docs: 补充回测结果摘要 + 策略文档 + Dashboard 后端 + 整理目录结构
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backtest/INDEX.md
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# Backtest Results Index
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## Today's Backtests (2026-06-08)
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| 文件 | 策略 | 时间范围 | 周期 | 大小 | 备注 |
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|------|------|----------|------|------|------|
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| backtest-result-2026-06-08_00-04-25.meta.json | StructureFlowStrategyV16 | 2022-01-01~2025-08-17 | 2022-01-01~2025-08-17 | 511KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-04-50.meta.json | StructureFlowStrategyV16 | 2022-01-01~2025-08-17 | 2022-01-01~2025-08-17 | 517KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-08-01.meta.json | StructureFlowStrategyV16 | 2022-01-01~2025-08-17 | 2022-01-01~2025-08-17 | 511KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-10-04.meta.json | StructureFlowStrategyV16 | 2022-01-01~2025-08-17 | 2022-01-01~2025-08-17 | 517KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-10-14.meta.json | StructureFlowStrategyV16 | 2022-01-01~2025-08-17 | 2022-01-01~2025-08-17 | 511KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-07-46.meta.json | StructureFlowStrategyV16 | 2022-01-01~2023-01-01 | 2022年度 | 149KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-07-45.meta.json | StructureFlowStrategyV16 | 2023-01-01~2024-01-01 | 2023年度 | 146KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-07-48.meta.json | StructureFlowStrategyV16 | 2023-01-01~2024-01-01 | 2023年度 | 143KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-07-49.meta.json | StructureFlowStrategyV16 | 2024-01-01~2025-01-01 | 2024年度 | 149KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-00-50.meta.json | StructureFlowStrategyV16 | 2025-01-01~2025-08-17 | 2025-01-01~2025-08-17 | 98KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-09-34.meta.json | StructureFlowStrategyV16 | 2025-01-01~2025-08-17 | 2025-01-01~2025-08-17 | 99KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-09-36.meta.json | StructureFlowStrategyV16 | 2025-01-01~2025-08-17 | 2025-01-01~2025-08-17 | 98KB | ✅ 基线版本 |
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| backtest-result-2026-06-08_00-25-02.meta.json | StructureFlowStrategyV18 | 2022-01-01~2026-06-07 | 全周期 | 611KB | ⚠️ 对比用 (2%缓冲) |
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| backtest-result-2026-06-08_00-25-05.meta.json | StructureFlowStrategyV18 | 2022-01-01~2026-06-07 | 全周期 | 615KB | ⚠️ 对比用 (2%缓冲) |
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| backtest-result-2026-06-08_00-17-35.meta.json | StructureFlowStrategyV17 | 2022-01-01~2026-06-07 | 全周期 | 607KB | ❌ 失败 (止损太宽) |
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| backtest-result-2026-06-08_14-50-07.meta.json | StructureFlowStrategyV161 | 2022-01-01~2026-06-07 | 全周期 | - | ❌ v1.6.1 过度过滤 |
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| backtest-result-2026-06-08_14-50-41.meta.json | StructureFlowStrategyV161 | 2022-01-01~2026-06-07 | 全周期 | - | ❌ v1.6.1 过度过滤 |
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| backtest-result-2026-06-08_15-07-41.meta.json | StructureFlowStrategyV162 | 2022-01-01~2026-06-07 | 全周期 | - | ❌ v1.6.2 Brooks二次确认 |
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| backtest-result-2026-06-08_07-22-32.zip | StructureFlowStrategyV163 | 2022-01-01~2026-06-07 | 全周期 | 622KB | ❌ v1.6.3 H4过滤器误杀盈利 |
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| backtest-result-2026-06-08_08-45-17.zip | StructureFlowStrategyV21 | 2022-01-01~2026-06-07 | 全周期 (ETH, Binance Futures) | 627KB | ⭐ v2.1 D1趋势强度 ETH+304.31% |
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| backtest-result-2026-06-08_08-46-37.zip | StructureFlowStrategyV21 | 2022-01-01~2026-06-07 | 全周期 (BTC, Binance Futures) | 632KB | ⭐ v2.1 D1趋势强度 BTC+95.96% |
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| backtest-result-2026-06-08_08-45-58.zip | StructureFlowStrategyV16 | 2022-01-01~2026-06-07 | 全周期 (ETH, Binance Futures) | 628KB | v1.6 基线 ETH+282.27% (同环境对比) |
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| backtest-result-2026-06-08_08-47-14.zip | StructureFlowStrategyV16 | 2022-01-01~2026-06-07 | 全周期 (BTC, Binance Futures) | 629KB | v1.6 基线 BTC+90.92% (同环境对比) |
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**v2.1 vs v1.6 同环境对比 (Binance Futures, 2022-2026):**
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- ETH: v1.6 +282.27% → v2.1 +304.31% (+7.8%相对提升)
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- BTC: v1.6 +90.92% → v2.1 +95.96% (+5.5%相对提升)
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- 两者PF/CAGR/DD均改善，v2.1全面超越v1.6
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**⚠️ 数据源说明：**
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- 之前的v1.6回测(3659.63%)用的是OKX Spot数据，fee结构不同
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- 今日v2.1回测用的是Binance Futures数据（0.05% fee）
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- 两种数据源下v2.1都超越v1.6，趋势一致
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"""
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Structure Flow Strategy v1.6
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=======================
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变更记录：
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v1.0 (2026-06-07): 纯价格结构策略，D1定方向→4H定位→1H入场
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v1.1 (2026-06-07): 1H futures，结构止损，首次回测成功(+61.52%)
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v1.2 (2026-06-07): Entry Candle止损，bug导致50笔硬止损全亏
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v1.3 (2026-06-07): ATR动态止损，结果-63.72%，胜率20.2%
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v1.4 (2026-06-07): 回归纯价格结构止损，+140.71%，胜率38.7%
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v1.5 (2026-06-07): 参数调优(stoploss -5%→-15%, max_stop_dist 3%→5%)，+140.83%
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v1.6 (2026-06-07): ===== 入场质量优化 =====
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- 6-bar冷却期：信号后6h内不重复入场（防止连挨多刀）
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- 活支撑/阻力检查：S/R必须被最近测试并守住才算有效
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设计原则：不降频，只砍最差的那几笔重复入场
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"""
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from datetime import datetime
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import numpy as np
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import pandas as pd
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy, IntParameter, informative
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from freqtrade.persistence import Trade
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class StructureFlowStrategyV16(IStrategy):
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"""
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Structure Flow Strategy v1.6 — 纯价格结构，零指标
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v1.6改动（相对于v1.5）：
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1. 6-bar冷却期：同方向信号触发后，6h内禁止同向再入场
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→ 解决"同一天同一个价位挨两刀"的问题
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2. 活支撑/阻力检查：4H Swing Point 必须被价格测试并守住才有效
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→ 解决"在死支撑上入场"的问题
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"""
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can_short = True
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stoploss = -0.15
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use_custom_stoploss = True
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minimal_roi = {"0": 100}
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max_open_trades = 1
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timeframe = "1h"
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# =====================
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# 可优化参数
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# =====================
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swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
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swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
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pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
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max_stop_dist = IntParameter(20, 50, default=50, space="buy")
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# v1.6 新增
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cooldown_bars = IntParameter(3, 12, default=6, space="buy")
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# =====================
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# 工具：Swing Point 检测
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# =====================
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@staticmethod
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def _detect_swing_points(
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high: pd.Series,
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low: pd.Series,
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window: int = 5,
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) -> tuple[pd.Series, pd.Series]:
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n = len(high)
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sh = pd.Series(np.nan, index=high.index, dtype=float)
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sl = pd.Series(np.nan, index=low.index, dtype=float)
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for i in range(window, n - window):
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if high.iloc[i] > high.iloc[i - window : i].max() and high.iloc[i] > high.iloc[i + 1 : i + window + 1].max():
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sh.iloc[i] = high.iloc[i]
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if low.iloc[i] < low.iloc[i - window : i].min() and low.iloc[i] < low.iloc[i + 1 : i + window + 1].min():
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sl.iloc[i] = low.iloc[i]
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return sh, sl
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# =====================
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# 工具：结构分析
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# =====================
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def _build_structure(
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self,
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high: pd.Series,
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low: pd.Series,
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close: pd.Series,
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swing_high: pd.Series,
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swing_low: pd.Series,
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) -> DataFrame:
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n = len(high)
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trend_up_arr = np.full(n, False)
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trend_down_arr = np.full(n, False)
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nearest_support = np.full(n, np.nan)
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nearest_resistance = np.full(n, np.nan)
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in_demand_zone = np.full(n, False)
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in_supply_zone = np.full(n, False)
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sh_prices = []
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sl_prices = []
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for i in range(n):
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if pd.notna(swing_high.iloc[i]):
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sh_prices.append(swing_high.iloc[i])
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if len(sh_prices) > 4:
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sh_prices.pop(0)
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if pd.notna(swing_low.iloc[i]):
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sl_prices.append(swing_low.iloc[i])
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if len(sl_prices) > 4:
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sl_prices.pop(0)
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if len(sh_prices) >= 2 and len(sl_prices) >= 2:
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if sh_prices[-1] > sh_prices[-2] and sl_prices[-1] > sl_prices[-2]:
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trend_up_arr[i] = True
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elif sh_prices[-1] < sh_prices[-2] and sl_prices[-1] < sl_prices[-2]:
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trend_down_arr[i] = True
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elif i > 0:
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trend_up_arr[i] = trend_up_arr[i - 1]
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trend_down_arr[i] = trend_down_arr[i - 1]
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elif i > 0:
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trend_up_arr[i] = trend_up_arr[i - 1]
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trend_down_arr[i] = trend_down_arr[i - 1]
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if sl_prices:
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nearest_support[i] = sl_prices[-1]
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if sh_prices:
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nearest_resistance[i] = sh_prices[-1]
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c = close.iloc[i]
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if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
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zone_range = nearest_resistance[i] - nearest_support[i]
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if zone_range > 0:
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pos_pct = (c - nearest_support[i]) / zone_range
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in_demand_zone[i] = pos_pct < 0.35
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in_supply_zone[i] = pos_pct > 0.65
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return DataFrame({
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"trend_up": trend_up_arr,
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"trend_down": trend_down_arr,
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"support": nearest_support,
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"resistance": nearest_resistance,
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"in_demand": in_demand_zone,
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"in_supply": in_supply_zone,
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}, index=high.index)
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# =====================
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# 工具：K线形态检测
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# =====================
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@staticmethod
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def _detect_candle_patterns(
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open_: pd.Series,
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high: pd.Series,
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low: pd.Series,
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close: pd.Series,
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pin_bar_wick_ratio: float = 0.6,
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) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
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body = (close - open_).abs()
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total_range = (high - low).replace(0, 0.0001)
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upper_wick = high - close.where(close > open_, open_)
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lower_wick = open_.where(close > open_, close) - low
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is_pin = (upper_wick + lower_wick) / total_range > pin_bar_wick_ratio
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bullish_pin = is_pin & (close > open_) & (lower_wick > upper_wick)
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bearish_pin = is_pin & (close < open_) & (upper_wick > lower_wick)
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prev_open = open_.shift(1)
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prev_close = close.shift(1)
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bullish_engulf = (close > prev_open) & (open_ < prev_close) & (close > open_)
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bearish_engulf = (close < prev_open) & (open_ > prev_close) & (close < open_)
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return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf
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# ================================================================
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# 信息时间框架 — D1 宏观结构
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# ================================================================
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@informative("1d")
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def populate_indicators_1d(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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sh, sl = self._detect_swing_points(
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dataframe["high"], dataframe["low"],
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self.swing_lookback_d1.value,
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)
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structure = self._build_structure(
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dataframe["high"], dataframe["low"], dataframe["close"],
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sh, sl,
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)
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dataframe["trend_up"] = structure["trend_up"]
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dataframe["trend_down"] = structure["trend_down"]
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return dataframe
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# ================================================================
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# 信息时间框架 — 4H 中期结构
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# ================================================================
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@informative("4h")
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def populate_indicators_4h(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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sh, sl = self._detect_swing_points(
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dataframe["high"], dataframe["low"],
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self.swing_lookback_h4.value,
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)
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structure = self._build_structure(
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dataframe["high"], dataframe["low"], dataframe["close"],
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sh, sl,
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)
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dataframe["trend_up"] = structure["trend_up"]
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dataframe["trend_down"] = structure["trend_down"]
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dataframe["support"] = structure["support"]
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dataframe["resistance"] = structure["resistance"]
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dataframe["in_demand"] = structure["in_demand"]
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dataframe["in_supply"] = structure["in_supply"]
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# ================================
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# v1.6 新增：活支撑/阻力检查
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# ================================
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# 支撑"活"的条件：在最近3根4H bar内，low 触及 support ±0.5%
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# 并且收盘价在支撑之上（即测试后撑住了）
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touched_support = (
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(dataframe["low"] <= dataframe["support"] * 1.005) &
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(dataframe["low"] >= dataframe["support"] * 0.995)
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)
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held_support = dataframe["close"] > dataframe["support"]
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support_tested_and_held = touched_support & held_support
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# 在过去3根4H bar内有至少一次"测试并守住"
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dataframe["support_alive"] = support_tested_and_held.rolling(3, min_periods=1).max() > 0
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# 阻力"活"的条件：high 触及 resistance ±0.5% 且 close 在阻力之下
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touched_resistance = (
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(dataframe["high"] >= dataframe["resistance"] * 0.995) &
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(dataframe["high"] <= dataframe["resistance"] * 1.005)
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)
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held_resistance = dataframe["close"] < dataframe["resistance"]
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resistance_tested_and_held = touched_resistance & held_resistance
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dataframe["resistance_alive"] = resistance_tested_and_held.rolling(3, min_periods=1).max() > 0
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return dataframe
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# ================================================================
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# 主时间框架 — 1H 指标
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# ================================================================
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def populate_indicators(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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"""1H 级别：K线形态（零指标）。"""
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bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
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self._detect_candle_patterns(
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dataframe["open"],
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dataframe["high"],
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dataframe["low"],
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dataframe["close"],
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self.pin_bar_wick_ratio.value / 100.0,
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)
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)
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dataframe["bullish_pinbar"] = bullish_pin
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dataframe["bearish_pinbar"] = bearish_pin
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dataframe["bullish_engulfing"] = bullish_engulf
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dataframe["bearish_engulfing"] = bearish_engulf
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dataframe["bullish_signal"] = bullish_pin | bullish_engulf
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dataframe["bearish_signal"] = bearish_pin | bearish_engulf
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# NaN 安全处理
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bool_cols = [
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"trend_up_1d", "trend_down_1d",
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"trend_up_4h", "trend_down_4h",
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"in_demand_4h", "in_supply_4h",
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"support_alive_4h", "resistance_alive_4h",
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"bullish_signal", "bearish_signal",
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]
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for col in bool_cols:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].fillna(False)
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return dataframe
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# =====================
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# 入场信号
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# =====================
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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入场逻辑（1H 时间框架）。
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做多条件:
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1. D1 上升结构（trend_up_1d）
|
||||
2. 4H 需求区域（in_demand_4h）
|
||||
3. 1H 看涨 K 线形态（bullish_signal）
|
||||
4. 止损距离 ≤ max_stop_dist%
|
||||
5. [v1.6] 支撑位是"活"的（support_alive_4h）
|
||||
6. [v1.6] 6h内没有过同方向入场信号（冷却期）
|
||||
|
||||
做空条件对称。
|
||||
"""
|
||||
max_dist = self.max_stop_dist.value / 100.0
|
||||
cooldown = self.cooldown_bars.value
|
||||
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand_4h", "in_supply_4h",
|
||||
"support_alive_4h", "resistance_alive_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
# ── 做多 ──
|
||||
long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
|
||||
|
||||
long_base = (
|
||||
dataframe["trend_up_1d"]
|
||||
& dataframe["in_demand_4h"]
|
||||
& dataframe["bullish_signal"]
|
||||
& (long_stop_dist <= max_dist)
|
||||
& (long_stop_dist > 0.003)
|
||||
)
|
||||
|
||||
# v1.6: 活支撑 — 支撑必须在最近3根4H内被测试并守住
|
||||
long_base = long_base & dataframe["support_alive_4h"]
|
||||
|
||||
# v1.6: 冷却期 — 过去N根1H bar内没有过满足条件的做多信号
|
||||
long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
long_conditions = long_base & long_recent
|
||||
|
||||
dataframe.loc[long_conditions, "enter_long"] = 1
|
||||
|
||||
# ── 做空 ──
|
||||
short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
|
||||
|
||||
short_base = (
|
||||
dataframe["trend_down_1d"]
|
||||
& dataframe["in_supply_4h"]
|
||||
& dataframe["bearish_signal"]
|
||||
& (short_stop_dist <= max_dist)
|
||||
& (short_stop_dist > 0.003)
|
||||
)
|
||||
|
||||
# v1.6: 活阻力 — 阻力必须在最近3根4H内被测试并守住
|
||||
short_base = short_base & dataframe["resistance_alive_4h"]
|
||||
|
||||
# v1.6: 冷却期 — 过去N根1H bar内没有过满足条件的做空信号
|
||||
short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
short_conditions = short_base & short_recent
|
||||
|
||||
dataframe.loc[short_conditions, "enter_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 出场信号
|
||||
# =====================
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""出场逻辑 — 由结构反转触发。"""
|
||||
exit_long = ~dataframe["trend_up_1d"].fillna(True)
|
||||
dataframe.loc[exit_long, "exit_long"] = 1
|
||||
|
||||
exit_short = dataframe["trend_up_1d"].fillna(False)
|
||||
dataframe.loc[exit_short, "exit_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 动态止损 — 纯价格结构（基于Swing Point）
|
||||
# =====================
|
||||
|
||||
def custom_stoploss(
|
||||
self,
|
||||
pair: str,
|
||||
trade: Trade,
|
||||
current_time: datetime,
|
||||
current_rate: float,
|
||||
current_profit: float,
|
||||
after_fill: bool,
|
||||
**kwargs,
|
||||
) -> float:
|
||||
"""
|
||||
止损逻辑：完全基于价格结构，零指标。
|
||||
|
||||
止损位：
|
||||
做多 → support_4h - 0.1%缓冲（最近4H Swing Low下方）
|
||||
做空 → resistance_4h + 0.1%缓冲（最近4H Swing High上方）
|
||||
|
||||
support_4h / resistance_4h 随新Swing Point自动更新，
|
||||
天然形成追踪止损效果。
|
||||
"""
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
if dataframe is None or len(dataframe) == 0:
|
||||
return -0.02 if not trade.is_short else 0.02
|
||||
|
||||
last = dataframe.iloc[-1]
|
||||
|
||||
if not trade.is_short:
|
||||
support = last.get("support_4h", np.nan)
|
||||
if pd.isna(support) or support <= 0:
|
||||
return -0.02
|
||||
sl_price = support * 0.999
|
||||
sl_ratio = (sl_price / current_rate) - 1.0
|
||||
return max(sl_ratio, -0.15)
|
||||
else:
|
||||
resistance = last.get("resistance_4h", np.nan)
|
||||
if pd.isna(resistance) or resistance <= 0:
|
||||
return 0.02
|
||||
sl_price = resistance * 1.001
|
||||
sl_ratio = 1.0 - (sl_price / current_rate)
|
||||
return min(sl_ratio, 0.15)
|
||||
|
||||
# =====================
|
||||
# Plot config
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def plot_config() -> dict:
|
||||
return {
|
||||
"main_plot": {
|
||||
"support_4h": {"color": "green", "type": "line"},
|
||||
"resistance_4h": {"color": "red", "type": "line"},
|
||||
},
|
||||
"subplots": {
|
||||
"signals": {
|
||||
"bullish_pinbar": {"color": "green", "type": "scatter"},
|
||||
"bearish_pinbar": {"color": "red", "type": "scatter"},
|
||||
},
|
||||
"filters": {
|
||||
"support_alive_4h": {"color": "green", "type": "line"},
|
||||
"resistance_alive_4h": {"color": "red", "type": "line"},
|
||||
},
|
||||
},
|
||||
}
|
||||
@ -0,0 +1 @@
|
||||
{"max_open_trades":1,"stake_currency":"USDT","stake_amount":"unlimited","tradable_balance_ratio":0.99,"fiat_display_currency":"USD","dry_run":true,"dry_run_wallet":10000,"trading_mode":"futures","margin_mode":"cross","liquidation_buffer":0.05,"exchange":{"name":"binance","key":"REDACTED","secret":"REDACTED","password":"REDACTED","ccxt_config":{"enableRateLimit":true},"ccxt_async_config":{"enableRateLimit":true},"pair_whitelist":["ETH/USDT:USDT"],"pair_blacklist":[]},"pairlists":[{"method":"StaticPairList"}],"telegram":{"enabled":false,"token":"REDACTED","chat_id":"REDACTED"},"api_server":{"enabled":false,"listen_ip_address":"0.0.0.0","listen_port":8080,"username":"freqtrader","password":"REDACTED","jwt_secret_key":"somethingRandom123"},"bot_name":"backtest","entry_pricing":{"price_side":"same","use_order_book":true,"order_book_top":1,"price_last_balance":0.0,"check_depth_of_market":{"enabled":false,"bids_to_ask_delta":1}},"exit_pricing":{"price_side":"same","use_order_book":true,"order_book_top":1},"config_files":["user_data/config_backtest.json"],"internals":{}}
|
||||
Binary file not shown.
File diff suppressed because one or more lines are too long
@ -0,0 +1,456 @@
|
||||
"""
|
||||
Structure Flow Strategy v2.1
|
||||
=======================
|
||||
变更记录：
|
||||
v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
|
||||
v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
|
||||
v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
|
||||
在4H级别评估趋势强度：最近2个Swing Point的间距变化。
|
||||
如果趋势在扩张（HH/HL间距增大），允许入场；
|
||||
如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
|
||||
目的：只在趋势明确时交易，避免震荡市反复止损。
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pandas import DataFrame
|
||||
from freqtrade.strategy import IStrategy, IntParameter, informative
|
||||
from freqtrade.persistence import Trade
|
||||
|
||||
|
||||
class StructureFlowStrategyV21(IStrategy):
|
||||
"""
|
||||
Structure Flow Strategy v2.1 — D1: 趋势强度过滤
|
||||
|
||||
v2.1改动（相对于v1.6）：
|
||||
在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
|
||||
只有趋势在扩张（或至少不收缩）时才允许入场。
|
||||
"""
|
||||
|
||||
can_short = True
|
||||
stoploss = -0.15
|
||||
use_custom_stoploss = True
|
||||
minimal_roi = {"0": 100}
|
||||
max_open_trades = 1
|
||||
timeframe = "1h"
|
||||
|
||||
# =====================
|
||||
# 可优化参数
|
||||
# =====================
|
||||
|
||||
swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
|
||||
swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
|
||||
pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
|
||||
max_stop_dist = IntParameter(20, 50, default=50, space="buy")
|
||||
cooldown_bars = IntParameter(3, 12, default=6, space="buy")
|
||||
# v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
|
||||
# 0 = 只要不收缩就行；越大要求趋势扩张越强
|
||||
trend_strength_min = IntParameter(-50, 20, default=-20, space="buy") # 扫描更宽范围
|
||||
|
||||
# =====================
|
||||
# 工具：Swing Point 检测
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def _detect_swing_points(
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
window: int = 5,
|
||||
) -> tuple[pd.Series, pd.Series]:
|
||||
n = len(high)
|
||||
sh = pd.Series(np.nan, index=high.index, dtype=float)
|
||||
sl = pd.Series(np.nan, index=low.index, dtype=float)
|
||||
|
||||
for i in range(window, n - window):
|
||||
if high.iloc[i] > high.iloc[i - window : i].max() and high.iloc[i] > high.iloc[i + 1 : i + window + 1].max():
|
||||
sh.iloc[i] = high.iloc[i]
|
||||
if low.iloc[i] < low.iloc[i - window : i].min() and low.iloc[i] < low.iloc[i + 1 : i + window + 1].min():
|
||||
sl.iloc[i] = low.iloc[i]
|
||||
|
||||
return sh, sl
|
||||
|
||||
# =====================
|
||||
# 工具：结构分析
|
||||
# =====================
|
||||
|
||||
def _build_structure(
|
||||
self,
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
close: pd.Series,
|
||||
swing_high: pd.Series,
|
||||
swing_low: pd.Series,
|
||||
) -> DataFrame:
|
||||
n = len(high)
|
||||
|
||||
trend_up_arr = np.full(n, False)
|
||||
trend_down_arr = np.full(n, False)
|
||||
nearest_support = np.full(n, np.nan)
|
||||
nearest_resistance = np.full(n, np.nan)
|
||||
in_demand_zone = np.full(n, False)
|
||||
in_supply_zone = np.full(n, False)
|
||||
|
||||
sh_prices = []
|
||||
sl_prices = []
|
||||
|
||||
for i in range(n):
|
||||
if pd.notna(swing_high.iloc[i]):
|
||||
sh_prices.append(swing_high.iloc[i])
|
||||
if len(sh_prices) > 4:
|
||||
sh_prices.pop(0)
|
||||
|
||||
if pd.notna(swing_low.iloc[i]):
|
||||
sl_prices.append(swing_low.iloc[i])
|
||||
if len(sl_prices) > 4:
|
||||
sl_prices.pop(0)
|
||||
|
||||
if len(sh_prices) >= 2 and len(sl_prices) >= 2:
|
||||
if sh_prices[-1] > sh_prices[-2] and sl_prices[-1] > sl_prices[-2]:
|
||||
trend_up_arr[i] = True
|
||||
elif sh_prices[-1] < sh_prices[-2] and sl_prices[-1] < sl_prices[-2]:
|
||||
trend_down_arr[i] = True
|
||||
elif i > 0:
|
||||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||||
elif i > 0:
|
||||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||||
|
||||
if sl_prices:
|
||||
nearest_support[i] = sl_prices[-1]
|
||||
if sh_prices:
|
||||
nearest_resistance[i] = sh_prices[-1]
|
||||
|
||||
c = close.iloc[i]
|
||||
if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
|
||||
zone_range = nearest_resistance[i] - nearest_support[i]
|
||||
if zone_range > 0:
|
||||
pos_pct = (c - nearest_support[i]) / zone_range
|
||||
in_demand_zone[i] = pos_pct < 0.35
|
||||
in_supply_zone[i] = pos_pct > 0.65
|
||||
|
||||
return DataFrame({
|
||||
"trend_up": trend_up_arr,
|
||||
"trend_down": trend_down_arr,
|
||||
"support": nearest_support,
|
||||
"resistance": nearest_resistance,
|
||||
"in_demand": in_demand_zone,
|
||||
"in_supply": in_supply_zone,
|
||||
}, index=high.index)
|
||||
|
||||
# =====================
|
||||
# 工具：K线形态检测
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def _detect_candle_patterns(
|
||||
open_: pd.Series,
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
close: pd.Series,
|
||||
pin_bar_wick_ratio: float = 0.6,
|
||||
) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
|
||||
body = (close - open_).abs()
|
||||
total_range = (high - low).replace(0, 0.0001)
|
||||
|
||||
upper_wick = high - close.where(close > open_, open_)
|
||||
lower_wick = open_.where(close > open_, close) - low
|
||||
is_pin = (upper_wick + lower_wick) / total_range > pin_bar_wick_ratio
|
||||
|
||||
bullish_pin = is_pin & (close > open_) & (lower_wick > upper_wick)
|
||||
bearish_pin = is_pin & (close < open_) & (upper_wick > lower_wick)
|
||||
|
||||
prev_open = open_.shift(1)
|
||||
prev_close = close.shift(1)
|
||||
bullish_engulf = (close > prev_open) & (open_ < prev_close) & (close > open_)
|
||||
bearish_engulf = (close < prev_open) & (open_ > prev_close) & (close < open_)
|
||||
|
||||
return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf
|
||||
|
||||
# ================================================================
|
||||
# 信息时间框架 — D1 宏观结构
|
||||
# ================================================================
|
||||
|
||||
@informative("1d")
|
||||
def populate_indicators_1d(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
sh, sl = self._detect_swing_points(
|
||||
dataframe["high"], dataframe["low"],
|
||||
self.swing_lookback_d1.value,
|
||||
)
|
||||
structure = self._build_structure(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||||
sh, sl,
|
||||
)
|
||||
dataframe["trend_up"] = structure["trend_up"]
|
||||
dataframe["trend_down"] = structure["trend_down"]
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 信息时间框架 — 4H 中期结构
|
||||
# ================================================================
|
||||
|
||||
@informative("4h")
|
||||
def populate_indicators_4h(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
sh, sl = self._detect_swing_points(
|
||||
dataframe["high"], dataframe["low"],
|
||||
self.swing_lookback_h4.value,
|
||||
)
|
||||
structure = self._build_structure(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||||
sh, sl,
|
||||
)
|
||||
dataframe["trend_up"] = structure["trend_up"]
|
||||
dataframe["trend_down"] = structure["trend_down"]
|
||||
dataframe["support"] = structure["support"]
|
||||
dataframe["resistance"] = structure["resistance"]
|
||||
dataframe["in_demand"] = structure["in_demand"]
|
||||
dataframe["in_supply"] = structure["in_supply"]
|
||||
|
||||
# ================================
|
||||
# v1.6 活支撑/阻力检查（保留）
|
||||
# ================================
|
||||
touched_support = (
|
||||
(dataframe["low"] <= dataframe["support"] * 1.005) &
|
||||
(dataframe["low"] >= dataframe["support"] * 0.995)
|
||||
)
|
||||
held_support = dataframe["close"] > dataframe["support"]
|
||||
support_tested_and_held = touched_support & held_support
|
||||
dataframe["support_alive"] = support_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||||
|
||||
touched_resistance = (
|
||||
(dataframe["high"] >= dataframe["resistance"] * 0.995) &
|
||||
(dataframe["high"] <= dataframe["resistance"] * 1.005)
|
||||
)
|
||||
held_resistance = dataframe["close"] < dataframe["resistance"]
|
||||
resistance_tested_and_held = touched_resistance & held_resistance
|
||||
dataframe["resistance_alive"] = resistance_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||||
|
||||
# ================================
|
||||
# v2.1 新增：趋势强度评估
|
||||
# ================================
|
||||
# 计算最近2个Swing Point之间的间距变化
|
||||
# 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
|
||||
# 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
|
||||
# 间距缩小 → 趋势减弱/震荡
|
||||
|
||||
sh_prices = []
|
||||
sl_prices = []
|
||||
trend_strength_up = np.full(len(dataframe), np.nan)
|
||||
trend_strength_down = np.full(len(dataframe), np.nan)
|
||||
|
||||
for i in range(len(dataframe)):
|
||||
if pd.notna(sh.iloc[i]):
|
||||
sh_prices.append(sh.iloc[i])
|
||||
if len(sh_prices) > 4:
|
||||
sh_prices.pop(0)
|
||||
if pd.notna(sl.iloc[i]):
|
||||
sl_prices.append(sl.iloc[i])
|
||||
if len(sl_prices) > 4:
|
||||
sl_prices.pop(0)
|
||||
|
||||
# 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
|
||||
if len(sh_prices) >= 2 and len(sl_prices) >= 2:
|
||||
# HH间距：最近两个Swing High的差值百分比
|
||||
hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
|
||||
# HL间距：最近两个Swing Low的差值百分比
|
||||
hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
|
||||
# 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
|
||||
trend_strength_up[i] = hh_dist + hl_dist
|
||||
|
||||
# 下降趋势强度（取反：间距缩小是负值）
|
||||
trend_strength_down[i] = -(hh_dist + hl_dist)
|
||||
|
||||
dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
|
||||
dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
|
||||
|
||||
# 趋势强度是否足够（扩张中）
|
||||
min_strength = self.trend_strength_min.value / 100.0 # 0~0.30
|
||||
dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
|
||||
dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
|
||||
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 主时间框架 — 1H 指标
|
||||
# ================================================================
|
||||
|
||||
def populate_indicators(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
"""1H 级别：K线形态（零指标）。"""
|
||||
bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
|
||||
self._detect_candle_patterns(
|
||||
dataframe["open"],
|
||||
dataframe["high"],
|
||||
dataframe["low"],
|
||||
dataframe["close"],
|
||||
self.pin_bar_wick_ratio.value / 100.0,
|
||||
)
|
||||
)
|
||||
dataframe["bullish_pinbar"] = bullish_pin
|
||||
dataframe["bearish_pinbar"] = bearish_pin
|
||||
dataframe["bullish_engulfing"] = bullish_engulf
|
||||
dataframe["bearish_engulfing"] = bearish_engulf
|
||||
dataframe["bullish_signal"] = bullish_pin | bullish_engulf
|
||||
dataframe["bearish_signal"] = bearish_pin | bearish_engulf
|
||||
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand_4h", "in_supply_4h",
|
||||
"support_alive_4h", "resistance_alive_4h",
|
||||
"strong_uptrend_4h", "strong_downtrend_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 入场信号
|
||||
# =====================
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
入场逻辑（1H 时间框架）。
|
||||
|
||||
v2.1 核心改动：D1 — 趋势强度过滤
|
||||
做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
|
||||
做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
|
||||
"""
|
||||
max_dist = self.max_stop_dist.value / 100.0
|
||||
cooldown = self.cooldown_bars.value
|
||||
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand_4h", "in_supply_4h",
|
||||
"support_alive_4h", "resistance_alive_4h",
|
||||
"strong_uptrend_4h", "strong_downtrend_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
# ── 做多 ──
|
||||
long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
|
||||
|
||||
long_base = (
|
||||
dataframe["trend_up_1d"]
|
||||
& dataframe["in_demand_4h"]
|
||||
& dataframe["bullish_signal"]
|
||||
& (long_stop_dist <= max_dist)
|
||||
& (long_stop_dist > 0.003)
|
||||
& dataframe["support_alive_4h"]
|
||||
# v2.1: 趋势强度 — 4H上升趋势必须在扩张
|
||||
& dataframe["strong_uptrend_4h"]
|
||||
)
|
||||
|
||||
long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[long_base & long_recent, "enter_long"] = 1
|
||||
|
||||
# ── 做空 ──
|
||||
short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
|
||||
|
||||
short_base = (
|
||||
dataframe["trend_down_1d"]
|
||||
& dataframe["in_supply_4h"]
|
||||
& dataframe["bearish_signal"]
|
||||
& (short_stop_dist <= max_dist)
|
||||
& (short_stop_dist > 0.003)
|
||||
& dataframe["resistance_alive_4h"]
|
||||
# v2.1: 趋势强度 — 4H下降趋势必须在扩张
|
||||
& dataframe["strong_downtrend_4h"]
|
||||
)
|
||||
|
||||
short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[short_base & short_recent, "enter_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 出场信号
|
||||
# =====================
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""出场逻辑 — 由结构反转触发。"""
|
||||
exit_long = ~dataframe["trend_up_1d"].fillna(True)
|
||||
dataframe.loc[exit_long, "exit_long"] = 1
|
||||
|
||||
exit_short = dataframe["trend_up_1d"].fillna(False)
|
||||
dataframe.loc[exit_short, "exit_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 动态止损 — 纯价格结构（基于Swing Point）
|
||||
# =====================
|
||||
|
||||
def custom_stoploss(
|
||||
self,
|
||||
pair: str,
|
||||
trade: Trade,
|
||||
current_time: datetime,
|
||||
current_rate: float,
|
||||
current_profit: float,
|
||||
after_fill: bool,
|
||||
**kwargs,
|
||||
) -> float:
|
||||
"""
|
||||
止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
|
||||
"""
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
if dataframe is None or len(dataframe) == 0:
|
||||
return -0.02 if not trade.is_short else 0.02
|
||||
|
||||
last = dataframe.iloc[-1]
|
||||
|
||||
if not trade.is_short:
|
||||
support = last.get("support_4h", np.nan)
|
||||
if pd.isna(support) or support <= 0:
|
||||
return -0.02
|
||||
sl_price = support * 0.999
|
||||
sl_ratio = (sl_price / current_rate) - 1.0
|
||||
return max(sl_ratio, -0.15)
|
||||
else:
|
||||
resistance = last.get("resistance_4h", np.nan)
|
||||
if pd.isna(resistance) or resistance <= 0:
|
||||
return 0.02
|
||||
sl_price = resistance * 1.001
|
||||
sl_ratio = 1.0 - (sl_price / current_rate)
|
||||
return min(sl_ratio, 0.15)
|
||||
|
||||
# =====================
|
||||
# Plot config
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def plot_config() -> dict:
|
||||
return {
|
||||
"main_plot": {
|
||||
"support_4h": {"color": "green", "type": "line"},
|
||||
"resistance_4h": {"color": "red", "type": "line"},
|
||||
},
|
||||
"subplots": {
|
||||
"signals": {
|
||||
"bullish_pinbar": {"color": "green", "type": "scatter"},
|
||||
"bearish_pinbar": {"color": "red", "type": "scatter"},
|
||||
},
|
||||
"filters": {
|
||||
"support_alive_4h": {"color": "green", "type": "line"},
|
||||
"resistance_alive_4h": {"color": "red", "type": "line"},
|
||||
"strong_uptrend_4h": {"color": "blue", "type": "line"},
|
||||
"strong_downtrend_4h": {"color": "orange", "type": "line"},
|
||||
},
|
||||
},
|
||||
}
|
||||
@ -0,0 +1 @@
|
||||
{"max_open_trades":3,"stake_currency":"USDT","stake_amount":"unlimited","tradable_balance_ratio":0.99,"fiat_display_currency":"USD","dry_run":true,"dry_run_wallet":1000,"cancel_open_orders_on_exit":false,"trading_mode":"futures","margin_mode":"isolated","unfilledtimeout":{"entry":10,"exit":10,"exit_timeout_count":0,"unit":"minutes"},"entry_pricing":{"price_side":"same","use_order_book":true,"order_book_top":1,"price_last_balance":0.0,"check_depth_of_market":{"enabled":false,"bids_to_ask_delta":1}},"exit_pricing":{"price_side":"same","use_order_book":true,"order_book_top":1},"exchange":{"name":"binance","key":"REDACTED","secret":"REDACTED","ccxt_config":{"proxies":{"http":"http://host.docker.internal:7890","https":"http://host.docker.internal:7890"}},"ccxt_async_config":{"proxies":{"http":"http://host.docker.internal:7890","https":"http://host.docker.internal:7890"}},"pair_whitelist":["ETH/USDT:USDT"],"pair_blacklist":[]},"pairlists":[{"method":"StaticPairList"}],"bot_name":"freqtrade","initial_state":"running","internals":{"process_throttle_secs":5},"config_files":["/tmp/futures_config.json"]}
|
||||
Binary file not shown.
File diff suppressed because one or more lines are too long
@ -0,0 +1,456 @@
|
||||
"""
|
||||
Structure Flow Strategy v2.1
|
||||
=======================
|
||||
变更记录：
|
||||
v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
|
||||
v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
|
||||
v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
|
||||
在4H级别评估趋势强度：最近2个Swing Point的间距变化。
|
||||
如果趋势在扩张（HH/HL间距增大），允许入场；
|
||||
如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
|
||||
目的：只在趋势明确时交易，避免震荡市反复止损。
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pandas import DataFrame
|
||||
from freqtrade.strategy import IStrategy, IntParameter, informative
|
||||
from freqtrade.persistence import Trade
|
||||
|
||||
|
||||
class StructureFlowStrategyV21(IStrategy):
|
||||
"""
|
||||
Structure Flow Strategy v2.1 — D1: 趋势强度过滤
|
||||
|
||||
v2.1改动（相对于v1.6）：
|
||||
在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
|
||||
只有趋势在扩张（或至少不收缩）时才允许入场。
|
||||
"""
|
||||
|
||||
can_short = True
|
||||
stoploss = -0.15
|
||||
use_custom_stoploss = True
|
||||
minimal_roi = {"0": 100}
|
||||
max_open_trades = 1
|
||||
timeframe = "1h"
|
||||
|
||||
# =====================
|
||||
# 可优化参数
|
||||
# =====================
|
||||
|
||||
swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
|
||||
swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
|
||||
pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
|
||||
max_stop_dist = IntParameter(20, 50, default=50, space="buy")
|
||||
cooldown_bars = IntParameter(3, 12, default=6, space="buy")
|
||||
# v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
|
||||
# 0 = 只要不收缩就行；越大要求趋势扩张越强
|
||||
trend_strength_min = IntParameter(-50, 20, default=-20, space="buy") # 扫描更宽范围
|
||||
|
||||
# =====================
|
||||
# 工具：Swing Point 检测
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def _detect_swing_points(
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
window: int = 5,
|
||||
) -> tuple[pd.Series, pd.Series]:
|
||||
n = len(high)
|
||||
sh = pd.Series(np.nan, index=high.index, dtype=float)
|
||||
sl = pd.Series(np.nan, index=low.index, dtype=float)
|
||||
|
||||
for i in range(window, n - window):
|
||||
if high.iloc[i] > high.iloc[i - window : i].max() and high.iloc[i] > high.iloc[i + 1 : i + window + 1].max():
|
||||
sh.iloc[i] = high.iloc[i]
|
||||
if low.iloc[i] < low.iloc[i - window : i].min() and low.iloc[i] < low.iloc[i + 1 : i + window + 1].min():
|
||||
sl.iloc[i] = low.iloc[i]
|
||||
|
||||
return sh, sl
|
||||
|
||||
# =====================
|
||||
# 工具：结构分析
|
||||
# =====================
|
||||
|
||||
def _build_structure(
|
||||
self,
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
close: pd.Series,
|
||||
swing_high: pd.Series,
|
||||
swing_low: pd.Series,
|
||||
) -> DataFrame:
|
||||
n = len(high)
|
||||
|
||||
trend_up_arr = np.full(n, False)
|
||||
trend_down_arr = np.full(n, False)
|
||||
nearest_support = np.full(n, np.nan)
|
||||
nearest_resistance = np.full(n, np.nan)
|
||||
in_demand_zone = np.full(n, False)
|
||||
in_supply_zone = np.full(n, False)
|
||||
|
||||
sh_prices = []
|
||||
sl_prices = []
|
||||
|
||||
for i in range(n):
|
||||
if pd.notna(swing_high.iloc[i]):
|
||||
sh_prices.append(swing_high.iloc[i])
|
||||
if len(sh_prices) > 4:
|
||||
sh_prices.pop(0)
|
||||
|
||||
if pd.notna(swing_low.iloc[i]):
|
||||
sl_prices.append(swing_low.iloc[i])
|
||||
if len(sl_prices) > 4:
|
||||
sl_prices.pop(0)
|
||||
|
||||
if len(sh_prices) >= 2 and len(sl_prices) >= 2:
|
||||
if sh_prices[-1] > sh_prices[-2] and sl_prices[-1] > sl_prices[-2]:
|
||||
trend_up_arr[i] = True
|
||||
elif sh_prices[-1] < sh_prices[-2] and sl_prices[-1] < sl_prices[-2]:
|
||||
trend_down_arr[i] = True
|
||||
elif i > 0:
|
||||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||||
elif i > 0:
|
||||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||||
|
||||
if sl_prices:
|
||||
nearest_support[i] = sl_prices[-1]
|
||||
if sh_prices:
|
||||
nearest_resistance[i] = sh_prices[-1]
|
||||
|
||||
c = close.iloc[i]
|
||||
if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
|
||||
zone_range = nearest_resistance[i] - nearest_support[i]
|
||||
if zone_range > 0:
|
||||
pos_pct = (c - nearest_support[i]) / zone_range
|
||||
in_demand_zone[i] = pos_pct < 0.35
|
||||
in_supply_zone[i] = pos_pct > 0.65
|
||||
|
||||
return DataFrame({
|
||||
"trend_up": trend_up_arr,
|
||||
"trend_down": trend_down_arr,
|
||||
"support": nearest_support,
|
||||
"resistance": nearest_resistance,
|
||||
"in_demand": in_demand_zone,
|
||||
"in_supply": in_supply_zone,
|
||||
}, index=high.index)
|
||||
|
||||
# =====================
|
||||
# 工具：K线形态检测
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def _detect_candle_patterns(
|
||||
open_: pd.Series,
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
close: pd.Series,
|
||||
pin_bar_wick_ratio: float = 0.6,
|
||||
) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
|
||||
body = (close - open_).abs()
|
||||
total_range = (high - low).replace(0, 0.0001)
|
||||
|
||||
upper_wick = high - close.where(close > open_, open_)
|
||||
lower_wick = open_.where(close > open_, close) - low
|
||||
is_pin = (upper_wick + lower_wick) / total_range > pin_bar_wick_ratio
|
||||
|
||||
bullish_pin = is_pin & (close > open_) & (lower_wick > upper_wick)
|
||||
bearish_pin = is_pin & (close < open_) & (upper_wick > lower_wick)
|
||||
|
||||
prev_open = open_.shift(1)
|
||||
prev_close = close.shift(1)
|
||||
bullish_engulf = (close > prev_open) & (open_ < prev_close) & (close > open_)
|
||||
bearish_engulf = (close < prev_open) & (open_ > prev_close) & (close < open_)
|
||||
|
||||
return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf
|
||||
|
||||
# ================================================================
|
||||
# 信息时间框架 — D1 宏观结构
|
||||
# ================================================================
|
||||
|
||||
@informative("1d")
|
||||
def populate_indicators_1d(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
sh, sl = self._detect_swing_points(
|
||||
dataframe["high"], dataframe["low"],
|
||||
self.swing_lookback_d1.value,
|
||||
)
|
||||
structure = self._build_structure(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||||
sh, sl,
|
||||
)
|
||||
dataframe["trend_up"] = structure["trend_up"]
|
||||
dataframe["trend_down"] = structure["trend_down"]
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 信息时间框架 — 4H 中期结构
|
||||
# ================================================================
|
||||
|
||||
@informative("4h")
|
||||
def populate_indicators_4h(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
sh, sl = self._detect_swing_points(
|
||||
dataframe["high"], dataframe["low"],
|
||||
self.swing_lookback_h4.value,
|
||||
)
|
||||
structure = self._build_structure(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||||
sh, sl,
|
||||
)
|
||||
dataframe["trend_up"] = structure["trend_up"]
|
||||
dataframe["trend_down"] = structure["trend_down"]
|
||||
dataframe["support"] = structure["support"]
|
||||
dataframe["resistance"] = structure["resistance"]
|
||||
dataframe["in_demand"] = structure["in_demand"]
|
||||
dataframe["in_supply"] = structure["in_supply"]
|
||||
|
||||
# ================================
|
||||
# v1.6 活支撑/阻力检查（保留）
|
||||
# ================================
|
||||
touched_support = (
|
||||
(dataframe["low"] <= dataframe["support"] * 1.005) &
|
||||
(dataframe["low"] >= dataframe["support"] * 0.995)
|
||||
)
|
||||
held_support = dataframe["close"] > dataframe["support"]
|
||||
support_tested_and_held = touched_support & held_support
|
||||
dataframe["support_alive"] = support_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||||
|
||||
touched_resistance = (
|
||||
(dataframe["high"] >= dataframe["resistance"] * 0.995) &
|
||||
(dataframe["high"] <= dataframe["resistance"] * 1.005)
|
||||
)
|
||||
held_resistance = dataframe["close"] < dataframe["resistance"]
|
||||
resistance_tested_and_held = touched_resistance & held_resistance
|
||||
dataframe["resistance_alive"] = resistance_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||||
|
||||
# ================================
|
||||
# v2.1 新增：趋势强度评估
|
||||
# ================================
|
||||
# 计算最近2个Swing Point之间的间距变化
|
||||
# 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
|
||||
# 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
|
||||
# 间距缩小 → 趋势减弱/震荡
|
||||
|
||||
sh_prices = []
|
||||
sl_prices = []
|
||||
trend_strength_up = np.full(len(dataframe), np.nan)
|
||||
trend_strength_down = np.full(len(dataframe), np.nan)
|
||||
|
||||
for i in range(len(dataframe)):
|
||||
if pd.notna(sh.iloc[i]):
|
||||
sh_prices.append(sh.iloc[i])
|
||||
if len(sh_prices) > 4:
|
||||
sh_prices.pop(0)
|
||||
if pd.notna(sl.iloc[i]):
|
||||
sl_prices.append(sl.iloc[i])
|
||||
if len(sl_prices) > 4:
|
||||
sl_prices.pop(0)
|
||||
|
||||
# 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
|
||||
if len(sh_prices) >= 2 and len(sl_prices) >= 2:
|
||||
# HH间距：最近两个Swing High的差值百分比
|
||||
hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
|
||||
# HL间距：最近两个Swing Low的差值百分比
|
||||
hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
|
||||
# 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
|
||||
trend_strength_up[i] = hh_dist + hl_dist
|
||||
|
||||
# 下降趋势强度（取反：间距缩小是负值）
|
||||
trend_strength_down[i] = -(hh_dist + hl_dist)
|
||||
|
||||
dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
|
||||
dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
|
||||
|
||||
# 趋势强度是否足够（扩张中）
|
||||
min_strength = self.trend_strength_min.value / 100.0 # 0~0.30
|
||||
dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
|
||||
dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
|
||||
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 主时间框架 — 1H 指标
|
||||
# ================================================================
|
||||
|
||||
def populate_indicators(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
"""1H 级别：K线形态（零指标）。"""
|
||||
bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
|
||||
self._detect_candle_patterns(
|
||||
dataframe["open"],
|
||||
dataframe["high"],
|
||||
dataframe["low"],
|
||||
dataframe["close"],
|
||||
self.pin_bar_wick_ratio.value / 100.0,
|
||||
)
|
||||
)
|
||||
dataframe["bullish_pinbar"] = bullish_pin
|
||||
dataframe["bearish_pinbar"] = bearish_pin
|
||||
dataframe["bullish_engulfing"] = bullish_engulf
|
||||
dataframe["bearish_engulfing"] = bearish_engulf
|
||||
dataframe["bullish_signal"] = bullish_pin | bullish_engulf
|
||||
dataframe["bearish_signal"] = bearish_pin | bearish_engulf
|
||||
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand_4h", "in_supply_4h",
|
||||
"support_alive_4h", "resistance_alive_4h",
|
||||
"strong_uptrend_4h", "strong_downtrend_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 入场信号
|
||||
# =====================
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
入场逻辑（1H 时间框架）。
|
||||
|
||||
v2.1 核心改动：D1 — 趋势强度过滤
|
||||
做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
|
||||
做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
|
||||
"""
|
||||
max_dist = self.max_stop_dist.value / 100.0
|
||||
cooldown = self.cooldown_bars.value
|
||||
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand_4h", "in_supply_4h",
|
||||
"support_alive_4h", "resistance_alive_4h",
|
||||
"strong_uptrend_4h", "strong_downtrend_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
# ── 做多 ──
|
||||
long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
|
||||
|
||||
long_base = (
|
||||
dataframe["trend_up_1d"]
|
||||
& dataframe["in_demand_4h"]
|
||||
& dataframe["bullish_signal"]
|
||||
& (long_stop_dist <= max_dist)
|
||||
& (long_stop_dist > 0.003)
|
||||
& dataframe["support_alive_4h"]
|
||||
# v2.1: 趋势强度 — 4H上升趋势必须在扩张
|
||||
& dataframe["strong_uptrend_4h"]
|
||||
)
|
||||
|
||||
long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[long_base & long_recent, "enter_long"] = 1
|
||||
|
||||
# ── 做空 ──
|
||||
short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
|
||||
|
||||
short_base = (
|
||||
dataframe["trend_down_1d"]
|
||||
& dataframe["in_supply_4h"]
|
||||
& dataframe["bearish_signal"]
|
||||
& (short_stop_dist <= max_dist)
|
||||
& (short_stop_dist > 0.003)
|
||||
& dataframe["resistance_alive_4h"]
|
||||
# v2.1: 趋势强度 — 4H下降趋势必须在扩张
|
||||
& dataframe["strong_downtrend_4h"]
|
||||
)
|
||||
|
||||
short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[short_base & short_recent, "enter_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 出场信号
|
||||
# =====================
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""出场逻辑 — 由结构反转触发。"""
|
||||
exit_long = ~dataframe["trend_up_1d"].fillna(True)
|
||||
dataframe.loc[exit_long, "exit_long"] = 1
|
||||
|
||||
exit_short = dataframe["trend_up_1d"].fillna(False)
|
||||
dataframe.loc[exit_short, "exit_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 动态止损 — 纯价格结构（基于Swing Point）
|
||||
# =====================
|
||||
|
||||
def custom_stoploss(
|
||||
self,
|
||||
pair: str,
|
||||
trade: Trade,
|
||||
current_time: datetime,
|
||||
current_rate: float,
|
||||
current_profit: float,
|
||||
after_fill: bool,
|
||||
**kwargs,
|
||||
) -> float:
|
||||
"""
|
||||
止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
|
||||
"""
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
if dataframe is None or len(dataframe) == 0:
|
||||
return -0.02 if not trade.is_short else 0.02
|
||||
|
||||
last = dataframe.iloc[-1]
|
||||
|
||||
if not trade.is_short:
|
||||
support = last.get("support_4h", np.nan)
|
||||
if pd.isna(support) or support <= 0:
|
||||
return -0.02
|
||||
sl_price = support * 0.999
|
||||
sl_ratio = (sl_price / current_rate) - 1.0
|
||||
return max(sl_ratio, -0.15)
|
||||
else:
|
||||
resistance = last.get("resistance_4h", np.nan)
|
||||
if pd.isna(resistance) or resistance <= 0:
|
||||
return 0.02
|
||||
sl_price = resistance * 1.001
|
||||
sl_ratio = 1.0 - (sl_price / current_rate)
|
||||
return min(sl_ratio, 0.15)
|
||||
|
||||
# =====================
|
||||
# Plot config
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def plot_config() -> dict:
|
||||
return {
|
||||
"main_plot": {
|
||||
"support_4h": {"color": "green", "type": "line"},
|
||||
"resistance_4h": {"color": "red", "type": "line"},
|
||||
},
|
||||
"subplots": {
|
||||
"signals": {
|
||||
"bullish_pinbar": {"color": "green", "type": "scatter"},
|
||||
"bearish_pinbar": {"color": "red", "type": "scatter"},
|
||||
},
|
||||
"filters": {
|
||||
"support_alive_4h": {"color": "green", "type": "line"},
|
||||
"resistance_alive_4h": {"color": "red", "type": "line"},
|
||||
"strong_uptrend_4h": {"color": "blue", "type": "line"},
|
||||
"strong_downtrend_4h": {"color": "orange", "type": "line"},
|
||||
},
|
||||
},
|
||||
}
|
||||
@ -0,0 +1 @@
|
||||
{"max_open_trades":3,"stake_currency":"USDT","stake_amount":"unlimited","tradable_balance_ratio":0.99,"fiat_display_currency":"USD","dry_run":true,"dry_run_wallet":1000,"cancel_open_orders_on_exit":false,"trading_mode":"futures","margin_mode":"isolated","unfilledtimeout":{"entry":10,"exit":10,"exit_timeout_count":0,"unit":"minutes"},"entry_pricing":{"price_side":"same","use_order_book":true,"order_book_top":1,"price_last_balance":0.0,"check_depth_of_market":{"enabled":false,"bids_to_ask_delta":1}},"exit_pricing":{"price_side":"same","use_order_book":true,"order_book_top":1},"exchange":{"name":"binance","key":"REDACTED","secret":"REDACTED","ccxt_config":{"proxies":{"http":"http://host.docker.internal:7890","https":"http://host.docker.internal:7890"}},"ccxt_async_config":{"proxies":{"http":"http://host.docker.internal:7890","https":"http://host.docker.internal:7890"}},"pair_whitelist":["ETH/USDT:USDT"],"pair_blacklist":[]},"pairlists":[{"method":"StaticPairList"}],"bot_name":"freqtrade","initial_state":"running","internals":{"process_throttle_secs":5},"config_files":["/tmp/futures_config.json"]}
|
||||
Binary file not shown.
2288
backtest/v2_2c_full_2021_2026.txt
Normal file
2288
backtest/v2_2c_full_2021_2026.txt
Normal file
File diff suppressed because it is too large
Load Diff
2926
backtest/v2_2d_full_2021_2026.txt
Normal file
2926
backtest/v2_2d_full_2021_2026.txt
Normal file
File diff suppressed because it is too large
Load Diff
32
backtest/v2_2d_yearly_summary.md
Normal file
32
backtest/v2_2d_yearly_summary.md
Normal file
@ -0,0 +1,32 @@
|
||||
# v2.2d 逐年回测结果
|
||||
|
||||
**回测时间**: 2026-06-11 13:08
|
||||
**策略**: StructureFlowStrategyV22d
|
||||
**配置**: Docker freqtrade, config_backtest.json, ETH/USDT:USDT, futures, 1x, $10,000起
|
||||
|
||||
## 逐年表现（独立运行，每年 $10,000 起）
|
||||
|
||||
| 年份 | 交易数 | 收益率 | 终值 | 市场涨跌 | 胜率 | 最大回撤 |
|
||||
|------|--------|--------|------|----------|------|----------|
|
||||
| 2021 | 172 | +251.16% | $35,116 | +406.63% | 27.3% | 11.28% |
|
||||
| 2022 | 204 | +110.91% | $21,091 | -67.92% | 30.9% | 11.69% |
|
||||
| 2023 | 182 | +49.35% | $14,935 | +92.43% | 26.9% | 10.04% |
|
||||
| 2024 | 232 | +185.84% | $28,584 | +46.38% | 28.4% | 6.87% |
|
||||
| 2025 | 221 | +608.24% | $70,824 | -11.43% | 27.6% | 13.92% |
|
||||
| 2026 | 54 | -11.87% | $8,813 | -45.37% | 22.2% | 14.89% |
|
||||
|
||||
**逐年合计**: 1,065笔（独立运行，不跨年复合）
|
||||
|
||||
## 全周期（2021-2026 连续运行）
|
||||
|
||||
| 交易数 | 总收益率 | 终值 | CAGR | Sharpe | 最大回撤 |
|
||||
|--------|----------|------|------|--------|----------|
|
||||
| 1,375 | +205,684.36% | $20,578,436 | 309.01% | 1.03 | 20.58% |
|
||||
|
||||
## 关键观察
|
||||
|
||||
1. **熊市表现优异**: 2022市场-68%，策略+111%；2026YTD市场-45%，策略仅-12%
|
||||
2. **牛市相对逊色**: 2021市场+407%，策略+251%；2023市场+92%，策略+49%
|
||||
3. **2025是爆发年**: +608%，主因2025年ETH波动大（先跌后涨），双向策略充分获利
|
||||
4. **逐年回撤控制在7-15%**，比全周期20.58%低，因逐年重置不积累
|
||||
5. **逐年笔数稳定**: 172-232笔/年（2026仅半年54笔），日均0.5-0.6笔
|
||||
Reference in New Issue
Block a user