf77 100644 --- a/engine/common/models.py +++ b/engine/common/models.py @@ -14,7 +14,7 @@ from pydantic import BaseModel, Field, field_validator # K 线周期类型 # ============================================================ -KlineInterval = Literal["1m", "5m", "15m", "30m", "1h", "4h", "1d", "1w"] +KlineInterval = Literal["1m", "5m", "15m", "30m", "1h", "2h", "4h", "6h", "1d", "1w"] # ============================================================ diff --git a/engine/data/service.py b/engine/data/service.py index 63dda8f..b965e52 100644 --- a/engine/data/service.py +++ b/engine/data/service.py @@ -36,7 +36,9 @@ INTERVAL_TO_TABLE: dict[KlineInterval, str] = { "15m": "klines_15m", "30m": "klines_30m", "1h": "klines_1h", + "2h": "klines_2h", "4h": "klines_4h", + "6h": "klines_6h", "1d": "klines_1d", "1w": "klines_1w", } @@ -48,7 +50,9 @@ INTERVAL_MS: dict[KlineInterval, int] = { "15m": 900_000, "30m": 1_800_000, "1h": 3_600_000, + "2h": 7_200_000, "4h": 14_400_000, + "6h": 21_600_000, "1d": 86_400_000, "1w": 604_800_000, } diff --git a/engine/example/analyze_comparison.py b/engine/example/analyze_comparison.py new file mode 100644 index 0000000..985cd59 --- /dev/null +++ b/engine/example/analyze_comparison.py @@ -0,0 +1,226 @@ +""" +读取 full_comparison_result.json 并生成多维度排序对比表 + +用法： + source .venv/bin/activate && python example/analyze_comparison.py +""" + +import json +import sys +from pathlib import Path + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +JSON_PATH = _project_root / "engine" / "example" / "full_comparison_result.json" + +with open(JSON_PATH, "r", encoding="utf-8") as f: + data = json.load(f) + +results = data["results"] +cfg = data["config"] + +# ── 通用排序列 ── +METRICS = [ + "总收益%", "年化收益%", "夏普比率", "最大回撤%", "胜率%", + "盈亏比", "交易次数", "平均盈亏", "最佳盈亏", "最差盈亏", "卡尔玛比率", +] + +# 哪些指标越大越好（正序），哪些越小越好（倒序） +DESCENDING = {"总收益%", "年化收益%", "夏普比率", "胜率%", "盈亏比", "平均盈亏", "最佳盈亏", "卡尔玛比率"} +ASCENDING = {"最大回撤%", "交易次数", "最差盈亏", "耗时s"} + + +def sort_results(items, key, descending=True): + """排序，返回 top N""" + return sorted(items, key=lambda x: x.get(key, -9999) if key in DESCENDING else -x.get(key, 9999), reverse=descending) + + +def print_table(title, rows, fields, col_widths): + """打印格式化表格""" + print() + print("═" * len(title)) + print(title) + print("═" * len(title)) + print() + + # header + header = "" + for i, f in enumerate(fields): + header += f" {f:<{col_widths[i]}}" + print(header) + + # separator + sep = " " + "─" * (sum(col_widths) + sum(c - len(str(fields[i])) for i, c in enumerate(col_widths))) + print(sep) + + for r in rows: + line = "" + for i, f in enumerate(fields): + val = r.get(f, "") + if isinstance(val, float): + if abs(val) >= 1000: + val_str = f"{val:>{col_widths[i]}.0f}" + elif abs(val) >= 100: + val_str = f"{val:>{col_widths[i]}.1f}" + elif abs(val) >= 10: + val_str = f"{val:>{col_widths[i]}.2f}" + else: + val_str = f"{val:>{col_widths[i]}.3f}" + else: + val_str = f"{str(val):<{col_widths[i]}}" + line += f" {val_str}" + print(line) + print() + + +# ════════════════════════════════════════════════════════ +# 表1：全局排名 — 按年化收益 Top 30 +# ════════════════════════════════════════════════════════ +top_annual = sort_results(results, "年化收益%", True)[:30] +print_table( + f" 全局排名 — 按年化收益 Top 30（共{len(results)}组） | 本金 {cfg['initial_capital']:,.0f} USDT", + top_annual, + fields=["策略名", "币种", "时间级别", "数据量", "总收益%", "年化收益%", "夏普比率", "最大回撤%", "胜率%", "盈亏比", "交易次数", "日期范围"], + col_widths=[22, 10, 8, 8, 9, 9, 8, 8, 7, 7, 6, 24], +) + +# ════════════════════════════════════════════════════════ +# 表2：按夏普比率 Top 30 +# ════════════════════════════════════════════════════════ +top_sharpe = sort_results(results, "夏普比率", True)[:30] +print_table( + " 全局排名 — 按夏普比率 Top 30", + top_sharpe, + fields=["策略名", "币种", "时间级别", "数据量", "年化收益%", "夏普比率", "最大回撤%", "胜率%", "盈亏比", "交易次数", "日期范围"], + col_widths=[22, 10, 8, 8, 9, 8, 8, 7, 7, 6, 24], +) + +# ════════════════════════════════════════════════════════ +# 表3：近半年+近一年（近期真实表现）按年化 Top 30 +# ════════════════════════════════════════════════════════ +recent = [r for r in results if r["数据量"] in ("近半年", "近一年")] +recent_top = sort_results(recent, "年化收益%", True)[:30] +print_table( + " 近期现实表现 — 近半年+近一年 — 按年化收益 Top 30", + recent_top, + fields=["策略名", "币种", "时间级别", "数据量", "总收益%", "年化收益%", "夏普比率", "最大回撤%", "胜率%", "盈亏比", "交易次数", "日期范围"], + col_widths=[22, 10, 8, 8, 9, 9, 8, 8, 7, 7, 6, 24], +) + +# ════════════════════════════════════════════════════════ +# 表4：全量数据（历史长期）按年化 Top 20 +# ════════════════════════════════════════════════════════ +full_data = [r for r in results if r["数据量"] == "全量"] +full_top = sort_results(full_data, "年化收益%", True)[:20] +print_table( + " 全量数据 — 按年化收益 Top 20", + full_top, + fields=["策略名", "币种", "时间级别", "总收益%", "年化收益%", "夏普比率", "最大回撤%", "胜率%", "盈亏比", "交易次数", "日期范围"], + col_widths=[22, 10, 8, 9, 9, 8, 8, 7, 7, 6, 24], +) + +# ════════════════════════════════════════════════════════ +# 表5：各策略在4h+1d上的近一年表现（最实用的中长线） +# ════════════════════════════════════════════════════════ +mid_long = [r for r in recent if r["时间级别"] in ("4h", "1d")] +mid_sorted = sort_results(mid_long, "年化收益%", True)[:30] +print_table( + " 中长线（4h/1d）近期表现 — 按年化收益 Top 30", + mid_sorted, + fields=["策略名", "币种", "时间级别", "数据量", "总收益%", "年化收益%", "夏普比率", "最大回撤%", "胜率%", "盈亏比", "交易次数", "日期范围"], + col_widths=[22, 10, 8, 8, 9, 9, 8, 8, 7, 7, 6, 24], +) + +# ════════════════════════════════════════════════════════ +# 表6：策略×币种×时间级别 盈利能力矩阵（近一年年化） +# ════════════════════════════════════════════════════════ +one_year = [r for r in results if r["数据量"] == "近一年"] + +print() +print("═" * 120) +print(" 近一年盈利能力矩阵 — 策略 × 币种 × 时间级别（年化收益%）") +print("═" * 120) + +for tf in cfg["timeframes"]: + tf_data = [r for r in one_year if r["时间级别"] == tf] + if not tf_data: + continue + print(f"\n ▲ 时间级别: {tf}") + print(f" {'策略名':<24}", end="") + for sym in cfg["symbols"]: + print(f" {sym:>12}", end="") + print() + print(" " + "─" * 80) + + strategies_seen = set() + for r in sort_results(tf_data, "年化收益%", True): + if r["策略名"] not in strategies_seen: + strategies_seen.add(r["策略名"]) + print(f" {r['策略名']:<24}", end="") + for sym in cfg["symbols"]: + match = [x for x in tf_data if x["策略名"] == r["策略名"] and x["币种"] == sym] + if match: + val = match[0]["年化收益%"] + color = "+" if val > 0 else "" + print(f" {color}{val:>11.1f}%", end="") + else: + print(f" {'—':>12}", end="") + print() + print() + +# ════════════════════════════════════════════════════════ +# 表7：每组(时间级别+数据量)下的最佳策略 +# ════════════════════════════════════════════════════════ +print("═" * 160) +print(" 每组(时间级别 + 数据量)下的最佳策略 — 按年化收益") +print("═" * 160) +print() + +for tf in cfg["timeframes"]: + for period in cfg["periods"]: + subset = [r for r in results if r["时间级别"] == tf and r["数据量"] == period] + if not subset: + continue + subset_sorted = sort_results(subset, "年化收益%", True) + + print(f" ▲ {tf} | {period}") + print(f" {'排名':<5} {'策略名':<24} {'币种':<10} {'总收益%':>9} {'年化%':>9} {'夏普':>7} {'回撤%':>8} {'胜率%':>7} {'盈亏比':>7} {'交易':>6} {'卡尔玛':>7} {'日期范围':<24}") + print(" " + "─" * 155) + for i, r in enumerate(subset_sorted[:8]): + rank = ["🥇1", "🥈2", "🥉3", " 4", " 5", " 6", " 7", " 8"][i] + print(f" {rank:<5} {r['策略名']:<24} {r['币种']:<10} {r['总收益%']:>8.1f}% {r['年化收益%']:>8.1f}% {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['卡尔玛比率']:>7.2f} {r['日期范围']:<24}") + print() + +# ════════════════════════════════════════════════════════ +# 表8：策略总览 — 每个策略在所有组合中的盈利比例 +# ════════════════════════════════════════════════════════ +print("═" * 120) +print(" 策略胜率统计 — 每个策略在所有回测组合中的盈利比例") +print("═" * 120) +print() + +strategy_stats = {} +for r in results: + sn = r["策略名"] + if sn not in strategy_stats: + strategy_stats[sn] = {"total": 0, "positive": 0, "positive_annual": 0, "sum_return": 0, "sum_sharpe": 0} + strategy_stats[sn]["total"] += 1 + if r["总收益%"] > 0: + strategy_stats[sn]["positive"] += 1 + if r["年化收益%"] > 0: + strategy_stats[sn]["positive_annual"] += 1 + strategy_stats[sn]["sum_return"] += r["年化收益%"] + strategy_stats[sn]["sum_sharpe"] += r["夏普比率"] + +print(f" {'策略名':<24} {'总回测':>6} {'总收益>0':>9} {'年化>0':>8} {'平均年化%':>10} {'平均夏普':>8}") +print(" " + "─" * 75) +for sn, st in sorted(strategy_stats.items(), key=lambda x: x[1]["positive_annual"] / x[1]["total"], reverse=True): + avg_ret = st["sum_return"] / st["total"] + avg_sharpe = st["sum_sharpe"] / st["total"] + pos_pct = st["positive_annual"] / st["total"] * 100 + print(f" {sn:<24} {st['total']:>6} {st['positive']:>8} ({st['positive']/st['total']*100:>5.1f}%) {st['positive_annual']:>7} ({pos_pct:>5.1f}%) {avg_ret:>9.1f}% {avg_sharpe:>8.2f}") + +print() +print("═" * 120) diff --git a/engine/example/full_comparison.py b/engine/example/full_comparison.py new file mode 100644 index 0000000..d8bd3b9 --- /dev/null +++ b/engine/example/full_comparison.py @@ -0,0 +1,974 @@ +""" +全维度策略对比回测 — 8策略 × 4币种 × 5时间级别 × 4数据量 + +策略均来自 Investopedia / BabyPips / TradingView 等知名交易社区，覆盖四大类： + - 趋势跟踪：海龟交易、超级趋势、三均线排列、EMA双均线多空 + - 动量：MACD金叉死叉 + - 波动率突破：布林收缩爆发、ATR波动率突破 + - 均值回归：RSI+布林带回归 + +用法： + source .venv/bin/activate && python example/full_comparison.py +""" + +import asyncio +import json +import statistics +import sys +import time +from datetime import datetime, timedelta, timezone +from pathlib import Path +from typing import Optional, Type + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.base import BaseStrategy, Signal, StrategyConfig +from engine.common.models import Kline +from engine.common.config import config +from engine.backtest.models import BacktestConfig, BacktestResult +from engine.data import DataService +from engine.indicators.incremental import EmaInc, AtrInc, RsiInc, BbInc +from engine.example.long_short import LongShortEngine + +# ── 全局常量 ── +SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] +TIMEFRAMES = ["15m", "30m", "1h", "4h", "1d"] +INITIAL = 10_000.0 +WARMUP = 150 +MAX_CONCURRENCY = 6 + +# ── 回测时间段定义 ── +NOW = datetime.now(timezone.utc) +PERIODS = { + "全量": (None, None), # 由数据库查询决定 + "近两年": (NOW - timedelta(days=730), NOW), + "近一年": (NOW - timedelta(days=365), NOW), + "近半年": (NOW - timedelta(days=182), NOW), +} + +# ── 最小数据量要求（跳过数据不足的组合）── +MIN_BARS_FOR_PERIOD = { + "全量": 500, + "近两年": 200, + "近一年": 100, + "近半年": 50, +} + + +# ════════════════════════════════════════════════════════ +# 策略 1：海龟交易 (Turtle Trading) +# Richard Dennis & William Eckhardt, 1983 +# 20日高点突破入场，10日低点突破出场，2N ATR 止损 +# ════════════════════════════════════════════════════════ + +class TurtleConfig(StrategyConfig): + entry_period: int = 20 + exit_period: int = 10 + atr_period: int = 20 + atr_stop: float = 2.0 + + +class TurtleStrategy(BaseStrategy): + strategy_type = "趋势跟踪" + strategy_desc = "Donchian 20/10通道突破 + 2N ATR止损，多空双向" + + def __init__(self, c: TurtleConfig): + super().__init__(c) + self.cfg = c + self._highs: list[float] = [] + self._lows: list[float] = [] + self._closes: list[float] = [] + self._atr = AtrInc(c.atr_period) + self._side: str = "" + self._entry_price: float = 0.0 + self._highest_since: float = 0.0 + self._lowest_since: float = float("inf") + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._highs.append(k.high) + self._lows.append(k.low) + self._closes.append(k.close) + self._atr.update(k.high, k.low, k.close) + n = len(self._closes) + min_bars = max(self.cfg.entry_period, self.cfg.atr_period) + 5 + if n < min_bars: + return None + ca = self._atr[-1] + if ca == 0: + return None + d_high = max(self._highs[-(self.cfg.entry_period + 1):-1]) + d_low = min(self._lows[-(self.cfg.entry_period + 1):-1]) + d_exit_high = max(self._highs[-(self.cfg.exit_period + 1):-1]) + d_exit_low = min(self._lows[-(self.cfg.exit_period + 1):-1]) + + if self._side == "long": + self._highest_since = max(self._highest_since, k.high) + stop = self._entry_price - self.cfg.atr_stop * ca + trail = self._highest_since - self.cfg.atr_stop * ca * 0.5 + if k.close < d_exit_low or k.close < max(stop, trail): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="海龟退出", timestamp=k.open_time, confidence=0.25) + elif self._side == "short": + self._lowest_since = min(self._lowest_since, k.low) + stop = self._entry_price + self.cfg.atr_stop * ca + trail = self._lowest_since + self.cfg.atr_stop * ca * 0.5 + if k.close > d_exit_high or k.close > min(stop, trail): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="海龟退出", timestamp=k.open_time, confidence=0.25) + else: + margin = 0.002 + if k.close > d_high * (1 + margin): + self._side = "long" + self._entry_price = k.close + self._highest_since = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="突破20日高", timestamp=k.open_time, confidence=0.25) + elif k.close < d_low * (1 - margin): + self._side = "short" + self._entry_price = k.close + self._lowest_since = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="跌破20日低", timestamp=k.open_time, confidence=0.25) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 2：超级趋势 (SuperTrend) +# Olivier Seban，广泛用于加密货币和商品 +# ATR(10)×3 动态跟踪止损，趋势翻转即反转 +# ════════════════════════════════════════════════════════ + +class SuperTrendConfig(StrategyConfig): + atr_period: int = 10 + multiplier: float = 3.0 + + +class SuperTrendStrategy(BaseStrategy): + strategy_type = "趋势跟踪" + strategy_desc = "ATR(10)×3倍动态跟踪止损带，趋势翻转即反转" + + def __init__(self, c: SuperTrendConfig): + super().__init__(c) + self.cfg = c + self._atr = AtrInc(c.atr_period) + self._highs: list[float] = [] + self._lows: list[float] = [] + self._closes: list[float] = [] + self._trend: int = 0 + self._final_upper: float = 0.0 + self._final_lower: float = 0.0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._highs.append(k.high) + self._lows.append(k.low) + self._closes.append(k.close) + self._atr.update(k.high, k.low, k.close) + n = len(self._closes) + if n < self.cfg.atr_period + 5: + return None + ca = self._atr[-1] + if ca == 0: + return None + hl2 = (k.high + k.low) / 2.0 + upper = hl2 + self.cfg.multiplier * ca + lower = hl2 - self.cfg.multiplier * ca + prev_upper = self._final_upper + prev_lower = self._final_lower + prev_trend = self._trend + + if k.close > prev_upper and prev_upper > 0: + self._trend = 1 + elif k.close < prev_lower and prev_lower > 0: + self._trend = -1 + + if self._trend == 1: + self._final_lower = max(lower, prev_lower) if prev_lower > 0 else lower + self._final_upper = float("inf") + elif self._trend == -1: + self._final_upper = min(upper, prev_upper) if prev_upper > 0 else upper + self._final_lower = float("-inf") + else: + self._final_upper = upper + self._final_lower = lower + + if prev_trend == self._trend: + return None + if self._trend == 1: + return Signal(symbol=self.cfg.symbol, side="BUY", reason="SuperTrend转多", timestamp=k.open_time, confidence=0.25) + elif self._trend == -1: + return Signal(symbol=self.cfg.symbol, side="SELL", reason="SuperTrend转空", timestamp=k.open_time, confidence=0.25) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 3：MACD 金叉死叉 +# Gerald Appel, 1970s +# MACD(12,26,9) 零轴附近金叉/死叉 + ATR 止损 +# ════════════════════════════════════════════════════════ + +class MacdCrossConfig(StrategyConfig): + fast: int = 12 + slow: int = 26 + signal: int = 9 + atr_period: int = 14 + atr_stop: float = 2.0 + + +class MacdCrossStrategy(BaseStrategy): + strategy_type = "动量" + strategy_desc = "MACD(12,26,9)零轴上金叉做多/零轴下死叉做空+ATR止损" + + def __init__(self, c: MacdCrossConfig): + super().__init__(c) + self.cfg = c + self._ema_fast = EmaInc(c.fast) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(c.atr_period) + self._macd_vals: list[float] = [] + self._signal_vals: list[float] = [] + self._side: str = "" + self._entry_price: float = 0.0 + self._bars_held: int = 0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + fe = self._ema_fast.update(k.close) + se = self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + n = len(self._ema_fast) + min_bars = max(self.cfg.slow, self.cfg.signal) + 10 + if n < min_bars: + return None + macd = fe - se + self._macd_vals.append(macd) + if len(self._macd_vals) < self.cfg.signal + 2: + self._signal_vals.append(0.0) + return None + if len(self._signal_vals) < self.cfg.signal: + self._signal_vals.append(0.0) + if len(self._signal_vals) == self.cfg.signal: + self._signal_vals[-1] = sum(self._macd_vals[-self.cfg.signal:]) / self.cfg.signal + return None + k_sig = 2.0 / (self.cfg.signal + 1) + sig_val = macd * k_sig + self._signal_vals[-1] * (1 - k_sig) + self._signal_vals.append(sig_val) + if len(self._signal_vals) < 3: + return None + cur_m, cur_s = self._macd_vals[-1], self._signal_vals[-1] + prev_m, prev_s = self._macd_vals[-2], self._signal_vals[-2] + ca = self._atr[-1] + if ca == 0: + return None + golden = prev_m <= prev_s and cur_m > cur_s + death = prev_m >= prev_s and cur_m < cur_s + + if self._side == "long": + self._bars_held += 1 + stop = self._entry_price - self.cfg.atr_stop * ca + if k.close < stop or (death and self._bars_held > 3): + self._side = ""; self._bars_held = 0 + return Signal(symbol=self.cfg.symbol, side="SELL", reason="MACD退出", timestamp=k.open_time, confidence=0.25) + elif self._side == "short": + self._bars_held += 1 + stop = self._entry_price + self.cfg.atr_stop * ca + if k.close > stop or (golden and self._bars_held > 3): + self._side = ""; self._bars_held = 0 + return Signal(symbol=self.cfg.symbol, side="BUY", reason="MACD退出", timestamp=k.open_time, confidence=0.25) + else: + if golden and cur_m > 0: + self._side = "long"; self._entry_price = k.close; self._bars_held = 0 + return Signal(symbol=self.cfg.symbol, side="BUY", reason="MACD零轴上金叉", timestamp=k.open_time, confidence=0.25) + elif death and cur_m < 0: + self._side = "short"; self._entry_price = k.close; self._bars_held = 0 + return Signal(symbol=self.cfg.symbol, side="SELL", reason="MACD零轴下死叉", timestamp=k.open_time, confidence=0.25) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 4：布林收缩爆发 (Bollinger Squeeze) +# John Bollinger, 2002 +# BB在KC内部收缩→扩张突破入场 +# ════════════════════════════════════════════════════════ + +class BBSqueezeConfig(StrategyConfig): + bb_period: int = 20 + bb_std: float = 2.0 + kc_period: int = 20 + kc_mult: float = 1.5 + squeeze_lookback: int = 30 + atr_stop: float = 2.0 + + +class BBSqueezeStrategy(BaseStrategy): + strategy_type = "波动率突破" + strategy_desc = "BB收缩至KC内部后扩张爆发，顺势入场 + ATR止损" + + def __init__(self, c: BBSqueezeConfig): + super().__init__(c) + self.cfg = c + self._bb = BbInc(c.bb_period, c.bb_std) + self._ema = EmaInc(c.kc_period) + self._atr_kc = AtrInc(c.kc_period) + self._atr_stop = AtrInc(14) + self._closes: list[float] = [] + self._side: str = "" + self._entry_price: float = 0.0 + self._bb_widths: list[float] = [] + self._kc_widths: list[float] = [] + self._was_squeezed: bool = False + self._squeeze_bars: int = 0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._closes.append(k.close) + bb_u, bb_m, bb_l = self._bb.update(k.close) + typical = (k.high + k.low + k.close) / 3.0 + kc_mid = self._ema.update(typical) + self._atr_kc.update(k.high, k.low, k.close) + self._atr_stop.update(k.high, k.low, k.close) + n = len(self._closes) + min_bars = max(self.cfg.bb_period, self.cfg.kc_period, self.cfg.squeeze_lookback) + 5 + if n < min_bars: + return None + atr_kc = self._atr_kc[-1] + ca = self._atr_stop[-1] + if atr_kc == 0 or ca == 0 or bb_u == 0: + return None + kc_u = kc_mid + self.cfg.kc_mult * atr_kc + kc_l = kc_mid - self.cfg.kc_mult * atr_kc + bb_width = bb_u - bb_l + kc_width = kc_u - kc_l + self._bb_widths.append(bb_width) + self._kc_widths.append(kc_width) + is_squeezed = bb_u < kc_u and bb_l > kc_l + lookback = min(self.cfg.squeeze_lookback, len(self._bb_widths)) + recent_bb_w = self._bb_widths[-lookback:] + min_bb_w = min(recent_bb_w) + width_squeeze = bb_width < min_bb_w * 1.2 + was_squeezed = self._was_squeezed + fired = False + if is_squeezed: + self._was_squeezed = True + self._squeeze_bars += 1 + elif self._was_squeezed: + self._was_squeezed = False + self._squeeze_bars = 0 + fired = True + ema5 = sum(self._closes[-5:]) / 5.0 if n >= 5 else k.close + up_momentum = k.close > bb_m and k.close > ema5 + down_momentum = k.close < bb_m and k.close < ema5 + + if self._side == "long": + stop = self._entry_price - self.cfg.atr_stop * ca + if k.close < stop or (down_momentum and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="BB退出", timestamp=k.open_time, confidence=0.25) + elif self._side == "short": + stop = self._entry_price + self.cfg.atr_stop * ca + if k.close > stop or (up_momentum and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="BB退出", timestamp=k.open_time, confidence=0.25) + else: + if was_squeezed and fired and width_squeeze: + if up_momentum: + self._side = "long"; self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="BB收缩爆发做多", timestamp=k.open_time, confidence=0.25) + elif down_momentum: + self._side = "short"; self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="BB收缩爆发做空", timestamp=k.open_time, confidence=0.25) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 5：三均线排列 (Triple EMA) +# 经典三线开花 — EMA(10,30,60) 多头/空头排列 + ATR 追踪止损 +# ════════════════════════════════════════════════════════ + +class TripleEmaConfig(StrategyConfig): + fast: int = 10 + mid: int = 30 + slow: int = 60 + atr_period: int = 14 + atr_stop: float = 2.0 + + +class TripleEmaStrategy(BaseStrategy): + strategy_type = "趋势跟踪" + strategy_desc = "EMA(10,30,60)多头/空头排列，快线金叉入场+ATR追踪止损" + + def __init__(self, c: TripleEmaConfig): + super().__init__(c) + self.cfg = c + self._ema_fast = EmaInc(c.fast) + self._ema_mid = EmaInc(c.mid) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(c.atr_period) + self._side: str = "" + self._entry_price: float = 0.0 + self._highest_since: float = 0.0 + self._lowest_since: float = float("inf") + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._ema_fast.update(k.close) + self._ema_mid.update(k.close) + self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + n = len(self._ema_slow) + if n < self.cfg.slow + 10: + return None + ef, em, es = self._ema_fast[-1], self._ema_mid[-1], self._ema_slow[-1] + pf, pm = self._ema_fast[-2], self._ema_mid[-2] + ca = self._atr[-1] + if ef == 0 or em == 0 or es == 0 or ca == 0: + return None + bull_align = ef > em > es + bear_align = ef < em < es + fast_cross_mid_up = pf <= pm and ef > em + fast_cross_mid_down = pf >= pm and ef < em + + if self._side == "long": + self._highest_since = max(self._highest_since, k.high) + trail = self._highest_since - self.cfg.atr_stop * ca + if fast_cross_mid_down or k.close < trail: + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="三均线退出", timestamp=k.open_time, confidence=0.25) + elif self._side == "short": + self._lowest_since = min(self._lowest_since, k.low) + trail = self._lowest_since + self.cfg.atr_stop * ca + if fast_cross_mid_up or k.close > trail: + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="三均线退出", timestamp=k.open_time, confidence=0.25) + else: + if fast_cross_mid_up and bull_align: + self._side = "long"; self._entry_price = k.close; self._highest_since = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="三线多头排列", timestamp=k.open_time, confidence=0.25) + elif fast_cross_mid_down and bear_align: + self._side = "short"; self._entry_price = k.close; self._lowest_since = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="三线空头排列", timestamp=k.open_time, confidence=0.25) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 6：RSI均值回归 (RSI Mean Reversion) +# 经典指标 — RSI(14)超买超卖 + 布林带确认 → 逆向交易 +# ════════════════════════════════════════════════════════ + +class MeanRevConfig(StrategyConfig): + rsi_period: int = 14 + rsi_oversold: float = 25.0 + rsi_overbought: float = 75.0 + bb_period: int = 20 + bb_std: float = 2.0 + atr_stop: float = 1.5 + require_bb_touch: bool = True + + +class MeanRevStrategy(BaseStrategy): + strategy_type = "均值回归" + strategy_desc = "RSI(14)超卖25/超买75 + 布林带触碰确认 → 逆向回归" + + def __init__(self, c: MeanRevConfig): + super().__init__(c) + self.cfg = c + self._rsi = RsiInc(c.rsi_period) + self._bb = BbInc(c.bb_period, c.bb_std) + self._atr = AtrInc(14) + self._side: str = "" + self._entry_price: float = 0.0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + r = self._rsi.update(k.close) + up, mid, lo = self._bb.update(k.close) + atr_v = self._atr.update(k.high, k.low, k.close) + if r == 0 or up == 0 or atr_v == 0: + return None + below_bb = k.close < lo if self.cfg.require_bb_touch else True + above_bb = k.close > up if self.cfg.require_bb_touch else True + + if self._side == "long": + stop = self._entry_price - self.cfg.atr_stop * atr_v + take = self._entry_price + self.cfg.atr_stop * atr_v * 1.5 + if k.close <= stop or k.close >= take or r > 55: + self._side = "" + reason = "止损" if k.close <= stop else ("止盈" if k.close >= take else "RSI回归中轨") + return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time) + elif self._side == "short": + stop = self._entry_price + self.cfg.atr_stop * atr_v + take = self._entry_price - self.cfg.atr_stop * atr_v * 1.5 + if k.close >= stop or k.close <= take or r < 45: + self._side = "" + reason = "止损" if k.close >= stop else ("止盈" if k.close <= take else "RSI回归中轨") + return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time) + else: + if r < self.cfg.rsi_oversold and below_bb: + self._side = "long"; self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason=f"RSI超卖{r:.0f}", timestamp=k.open_time) + elif r > self.cfg.rsi_overbought and above_bb: + self._side = "short"; self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"RSI超买{r:.0f}", timestamp=k.open_time) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 7：ATR波动率突破 (Volatility Breakout) +# 经典波动率策略 — ATR收缩至极低后扩张 → 顺势突破 +# ════════════════════════════════════════════════════════ + +class VolBreakConfig(StrategyConfig): + atr_period: int = 14 + squeeze_period: int = 20 + squeeze_ratio: float = 0.7 + atr_stop: float = 2.0 + + +class VolBreakStrategy(BaseStrategy): + strategy_type = "波动率突破" + strategy_desc = "ATR(14)收缩至极低后扩张突破 + EMA(10/30)方向确认" + + def __init__(self, c: VolBreakConfig): + super().__init__(c) + self.cfg = c + self._atr = AtrInc(c.atr_period) + self._ema_fast = EmaInc(10) + self._ema_slow = EmaInc(30) + self._closes: list[float] = [] + self._highs: list[float] = [] + self._lows: list[float] = [] + self._side: str = "" + self._entry_price: float = 0.0 + self._was_squeezed = False + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._closes.append(k.close) + self._highs.append(k.high) + self._lows.append(k.low) + self._atr.update(k.high, k.low, k.close) + self._ema_fast.update(k.close) + self._ema_slow.update(k.close) + n = len(self._closes) + if n < self.cfg.atr_period + self.cfg.squeeze_period: + return None + atr_now = self._atr[-1] + atr_prev = self._atr[-2] if n >= 2 else 0 + ca = atr_now + if ca == 0: + return None + atr_window = [self._atr[i] for i in range(max(0, n - self.cfg.squeeze_period), n) if self._atr[i] > 0] + if not atr_window: + return None + min_atr = min(atr_window) + is_squeezed = atr_now < min_atr * (1 + (1 - self.cfg.squeeze_ratio)) + atr_expanding = atr_now > atr_prev * 1.05 if atr_prev > 0 else False + cf, cs = self._ema_fast[-1], self._ema_slow[-1] + trend_up = cf > cs + + if self._side == "long": + self._was_squeezed = False + stop = self._entry_price - self.cfg.atr_stop * ca + if k.close < stop or (cf < cs and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="ATR退出", timestamp=k.open_time) + elif self._side == "short": + self._was_squeezed = False + stop = self._entry_price + self.cfg.atr_stop * ca + if k.close > stop or (cf > cs and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="ATR退出", timestamp=k.open_time) + else: + if is_squeezed: + self._was_squeezed = True + elif self._was_squeezed and atr_expanding: + self._was_squeezed = False + if trend_up: + self._side = "long"; self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="ATR扩张突破做多", timestamp=k.open_time) + else: + self._side = "short"; self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="ATR扩张突破做空", timestamp=k.open_time) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 8：EMA双均线多空 (EMA Crossover) +# 最经典的均线交叉 — 始终在场，金叉做多死叉做空 +# ════════════════════════════════════════════════════════ + +class EmaCrossConfig(StrategyConfig): + fast: int = 10 + slow: int = 50 + atr_stop: float = 2.5 + + +class EmaCrossStrategy(BaseStrategy): + strategy_type = "趋势跟踪" + strategy_desc = "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场" + + def __init__(self, c: EmaCrossConfig): + super().__init__(c) + self.cfg = c + self._ema_fast = EmaInc(c.fast) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(14) + self._closes: list[float] = [] + self._highs: list[float] = [] + self._lows: list[float] = [] + self._highest: float = 0.0 + self._lowest: float = float('inf') + self._position_side: str = "" + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._closes.append(k.close) + self._highs.append(k.high) + self._lows.append(k.low) + self._ema_fast.update(k.close) + self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + n = len(self._closes) + if n < self.cfg.slow + 5: + return None + cur_f, cur_s = self._ema_fast[-1], self._ema_slow[-1] + cur_atr = self._atr[-1] + prev_f, prev_s = self._ema_fast[-2], self._ema_slow[-2] + if cur_f == 0 or cur_s == 0 or cur_atr == 0: + return None + golden = prev_f <= prev_s and cur_f > cur_s + death = prev_f >= prev_s and cur_f < cur_s + + if self._position_side == "long": + self._highest = max(self._highest, k.high) + stop = self._highest - self.cfg.atr_stop * cur_atr + if death: + self._position_side = "short" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="EMA死叉→做空", timestamp=k.open_time) + if k.close < stop: + self._position_side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="ATR止损→空仓", timestamp=k.open_time) + elif self._position_side == "short": + self._lowest = min(self._lowest, k.low) + stop = self._lowest + self.cfg.atr_stop * cur_atr + if golden: + self._position_side = "long" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="EMA金叉→做多", timestamp=k.open_time) + if k.close > stop: + self._position_side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="ATR止损→空仓", timestamp=k.open_time) + else: + if golden: + self._position_side = "long"; self._highest = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="金叉→做多", timestamp=k.open_time) + elif death: + self._position_side = "short"; self._lowest = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="死叉→做空", timestamp=k.open_time) + return None + + +# ════════════════════════════════════════════════════════ +# 策略注册表 +# ════════════════════════════════════════════════════════ + +STRATEGY_REGISTRY = { + "1.海龟交易": { + "config_cls": TurtleConfig, + "strategy_cls": TurtleStrategy, + "make_config": lambda s: TurtleConfig(symbol=s, entry_period=20, exit_period=10, atr_period=20, atr_stop=2.0), + }, + "2.超级趋势": { + "config_cls": SuperTrendConfig, + "strategy_cls": SuperTrendStrategy, + "make_config": lambda s: SuperTrendConfig(symbol=s, atr_period=10, multiplier=3.0), + }, + "3.MACD金叉死叉": { + "config_cls": MacdCrossConfig, + "strategy_cls": MacdCrossStrategy, + "make_config": lambda s: MacdCrossConfig(symbol=s, fast=12, slow=26, signal=9, atr_period=14, atr_stop=2.0), + }, + "4.布林收缩爆发": { + "config_cls": BBSqueezeConfig, + "strategy_cls": BBSqueezeStrategy, + "make_config": lambda s: BBSqueezeConfig(symbol=s, bb_period=20, bb_std=2.0, kc_period=20, kc_mult=1.5, squeeze_lookback=30, atr_stop=2.0), + }, + "5.三均线排列": { + "config_cls": TripleEmaConfig, + "strategy_cls": TripleEmaStrategy, + "make_config": lambda s: TripleEmaConfig(symbol=s, fast=10, mid=30, slow=60, atr_period=14, atr_stop=2.0), + }, + "6.RSI均值回归": { + "config_cls": MeanRevConfig, + "strategy_cls": MeanRevStrategy, + "make_config": lambda s: MeanRevConfig(symbol=s, rsi_period=14, rsi_oversold=25, rsi_overbought=75, bb_period=20, bb_std=2.0, atr_stop=1.5), + }, + "7.ATR波动率突破": { + "config_cls": VolBreakConfig, + "strategy_cls": VolBreakStrategy, + "make_config": lambda s: VolBreakConfig(symbol=s, atr_period=14, squeeze_period=20, squeeze_ratio=0.7, atr_stop=2.0), + }, + "8.EMA双均线多空": { + "config_cls": EmaCrossConfig, + "strategy_cls": EmaCrossStrategy, + "make_config": lambda s: EmaCrossConfig(symbol=s, fast=10, slow=50, atr_stop=2.5), + }, +} + + +# ════════════════════════════════════════════════════════ +# 参数用于表格的简洁呈现 +# ════════════════════════════════════════════════════════ + +STRATEGY_PARAMS_STR = { + "1.海龟交易": "entry=20/exit=10/ATR(20)x2.0", + "2.超级趋势": "ATR(10)x3.0", + "3.MACD金叉死叉": "MACD(12,26,9)/ATR(14)x2.0", + "4.布林收缩爆发": "BB(20,2.0)/KC(20,1.5)/squeeze=30", + "5.三均线排列": "EMA(10,30,60)/ATR(14)x2.0", + "6.RSI均值回归": "RSI(14)25/75+BB(20,2.0)/ATR(14)x1.5", + "7.ATR波动率突破": "ATR(14)/squeeze=20x0.7/EMA(10,30)", + "8.EMA双均线多空": "EMA(10,50)/ATR(14)x2.5", +} + + +# ════════════════════════════════════════════════════════ +# 执行 +# ════════════════════════════════════════════════════════ + +async def run_one(entry, symbol, interval, period_label, start, end): + """执行单次回测""" + make_config = entry["make_config"] + strategy_cls = entry["strategy_cls"] + sc = make_config(symbol) + bt = BacktestConfig( + symbol=symbol, interval=interval, + start_time=start, end_time=end, + initial_capital=INITIAL, warmup_bars=WARMUP, + ) + engine = LongShortEngine(bt, db_config=config.db) + t0 = time.time() + try: + r = await engine.run(strategy_cls, sc) + elapsed = time.time() - t0 + return r, elapsed, None + except Exception as ex: + elapsed = time.time() - t0 + return None, elapsed, str(ex) + + +def safe(val, default=0): + """安全取值，避免 None""" + return default if val is None else val + + +async def main(): + # 第一步：预取所有数据范围 + ds = DataService(config.db) + await ds.connect() + + print("正在获取数据范围...") + date_ranges: dict[tuple[str, str], tuple] = {} # (symbol, interval) -> (start_dt, end_dt, bar_count_estimate) + for symbol in SYMBOLS: + for tf in TIMEFRAMES: + try: + s, e = await ds.fetch_symbol_date_range(symbol, tf) + # 粗略估计 bar 数量 + bar_ms = {"15m": 900_000, "30m": 1_800_000, "1h": 3_600_000, "4h": 14_400_000, "1d": 86_400_000} + estimated_bars = int((e - s).total_seconds() * 1000 / bar_ms[tf]) + date_ranges[(symbol, tf)] = (s, e, estimated_bars) + print(f" {symbol} {tf:<4}: {s.date()} ~ {e.date()} (约{estimated_bars:,}根)") + except Exception as ex: + print(f" {symbol} {tf:<4}: 获取失败 — {ex}") + + await ds.close() + + # 第二步：构建任务列表 (跳过数据不足的组合) + sem = asyncio.Semaphore(MAX_CONCURRENCY) + tasks_info: list[dict] = [] + + for strat_name, entry in STRATEGY_REGISTRY.items(): + for symbol in SYMBOLS: + for tf in TIMEFRAMES: + key = (symbol, tf) + if key not in date_ranges: + continue + fs, fe, est_bars = date_ranges[key] + + for period_label, (period_start, period_end) in PERIODS.items(): + actual_start = period_start or fs + actual_end = period_end or fe + if actual_start >= actual_end: + continue + + # 数据量检查 + min_bars = MIN_BARS_FOR_PERIOD.get(period_label, 50) + actual_bars = est_bars + if period_label != "全量": + actual_bars = int((actual_end - actual_start).total_seconds() * 1000 / { + "15m": 900_000, "30m": 1_800_000, "1h": 3_600_000, "4h": 14_400_000, "1d": 86_400_000 + }[tf]) + + if actual_bars < min_bars: + continue + + # 跳过日线+近半年（bar太少） + if tf == "1d" and period_label == "近半年": + continue + + tasks_info.append({ + "strat_name": strat_name, + "entry": entry, + "symbol": symbol, + "tf": tf, + "period_label": period_label, + "start": actual_start, + "end": actual_end, + }) + + total = len(tasks_info) + print(f"\n共 {total} 组回测任务 (8策略×4币种×5时间×4数据量 - 跳过数据不足和日线近半年)") + + # 第三步：并发执行 + results: list[dict] = [] + completed = 0 + errors = 0 + + async def run_one_safe(info): + nonlocal completed, errors + async with sem: + r, elapsed, err = await run_one( + info["entry"], info["symbol"], info["tf"], + info["period_label"], info["start"], info["end"], + ) + completed += 1 + if err: + errors += 1 + status = f"✗ {err[:40]}" + elif r is None: + errors += 1 + status = "✗ 无结果" + else: + m = r.metrics + status = f"✓ {m.annual_return_pct:+.1f}%/yr" + print(f" [{completed}/{total}] {info['strat_name']} {info['symbol']} {info['tf']} {info['period_label']} ({elapsed:.1f}s) {status}", flush=True) + + row = { + "策略名": info["strat_name"], + "币种": info["symbol"], + "时间级别": info["tf"], + "数据量": info["period_label"], + "策略类型": info["entry"]["strategy_cls"].strategy_type if r else "", + "策略参数": STRATEGY_PARAMS_STR.get(info["strat_name"], ""), + "策略描述": info["entry"]["strategy_cls"].strategy_desc if r else "", + "日期范围": f"{info['start'].date()}~{info['end'].date()}", + } + + if r is not None: + m = r.metrics + row.update({ + "初始资金": INITIAL, + "最终权益": round(m.final_equity, 2), + "总收益%": round(m.total_return_pct, 2), + "年化收益%": round(m.annual_return_pct, 2), + "夏普比率": round(m.sharpe_ratio, 2), + "最大回撤%": round(m.max_drawdown_pct, 2), + "胜率%": round(m.win_rate * 100, 2), + "盈亏比": round(m.profit_factor, 2), + "交易次数": m.total_trades, + "平均盈亏": round(m.avg_trade_pnl, 2), + "最佳盈亏": round(m.best_trade_pnl, 2), + "最差盈亏": round(m.worst_trade_pnl, 2), + "卡尔玛比率": round(m.calmar_ratio, 2), + "耗时s": round(elapsed, 1), + }) + else: + row.update({ + "初始资金": INITIAL, + "最终权益": 0, + "总收益%": 0, + "年化收益%": 0, + "夏普比率": 0, + "最大回撤%": 0, + "胜率%": 0, + "盈亏比": 0, + "交易次数": 0, + "平均盈亏": 0, + "最佳盈亏": 0, + "最差盈亏": 0, + "卡尔玛比率": 0, + "耗时s": round(elapsed, 1), + "错误": err or "未知错误", + }) + + results.append(row) + return row + + t_total = time.time() + await asyncio.gather(*[run_one_safe(info) for info in tasks_info]) + total_elapsed = time.time() - t_total + + print(f"\n全部完成！成功 {total - errors}/{total}，错误 {errors}，总耗时 {total_elapsed:.0f}s") + + # 第四步：打印完整表格 + print() + print("═" * 195) + print(" 全维度策略对比回测结果") + print("═" * 195) + print() + + # 按策略分组打印 + for strat_name in STRATEGY_REGISTRY: + strat_results = [r for r in results if r["策略名"] == strat_name] + if not strat_results: + continue + first = strat_results[0] + print(f"■ {strat_name} | 类型: {first['策略类型']} | {first['策略描述']}") + print(f" 参数: {first['策略参数']}") + 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}") + print(" " + "─" * 185) + + # 排序：币种、时间级别、数据量 + 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"]))) + + for r in strat_results: + 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}") + print() + + # 第五步：终极汇总 — 每种时间级别+数据量下的最佳策略 + print("═" * 195) + 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）") + print("═" * 195) + print() + + for tf in TIMEFRAMES: + for period_label in PERIODS: + 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] + if not subset: + continue + subset.sort(key=lambda x: x.get("年化收益%", -9999), reverse=True) + + print(f" ▲ {tf} | {period_label}") + 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}") + print(" " + "─" * 130) + for i, r in enumerate(subset[:5]): + marker = ["🥇", "🥈", "🥉", " 4", " 5"][i] + 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}") + print() + + # 第六步：保存 JSON + output_file = _project_root / "engine" / "example" / "full_comparison_result.json" + with open(output_file, "w", encoding="utf-8") as f: + json.dump({ + "config": { + "symbols": SYMBOLS, + "timeframes": TIMEFRAMES, + "periods": list(PERIODS.keys()), + "initial_capital": INITIAL, + "warmup_bars": WARMUP, + "total_tasks": total, + "total_errors": errors, + "elapsed_seconds": total_elapsed, + "run_time": datetime.now(timezone.utc).isoformat(), + }, + "results": results, + }, f, ensure_ascii=False, indent=2, default=str) + print(f" 详细结果已保存至: {output_file}") + print() + print("═" * 195) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/full_comparison_result.json b/engine/example/full_comparison_result.json new file mode 100644 index 0000000..4126368 --- /dev/null +++ b/engine/example/full_comparison_result.json @@ -0,0 +1,14623 @@ +{ + "config": { + "symbols": [ + "BTCUSDT", + "ETHUSDT", + "BNBUSDT", + "SOLUSDT" + ], + "timeframes": [ + "15m", + "30m", + "1h", + "4h", + "1d" + ], + "periods": [ + "全量", + "近两年", + "近一年", + "近半年" + ], + "initial_capital": 10000.0, + "warmup_bars": 150, + "total_tasks": 608, + "total_errors": 0, + "elapsed_seconds": 266.8973982334137, + "run_time": "2026-06-13T11:15:01.252938+00:00" + }, + "results": [ + { + "策略名": "1.海龟交易", + "币种": "BTCUSDT", + "时间级别": "15m", + "数据量": "近半年", + "策略类型": "趋势跟踪", + "策略参数": "entry=20/exit=10/ATR(20)x2.0", + "策略描述": "Donchian 20/10通道突破 + 2N ATR止损，多空双向", + "日期范围": "2025-12-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 6967.17, + "总收益%": -30.33, + "年化收益%": -52.31, + "夏普比率": -15.5, + "最大回撤%": -30.64, + "胜率%": 19.17, + "盈亏比": 0.32, + "交易次数": 412, + "平均盈亏": -5.26, + "最佳盈亏": 73.69, + "最差盈亏": -55.65, + "卡尔玛比率": -1.71, + "耗时s": 0.6 + }, + { + "策略名": "1.海龟交易", + "币种": "BTCUSDT", + "时间级别": "30m", + "数据量": "近两年", + "策略类型": "趋势跟踪", + "策略参数": "entry=20/exit=10/ATR(20)x2.0", + "策略描述": "Donchian 20/10通道突破 + 2N ATR止损，多空双向", + "日期范围": "2024-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 4417.37, + "总收益%": -55.83, + "年化收益%": -33.74, + "夏普比率": -8.35, + "最大回撤%": -55.84, + "胜率%": 23.75, + "盈亏比": 0.46, + "交易次数": 1040, + "平均盈亏": -3.67, + "最佳盈亏": 89.84, + "最差盈亏": -88.89, + "卡尔玛比率": -0.6, + "耗时s": 1.4 + }, + { + "策略名": "1.海龟交易", + "币种": "BTCUSDT", + "时间级别": "15m", + "数据量": "近一年", + "策略类型": "趋势跟踪", + "策略参数": "entry=20/exit=10/ATR(20)x2.0", + "策略描述": "Donchian 20/10通道突破 + 2N ATR止损，多空双向", + "日期范围": "2025-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 5699.41, + "总收益%": -43.01, + "年化收益%": -43.35, + "夏普比率": -13.03, + "最大回撤%": -43.01, + "胜率%": 21.24, + "盈亏比": 0.38, + "交易次数": 739, + "平均盈亏": -3.88, + "最佳盈亏": 68.93, + "最差盈亏": -45.52, + "卡尔玛比率": -1.01, + "耗时s": 1.4 + }, + { + "策略名": "1.海龟交易", + "币种": "BTCUSDT", + "时间级别": "15m", + "数据量": "近两年", + "策略类型": "趋势跟踪", + "策略参数": "entry=20/exit=10/ATR(20)x2.0", + "策略描述": "Donchian 20/10通道突破 + 2N ATR止损，多空双向", + "日期范围": "2024-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 2977.16, + "总收益%": -70.23, + "年化收益%": -45.61, + "夏普比率": -12.56, + "最大回撤%": -70.32, + "胜率%": 20.88, + "盈亏比": 0.39, + "交易次数": 1571, + "平均盈亏": -3.01, + "最佳盈亏": 137.03, + "最差盈亏": -42.77, + "卡尔玛比率": -0.65, + "耗时s": 2.3 + }, + { + "策略名": "1.海龟交易", + "币种": "BTCUSDT", + "时间级别": "30m", + "数据量": "近一年", + "策略类型": "趋势跟踪", + "策略参数": "entry=20/exit=10/ATR(20)x2.0", + "策略描述": "Donchian 20/10通道突破 + 2N ATR止损，多空双向", + "日期范围": "2025-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 7074.29, + "总收益%": -29.26, + "年化收益%": -29.63, + "夏普比率": -8.59, + "最大回撤%": -29.28, + "胜率%": 24.95, + "盈亏比": 0.48, + "交易次数": 481, + "平均盈亏": -3.95, + "最佳盈亏": 98.63, + "最差盈亏": -48.1, + "卡尔玛比率": -1.01, + "耗时s": 2.1 + }, + { + "策略名": "1.海龟交易", + "币种": "BTCUSDT", + "时间级别": "30m", + "数据量": "近半年", + "策略类型": "趋势跟踪", + "策略参数": "entry=20/exit=10/ATR(20)x2.0", + "策略描述": "Donchian 20/10通道突破 + 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ATR追踪止损，始终在场", + "日期范围": "2024-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 995.23, + "总收益%": -90.05, + "年化收益%": -68.73, + "夏普比率": -1.15, + "最大回撤%": -94.16, + "胜率%": 34.49, + "盈亏比": 0.85, + "交易次数": 722, + "平均盈亏": -7.52, + "最佳盈亏": 1770.87, + "最差盈亏": -2618.68, + "卡尔玛比率": -0.73, + "耗时s": 2.2 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "30m", + "数据量": "近一年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2025-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 5358.22, + "总收益%": -46.42, + "年化收益%": -46.92, + "夏普比率": -0.63, + "最大回撤%": -62.91, + "胜率%": 34.39, + "盈亏比": 0.95, + "交易次数": 346, + "平均盈亏": -4.04, + "最佳盈亏": 1619.41, + "最差盈亏": -1606.35, + "卡尔玛比率": -0.75, + "耗时s": 1.8 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "30m", + "数据量": "近半年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2025-12-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 8185.53, + "总收益%": -18.14, + "年化收益%": -33.9, + "夏普比率": -0.32, + "最大回撤%": -40.72, + "胜率%": 35.67, + "盈亏比": 1.03, + "交易次数": 171, + "平均盈亏": 2.64, + "最佳盈亏": 1520.58, + "最差盈亏": -2215.82, + "卡尔玛比率": -0.83, + "耗时s": 1.3 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "30m", + "数据量": "全量", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2020-08-11~2026-06-11", + "初始资金": 10000.0, + "最终权益": 0.13, + "总收益%": -100.0, + "年化收益%": -85.58, + "夏普比率": -1.61, + "最大回撤%": -100.0, + "胜率%": 34.34, + "盈亏比": 0.7, + "交易次数": 1977, + "平均盈亏": -4.33, + "最佳盈亏": 3144.43, + "最差盈亏": -1359.48, + "卡尔玛比率": -0.86, + "耗时s": 4.1 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "1h", + "数据量": "全量", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2020-08-11~2026-06-11", + "初始资金": 10000.0, + "最终权益": 12.02, + "总收益%": -99.88, + "年化收益%": -68.51, + "夏普比率": -0.85, + "最大回撤%": -99.92, + "胜率%": 34.23, + "盈亏比": 0.86, + "交易次数": 1043, + "平均盈亏": -7.12, + "最佳盈亏": 3786.89, + "最差盈亏": -4192.55, + "卡尔玛比率": -0.69, + "耗时s": 2.8 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "1h", + "数据量": "近两年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2024-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 1035.23, + "总收益%": -89.65, + "年化收益%": -68.26, + "夏普比率": -1.03, + "最大回撤%": -95.44, + "胜率%": 33.88, + "盈亏比": 0.9, + "交易次数": 369, + "平均盈亏": -14.56, + "最佳盈亏": 6346.87, + "最差盈亏": -3236.24, + "卡尔玛比率": -0.72, + "耗时s": 3.7 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "1h", + "数据量": "近一年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2025-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 1111.66, + "总收益%": -88.88, + "年化收益%": -89.46, + "夏普比率": -3.08, + "最大回撤%": -89.8, + "胜率%": 30.53, + "盈亏比": 0.46, + "交易次数": 190, + "平均盈亏": -41.95, + "最佳盈亏": 614.36, + "最差盈亏": -1296.45, + "卡尔玛比率": -1.0, + "耗时s": 3.5 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "1h", + "数据量": "近半年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2025-12-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 4518.76, + "总收益%": -54.81, + "年化收益%": -81.22, + "夏普比率": -2.73, + "最大回撤%": -55.15, + "胜率%": 28.42, + "盈亏比": 0.58, + "交易次数": 95, + "平均盈亏": -48.58, + "最佳盈亏": 1337.87, + "最差盈亏": -1206.03, + "卡尔玛比率": -1.47, + "耗时s": 3.1 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "15m", + "数据量": "全量", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2020-08-11~2026-06-11", + "初始资金": 10000.0, + "最终权益": 0.06, + "总收益%": -100.0, + "年化收益%": -87.22, + "夏普比率": -2.11, + "最大回撤%": -100.0, + "胜率%": 33.98, + "盈亏比": 0.8, + "交易次数": 2855, + "平均盈亏": -2.58, + "最佳盈亏": 1832.61, + "最差盈亏": -2808.16, + "卡尔玛比率": -0.87, + "耗时s": 8.8 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "4h", + "数据量": "全量", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2020-08-11~2026-06-11", + "初始资金": 10000.0, + "最终权益": 1330.36, + "总收益%": -86.7, + "年化收益%": -29.52, + "夏普比率": 0.13, + "最大回撤%": -92.93, + "胜率%": 35.08, + "盈亏比": 0.84, + "交易次数": 248, + "平均盈亏": -29.3, + "最佳盈亏": 4637.5, + "最差盈亏": -5213.24, + "卡尔玛比率": -0.32, + "耗时s": 2.5 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "4h", + "数据量": "近半年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2025-12-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 8304.27, + "总收益%": -16.96, + "年化收益%": -35.53, + "夏普比率": -0.87, + "最大回撤%": -33.76, + "胜率%": 35.0, + "盈亏比": 0.6, + "交易次数": 20, + "平均盈亏": -74.62, + "最佳盈亏": 854.64, + "最差盈亏": -735.6, + "卡尔玛比率": -1.05, + "耗时s": 0.3 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "1d", + "数据量": "全量", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2020-08-11~2026-06-11", + "初始资金": 10000.0, + "最终权益": -14160.13, + "总收益%": -241.6, + "年化收益%": 0.0, + "夏普比率": -2.68, + "最大回撤%": -346.77, + "胜率%": 0.0, + "盈亏比": 0.0, + "交易次数": 1, + "平均盈亏": -24130.14, + "最佳盈亏": -24130.14, + "最差盈亏": -24130.14, + "卡尔玛比率": 0.0, + "耗时s": 0.3 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "4h", + "数据量": "近两年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2024-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 4294.52, + "总收益%": -57.05, + "年化收益%": -35.54, + "夏普比率": -0.27, + "最大回撤%": -79.22, + "胜率%": 31.76, + "盈亏比": 0.8, + "交易次数": 85, + "平均盈亏": -57.09, + "最佳盈亏": 2538.22, + "最差盈亏": -2408.99, + "卡尔玛比率": -0.45, + "耗时s": 1.9 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "4h", + "数据量": "近一年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2025-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 7679.48, + "总收益%": -23.21, + "年化收益%": -24.84, + "夏普比率": -0.04, + "最大回撤%": -57.84, + "胜率%": 25.58, + "盈亏比": 0.82, + "交易次数": 43, + "平均盈亏": -41.39, + "最佳盈亏": 3793.36, + "最差盈亏": -910.01, + "卡尔玛比率": -0.43, + "耗时s": 0.4 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "1d", + "数据量": "近两年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2024-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 13051.66, + "总收益%": 30.52, + "年化收益%": 18.35, + "夏普比率": 0.59, + "最大回撤%": -33.05, + "胜率%": 54.55, + "盈亏比": 1.67, + "交易次数": 11, + "平均盈亏": 304.4, + "最佳盈亏": 2911.8, + "最差盈亏": -2038.38, + "卡尔玛比率": 0.56, + "耗时s": 0.3 + }, + { + "策略名": "8.EMA双均线多空", + "币种": "SOLUSDT", + "时间级别": "1d", + "数据量": "近一年", + "策略类型": "趋势跟踪", + "策略参数": "EMA(10,50)/ATR(14)x2.5", + "策略描述": "EMA(10,50)金叉做多死叉做空 + ATR追踪止损，始终在场", + "日期范围": "2025-06-13~2026-06-13", + "初始资金": 10000.0, + "最终权益": 11911.16, + "总收益%": 19.11, + "年化收益%": 35.14, + "夏普比率": 0.91, + "最大回撤%": -22.11, + "胜率%": 33.33, + "盈亏比": 5.3, + "交易次数": 3, + "平均盈亏": 706.36, + "最佳盈亏": 2611.34, + "最差盈亏": -306.28, + "卡尔玛比率": 1.59, + "耗时s": 0.1 + } + ] +} \ No newline at end of file diff --git a/engine/example/intraday_explore.py b/engine/example/intraday_explore.py new file mode 100644 index 0000000..f59b43f --- /dev/null +++ b/engine/example/intraday_explore.py @@ -0,0 +1,563 @@ +""" +日内策略探索 — 4 种思路 (15m / 30m / 1h 全币种) + +1. 均值回归：RSI超买超卖 + 布林带触碰，震荡市中做回归 +2. 多时间框架：4h 牛熊判定方向过滤 + 1h EMA交叉入场 +3. 波动率突破：ATR 收缩后扩张，顺势突破 +4. 成交量：OBV 背离 + VWAP 回归 + +用法： + source .venv/bin/activate && python example/intraday_explore.py +""" + +import asyncio +import sys +import time +from datetime import datetime, timezone +from pathlib import Path +from typing import Optional + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.base import BaseStrategy, Signal, StrategyConfig +from engine.common.models import Kline +from engine.common.config import config +from engine.backtest import BacktestConfig +from engine.data import DataService +from engine.indicators.incremental import EmaInc, AtrInc, RsiInc, BbInc +from engine.example.long_short import LongShortEngine +from engine.example.regime_all import RegimeDetector3, RegimeEmaConfig, RegimeEmaStrategy + + +# ════════════════════════════════════════════════════════ +# 策略 1：均值回归 — RSI + 布林带 +# ════════════════════════════════════════════════════════ + + +class MeanRevConfig(StrategyConfig): + rsi_period: int = 14 + rsi_oversold: float = 25.0 + rsi_overbought: float = 75.0 + bb_period: int = 20 + bb_std: float = 2.0 + atr_stop: float = 1.5 + require_bb_touch: bool = True # 是否要求价格触碰布林带 + + +class MeanRevStrategy(BaseStrategy): + """RSI 极端 + 布林带确认 → 均值回归，ATR 止损""" + + strategy_type = "mean_rev" + + def __init__(self, c: MeanRevConfig): + super().__init__(c) + self.cfg = c + self._rsi = RsiInc(c.rsi_period) + self._bb = BbInc(c.bb_period, c.bb_std) + self._atr = AtrInc(14) + self._side: str = "" # "long" / "short" + self._entry_price: float = 0.0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + r = self._rsi.update(k.close) + up, mid, lo = self._bb.update(k.close) + atr_v = self._atr.update(k.high, k.low, k.close) + + if r == 0 or up == 0 or atr_v == 0: + return None + + below_bb = k.close < lo if self.cfg.require_bb_touch else True + above_bb = k.close > up if self.cfg.require_bb_touch else True + + # ── 持仓管理 ── + if self._side == "long": + stop = self._entry_price - self.cfg.atr_stop * atr_v + take = self._entry_price + self.cfg.atr_stop * atr_v * 1.5 + if k.close <= stop or k.close >= take or r > 55: # 回归中轨或超止损 + self._side = "" + reason = "止损" if k.close <= stop else ("止盈" if k.close >= take else "RSI回归中轨") + return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time) + + elif self._side == "short": + stop = self._entry_price + self.cfg.atr_stop * atr_v + take = self._entry_price - self.cfg.atr_stop * atr_v * 1.5 + if k.close >= stop or k.close <= take or r < 45: + self._side = "" + reason = "止损" if k.close >= stop else ("止盈" if k.close <= take else "RSI回归中轨") + return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time) + + # ── 入场 ── + else: + if r < self.cfg.rsi_oversold and below_bb: + self._side = "long" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason=f"RSI超卖{r:.0f}", timestamp=k.open_time) + elif r > self.cfg.rsi_overbought and above_bb: + self._side = "short" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"RSI超买{r:.0f}", timestamp=k.open_time) + + return None + + +# ════════════════════════════════════════════════════════ +# 策略 2：多时间框架 — 4h 方向 + 1h 入场 +# ════════════════════════════════════════════════════════ + + +class MultiTFConfig(StrategyConfig): + fast: int = 20 + slow: int = 100 + atr_stop: float = 2.0 + # 4h 数据由策略内部自动加载 + + +class MultiTFStrategy(BaseStrategy): + """4h 牛熊判定方向过滤，1h EMA 交叉入场，只顺大势""" + + strategy_type = "multi_tf" + + def __init__(self, c: MultiTFConfig): + super().__init__(c) + self.cfg = c + self._ema_fast = EmaInc(c.fast) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(14) + self._side: str = "" + self._hp: float = 0.0 + self._lp: float = float("inf") + # 4h 牛熊判定 — 在 on_start 中加载 + self._regime_map: dict[int, str] = {} # timestamp_hour -> regime + self._4h_loaded = False + + async def on_start(self) -> None: + """加载 4h 数据并预计算牛熊判定""" + await super().on_start() + if self._4h_loaded: + return + try: + ds = DataService(config.db) + await ds.connect() + try: + klines_4h = await ds.fetch_klines( + symbol=self.cfg.symbol, interval="4h", + start_time=datetime(2017, 1, 1), + end_time=datetime(2026, 12, 31), + limit=1_000_000, + ) + detector = RegimeDetector3() + for k in klines_4h: + detector.update(k.close) + idx = len(detector._e200) - 1 + if idx >= 220: + regime = detector.detect(k.close, idx) + # 4h bar 覆盖的时间窗口（按小时取整） + hour_key = int(k.open_time / 3_600_000) + for h in range(4): + self._regime_map[hour_key + h] = regime + self._4h_loaded = True + finally: + await ds.close() + except Exception: + pass # 加载失败则不做过滤 + + def _get_regime(self, ts_ms: float) -> str: + """根据 1h bar 时间戳查找对应 4h 牛熊状态""" + hour_key = int(ts_ms / 3_600_000) + return self._regime_map.get(hour_key, "sideways") + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._ema_fast.update(k.close) + self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + + n = len(self._ema_fast) + if n < self.cfg.slow + 5: + return None + + cf, cs = self._ema_fast[-1], self._ema_slow[-1] + pf, ps = self._ema_fast[-2], self._ema_slow[-2] + ca = self._atr[-1] + if cf == 0 or cs == 0 or ca == 0: + return None + + golden = pf <= ps and cf > cs + death = pf >= ps and cf < cs + regime = self._get_regime(k.open_time) + + # ── 多头持仓 ── + if self._side == "long": + self._hp = max(self._hp, k.high) + stop = self._hp - self.cfg.atr_stop * ca + if death or k.close < stop: + self._side = "" + reason = "死叉" if death else "ATR止损" + return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time) + + # ── 空头持仓 ── + elif self._side == "short": + self._lp = min(self._lp, k.low) + stop = self._lp + self.cfg.atr_stop * ca + if golden or k.close > stop: + self._side = "" + reason = "金叉" if golden else "ATR止损" + return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time) + + # ── 入场：必须顺4h方向 ── + else: + if golden and regime == "bull": + self._side = "long" + self._hp = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="4h牛+金叉", timestamp=k.open_time) + elif death and regime == "bear": + self._side = "short" + self._lp = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="4h熊+死叉", timestamp=k.open_time) + + return None + + +# ════════════════════════════════════════════════════════ +# 策略 3：波动率突破 — ATR 收缩扩张 +# ════════════════════════════════════════════════════════ + + +class VolBreakConfig(StrategyConfig): + atr_period: int = 14 + squeeze_period: int = 20 # ATR 收缩回看窗口 + squeeze_ratio: float = 0.7 # 当前 ATR < 最低 ATR * ratio 时视为收缩 + atr_stop: float = 2.0 + + +class VolBreakStrategy(BaseStrategy): + """ATR 收缩到极致后扩张 → 顺势突破，ATR 止损""" + + strategy_type = "vol_break" + + def __init__(self, c: VolBreakConfig): + super().__init__(c) + self.cfg = c + self._atr = AtrInc(c.atr_period) + self._ema_fast = EmaInc(10) + self._ema_slow = EmaInc(30) + self._closes: list[float] = [] + self._highs: list[float] = [] + self._lows: list[float] = [] + self._side: str = "" + self._entry_price: float = 0.0 + self._was_squeezed = False + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._closes.append(k.close) + self._highs.append(k.high) + self._lows.append(k.low) + self._atr.update(k.high, k.low, k.close) + self._ema_fast.update(k.close) + self._ema_slow.update(k.close) + + n = len(self._closes) + if n < self.cfg.atr_period + self.cfg.squeeze_period: + return None + + atr_now = self._atr[-1] + atr_prev = self._atr[-2] if n >= 2 else 0 + ca = atr_now + if ca == 0: + return None + + # ATR 收缩检测：当前 ATR 是否处于 squeeze_period 内的最低水平 + atr_window = [self._atr[i] for i in range(max(0, n - self.cfg.squeeze_period), n) if self._atr[i] > 0] + if not atr_window: + return None + min_atr = min(atr_window) + is_squeezed = atr_now < min_atr * (1 + (1 - self.cfg.squeeze_ratio)) + + # ATR 扩张信号 + atr_expanding = atr_now > atr_prev * 1.05 if atr_prev > 0 else False + + # 趋势方向 + cf = self._ema_fast[-1] + cs = self._ema_slow[-1] + trend_up = cf > cs + + # ── 持仓管理 ── + if self._side == "long": + self._was_squeezed = False + stop = self._entry_price - self.cfg.atr_stop * ca + if k.close < stop or (cf < cs and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="ATR止损或转弱", timestamp=k.open_time) + + elif self._side == "short": + self._was_squeezed = False + stop = self._entry_price + self.cfg.atr_stop * ca + if k.close > stop or (cf > cs and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="ATR止损或转强", timestamp=k.open_time) + + # ── 入场 ── + else: + if is_squeezed: + self._was_squeezed = True + elif self._was_squeezed and atr_expanding: + self._was_squeezed = False + if trend_up: + self._side = "long" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="ATR扩张突破做多", timestamp=k.open_time) + else: + self._side = "short" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="ATR扩张突破做空", timestamp=k.open_time) + + return None + + +# ════════════════════════════════════════════════════════ +# 策略 4：成交量 — OBV 背离 + VWAP 回归 +# ════════════════════════════════════════════════════════ + + +class VolumeConfig(StrategyConfig): + obv_lookback: int = 20 # OBV 背离检测窗口 + vwap_std: float = 2.0 # VWAP 偏离标准差倍数 + atr_stop: float = 2.0 + + +class VolumeStrategy(BaseStrategy): + """OBV 背离（价格新低但 OBV 未新低→看涨）+ VWAP 偏离回归""" + + strategy_type = "volume" + + def __init__(self, c: VolumeConfig): + super().__init__(c) + self.cfg = c + self._closes: list[float] = [] + self._highs: list[float] = [] + self._lows: list[float] = [] + self._volumes: list[float] = [] + self._obv: list[float] = [] # 增量 OBV + self._obv_val: float = 0.0 + self._atr = AtrInc(14) + self._cum_pv: float = 0.0 # 累积 price*volume + self._cum_vol: float = 0.0 # 累积 volume + self._side: str = "" + self._entry_price: float = 0.0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._closes.append(k.close) + self._highs.append(k.high) + self._lows.append(k.low) + self._volumes.append(k.volume) + self._atr.update(k.high, k.low, k.close) + + # 增量 OBV + n = len(self._closes) + if n == 1: + self._obv_val = k.volume + else: + if k.close > self._closes[-2]: + self._obv_val += k.volume + elif k.close < self._closes[-2]: + self._obv_val -= k.volume + self._obv.append(self._obv_val) + + # 增量 VWAP + typical = (k.high + k.low + k.close) / 3.0 + self._cum_pv += typical * k.volume + self._cum_vol += k.volume + vwap = self._cum_pv / self._cum_vol if self._cum_vol > 0 else k.close + + if n < self.cfg.obv_lookback + 5: + return None + + ca = self._atr[-1] + if ca == 0: + return None + + # OBV 背离检测：价格新低但 OBV 未新低 → 潜在反转 + lookback = self.cfg.obv_lookback + price_window = self._closes[-lookback:] + obv_window = self._obv[-lookback:] + price_made_new_low = min(price_window) == price_window[-1] + obv_not_new_low = min(obv_window) < obv_window[-1] + obv_bull_div = price_made_new_low and obv_not_new_low + + # OBV 负背离：价格新高但 OBV 未新高 + price_made_new_high = max(price_window) == price_window[-1] + obv_not_new_high = max(obv_window) > obv_window[-1] + obv_bear_div = price_made_new_high and obv_not_new_high + + # VWAP 偏离度 + vwap_dev = (k.close - vwap) / vwap if vwap > 0 else 0 + + # ── 持仓管理 ── + if self._side == "long": + stop = self._entry_price - self.cfg.atr_stop * ca + if k.close < stop or vwap_dev < -0.01: # 回到 VWAP 下方 + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="止损或回VWAP", timestamp=k.open_time) + + elif self._side == "short": + stop = self._entry_price + self.cfg.atr_stop * ca + if k.close > stop or vwap_dev > 0.01: + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="止损或回VWAP", timestamp=k.open_time) + + # ── 入场 ── + else: + if obv_bull_div and vwap_dev < -self.cfg.vwap_std * 0.02: # 价格显著低于 VWAP + self._side = "long" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason=f"OBV底背离+VWAP下方", timestamp=k.open_time) + elif obv_bear_div and vwap_dev > self.cfg.vwap_std * 0.02: + self._side = "short" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"OBV顶背离+VWAP上方", timestamp=k.open_time) + + return None + + +# ════════════════════════════════════════════════════════ +# 执行 +# ════════════════════════════════════════════════════════ + +SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] +INTERVALS = ["15m", "30m", "1h"] + +STRATEGIES = { + "1.均值回归": (MeanRevConfig, MeanRevStrategy), + "2.多TF(4h+1h)": (MultiTFConfig, MultiTFStrategy), + "3.波动突破": (VolBreakConfig, VolBreakStrategy), + "4.成交量": (VolumeConfig, VolumeStrategy), +} + +# 均值回归参数 +MEAN_REV_PARAMS = { + "BTCUSDT": {"rsi_period": 14, "rsi_oversold": 25, "rsi_overbought": 75, "bb_period": 20, "bb_std": 2.0, "atr_stop": 1.5}, + "ETHUSDT": {"rsi_period": 14, "rsi_oversold": 25, "rsi_overbought": 75, "bb_period": 20, "bb_std": 2.0, "atr_stop": 1.5}, + "BNBUSDT": {"rsi_period": 14, "rsi_oversold": 25, "rsi_overbought": 75, "bb_period": 20, "bb_std": 2.0, "atr_stop": 1.5}, + "SOLUSDT": {"rsi_period": 14, "rsi_oversold": 25, "rsi_overbought": 75, "bb_period": 20, "bb_std": 2.0, "atr_stop": 1.5}, +} + +# 多TF参数 +MULTI_TF_PARAMS = { + "BTCUSDT": {"fast": 20, "slow": 100, "atr_stop": 2.0}, + "ETHUSDT": {"fast": 20, "slow": 100, "atr_stop": 2.0}, + "BNBUSDT": {"fast": 20, "slow": 100, "atr_stop": 2.0}, + "SOLUSDT": {"fast": 20, "slow": 100, "atr_stop": 2.0}, +} + +# 波动突破参数 +VOL_BREAK_PARAMS = { + "BTCUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "ETHUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "BNBUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "SOLUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, +} + +# 成交量参数 +VOLUME_PARAMS = { + "BTCUSDT": {"obv_lookback": 20, "vwap_std": 2.0, "atr_stop": 2.0}, + "ETHUSDT": {"obv_lookback": 20, "vwap_std": 2.0, "atr_stop": 2.0}, + "BNBUSDT": {"obv_lookback": 20, "vwap_std": 2.0, "atr_stop": 2.0}, + "SOLUSDT": {"obv_lookback": 20, "vwap_std": 2.0, "atr_stop": 2.0}, +} + + +async def run_one(engine_factory, strategy_cls, config_cls, params, symbol, interval, start, end): + """运行单组回测""" + INITIAL = 10_000.0 + sc = config_cls(symbol=symbol, **params) + bt = BacktestConfig(symbol=symbol, interval=interval, start_time=start, end_time=end, initial_capital=INITIAL) + engine = engine_factory(bt) + r = await engine.run(strategy_cls, sc) + m = r.metrics + return m, INITIAL, m.final_equity + + +async def main(): + ds = DataService(config.db) + await ds.connect() + + # 预加载所有数据范围 + ranges: dict[str, dict[str, tuple[datetime, datetime]]] = {} + for interval in INTERVALS: + ranges[interval] = {} + for symbol in SYMBOLS: + try: + s, e = await ds.fetch_symbol_date_range(symbol, interval) + ranges[interval][symbol] = (s, e) + except Exception: + pass + await ds.close() + + all_results: list[dict] = [] + + print() + print("═" * 135) + print(" 日内策略探索 — 4思路 × 4币种 × 3周期") + print("═" * 135) + + for strategy_name, (config_cls, strategy_cls) in STRATEGIES.items(): + print(f"\n ■ {strategy_name}") + print(f" {'币种':<10} {'周期':<6} {'本金':>7} {'终值':>9} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>8} {'交易':>6} {'耗时s':>7}") + print(" " + "─" * 115) + + for interval in INTERVALS: + for symbol in SYMBOLS: + if symbol not in ranges.get(interval, {}): + continue + start, end = ranges[interval][symbol] + + # 选择参数 + if strategy_name == "1.均值回归": + params = MEAN_REV_PARAMS[symbol] + elif strategy_name == "2.多TF(4h+1h)": + params = MULTI_TF_PARAMS[symbol] + elif strategy_name == "3.波动突破": + params = VOL_BREAK_PARAMS[symbol] + else: + params = VOLUME_PARAMS[symbol] + + t0 = time.time() + try: + m, initial, final_equity = await run_one( + lambda bt: LongShortEngine(bt, db_config=config.db), + strategy_cls, config_cls, params, + symbol, interval, start, end, + ) + elapsed = time.time() - t0 + except Exception as ex: + print(f" {symbol:<10} {interval:<6} {'错误: ' + str(ex)[:40]}") + continue + + print(f" {symbol:<10} {interval:<6} {initial:>7.0f} {final_equity:>9.0f} {m.total_return_pct:>7.1f}% {m.annual_return_pct:>7.1f}% {m.sharpe_ratio:>7.2f} {m.max_drawdown_pct:>7.1f}% {m.total_trades:>6} {elapsed:>6.1f}s") + + all_results.append({ + "strategy": strategy_name, "interval": interval, "symbol": symbol, + "return": m.total_return_pct, "annual": m.annual_return_pct, + "sharpe": m.sharpe_ratio, "dd": m.max_drawdown_pct, + "trades": m.total_trades, "initial": initial, "final": final_equity, + }) + + # ── 汇总：每种策略的最佳组合 ── + print(f"\n\n ■ 各策略最佳组合 (按夏普排名)") + print(f" {'策略':<18} {'级别':<6} {'币种':<10} {'本金':>7} {'终值':>9} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>8} {'交易':>6}") + print(" " + "─" * 115) + + # 每种策略取最佳 + for sn in STRATEGIES: + candidates = [r for r in all_results if r["strategy"] == sn] + if not candidates: + continue + best = max(candidates, key=lambda x: x["sharpe"]) + print(f" {sn:<18} {best['interval']:<6} {best['symbol']:<10} {best['initial']:>7.0f} {best['final']:>9.0f} {best['return']:>7.1f}% {best['annual']:>7.1f}% {best['sharpe']:>7.2f} {best['dd']:>7.1f}% {best['trades']:>6}") + + print("\n═" * 135) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/long_short.py b/engine/example/long_short.py index 4eeae9e..d659628 100644 --- a/engine/example/long_short.py +++ b/engine/example/long_short.py @@ -31,7 +31,7 @@ from engine.common.base import BaseStrategy, Signal, StrategyConfig from engine.common.models import Kline from engine.common.config import config, DBConfig from engine.data import DataService -from engine.indicators import ema, atr +from engine.indicators.incremental import EmaInc, AtrInc from engine.backtest.models import BacktestConfig, BacktestMetrics, BacktestResult, BacktestTrade @@ -364,7 +364,7 @@ class LongShortEmaConfig(StrategyConfig): class LongShortEmaStrategy(BaseStrategy): - """EMA金叉做多、死叉做空，始终在场""" + """EMA金叉做多、死叉做空，始终在场 — 全部指标增量计算""" strategy_type = "long_short_ema" @@ -374,6 +374,9 @@ class LongShortEmaStrategy(BaseStrategy): self._closes: list[float] = [] self._highs: list[float] = [] self._lows: list[float] = [] + self._ema_fast = EmaInc(c.fast) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(14) self._highest: float = 0.0 self._lowest: float = float('inf') self._position_side: str = "" # "long" / "short" @@ -382,15 +385,19 @@ class LongShortEmaStrategy(BaseStrategy): self._closes.append(k.close) self._highs.append(k.high) self._lows.append(k.low) + + # 增量更新（即使在热身期也要更新，保证后续状态正确） + self._ema_fast.update(k.close) + self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + n = len(self._closes) if n < self.cfg.slow + 5: return None - fast = ema(self._closes, self.cfg.fast) - slow = ema(self._closes, self.cfg.slow) - atr_vals = atr(self._highs, self._lows, self._closes, 14) - cur_f, cur_s, cur_atr = fast[-1], slow[-1], atr_vals[-1] - prev_f, prev_s = fast[-2], slow[-2] + cur_f, cur_s = self._ema_fast[-1], self._ema_slow[-1] + cur_atr = self._atr[-1] + prev_f, prev_s = self._ema_fast[-2], self._ema_slow[-2] if cur_f == 0 or cur_s == 0 or cur_atr == 0: return None diff --git a/engine/example/multi_strategy_report.py b/engine/example/multi_strategy_report.py new file mode 100644 index 0000000..0be1faa --- /dev/null +++ b/engine/example/multi_strategy_report.py @@ -0,0 +1,393 @@ +""" +多策略多级别分类回测报告 +日内交易 (30m/1h) ｜ 中线交易 (2h/4h/6h) ｜ 长线交易 (1d/1w) + +策略：牛熊自适应 / MACD / EMA双均线 / RSI / 布林突破 +币种：BTC / ETH / BNB / SOL +数据：日内近两年，中线+长线全量 + +用法： + source .venv/bin/activate && python example/multi_strategy_report.py +""" + +import asyncio +import sys +from collections import defaultdict +from datetime import datetime, timezone +from pathlib import Path +from typing import Optional + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.base import BaseStrategy, Signal, StrategyConfig +from engine.common.models import Kline +from engine.common.config import config +from engine.backtest import BacktestEngine, BacktestConfig, BacktestResult +from engine.indicators import macd, ema, rsi, bollinger, atr + +# ═══════════════════════════════════════════ +# 策略定义 +# ═══════════════════════════════════════════ + +# --- MACD --- +class MacdConfig(StrategyConfig): + fast: int = 12; slow: int = 26; signal: int = 9 + +class MacdStrategy(BaseStrategy): + strategy_type = "macd" + def __init__(self, c: MacdConfig): + super().__init__(c); self.cfg = c; self._c: list[float] = [] + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._c.append(k.close) + ml, sl, _ = macd(self._c, self.cfg.fast, self.cfg.slow, self.cfg.signal) + if len(ml) < 3 or ml[-1] == 0: return None + if ml[-2] <= sl[-2] and ml[-1] > sl[-1]: + return Signal(symbol=self.cfg.symbol, side="BUY", reason="MACD金叉", timestamp=k.open_time) + if ml[-2] >= sl[-2] and ml[-1] < sl[-1]: + return Signal(symbol=self.cfg.symbol, side="SELL", reason="MACD死叉", timestamp=k.open_time) + return None + +# --- EMA双均线 --- +class EmaCrossConfig(StrategyConfig): + fast: int = 20; slow: int = 50 + +class EmaCrossStrategy(BaseStrategy): + strategy_type = "ema_cross" + def __init__(self, c: EmaCrossConfig): + super().__init__(c); self.cfg = c; self._c: list[float] = [] + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._c.append(k.close) + f = ema(self._c, self.cfg.fast); s = ema(self._c, self.cfg.slow) + if len(f) < 3 or f[-1] == 0 or s[-1] == 0: return None + if f[-2] <= s[-2] and f[-1] > s[-1]: + return Signal(symbol=self.cfg.symbol, side="BUY", reason="EMA金叉", timestamp=k.open_time) + if f[-2] >= s[-2] and f[-1] < s[-1]: + return Signal(symbol=self.cfg.symbol, side="SELL", reason="EMA死叉", timestamp=k.open_time) + return None + +# --- RSI --- +class RsiConfig(StrategyConfig): + period: int = 14; oversold: float = 30.0; overbought: float = 70.0 + +class RsiStrategy(BaseStrategy): + strategy_type = "rsi" + def __init__(self, c: RsiConfig): + super().__init__(c); self.cfg = c; self._c: list[float] = []; self._in = False + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._c.append(k.close) + v = rsi(self._c, self.cfg.period)[-1] + if v == 0: return None + if v < self.cfg.oversold and not self._in: + self._in = True + return Signal(symbol=self.cfg.symbol, side="BUY", reason=f"RSI超卖({v:.1f})", timestamp=k.open_time) + if v > self.cfg.overbought and self._in: + self._in = False + return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"RSI超买({v:.1f})", timestamp=k.open_time) + return None + +# --- 布林突破 --- +class BollConfig(StrategyConfig): + period: int = 20; std: float = 2.0 + +class BollStrategy(BaseStrategy): + strategy_type = "boll" + def __init__(self, c: BollConfig): + super().__init__(c); self.cfg = c; self._c: list[float] = [] + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._c.append(k.close) + upper, mid, lower = bollinger(self._c, self.cfg.period, self.cfg.std) + if len(upper) < 3 or mid[-1] == 0: return None + p, md = k.close, mid[-1] + pp, pm = self._c[-2], mid[-2] + if pp <= pm and p > md and upper[-1] > 0 and mid[-1] > mid[-2]: + return Signal(symbol=self.cfg.symbol, side="BUY", reason=f"突破BB中轨", timestamp=k.open_time) + if pp >= pm and p < md: + return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"跌破BB中轨", timestamp=k.open_time) + return None + +# --- 牛熊自适应 (多空双向) --- +class RegimeDetector: + def __init__(self): + self._ath = 0.0 + def update_ath(self, price: float): + if price > self._ath: self._ath = price + def ema200_slope(self, closes, idx): + if idx < 210: return "unknown" + e200 = ema(closes, 200) + if e200[idx - 20] == 0: return "unknown" + slope = (e200[idx] - e200[idx - 20]) / e200[idx - 20] + if slope > 0.002: return "bull" + if slope < -0.002: return "bear" + return "sideways" + def price_vs_ema200(self, closes, idx): + if idx < 210: return "unknown" + e200 = ema(closes, 200) + return "bull" if closes[idx] > e200[idx] else "bear" + def ath_drawdown(self, closes, idx): + if self._ath == 0: return "unknown" + dd = (closes[idx] - self._ath) / self._ath + if dd > -0.15: return "bull" + if dd < -0.35: return "bear" + return "sideways" + def detect(self, closes, idx): + r1 = self.ema200_slope(closes, idx); r2 = self.price_vs_ema200(closes, idx); r3 = self.ath_drawdown(closes, idx) + b = sum(1 for r in [r1,r2,r3] if r=="bull"); br = sum(1 for r in [r1,r2,r3] if r=="bear") + if b >= 2: return "bull" + if br >= 2: return "bear" + return "sideways" + +class RegimeConfig(StrategyConfig): + fast: int = 10; slow: int = 50; atr_stop: float = 2.5 + +class RegimeStrategy(BaseStrategy): + strategy_type = "regime" + def __init__(self, c: RegimeConfig): + super().__init__(c); self.cfg = c + self._c: list[float] = []; self._h: list[float] = []; self._l: list[float] = [] + self._detector = RegimeDetector(); self._side = ""; self._hp = 0.0; self._lp = float('inf') + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._c.append(k.close); self._h.append(k.high); self._l.append(k.low) + self._detector.update_ath(k.close) + n = len(self._c) + if n < 220: return None + regime = self._detector.detect(self._c, n - 1) + f = ema(self._c, self.cfg.fast); s = ema(self._c, self.cfg.slow) + a = atr(self._h, self._l, self._c, 14) + cf, cs, ca, pf, ps = f[-1], s[-1], a[-1], f[-2], s[-2] + if cf == 0 or cs == 0 or ca == 0: return None + golden = pf <= ps and cf > cs; death = pf >= ps and cf < cs + if self._side == "long": + self._hp = max(self._hp, k.high); stop = self._hp - self.cfg.atr_stop * ca + if death or k.close < stop or regime == "bear": + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="平多", timestamp=k.open_time) + elif self._side == "short": + self._lp = min(self._lp, k.low); stop = self._lp + self.cfg.atr_stop * ca + if golden or k.close > stop or regime == "bull": + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="平空", timestamp=k.open_time) + else: + if regime == "bull" and golden: + self._side = "long"; self._hp = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="牛市金叉", timestamp=k.open_time) + elif regime == "bear" and death: + self._side = "short"; self._lp = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="熊市死叉", timestamp=k.open_time) + return None + +# ═══════════════════════════════════════════ +# 注册 +# ═══════════════════════════════════════════ + +STRATEGY_REGISTRY = { + "牛熊自适应": (RegimeStrategy, RegimeConfig, "regime"), + "MACD": (MacdStrategy, MacdConfig, "trend"), + "EMA双均线": (EmaCrossStrategy, EmaCrossConfig, "trend"), + "RSI超卖反弹": (RsiStrategy, RsiConfig, "reversal"), + "布林突破": (BollStrategy, BollConfig, "breakout"), +} + +SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] + +REGIME_PARAMS = { + "BTCUSDT": (10, 50), "ETHUSDT": (10, 75), "BNBUSDT": (20, 50), "SOLUSDT": (30, 50), +} + +CATEGORIES = { + "日内交易": { + "intervals": ["30m", "1h"], + "strategies": ["MACD", "EMA双均线", "RSI超卖反弹", "布林突破", "牛熊自适应"], + "data": "recent", + }, + "中线交易": { + "intervals": ["2h", "4h", "6h"], + "strategies": ["牛熊自适应", "MACD", "EMA双均线", "RSI超卖反弹", "布林突破"], + "data": "full", + }, + "长线交易": { + "intervals": ["1d", "1w"], + "strategies": ["牛熊自适应", "MACD", "EMA双均线"], + "data": "full", + }, +} + +RECENT_START = datetime(2024, 6, 1) +RECENT_END = datetime(2026, 6, 12) +FULL_DEFAULT = datetime(2017, 1, 1) + + +async def run_simple(symbol, interval, strategy_cls, strategy_cfg, start, end) -> BacktestResult | None: + """使用 BacktestEngine（只做多）运行回测""" + bt = BacktestConfig( + symbol=symbol, interval=interval, start_time=start, end_time=end, + initial_capital=10_000.0, warmup_bars=100, + ) + strategy_cfg.symbol = symbol + engine = BacktestEngine(bt, db_config=config.db) + return await engine.run(strategy_cls, strategy_cfg) + + +async def run_regime(symbol, interval, start, end) -> BacktestResult | None: + """使用 LongShortEngine（多空双向）运行牛熊自适应策略""" + from engine.example.long_short import LongShortEngine + fast, slow = REGIME_PARAMS.get(symbol, (10, 50)) + sc = RegimeConfig(symbol=symbol, fast=fast, slow=slow) + bt = BacktestConfig( + symbol=symbol, interval=interval, start_time=start, end_time=end, + initial_capital=10_000.0, warmup_bars=250, + ) + engine = LongShortEngine(bt, db_config=config.db) + return await engine.run(RegimeStrategy, sc) + + +# ═══════════════════════════════════════════ +# 主流程 +# ═══════════════════════════════════════════ + +async def main(): + sem = asyncio.Semaphore(2) # 并发控制 + + async def with_sem(coro): + async with sem: + return await coro + + out: list[str] = [] + def w(line=""): + out.append(line); print(line) + + # 收集所有结果: (category, interval, symbol, strategy_name, result) + all_results: list[dict] = [] + + w("# 多策略多级别分类回测报告") + w() + w(f"> 生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M')}") + w(f"> 初始资金：10,000 USDT | 手续费：0.1% | 滑点：0.05%") + w(f"> 日内交易使用近两年数据 (2024.06-2026.06)，中线/长线使用全量历史数据") + w() + + for cat_name, cat_cfg in CATEGORIES.items(): + intervals = cat_cfg["intervals"] + strategy_names = cat_cfg["strategies"] + use_full = cat_cfg["data"] == "full" + + w(f"## {cat_name} ({'/'.join(intervals)})") + w() + + for interval in intervals: + w(f"### {interval}") + w() + # 表头 + cols = "| 币种 |" + sep = "|------|" + for sn in strategy_names: + cols += f" {sn} 收益% | {sn} 夏普 | {sn} 回撤% | {sn} 交易 | {sn} 胜率% |" + sep += "--------|------|------|------|------|" + w(cols) + w(sep) + + for symbol in SYMBOLS: + row = f"| {symbol:<10} |" + + tasks = [] + for sn in strategy_names: + if sn == "牛熊自适应": + # 牛熊自适应使用 LongShortEngine + if use_full: + start, end = FULL_DEFAULT, RECENT_END + else: + start, end = RECENT_START, RECENT_END + tasks.append((sn, with_sem(run_regime(symbol, interval, start, end)))) + else: + cls, cfg_cls, _ = STRATEGY_REGISTRY[sn] + if use_full: + start, end = FULL_DEFAULT, RECENT_END + else: + start, end = RECENT_START, RECENT_END + cfg = cfg_cls() + tasks.append((sn, with_sem(run_simple(symbol, interval, cls, cfg, start, end)))) + + # 并行执行当前币种的所有策略 + results_list = await asyncio.gather(*[t[1] for t in tasks], return_exceptions=True) + + for (sn, _), r in zip(tasks, results_list): + if isinstance(r, Exception): + row += f" ERR | — | — | — | — |" + print(f" ✗ {symbol} {interval} {sn}: {r}") + elif r is None: + row += f" N/A | — | — | — | — |" + else: + m = r.metrics + row += f" {m.total_return_pct:>+6.1f}% | {m.sharpe_ratio:>4.2f} | {m.max_drawdown_pct:>5.1f}% | {m.total_trades:>4} | {m.win_rate*100:>4.1f}% |" + all_results.append({ + "category": cat_name, "interval": interval, + "symbol": symbol, "strategy": sn, + "return": m.total_return_pct, "sharpe": m.sharpe_ratio, + "dd": m.max_drawdown_pct, "trades": m.total_trades, + "win": m.win_rate, "pf": m.profit_factor, + }) + w(row) + w() + + # ═══════════════════════════════════════ + # 汇总分析 + # ═══════════════════════════════════════ + w("---") + w() + w("## 汇总分析") + w() + + for cat_name in CATEGORIES: + cat_results = [r for r in all_results if r["category"] == cat_name] + if not cat_results: + continue + w(f"### {cat_name} — 各币种最优策略") + w() + w("| 币种 | 最佳周期 | 最佳策略 | 总收益% | 夏普 | 回撤% | 交易 | 胜率% |") + w("|------|---------|---------|--------|------|------|------|------|") + + for symbol in SYMBOLS: + candidates = [r for r in cat_results if r["symbol"] == symbol] + if not candidates: + continue + best = max(candidates, key=lambda x: x["sharpe"]) + w(f"| {symbol} | {best['interval']} | {best['strategy']} | {best['return']:>+7.1f}% | {best['sharpe']:.2f} | {best['dd']:.1f}% | {best['trades']} | {best['win']*100:.1f}% |") + w() + + # 全市场最优 + w("### 全市场 TOP 10（按夏普排序）") + w() + w("| 排名 | 分类 | 币种 | 周期 | 策略 | 总收益% | 夏普 | 回撤% | 交易 | 胜率% |") + w("|------|------|------|------|------|--------|------|------|------|------|") + ranked = sorted(all_results, key=lambda x: x["sharpe"], reverse=True) + for i, r in enumerate(ranked[:10]): + w(f"| {i+1} | {r['category']} | {r['symbol']} | {r['interval']} | {r['strategy']} | {r['return']:>+7.1f}% | {r['sharpe']:.2f} | {r['dd']:.1f}% | {r['trades']} | {r['win']*100:.1f}% |") + w() + + # 按策略类型汇总 + w("### 各策略类型平均表现") + w() + w("| 策略 | 分类 | 平均收益% | 平均夏普 | 平均回撤% | 平均胜率% |") + w("|------|------|---------|---------|---------|---------|") + for sn in ["牛熊自适应", "MACD", "EMA双均线", "RSI超卖反弹", "布林突破"]: + for cat_name in CATEGORIES: + sr = [r for r in all_results if r["strategy"] == sn and r["category"] == cat_name] + if not sr: + continue + avg_ret = sum(r["return"] for r in sr) / len(sr) + avg_sh = sum(r["sharpe"] for r in sr) / len(sr) + avg_dd = sum(r["dd"] for r in sr) / len(sr) + avg_win = sum(r["win"] for r in sr) / len(sr) + w(f"| {sn} | {cat_name} | {avg_ret:>+7.1f}% | {avg_sh:.2f} | {avg_dd:.1f}% | {avg_win*100:.1f}% |") + w() + + # 写出文件 + out_path = Path(__file__).resolve().parent.parent / "backtest" / "MULTI_STRATEGY_REPORT.md" + with open(out_path, "w", encoding="utf-8") as f: + f.write("\n".join(out) + "\n") + print(f"\n✓ 报告已保存到: {out_path}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/regime_all.py b/engine/example/regime_all.py index f38bc38..e03a634 100644 --- a/engine/example/regime_all.py +++ b/engine/example/regime_all.py @@ -23,50 +23,55 @@ from engine.common.base import BaseStrategy, Signal, StrategyConfig from engine.common.models import Kline from engine.common.config import config from engine.backtest import BacktestConfig -from engine.indicators import ema, atr +from engine.indicators.incremental import EmaInc, AtrInc from engine.data import DataService from engine.example.long_short import LongShortEngine # ════════════════════════════════════════════════════════ -# 3法判定器 +# 3法判定器（增量 EMA200，O(1) per bar） # ════════════════════════════════════════════════════════ class RegimeDetector3: + """牛熊判定器，内部维护增量 EMA(200)，避免每次从头重算""" + def __init__(self): self._ath = 0.0 + self._e200 = EmaInc(200) - def update_ath(self, price: float): + def update(self, price: float): + """每根 bar 调一次：更新 ATH + EMA(200)""" if price > self._ath: self._ath = price + self._e200.update(price) - def ema200_slope(self, closes: list[float], idx: int) -> str: - if idx < 210: return "unknown" - e200 = ema(closes, 200) + def _ema200_slope(self, idx: int) -> str: + if idx < 220: return "unknown" + e200 = self._e200 if e200[idx - 20] == 0: return "unknown" slope = (e200[idx] - e200[idx - 20]) / e200[idx - 20] if slope > 0.002: return "bull" if slope < -0.002: return "bear" return "sideways" - def price_vs_ema200(self, closes: list[float], idx: int) -> str: + def _price_vs_ema200(self, price: float, idx: int) -> str: if idx < 210: return "unknown" - e200 = ema(closes, 200) - if e200[idx] == 0: return "unknown" - return "bull" if closes[idx] > e200[idx] else "bear" + e = self._e200[idx] + if e == 0: return "unknown" + return "bull" if price > e else "bear" - def ath_drawdown(self, closes: list[float], idx: int) -> str: + def _ath_drawdown(self, price: float) -> str: if self._ath == 0: return "unknown" - dd = (closes[idx] - self._ath) / self._ath + dd = (price - self._ath) / self._ath if dd > -0.15: return "bull" if dd < -0.35: return "bear" return "sideways" - def detect(self, closes: list[float], idx: int) -> str: - r1 = self.ema200_slope(closes, idx) - r2 = self.price_vs_ema200(closes, idx) - r3 = self.ath_drawdown(closes, idx) + def detect(self, price: float, idx: int) -> str: + r1 = self._ema200_slope(idx) + r2 = self._price_vs_ema200(price, idx) + r3 = self._ath_drawdown(price) b = sum(1 for r in [r1, r2, r3] if r == "bull") br = sum(1 for r in [r1, r2, r3] if r == "bear") if b >= 2: return "bull" @@ -84,7 +89,7 @@ class RegimeEmaConfig(StrategyConfig): class RegimeEmaStrategy(BaseStrategy): - """按市场状态自适应做多/做空""" + """按市场状态自适应做多/做空 — 全部指标增量计算，O(1) per bar""" strategy_type = "regime_ema" @@ -93,19 +98,28 @@ class RegimeEmaStrategy(BaseStrategy): self.cfg = c self._c: list[float] = []; self._h: list[float] = []; self._l: list[float] = [] self._detector = RegimeDetector3() + self._ema_fast = EmaInc(c.fast) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(14) self._side: str = ""; self._hp: float = 0.0; self._lp: float = float('inf') async def on_kline(self, k: Kline) -> Optional[Signal]: self._c.append(k.close); self._h.append(k.high); self._l.append(k.low) - self._detector.update_ath(k.close) + + # 增量更新所有指标（O(1) each） + self._detector.update(k.close) + self._ema_fast.update(k.close) + self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + n = len(self._c) if n < 220: return None - regime = self._detector.detect(self._c, n - 1) + regime = self._detector.detect(k.close, n - 1) - f = ema(self._c, self.cfg.fast); s = ema(self._c, self.cfg.slow) - a = atr(self._h, self._l, self._c, 14) - cf, cs, ca, pf, ps = f[-1], s[-1], a[-1], f[-2], s[-2] + cf, cs = self._ema_fast[-1], self._ema_slow[-1] + ca = self._atr[-1] + pf, ps = self._ema_fast[-2], self._ema_slow[-2] if cf == 0 or cs == 0 or ca == 0: return None golden = pf <= ps and cf > cs; death = pf >= ps and cf < cs diff --git a/engine/example/regime_intraday.py b/engine/example/regime_intraday.py new file mode 100644 index 0000000..bbcc7b7 --- /dev/null +++ b/engine/example/regime_intraday.py @@ -0,0 +1,122 @@ +""" +牛熊自适应策略 — 日内级别全币种扫描 (15m / 30m / 1h) + +用法： + source .venv/bin/activate && python example/regime_intraday.py +""" + +import asyncio +import sys +import time +from datetime import datetime, timezone +from pathlib import Path + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.config import config +from engine.backtest import BacktestConfig +from engine.data import DataService +from engine.example.regime_all import RegimeEmaStrategy, RegimeEmaConfig +from engine.example.long_short import LongShortEngine + +SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] +INTERVALS = ["15m", "30m", "1h"] + +# 沿用 4h 级别优化参数（日内级别可能需单独调参） +PARAMS = { + "BTCUSDT": (10, 50), + "ETHUSDT": (10, 75), + "BNBUSDT": (20, 50), + "SOLUSDT": (30, 50), +} + + +async def get_actual_range(ds: DataService, symbol: str, interval: str): + """获取币种指定周期的实际数据范围""" + start, end = await ds.fetch_symbol_date_range(symbol, interval) + return start, end + + +async def main(): + ds = DataService(config.db) + await ds.connect() + + print() + print("═" * 130) + print(" 牛熊自适应策略 — 日内级别全币种扫描 | 牛市只多/熊市只空/震荡空仓") + print("═" * 130) + + total_start = time.time() + results: list[dict] = [] + + for interval in INTERVALS: + print(f"\n ■ {interval} 级别") + print(f" {'币种':<10} {'数据范围':<22} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>8} {'交易':>6} {'多头P&L':>11} {'空头P&L':>11} {'耗时s':>7}") + print(" " + "─" * 125) + + for symbol in SYMBOLS: + fast, slow = PARAMS[symbol] + sc = RegimeEmaConfig(symbol=symbol, fast=fast, slow=slow) + + try: + act_start, act_end = await get_actual_range(ds, symbol, interval) + range_str = f"{act_start.date()}~{act_end.date()}" + except Exception: + # 数据不存在，跳过 + print(f" {symbol:<10} {'无数据':<22}") + continue + + bt = BacktestConfig( + symbol=symbol, interval=interval, + start_time=act_start, end_time=act_end, + initial_capital=10_000.0, + ) + engine = LongShortEngine(bt, db_config=config.db) + + t0 = time.time() + try: + r = await engine.run(RegimeEmaStrategy, sc) + elapsed = time.time() - t0 + except Exception as ex: + print(f" {symbol:<10} {range_str:<22} {'错误: ' + str(ex)[:30]}") + continue + + m = r.metrics + long_t = [t for t in r.trades if t.pnl is not None and t.side == "SELL"] + short_t = [t for t in r.trades if t.pnl is not None and t.side == "BUY"] + long_pnl = sum(t.pnl for t in long_t) if long_t else 0 + short_pnl = sum(t.pnl for t in short_t) if short_t else 0 + + print(f" {symbol:<10} {range_str:<22} {m.total_return_pct:>7.1f}% {m.annual_return_pct:>7.1f}% {m.sharpe_ratio:>7.2f} {m.max_drawdown_pct:>7.1f}% {m.total_trades:>6} {long_pnl:>+10.0f} {short_pnl:>+10.0f} {elapsed:>6.1f}s") + + results.append({ + "interval": interval, "symbol": symbol, + "return": m.total_return_pct, "annual": m.annual_return_pct, + "sharpe": m.sharpe_ratio, "dd": m.max_drawdown_pct, + "trades": m.total_trades, "win_rate": m.win_rate, + "profit_factor": m.profit_factor, + "long_pnl": long_pnl, "short_pnl": short_pnl, + "elapsed": elapsed, + }) + + await ds.close() + + # ── 汇总排名 ── + total_elapsed = time.time() - total_start + print(f"\n ■ 最佳组合 (按夏普排名)") + print(f" {'排名':<5} {'级别':<6} {'币种':<10} {'总收益%':>8} {'夏普':>7} {'回撤%':>8} {'交易':>6} {'胜率%':>7} {'盈亏比':>7}") + print(" " + "─" * 75) + + sorted_results = sorted(results, key=lambda x: x["sharpe"], reverse=True) + for i, r in enumerate(sorted_results): + medal = ["🥇", "🥈", "🥉"][i] if i < 3 else f"{i+1:>2}." + print(f" {medal:<5} {r['interval']:<6} {r['symbol']:<10} {r['return']:>7.1f}% {r['sharpe']:>7.2f} {r['dd']:>7.1f}% {r['trades']:>6} {r['win_rate']*100:>6.1f}% {r['profit_factor']:>7.2f}") + + print(f"\n 总耗时: {total_elapsed:.1f}s") + print("═" * 130) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/regime_timeframe_comparison.py b/engine/example/regime_timeframe_comparison.py index aa3b4a3..93ffb71 100644 --- a/engine/example/regime_timeframe_comparison.py +++ b/engine/example/regime_timeframe_comparison.py @@ -1,6 +1,6 @@ """ 牛熊自适应策略 — 多时间级别回测对比 -4h / 1d × 全量数据 / 近两年 (2024.06-2026.06) +2h / 4h / 6h / 1d × 全量数据 / 近两年 (2024.06-2026.06) 用法： source .venv/bin/activate && python example/regime_timeframe_comparison.py @@ -37,7 +37,7 @@ PARAMS = { "SOLUSDT": (30, 50), } -INTERVALS = ["4h", "1d"] +INTERVALS = ["2h", "4h", "6h", "1d"] # 近两年：2024年6月 → 2026年6月 YEAR_START = datetime(2024, 6, 1) @@ -267,7 +267,7 @@ async def main(): # 写出文件 out_path = ( - Path(__file__).resolve().parent.parent / "backtest" / "TIMEFRAME_COMPARISON_2Y.md" + Path(__file__).resolve().parent.parent / "backtest" / "TIMEFRAME_COMPARISON_2H_6H.md" ) with open(out_path, "w", encoding="utf-8") as f: f.write("\n".join(out) + "\n") diff --git a/engine/example/top3_trades.py b/engine/example/top3_trades.py new file mode 100644 index 0000000..8397eed --- /dev/null +++ b/engine/example/top3_trades.py @@ -0,0 +1,96 @@ +"""获取近一年Top3策略的详细交易记录""" +import asyncio, sys, json +from datetime import datetime, timedelta, timezone +from pathlib import Path + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.config import config +from engine.backtest.models import BacktestConfig +from engine.example.long_short import LongShortEngine +from engine.example.full_comparison import ( + VolBreakStrategy, VolBreakConfig, + EmaCrossStrategy, EmaCrossConfig, +) +from engine.common.base import Signal + +NOW = datetime.now(timezone.utc) +ONE_YEAR_AGO = NOW - timedelta(days=365) +INITIAL = 10_000.0 + +TASKS = [ + ("ATR波动率突破 ETHUSDT 4h", VolBreakConfig, VolBreakStrategy, + "ETHUSDT", "4h", lambda s: VolBreakConfig(symbol=s, atr_period=14, squeeze_period=20, squeeze_ratio=0.7, atr_stop=2.0)), + ("EMA双均线多空 ETHUSDT 1d", EmaCrossConfig, EmaCrossStrategy, + "ETHUSDT", "1d", lambda s: EmaCrossConfig(symbol=s, fast=10, slow=50, atr_stop=2.5)), + ("EMA双均线多空 BTCUSDT 1d", EmaCrossConfig, EmaCrossStrategy, + "BTCUSDT", "1d", lambda s: EmaCrossConfig(symbol=s, fast=10, slow=50, atr_stop=2.5)), +] + + +def fmt_ts(ts_ms): + return datetime.fromtimestamp(ts_ms / 1000, tz=timezone.utc).strftime("%Y-%m-%d %H:%M") + + +async def run_one(label, config_cls, strategy_cls, symbol, interval, mkcfg): + sc = mkcfg(symbol) + bt = BacktestConfig(symbol=symbol, interval=interval, + start_time=ONE_YEAR_AGO, end_time=NOW, + initial_capital=INITIAL, warmup_bars=150) + engine = LongShortEngine(bt, db_config=config.db) + r = await engine.run(strategy_cls, sc) + return label, r + + +async def main(): + results = [] + for task in TASKS: + label, r = await run_one(*task) + results.append((label, r)) + + for label, r in results: + m = r.metrics + trades = r.trades + + # 配对交易 + paired = [] + pending = None + for t in trades: + if t.side == "BUY" and t.pnl is None: + pending = {"entry_ts": t.timestamp, "entry_price": t.price, "entry_reason": t.reason} + elif t.side == "SELL" and pending and t.pnl is not None: + paired.append({**pending, "exit_ts": t.timestamp, "exit_price": t.price, + "exit_reason": t.reason, "pnl": t.pnl}) + pending = None + elif t.side == "SELL" and t.pnl is None: + pending = {"entry_ts": t.timestamp, "entry_price": t.price, "entry_reason": t.reason, "short": True} + elif t.side == "BUY" and pending and t.pnl is not None and pending.get("short"): + paired.append({**pending, "exit_ts": t.timestamp, "exit_price": t.price, + "exit_reason": t.reason, "pnl": t.pnl}) + pending = None + + cfg = r.config + print(f"\n═══ {label} ═══") + print(f" 本金 {INITIAL:,.0f} → 终值 {m.final_equity:,.0f} | {m.total_return_pct:+.1f}% | 年化 {m.annual_return_pct:+.1f}%") + print(f" 夏普 {m.sharpe_ratio:.2f} | 回撤 {m.max_drawdown_pct:.1f}% | 胜率 {m.win_rate*100:.1f}% | 盈亏比 {m.profit_factor:.2f} | {m.total_trades}笔") + print(f" 日期 {cfg.start_time.date()} ~ {cfg.end_time.date()}") + print() + print(f" {'#':>3} {'入场时间':<19} {'入场价':>10} {'入场原因':<25} {'出场时间':<19} {'出场价':>10} {'出场原因':<25} {'盈亏':>10}") + print(" " + "─" * 130) + + total_pnl = 0 + for i, p in enumerate(paired): + total_pnl += p["pnl"] + side = "做空" if p.get("short") else "做多" + print(f" {i+1:>3} {fmt_ts(p['entry_ts']):<19} {p['entry_price']:>10.4f} {p['entry_reason']:<25} {fmt_ts(p['exit_ts']):<19} {p['exit_price']:>10.4f} {p['exit_reason']:<25} {p['pnl']:>+10.2f}") + + print(" " + "─" * 130) + wins = sum(1 for p in paired if p["pnl"] > 0) + print(f" 合计 {len(paired)} 笔 | 盈利 {wins} 笔 | 总盈亏 {total_pnl:+,.2f} USDT") + print() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/vol_break_compare.py b/engine/example/vol_break_compare.py new file mode 100644 index 0000000..1429d20 --- /dev/null +++ b/engine/example/vol_break_compare.py @@ -0,0 +1,189 @@ +""" +策略3 ATR波动率突破 — 1h 全量 vs 近两年对比 + 近两年详细订单 + +用法： + source .venv/bin/activate && python example/vol_break_compare.py +""" + +import asyncio +import sys +import time +from datetime import datetime, timezone, timedelta +from pathlib import Path + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.config import config +from engine.backtest import BacktestConfig +from engine.data import DataService +from engine.example.long_short import LongShortEngine +from engine.example.intraday_explore import VolBreakStrategy, VolBreakConfig + +SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] +INTERVAL = "1h" +INITIAL = 10_000.0 + +PARAMS = { + "BTCUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "ETHUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "BNBUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "SOLUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, +} + +# 近两年截止日期 +RECENT_END = datetime(2025, 6, 1, tzinfo=timezone.utc) +RECENT_START = RECENT_END - timedelta(days=365 * 2) # 2023-06 + + +def fmt_ts(ts_ms: float) -> str: + dt = datetime.fromtimestamp(ts_ms / 1000, tz=timezone.utc) + return dt.strftime("%Y-%m-%d %H:%M") + + +def pair_trades(trades: list) -> list[dict]: + """将 BUY/SELL 配对为完整交易""" + paired = [] + pending_open = None # (side, price, reason, ts) + + for t in trades: + if t.side == "BUY": + if pending_open and pending_open["side"] == "short": + # 平空仓 (BUY to close short) + paired.append({ + "type": "做空", + "entry_ts": pending_open["ts"], + "entry_price": pending_open["price"], + "entry_reason": pending_open["reason"], + "exit_ts": t.timestamp, + "exit_price": t.price, + "exit_reason": t.reason, + "pnl": t.pnl or 0, + }) + pending_open = None + elif t.pnl is None: + # 开多仓 + pending_open = {"side": "long", "price": t.price, "reason": t.reason, "ts": t.timestamp} + + elif t.side == "SELL": + if pending_open and pending_open["side"] == "long": + # 平多仓 (SELL to close long) + paired.append({ + "type": "做多", + "entry_ts": pending_open["ts"], + "entry_price": pending_open["price"], + "entry_reason": pending_open["reason"], + "exit_ts": t.timestamp, + "exit_price": t.price, + "exit_reason": t.reason, + "pnl": t.pnl or 0, + }) + pending_open = None + elif t.pnl is None: + # 开空仓 + pending_open = {"side": "short", "price": t.price, "reason": t.reason, "ts": t.timestamp} + + return paired + + +async def run_backtest(symbol, start, end): + sc = VolBreakConfig(symbol=symbol, **PARAMS[symbol]) + bt = BacktestConfig(symbol=symbol, interval=INTERVAL, start_time=start, end_time=end, initial_capital=INITIAL) + engine = LongShortEngine(bt, db_config=config.db) + r = await engine.run(VolBreakStrategy, sc) + return r + + +async def main(): + ds = DataService(config.db) + await ds.connect() + + # 获取数据范围 + full_ranges = {} + for symbol in SYMBOLS: + try: + s, e = await ds.fetch_symbol_date_range(symbol, INTERVAL) + full_ranges[symbol] = (s, e) + except Exception: + pass + + # ── 运行全量 + 近两年 ── + full_results = {} + recent_results = {} + for symbol in SYMBOLS: + if symbol not in full_ranges: + continue + fs, fe = full_ranges[symbol] + + print(f" 运行 {symbol} 全量 ({fs.date()}~{fe.date()})...", end=" ", flush=True) + t0 = time.time() + full_results[symbol] = await run_backtest(symbol, fs, fe) + print(f"{time.time()-t0:.1f}s") + + rs = max(RECENT_START, fs) + re = min(RECENT_END, fe) + print(f" 运行 {symbol} 近两年 ({rs.date()}~{re.date()})...", end=" ", flush=True) + t0 = time.time() + recent_results[symbol] = await run_backtest(symbol, rs, re) + print(f"{time.time()-t0:.1f}s") + + await ds.close() + + # ── 全量 vs 近两年 对比表 ── + print() + print("═" * 145) + print(" ATR波动率突破 1h — 全量 vs 近两年 (2023.06~2025.06)") + print("═" * 145) + print() + header = f" {'币种':<10} | {'—————— 全量数据 ——————':>55} | {'—————— 近两年 ——————':>55}" + print(header) + sub = f" {'':<10} | {'本金':>6} {'终值':>8} {'总收益%':>8} {'年化%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5} | {'本金':>6} {'终值':>8} {'总收益%':>8} {'年化%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5}" + print(sub) + print(" " + "─" * 143) + + for symbol in SYMBOLS: + if symbol not in full_results: + continue + f = full_results[symbol].metrics + r = recent_results[symbol].metrics + print(f" {symbol:<10} | {INITIAL:>6.0f} {f.final_equity:>8.0f} {f.total_return_pct:>7.1f}% {f.annual_return_pct:>6.1f}% {f.sharpe_ratio:>6.2f} {f.max_drawdown_pct:>6.1f}% {f.total_trades:>5} | {INITIAL:>6.0f} {r.final_equity:>8.0f} {r.total_return_pct:>7.1f}% {r.annual_return_pct:>6.1f}% {r.sharpe_ratio:>6.2f} {r.max_drawdown_pct:>6.1f}% {r.total_trades:>5}") + + print("\n═" * 145) + + # ── 近两年详细订单 ── + for symbol in SYMBOLS: + if symbol not in recent_results: + continue + result = recent_results[symbol] + m = result.metrics + rng = recent_results[symbol].config + paired = pair_trades(result.trades) + + print(f"\n{'─' * 145}") + print(f" {symbol} 近两年 ({rng.start_time.date()}~{rng.end_time.date()}) — {len(paired)} 笔完整交易") + print(f" 本金 {INITIAL:,.0f} → 终值 {m.final_equity:,.0f} | 总收益 {m.total_return_pct:.1f}% | 年化 {m.annual_return_pct:.1f}% | 夏普 {m.sharpe_ratio:.2f} | 回撤 {m.max_drawdown_pct:.1f}%") + print(f" 胜率 {m.win_rate*100:.1f}% | 盈亏比 {m.profit_factor:.2f} | 最佳单笔 {m.best_trade_pnl:+,.0f} | 最差 {m.worst_trade_pnl:+,.0f} | 平均 {m.avg_trade_pnl:+,.0f}") + print(f"{'─' * 145}") + print(f" {'#':>3} {'类型':<5} {'入场时间':<19} {'入场价':>10} {'出场时间':<19} {'出场价':>10} {'盈亏':>12} {'入场原因':<30} {'出场原因':<30}") + print(f" {'─' * 141}") + + total_pnl = 0 + wins = 0 + for i, p in enumerate(paired): + pnl = p["pnl"] + total_pnl += pnl + if pnl > 0: + wins += 1 + pnl_str = f"{pnl:+,.0f}" + print(f" {i+1:>3} {p['type']:<5} {p['entry_ts']:<19} {p['entry_price']:>10.4f} {p['exit_ts']:<19} {p['exit_price']:>10.4f} {pnl_str:>12} {p['entry_reason']:<30} {p['exit_reason']:<30}") + + print(f" {'─' * 141}") + print(f" 合计: {len(paired)} 笔 | 盈利 {wins} 笔 ({wins/len(paired)*100 if paired else 0:.0f}%) | 总盈亏 {total_pnl:+,.0f} USDT") + + print(f"\n{'─' * 145}") + print() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/vol_break_periods.py b/engine/example/vol_break_periods.py new file mode 100644 index 0000000..5f57082 --- /dev/null +++ b/engine/example/vol_break_periods.py @@ -0,0 +1,212 @@ +""" +策略3 ATR波动率突破 — 全量 / 近两年 / 近一年 / 近半年 对比 + +用法： + source .venv/bin/activate && python example/vol_break_periods.py +""" + +import asyncio +import sys +import time +from datetime import datetime, timezone, timedelta +from pathlib import Path + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.config import config +from engine.backtest import BacktestConfig +from engine.data import DataService +from engine.example.long_short import LongShortEngine +from engine.example.intraday_explore import VolBreakStrategy, VolBreakConfig + +SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] +INTERVAL = "1h" +INITIAL = 10_000.0 + +TODAY = datetime.now(timezone.utc) + +# 时间段定义 +PERIODS = { + "全量": (None, TODAY), # start 动态获取 + "近两年": (TODAY - timedelta(days=365 * 2), TODAY), + "近一年": (TODAY - timedelta(days=365), TODAY), + "近半年": (TODAY - timedelta(days=182), TODAY), +} + +PARAMS = { + "BTCUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "ETHUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "BNBUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, + "SOLUSDT": {"atr_period": 14, "squeeze_period": 20, "squeeze_ratio": 0.7, "atr_stop": 2.0}, +} + + +def fmt_ts(ts_ms: float) -> str: + dt = datetime.fromtimestamp(ts_ms / 1000, tz=timezone.utc) + return dt.strftime("%Y-%m-%d %H:%M") + + +def pair_trades(trades: list) -> list[dict]: + paired = [] + pending_open = None + + for t in trades: + if t.side == "BUY": + if pending_open and pending_open["side"] == "short": + paired.append({ + "type": "做空", + "entry_ts": pending_open["ts"], + "entry_price": pending_open["price"], + "entry_reason": pending_open["reason"], + "exit_ts": t.timestamp, + "exit_price": t.price, + "exit_reason": t.reason, + "pnl": t.pnl or 0, + }) + pending_open = None + elif t.pnl is None: + pending_open = {"side": "long", "price": t.price, "reason": t.reason, "ts": t.timestamp} + + elif t.side == "SELL": + if pending_open and pending_open["side"] == "long": + paired.append({ + "type": "做多", + "entry_ts": pending_open["ts"], + "entry_price": pending_open["price"], + "entry_reason": pending_open["reason"], + "exit_ts": t.timestamp, + "exit_price": t.price, + "exit_reason": t.reason, + "pnl": t.pnl or 0, + }) + pending_open = None + elif t.pnl is None: + pending_open = {"side": "short", "price": t.price, "reason": t.reason, "ts": t.timestamp} + + return paired + + +async def run_backtest(symbol, start, end): + sc = VolBreakConfig(symbol=symbol, **PARAMS[symbol]) + bt = BacktestConfig(symbol=symbol, interval=INTERVAL, start_time=start, end_time=end, initial_capital=INITIAL) + engine = LongShortEngine(bt, db_config=config.db) + r = await engine.run(VolBreakStrategy, sc) + return r + + +async def main(): + ds = DataService(config.db) + await ds.connect() + + full_ranges = {} + for symbol in SYMBOLS: + try: + s, e = await ds.fetch_symbol_date_range(symbol, INTERVAL) + full_ranges[symbol] = (s, e) + print(f" {symbol}: {s.date()} ~ {e.date()}") + except Exception as ex: + print(f" {symbol}: 获取范围失败 {ex}") + + # 运行所有时间段 + all_results: dict[str, dict[str, object]] = {} # symbol -> period_name -> result + + for symbol in SYMBOLS: + if symbol not in full_ranges: + continue + all_results[symbol] = {} + fs, fe = full_ranges[symbol] + + for period_name, (ps, pe) in PERIODS.items(): + start = fs if period_name == "全量" else max(ps, fs) + end = min(pe, fe) + + if start >= end: + print(f" {symbol} {period_name}: 无有效数据范围，跳过") + continue + + print(f" 运行 {symbol} {period_name} ({start.date()}~{end.date()})...", end=" ", flush=True) + t0 = time.time() + try: + all_results[symbol][period_name] = await run_backtest(symbol, start, end) + print(f"{time.time()-t0:.1f}s") + except Exception as ex: + print(f"错误: {ex}") + + await ds.close() + + # ════════════════════════════════════════════════════ + # 对比表 + # ════════════════════════════════════════════════════ + print() + print("═" * 165) + print(f" ATR波动率突破 1h — 全量 / 近两年 / 近一年 / 近半年 对比 ({TODAY.strftime('%Y-%m-%d')} 截止)") + print("═" * 165) + print() + + col_w = 36 + for symbol in SYMBOLS: + if symbol not in all_results: + continue + print(f" ■ {symbol}") + print(f" {'时段':<8} {'本金':>7} {'终值':>9} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>8} {'胜率%':>7} {'盈亏比':>7} {'交易':>5} {'数据范围'}") + print(" " + "─" * 163) + + for period_name in PERIODS: + if period_name not in all_results[symbol]: + continue + r = all_results[symbol][period_name] + m = r.metrics + data_range = f"{r.config.start_time.date()}~{r.config.end_time.date()}" + print(f" {period_name:<8} {INITIAL:>7.0f} {m.final_equity:>9.0f} {m.total_return_pct:>7.1f}% {m.annual_return_pct:>7.1f}% {m.sharpe_ratio:>7.2f} {m.max_drawdown_pct:>7.1f}% {m.win_rate*100:>6.1f}% {m.profit_factor:>7.2f} {m.total_trades:>5} {data_range}") + print() + + # ════════════════════════════════════════════════════ + # 详细订单 + # ════════════════════════════════════════════════════ + print("═" * 165) + print(" 各时段详细订单") + print("═" * 165) + + for symbol in SYMBOLS: + if symbol not in all_results: + continue + for period_name in PERIODS: + if period_name not in all_results[symbol]: + continue + result = all_results[symbol][period_name] + m = result.metrics + paired = pair_trades(result.trades) + + if len(paired) == 0: + continue + + print(f"\n{'─' * 155}") + print(f" {symbol} {period_name} — {len(paired)} 笔完整交易") + print(f" 本金 {INITIAL:,.0f} → 终值 {m.final_equity:,.0f} | 总收益 {m.total_return_pct:.1f}% | 年化 {m.annual_return_pct:.1f}% | 夏普 {m.sharpe_ratio:.2f} | 回撤 {m.max_drawdown_pct:.1f}%") + print(f" 胜率 {m.win_rate*100:.1f}% | 盈亏比 {m.profit_factor:.2f} | 最佳 {m.best_trade_pnl:+,.0f} | 最差 {m.worst_trade_pnl:+,.0f} | 平均 {m.avg_trade_pnl:+,.0f}") + print(f" 数据范围: {result.config.start_time.date()} ~ {result.config.end_time.date()}") + print(f"{'─' * 155}") + print(f" {'#':>3} {'类型':<5} {'入场时间':<19} {'入场价':>10} {'出场时间':<19} {'出场价':>10} {'盈亏':>12} {'入场原因':<35} {'出场原因':<30}") + print(f" {'─' * 153}") + + total_pnl = 0 + wins = 0 + for i, p in enumerate(paired): + pnl = p["pnl"] + total_pnl += pnl + if pnl > 0: + wins += 1 + pnl_str = f"{pnl:+,.0f}" + print(f" {i+1:>3} {p['type']:<5} {p['entry_ts']:<19} {p['entry_price']:>10.4f} {p['exit_ts']:<19} {p['exit_price']:>10.4f} {pnl_str:>12} {p['entry_reason']:<35} {p['exit_reason']:<30}") + + print(f" {'─' * 153}") + print(f" 合计: {len(paired)} 笔 | 盈利 {wins} 笔 ({wins/len(paired)*100 if paired else 0:.0f}%) | 总盈亏 {total_pnl:+,.0f} USDT") + + print(f"\n{'─' * 155}") + print() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/web_strategies.py b/engine/example/web_strategies.py new file mode 100644 index 0000000..fe3b716 --- /dev/null +++ b/engine/example/web_strategies.py @@ -0,0 +1,701 @@ +""" +网络验证策略探索 — 5 个业界知名策略 × 全币种 × 1h + +1. 海龟交易 (Turtle) — Donchian 20/10 通道突破 + 2N ATR 止损 +2. 超级趋势 (SuperTrend) — ATR(10)×3 动态跟踪止损 +3. MACD金叉死叉 — MACD(12,26,9) 零轴交叉 + ATR 止损 +4. 布林收缩爆发 (BBSqueeze) — BB 收缩至 KC 内部后扩张突破 +5. 三均线排列 (TripleEMA) — EMA(10,30,60) 多头/空头排列 + ATR 追踪 + +用法： + source .venv/bin/activate && python example/web_strategies.py +""" + +import asyncio +import json +import sys +import time +from datetime import datetime, timezone +from pathlib import Path +from typing import Optional + +_project_root = Path(__file__).resolve().parent.parent.parent +if str(_project_root) not in sys.path: + sys.path.insert(0, str(_project_root)) + +from engine.common.base import BaseStrategy, Signal, StrategyConfig +from engine.common.models import Kline +from engine.common.config import config +from engine.backtest import BacktestConfig +from engine.data import DataService +from engine.indicators.incremental import EmaInc, AtrInc, RsiInc, BbInc +from engine.example.long_short import LongShortEngine + +SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] +INTERVAL = "1h" +INITIAL = 10_000.0 + + +# ════════════════════════════════════════════════════════ +# 策略 1：海龟交易 (Turtle Trading) +# Richard Dennis & William Eckhardt, 1983 +# 20 日高点突破入场，10 日低点突破出场，2N ATR 止损 +# ════════════════════════════════════════════════════════ + + +class TurtleConfig(StrategyConfig): + entry_period: int = 20 + exit_period: int = 10 + atr_period: int = 20 + atr_stop: float = 2.0 + + +class TurtleStrategy(BaseStrategy): + """海龟交易 — Donchian 通道突破 + ATR 动态止损""" + + strategy_type = "turtle" + + def __init__(self, c: TurtleConfig): + super().__init__(c) + self.cfg = c + self._highs: list[float] = [] + self._lows: list[float] = [] + self._closes: list[float] = [] + self._atr = AtrInc(c.atr_period) + self._side: str = "" + self._entry_price: float = 0.0 + self._highest_since_entry: float = 0.0 + self._lowest_since_entry: float = float("inf") + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._highs.append(k.high) + self._lows.append(k.low) + self._closes.append(k.close) + self._atr.update(k.high, k.low, k.close) + + n = len(self._closes) + min_bars = max(self.cfg.entry_period, self.cfg.atr_period) + 5 + if n < min_bars: + return None + + ca = self._atr[-1] + if ca == 0: + return None + + # Donchian 通道应排除当前 bar（用前 N 根 bar 计算） + donchian_high = max(self._highs[-(self.cfg.entry_period + 1):-1]) + donchian_low = min(self._lows[-(self.cfg.entry_period + 1):-1]) + donchian_exit_high = max(self._highs[-(self.cfg.exit_period + 1):-1]) + donchian_exit_low = min(self._lows[-(self.cfg.exit_period + 1):-1]) + + # ── 持仓管理 ── + if self._side == "long": + self._highest_since_entry = max(self._highest_since_entry, k.high) + stop = self._entry_price - self.cfg.atr_stop * ca + trail_stop = self._highest_since_entry - self.cfg.atr_stop * ca * 0.5 + if k.close < donchian_exit_low or k.close < max(stop, trail_stop): + self._side = "" + reason = "跌破10日低点" if k.close < donchian_exit_low else "ATR止损" + return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time, confidence=0.25) + + elif self._side == "short": + self._lowest_since_entry = min(self._lowest_since_entry, k.low) + stop = self._entry_price + self.cfg.atr_stop * ca + trail_stop = self._lowest_since_entry + self.cfg.atr_stop * ca * 0.5 + if k.close > donchian_exit_high or k.close > min(stop, trail_stop): + self._side = "" + reason = "突破10日高点" if k.close > donchian_exit_high else "ATR止损" + return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time, confidence=0.25) + + # ── 入场：仅在有明显突破幅度时入场 ── + else: + breakout_margin = 0.002 # 需突破通道 0.2% 以上 + if k.close > donchian_high * (1 + breakout_margin): + self._side = "long" + self._entry_price = k.close + self._highest_since_entry = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="突破20日高点", timestamp=k.open_time, confidence=0.25) + elif k.close < donchian_low * (1 - breakout_margin): + self._side = "short" + self._entry_price = k.close + self._lowest_since_entry = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="跌破20日低点", timestamp=k.open_time, confidence=0.25) + + return None + + +# ════════════════════════════════════════════════════════ +# 策略 2：超级趋势 (SuperTrend) +# Olivier Seban, 广泛用于加密货币和商品 +# ATR 动态跟踪止损，趋势翻转即反转 +# ════════════════════════════════════════════════════════ + + +class SuperTrendConfig(StrategyConfig): + atr_period: int = 10 + multiplier: float = 3.0 + + +class SuperTrendStrategy(BaseStrategy): + """超级趋势 — ATR 动态跟踪止损，趋势跟踪""" + + strategy_type = "supertrend" + + def __init__(self, c: SuperTrendConfig): + super().__init__(c) + self.cfg = c + self._atr = AtrInc(c.atr_period) + self._highs: list[float] = [] + self._lows: list[float] = [] + self._closes: list[float] = [] + self._trend: int = 0 # 1=多, -1=空 + self._final_upper: float = 0.0 + self._final_lower: float = 0.0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._highs.append(k.high) + self._lows.append(k.low) + self._closes.append(k.close) + self._atr.update(k.high, k.low, k.close) + + n = len(self._closes) + if n < self.cfg.atr_period + 5: + return None + + ca = self._atr[-1] + if ca == 0: + return None + + hl2 = (k.high + k.low) / 2.0 + upper = hl2 + self.cfg.multiplier * ca + lower = hl2 - self.cfg.multiplier * ca + + # 前一根的最终带用于趋势判断 + prev_upper = self._final_upper + prev_lower = self._final_lower + + # 判断趋势方向 (使用前一根的最终带) + prev_trend = self._trend + if prev_trend == prev_trend: # always true, just need placeholder + pass + if k.close > prev_upper and prev_upper > 0: + self._trend = 1 + elif k.close < prev_lower and prev_lower > 0: + self._trend = -1 + # 否则保持原趋势 + + # 带连续性修正 + if self._trend == 1: + self._final_lower = max(lower, prev_lower) if prev_lower > 0 else lower + self._final_upper = float("inf") + elif self._trend == -1: + self._final_upper = min(upper, prev_upper) if prev_upper > 0 else upper + self._final_lower = float("-inf") + else: + self._final_upper = upper + self._final_lower = lower + + if prev_trend == self._trend: + return None + + # 趋势翻转 → 信号 + if self._trend == 1: + return Signal(symbol=self.cfg.symbol, side="BUY", reason="SuperTrend转多", timestamp=k.open_time, confidence=0.25) + elif self._trend == -1: + return Signal(symbol=self.cfg.symbol, side="SELL", reason="SuperTrend转空", timestamp=k.open_time, confidence=0.25) + return None + + +# ════════════════════════════════════════════════════════ +# 策略 3：MACD 金叉死叉 +# Gerald Appel, 1970s +# MACD(12,26,9) 零轴附近金叉/死叉 + ATR 止损 +# ════════════════════════════════════════════════════════ + + +class MacdCrossConfig(StrategyConfig): + fast: int = 12 + slow: int = 26 + signal: int = 9 + atr_period: int = 14 + atr_stop: float = 2.0 + + +class MacdCrossStrategy(BaseStrategy): + """MACD 金叉死叉 — 零轴以上只做多，零轴以下只做空""" + + strategy_type = "macd_cross" + + def __init__(self, c: MacdCrossConfig): + super().__init__(c) + self.cfg = c + self._ema_fast = EmaInc(c.fast) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(c.atr_period) + self._macd_vals: list[float] = [] # MACD 线值 + self._signal_vals: list[float] = [] # 信号线值 + self._side: str = "" + self._entry_price: float = 0.0 + self._bars_held: int = 0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + fe = self._ema_fast.update(k.close) + se = self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + + n = len(self._ema_fast) + min_bars = max(self.cfg.slow, self.cfg.signal) + 10 + if n < min_bars: + return None + + macd = fe - se + self._macd_vals.append(macd) + + if len(self._macd_vals) < self.cfg.signal + 2: + self._signal_vals.append(0.0) + return None + + # 信号线 = EMA of MACD，简化：用列表算 + if len(self._signal_vals) < self.cfg.signal: + self._signal_vals.append(0.0) + if len(self._signal_vals) == self.cfg.signal: + self._signal_vals[-1] = sum(self._macd_vals[-self.cfg.signal:]) / self.cfg.signal + return None + k_sig = 2.0 / (self.cfg.signal + 1) + sig_val = macd * k_sig + self._signal_vals[-1] * (1 - k_sig) + self._signal_vals.append(sig_val) + + if len(self._signal_vals) < 3: + return None + + cur_m = self._macd_vals[-1] + cur_s = self._signal_vals[-1] + prev_m = self._macd_vals[-2] + prev_s = self._signal_vals[-2] + ca = self._atr[-1] + if ca == 0: + return None + + golden = prev_m <= prev_s and cur_m > cur_s + death = prev_m >= prev_s and cur_m < cur_s + + # ── 持仓管理 ── + if self._side == "long": + self._bars_held += 1 + stop = self._entry_price - self.cfg.atr_stop * ca + if k.close < stop or (death and self._bars_held > 3): + self._side = "" + self._bars_held = 0 + reason = "ATR止损" if k.close < stop else "MACD死叉" + return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time, confidence=0.25) + + elif self._side == "short": + self._bars_held += 1 + stop = self._entry_price + self.cfg.atr_stop * ca + if k.close > stop or (golden and self._bars_held > 3): + self._side = "" + self._bars_held = 0 + reason = "ATR止损" if k.close > stop else "MACD金叉" + return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time, confidence=0.25) + + # ── 入场：零轴同向确认 + 金叉/死叉必须刚发生 ── + else: + if golden and cur_m > 0: + self._side = "long" + self._entry_price = k.close + self._bars_held = 0 + return Signal(symbol=self.cfg.symbol, side="BUY", reason="MACD零轴上金叉", timestamp=k.open_time, confidence=0.25) + elif death and cur_m < 0: + self._side = "short" + self._entry_price = k.close + self._bars_held = 0 + return Signal(symbol=self.cfg.symbol, side="SELL", reason="MACD零轴下死叉", timestamp=k.open_time, confidence=0.25) + + return None + + +# ════════════════════════════════════════════════════════ +# 策略 4：布林收缩爆发 (Bollinger Squeeze) +# John Bollinger, 2002 +# BB 在 KC 内部收缩 → 扩张突破入场 +# ════════════════════════════════════════════════════════ + + +class BBSqueezeConfig(StrategyConfig): + bb_period: int = 20 + bb_std: float = 2.0 + kc_period: int = 20 + kc_mult: float = 1.5 + squeeze_lookback: int = 30 # 判断收缩的回看窗口 + atr_stop: float = 2.0 + + +class BBSqueezeStrategy(BaseStrategy): + """布林收缩爆发 — BB 收缩到极限后扩张，顺势入场""" + + strategy_type = "bb_squeeze" + + def __init__(self, c: BBSqueezeConfig): + super().__init__(c) + self.cfg = c + self._bb = BbInc(c.bb_period, c.bb_std) + self._ema = EmaInc(c.kc_period) # Keltner 中轨 + self._atr_kc = AtrInc(c.kc_period) # Keltner 宽度的 ATR + self._atr_stop = AtrInc(14) # 止损 ATR + self._closes: list[float] = [] + self._side: str = "" + self._entry_price: float = 0.0 + # 收缩检测 + self._bb_widths: list[float] = [] + self._kc_widths: list[float] = [] + self._was_squeezed: bool = False + self._squeeze_bars: int = 0 + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._closes.append(k.close) + bb_u, bb_m, bb_l = self._bb.update(k.close) + typical = (k.high + k.low + k.close) / 3.0 + kc_mid = self._ema.update(typical) + self._atr_kc.update(k.high, k.low, k.close) + self._atr_stop.update(k.high, k.low, k.close) + + n = len(self._closes) + min_bars = max(self.cfg.bb_period, self.cfg.kc_period, self.cfg.squeeze_lookback) + 5 + if n < min_bars: + return None + + atr_kc = self._atr_kc[-1] + ca = self._atr_stop[-1] + if atr_kc == 0 or ca == 0 or bb_u == 0: + return None + + kc_u = kc_mid + self.cfg.kc_mult * atr_kc + kc_l = kc_mid - self.cfg.kc_mult * atr_kc + + bb_width = bb_u - bb_l + kc_width = kc_u - kc_l + self._bb_widths.append(bb_width) + self._kc_widths.append(kc_width) + + # BB 在 KC 内部 = 收缩 + is_squeezed = bb_u < kc_u and bb_l > kc_l + # BB 宽度处于近期最低水平 + lookback = min(self.cfg.squeeze_lookback, len(self._bb_widths)) + recent_bb_w = self._bb_widths[-lookback:] + min_bb_w = min(recent_bb_w) + width_squeeze = bb_width < min_bb_w * 1.2 + + # 收缩释放信号：之前收缩，现在 BB 扩张出 KC + was_squeezed = self._was_squeezed + fired = False + if is_squeezed: + self._was_squeezed = True + self._squeeze_bars += 1 + elif self._was_squeezed: + # BB 不再在 KC 内部 → 收缩释放 + self._was_squeezed = False + self._squeeze_bars = 0 + fired = True + + # 方向判断：用价格与 BB 中轨关系 + EMA(5) 动量 + ema5 = sum(self._closes[-5:]) / 5.0 if n >= 5 else k.close + up_momentum = k.close > bb_m and k.close > ema5 + down_momentum = k.close < bb_m and k.close < ema5 + + # ── 持仓管理 ── + if self._side == "long": + stop = self._entry_price - self.cfg.atr_stop * ca + if k.close < stop or (down_momentum and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="SELL", reason="ATR止损或转弱", timestamp=k.open_time, confidence=0.25) + + elif self._side == "short": + stop = self._entry_price + self.cfg.atr_stop * ca + if k.close > stop or (up_momentum and not is_squeezed): + self._side = "" + return Signal(symbol=self.cfg.symbol, side="BUY", reason="ATR止损或转强", timestamp=k.open_time, confidence=0.25) + + # ── 入场 ── + else: + if was_squeezed and fired and width_squeeze: + if up_momentum: + self._side = "long" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="BB收缩爆发做多", timestamp=k.open_time, confidence=0.25) + elif down_momentum: + self._side = "short" + self._entry_price = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="BB收缩爆发做空", timestamp=k.open_time, confidence=0.25) + + return None + + +# ════════════════════════════════════════════════════════ +# 策略 5：三均线排列 (Triple EMA) +# 经典三线开花 — EMA(10,30,60) 多头/空头排列 + ATR 追踪止损 +# ════════════════════════════════════════════════════════ + + +class TripleEmaConfig(StrategyConfig): + fast: int = 10 + mid: int = 30 + slow: int = 60 + atr_period: int = 14 + atr_stop: float = 2.0 + + +class TripleEmaStrategy(BaseStrategy): + """三均线排列 — 多头排列做多，空头排列做空""" + + strategy_type = "triple_ema" + + def __init__(self, c: TripleEmaConfig): + super().__init__(c) + self.cfg = c + self._ema_fast = EmaInc(c.fast) + self._ema_mid = EmaInc(c.mid) + self._ema_slow = EmaInc(c.slow) + self._atr = AtrInc(c.atr_period) + self._side: str = "" + self._entry_price: float = 0.0 + self._highest_since_entry: float = 0.0 + self._lowest_since_entry: float = float("inf") + + async def on_kline(self, k: Kline) -> Optional[Signal]: + self._ema_fast.update(k.close) + self._ema_mid.update(k.close) + self._ema_slow.update(k.close) + self._atr.update(k.high, k.low, k.close) + + n = len(self._ema_slow) + if n < self.cfg.slow + 10: + return None + + ef = self._ema_fast[-1] + em = self._ema_mid[-1] + es = self._ema_slow[-1] + pf = self._ema_fast[-2] + pm = self._ema_mid[-2] + ca = self._atr[-1] + + if ef == 0 or em == 0 or es == 0 or ca == 0: + return None + + # 排列状态 + bull_align = ef > em > es + bear_align = ef < em < es + # 金叉：快线从下向上穿中线和慢线 + fast_cross_mid_up = pf <= pm and ef > em + fast_cross_mid_down = pf >= pm and ef < em + + # ── 多头持仓 ── + if self._side == "long": + self._highest_since_entry = max(self._highest_since_entry, k.high) + trail = self._highest_since_entry - self.cfg.atr_stop * ca + if fast_cross_mid_down or k.close < trail: + self._side = "" + reason = "快线下穿中线" if fast_cross_mid_down else "ATR追踪止损" + return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time, confidence=0.25) + + # ── 空头持仓 ── + elif self._side == "short": + self._lowest_since_entry = min(self._lowest_since_entry, k.low) + trail = self._lowest_since_entry + self.cfg.atr_stop * ca + if fast_cross_mid_up or k.close > trail: + self._side = "" + reason = "快线上穿中线" if fast_cross_mid_up else "ATR追踪止损" + return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time, confidence=0.25) + + # ── 入场 ── + else: + if fast_cross_mid_up and bull_align: + self._side = "long" + self._entry_price = k.close + self._highest_since_entry = k.close + return Signal(symbol=self.cfg.symbol, side="BUY", reason="三线多头排列+快线金叉", timestamp=k.open_time, confidence=0.25) + elif fast_cross_mid_down and bear_align: + self._side = "short" + self._entry_price = k.close + self._lowest_since_entry = k.close + return Signal(symbol=self.cfg.symbol, side="SELL", reason="三线空头排列+快线死叉", timestamp=k.open_time, confidence=0.25) + + return None + + +# ════════════════════════════════════════════════════════ +# 执行 +# ════════════════════════════════════════════════════════ + +STRATEGIES = { + "1.海龟交易(Turtle)": (TurtleConfig, TurtleStrategy), + "2.超级趋势(SuperTrend)": (SuperTrendConfig, SuperTrendStrategy), + "3.MACD金叉死叉": (MacdCrossConfig, MacdCrossStrategy), + "4.布林收缩爆发(BBSqueeze)": (BBSqueezeConfig, BBSqueezeStrategy), + "5.三均线排列(TripleEMA)": (TripleEmaConfig, TripleEmaStrategy), +} + + +def make_config(config_cls, symbol): + """根据策略类型创建默认参数配置""" + if config_cls == TurtleConfig: + return config_cls(symbol=symbol, entry_period=20, exit_period=10, atr_period=20, atr_stop=2.0) + elif config_cls == SuperTrendConfig: + return config_cls(symbol=symbol, atr_period=10, multiplier=3.0) + elif config_cls == MacdCrossConfig: + return config_cls(symbol=symbol, fast=12, slow=26, signal=9, atr_period=14, atr_stop=2.0) + elif config_cls == BBSqueezeConfig: + return config_cls(symbol=symbol, bb_period=20, bb_std=2.0, kc_period=20, kc_mult=1.5, squeeze_lookback=30, atr_stop=2.0) + elif config_cls == TripleEmaConfig: + return config_cls(symbol=symbol, fast=10, mid=30, slow=60, atr_period=14, atr_stop=2.0) + else: + raise ValueError(f"未知策略: {config_cls}") + + +async def run_one(config_cls, strategy_cls, symbol, start, end): + sc = make_config(config_cls, symbol) + bt = BacktestConfig(symbol=symbol, interval=INTERVAL, start_time=start, end_time=end, initial_capital=INITIAL) + engine = LongShortEngine(bt, db_config=config.db) + r = await engine.run(strategy_cls, sc) + return r + + +async def main(): + ds = DataService(config.db) + await ds.connect() + + # 获取数据范围 + ranges: dict[str, tuple] = {} + for symbol in SYMBOLS: + try: + s, e = await ds.fetch_symbol_date_range(symbol, INTERVAL) + ranges[symbol] = (s, e) + print(f" {symbol}: {s.date()} ~ {e.date()}") + except Exception as ex: + print(f" {symbol}: 获取范围失败 {ex}") + + await ds.close() + + # 汇总数据 + all_results: list[dict] = [] + detail_results: dict[str, dict[str, dict]] = {} # 用于保存详细结果 + + print() + print("═" * 140) + print(" 5 策略 × 4 币种 × 1h — 网络验证策略扫描") + print("═" * 140) + print() + + for strategy_name, (config_cls, strategy_cls) in STRATEGIES.items(): + print(f" ■ {strategy_name}") + print(f" {'币种':<10} {'本金':>7} {'终值':>9} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>8} {'胜率%':>7} {'盈亏比':>7} {'交易':>6} {'耗时s':>7}") + print(" " + "─" * 120) + + detail_results[strategy_name] = {} + + for symbol in SYMBOLS: + if symbol not in ranges: + continue + start, end = ranges[symbol] + + t0 = time.time() + try: + r = await run_one(config_cls, strategy_cls, symbol, start, end) + elapsed = time.time() - t0 + except Exception as ex: + print(f" {symbol:<10} {'错误: ' + str(ex)[:50]}") + continue + + m = r.metrics + final = m.final_equity + print(f" {symbol:<10} {INITIAL:>7.0f} {final:>9.0f} {m.total_return_pct:>7.1f}% {m.annual_return_pct:>7.1f}% {m.sharpe_ratio:>7.2f} {m.max_drawdown_pct:>7.1f}% {m.win_rate*100:>6.1f}% {m.profit_factor:>7.2f} {m.total_trades:>6} {elapsed:>6.1f}s") + + all_results.append({ + "strategy": strategy_name, + "symbol": symbol, + "interval": INTERVAL, + "initial": INITIAL, + "final": final, + "total_return": m.total_return_pct, + "annual_return": m.annual_return_pct, + "sharpe": m.sharpe_ratio, + "drawdown": m.max_drawdown_pct, + "win_rate": m.win_rate * 100, + "profit_factor": m.profit_factor, + "trades": m.total_trades, + "best": m.best_trade_pnl, + "worst": m.worst_trade_pnl, + "avg": m.avg_trade_pnl, + "calmar": m.calmar_ratio, + "start_date": str(start.date()), + "end_date": str(end.date()), + }) + + # 详细交易记录 + detail_results[strategy_name][symbol] = { + "config": { + "symbol": symbol, + "interval": INTERVAL, + "start": str(start.date()), + "end": str(end.date()), + "initial_capital": INITIAL, + }, + "metrics": { + "total_return_pct": m.total_return_pct, + "annual_return_pct": m.annual_return_pct, + "sharpe_ratio": m.sharpe_ratio, + "max_drawdown_pct": m.max_drawdown_pct, + "win_rate": m.win_rate * 100, + "profit_factor": m.profit_factor, + "total_trades": m.total_trades, + "avg_trade_pnl": m.avg_trade_pnl, + "best_trade_pnl": m.best_trade_pnl, + "worst_trade_pnl": m.worst_trade_pnl, + "final_equity": m.final_equity, + "calmar_ratio": m.calmar_ratio, + }, + "trades": [ + { + "side": t.side, + "price": t.price, + "quantity": t.quantity, + "pnl": t.pnl, + "reason": t.reason, + "timestamp": t.timestamp, + "timestamp_str": datetime.fromtimestamp(t.timestamp / 1000, tz=timezone.utc).strftime("%Y-%m-%d %H:%M"), + } + for t in r.trades + ], + } + print() + + # ── 汇总：每种策略的最佳/最差 ── + print("═" * 140) + print(" ■ 各策略汇总 (按年化收益排序)") + print(f" {'策略':<28} {'币种':<10} {'本金':>7} {'终值':>9} {'总收益%':>8} {'年化%':>8} {'夏普':>7} {'回撤%':>8} {'胜率%':>7} {'盈亏比':>7} {'交易':>6}") + print(" " + "─" * 120) + + for sn in STRATEGIES: + candidates = [r for r in all_results if r["strategy"] == sn] + if not candidates: + continue + # 按年化排序，显示所有币种 + candidates.sort(key=lambda x: x["annual_return"], reverse=True) + for c in candidates: + marker = " ★" if c == candidates[0] else " " + print(f" {sn:<26}{marker} {c['symbol']:<10} {c['initial']:>7.0f} {c['final']:>9.0f} {c['total_return']:>7.1f}% {c['annual_return']:>7.1f}% {c['sharpe']:>7.2f} {c['drawdown']:>7.1f}% {c['win_rate']:>6.1f}% {c['profit_factor']:>7.2f} {c['trades']:>6}") + + print("\n═" * 140) + + # ── 保存结果到 JSON ── + output_file = _project_root / "engine" / "example" / "web_strategies_result.json" + with open(output_file, "w", encoding="utf-8") as f: + json.dump({ + "summary": all_results, + "detail": detail_results, + "run_time": datetime.now(timezone.utc).isoformat(), + }, f, ensure_ascii=False, indent=2, default=str) + print(f"\n 详细结果已保存至: {output_file}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/engine/example/web_strategies_result.json b/engine/example/web_strategies_result.json new file mode 100644 index 0000000..65c244a --- /dev/null +++ b/engine/example/web_strategies_result.json @@ -0,0 +1,466001 @@ +{ + "summary": [ + { + "strategy": "1.海龟交易(Turtle)", + "symbol": "BTCUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 1221.9279533509464, + "total_return": -87.78072046649052, + "annual_return": -21.227221861260993, + "sharpe": -3.1837088009687964, + "drawdown": -87.79502053665757, + "win_rate": 27.52454417952314, + "profit_factor": 0.6796751558963826, + "trades": 2852, + "best": 216.4364129688011, + "worst": -253.80738725201374, + "avg": -2.099902629982713, + "calmar": -0.2417816150791589, + "start_date": "2017-08-17", + "end_date": "2026-06-11" + }, + { + "strategy": "1.海龟交易(Turtle)", + "symbol": "ETHUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 741.1647950740837, + "total_return": -92.58835204925916, + "annual_return": -25.572920288455347, + "sharpe": -3.159879974594997, + "drawdown": -92.68774171559222, + "win_rate": 28.44882390336936, + "profit_factor": 0.687891546563954, + "trades": 3146, + "best": 352.8402032949396, + "worst": -337.8876890103728, + "avg": -2.1029540292036226, + "calmar": -0.27590401724237273, + "start_date": "2017-08-17", + "end_date": "2026-06-11" + }, + { + "strategy": "1.海龟交易(Turtle)", + "symbol": "BNBUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 1199.6822808216411, + "total_return": -88.00317719178358, + "annual_return": -21.878437352763214, + "sharpe": -2.143614391818023, + "drawdown": -88.3876602845991, + "win_rate": 28.880866425992778, + "profit_factor": 0.7693435844079854, + "trades": 3047, + "best": 664.8622639865916, + "worst": -496.6486583905924, + "avg": -1.9468760463780126, + "calmar": -0.2475281875582736, + "start_date": "2017-11-06", + "end_date": "2026-06-11" + }, + { + "strategy": "1.海龟交易(Turtle)", + "symbol": "SOLUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 1161.6933979898497, + "total_return": -88.38306602010151, + "annual_return": -30.901903497826343, + "sharpe": -2.773810973195083, + "drawdown": -89.12423303588703, + "win_rate": 29.799914857386124, + "profit_factor": 0.7575984585197537, + "trades": 2349, + "best": 658.6164997593578, + "worst": -289.6358956114907, + "avg": -2.748695493308646, + "calmar": -0.34672840870768884, + "start_date": "2020-08-11", + "end_date": "2026-06-11" + }, + { + "strategy": "2.超级趋势(SuperTrend)", + "symbol": "BTCUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 4843.635655622169, + "total_return": -51.563643443778304, + "annual_return": -7.898702170447091, + "sharpe": -0.641036262386469, + "drawdown": -60.55831723409949, + "win_rate": 33.67477592829706, + "profit_factor": 0.863070904649423, + "trades": 781, + "best": 643.9858717972609, + "worst": -393.3257862439302, + "avg": -4.6721624131288175, + "calmar": -0.13043133513623212, + "start_date": "2017-08-17", + "end_date": "2026-06-11" + }, + { + "strategy": "2.超级趋势(SuperTrend)", + "symbol": "ETHUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 5189.177785477416, + "total_return": -48.10822214522584, + "annual_return": -7.1755063037095335, + "sharpe": -0.4350756004989281, + "drawdown": -57.66563630489837, + "win_rate": 32.756489493201485, + "profit_factor": 0.8949782298393755, + "trades": 809, + "best": 1092.4589129239198, + "worst": -428.11574667135403, + "avg": -4.086228135230754, + "calmar": -0.12443296846271017, + "start_date": "2017-08-17", + "end_date": "2026-06-11" + }, + { + "strategy": "2.超级趋势(SuperTrend)", + "symbol": "BNBUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 20460.764786424756, + "total_return": 104.60764786424757, + "annual_return": 8.693166220913163, + "sharpe": 0.49012077742169446, + "drawdown": -38.553544935019694, + "win_rate": 37.10843373493976, + "profit_factor": 1.185975330550641, + "trades": 830, + "best": 4358.790752760513, + "worst": -1051.898041651052, + "avg": 17.813913001470596, + "calmar": 0.22548292862731853, + "start_date": "2017-11-06", + "end_date": "2026-06-11" + }, + { + "strategy": "2.超级趋势(SuperTrend)", + "symbol": "SOLUSDT", + "interval": "1h", + "initial": 10000.0, + "final": 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"timestamp_str": "2026-05-30 01:00" + } + ] + } + } + }, + "run_time": "2026-06-13T11:01:28.177375+00:00" +} \ No newline at end of file diff --git a/engine/indicators/__init__.py b/engine/indicators/__init__.py index 322b96c..fe6b00c 100644 --- a/engine/indicators/__init__.py +++ b/engine/indicators/__init__.py @@ -17,6 +17,7 @@ from .trend import sma, ema, macd, macd_signal, macd_histogram, adx from .momentum import rsi, stoch, stoch_k, stoch_d from .volatility import bollinger, bollinger_upper, bollinger_mid, bollinger_lower, atr from .volume import obv, vwap +from .incremental import EmaInc, AtrInc, RsiInc, BbInc __all__ = [ # 趋势 @@ -27,4 +28,6 @@ __all__ = [ "bollinger", "bollinger_upper", "bollinger_mid", "bollinger_lower", "atr", # 成交量 "obv", "vwap", + # 增量 + "EmaInc", "AtrInc", "RsiInc", "BbInc", ] diff --git a/engine/indicators/incremental.py b/engine/indicators/incremental.py new file mode 100644 index 0000000..c26626f --- /dev/null +++ b/engine/indicators/incremental.py @@ -0,0 +1,244 @@ +""" +增量指标 — O(1) 每 bar 更新，避免每次从头重算整条序列 + +策略在 on_kline 中对每根 bar 调用 update()，内部只计算增量值， +对外暴露 values 属性（完整序列，支持索引回溯），兼顾性能与易用性。 + +用法： + from engine.indicators.incremental import EmaInc, AtrInc + + e200 = EmaInc(200) + for price in prices: + e200.update(price) + print(e200[-1]) # 最新 EMA 值 + print(e200[-20]) # 20 根前的 EMA 值（斜率计算用） +""" + +from typing import Optional + + +class EmaInc: + """增量 EMA + + 内部维护完整序列，update() 为 O(1)，values 为 list[float] 可直接索引。 + """ + + def __init__(self, period: int): + self.period = period + self.k = 2.0 / (period + 1) + self._values: list[float] = [] + self._warm: list[float] = [] + self._ready = False + + def update(self, price: float) -> float: + """输入新价格，返回最新 EMA 值（不足周期时返回 0）""" + if not self._ready: + self._warm.append(price) + self._values.append(0.0) + if len(self._warm) == self.period: + self._values[-1] = sum(self._warm) / self.period + self._warm.clear() + self._ready = True + return self._values[-1] + return 0.0 + val = price * self.k + self._values[-1] * (1 - self.k) + self._values.append(val) + return val + + @property + def values(self) -> list[float]: + return self._values + + @property + def current(self) -> float: + return self._values[-1] if self._values else 0.0 + + def __getitem__(self, idx: int) -> float: + return self._values[idx] + + def __len__(self) -> int: + return len(self._values) + + +class AtrInc: + """增量 ATR（Wilder 平滑） + + 内部维护完整序列，update() 为 O(1)，values 为 list[float] 可直接索引。 + """ + + def __init__(self, period: int = 14): + self.period = period + self._values: list[float] = [] + self._tr_buffer: list[float] = [] + self._prev_close: Optional[float] = None + self._ready = False + + def update(self, high: float, low: float, close: float) -> float: + """输入新 bar 的 HLC，返回最新 ATR 值（不足周期时返回 0）""" + # 第一根 bar：记录收盘价，无法计算 TR + if self._prev_close is None: + self._prev_close = close + self._values.append(0.0) + return 0.0 + + tr = max(high - low, abs(high - self._prev_close), abs(low - self._prev_close)) + self._prev_close = close + + if not self._ready: + self._tr_buffer.append(tr) + self._values.append(0.0) + if len(self._tr_buffer) == self.period: + atr_val = sum(self._tr_buffer) / self.period + self._values[-1] = atr_val + self._tr_buffer.clear() + self._ready = True + return atr_val + return 0.0 + + # Wilder 平滑 + atr_val = (self._values[-1] * (self.period - 1) + tr) / self.period + self._values.append(atr_val) + return atr_val + + @property + def values(self) -> list[float]: + return self._values + + @property + def current(self) -> float: + return self._values[-1] if self._values else 0.0 + + def __getitem__(self, idx: int) -> float: + return self._values[idx] + + def __len__(self) -> int: + return len(self._values) + + +class RsiInc: + """增量 RSI（Wilder 平滑） + + 内部维护完整序列，update() 为 O(1)。 + """ + + def __init__(self, period: int = 14): + self.period = period + self._values: list[float] = [] + self._prev_price: Optional[float] = None + self._avg_gain: float = 0.0 + self._avg_loss: float = 0.0 + self._changes: list[float] = [] + self._ready = False + + def update(self, price: float) -> float: + if self._prev_price is None: + self._prev_price = price + self._values.append(0.0) + return 0.0 + + change = price - self._prev_price + self._prev_price = price + + if not self._ready: + self._changes.append(change) + self._values.append(0.0) + if len(self._changes) == self.period: + gains = [max(c, 0.0) for c in self._changes] + losses = [abs(min(c, 0.0)) for c in self._changes] + self._avg_gain = sum(gains) / self.period + self._avg_loss = sum(losses) / self.period + self._changes.clear() + self._ready = True + rs = self._avg_gain / self._avg_loss if self._avg_loss > 0 else float("inf") + rsi = 100.0 - (100.0 / (1.0 + rs)) if self._avg_loss > 0 else 100.0 + self._values[-1] = rsi + return rsi + return 0.0 + + gain = max(change, 0.0) + loss = abs(min(change, 0.0)) + self._avg_gain = (self._avg_gain * (self.period - 1) + gain) / self.period + self._avg_loss = (self._avg_loss * (self.period - 1) + loss) / self.period + + if self._avg_loss == 0: + rsi = 100.0 + else: + rs = self._avg_gain / self._avg_loss + rsi = 100.0 - (100.0 / (1.0 + rs)) + + self._values.append(rsi) + return rsi + + @property + def values(self) -> list[float]: + return self._values + + @property + def current(self) -> float: + return self._values[-1] if self._values else 0.0 + + def __getitem__(self, idx: int) -> float: + return self._values[idx] + + def __len__(self) -> int: + return len(self._values) + + +class BbInc: + """增量布林带 + + 内部维护完整序列，update() 返回 (upper, mid, lower) 三元组。 + """ + + def __init__(self, period: int = 20, std: float = 2.0): + self.period = period + self.std = std + self._upper: list[float] = [] + self._mid: list[float] = [] + self._lower: list[float] = [] + self._window: list[float] = [] + self._window_sum: float = 0.0 + self._window_sum_sq: float = 0.0 + + def update(self, price: float) -> tuple[float, float, float]: + self._window.append(price) + self._window_sum += price + self._window_sum_sq += price * price + + if len(self._window) < self.period: + self._upper.append(0.0) + self._mid.append(0.0) + self._lower.append(0.0) + return 0.0, 0.0, 0.0 + + if len(self._window) > self.period: + old = self._window.pop(0) + self._window_sum -= old + self._window_sum_sq -= old * old + + mean = self._window_sum / self.period + variance = (self._window_sum_sq / self.period) - (mean * mean) + stdev = max(variance, 0.0) ** 0.5 + + upper = mean + self.std * stdev + lower = mean - self.std * stdev + + self._upper.append(upper) + self._mid.append(mean) + self._lower.append(lower) + return upper, mean, lower + + @property + def upper(self) -> list[float]: + return self._upper + + @property + def mid(self) -> list[float]: + return self._mid + + @property + def lower(self) -> list[float]: + return self._lower + + def __len__(self) -> int: + return len(self._mid)