Rekey
edc50e8809
- 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
227 lines
11 KiB
Python
227 lines
11 KiB
Python
"""
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读取 full_comparison_result.json 并生成多维度排序对比表
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用法：
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source .venv/bin/activate && python example/analyze_comparison.py
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"""
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import json
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import sys
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from pathlib import Path
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_project_root = Path(__file__).resolve().parent.parent.parent
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if str(_project_root) not in sys.path:
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sys.path.insert(0, str(_project_root))
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JSON_PATH = _project_root / "engine" / "example" / "full_comparison_result.json"
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with open(JSON_PATH, "r", encoding="utf-8") as f:
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data = json.load(f)
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results = data["results"]
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cfg = data["config"]
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# ── 通用排序列 ──
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METRICS = [
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"总收益%", "年化收益%", "夏普比率", "最大回撤%", "胜率%",
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"盈亏比", "交易次数", "平均盈亏", "最佳盈亏", "最差盈亏", "卡尔玛比率",
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]
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# 哪些指标越大越好（正序），哪些越小越好（倒序）
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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"}
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ASCENDING = {"最大回撤%", "交易次数", "最差盈亏", "耗时s"}
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def sort_results(items, key, descending=True):
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"""排序，返回 top N"""
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return sorted(items, key=lambda x: x.get(key, -9999) if key in DESCENDING else -x.get(key, 9999), reverse=descending)
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def print_table(title, rows, fields, col_widths):
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"""打印格式化表格"""
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print()
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print("═" * len(title))
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print(title)
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print("═" * len(title))
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print()
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# header
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header = ""
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for i, f in enumerate(fields):
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header += f" {f:<{col_widths[i]}}"
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print(header)
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# separator
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sep = " " + "─" * (sum(col_widths) + sum(c - len(str(fields[i])) for i, c in enumerate(col_widths)))
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print(sep)
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for r in rows:
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line = ""
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for i, f in enumerate(fields):
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val = r.get(f, "")
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if isinstance(val, float):
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if abs(val) >= 1000:
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val_str = f"{val:>{col_widths[i]}.0f}"
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elif abs(val) >= 100:
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val_str = f"{val:>{col_widths[i]}.1f}"
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elif abs(val) >= 10:
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val_str = f"{val:>{col_widths[i]}.2f}"
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else:
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val_str = f"{val:>{col_widths[i]}.3f}"
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else:
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val_str = f"{str(val):<{col_widths[i]}}"
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line += f" {val_str}"
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print(line)
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print()
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# ════════════════════════════════════════════════════════
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# 表1：全局排名 — 按年化收益 Top 30
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# ════════════════════════════════════════════════════════
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top_annual = sort_results(results, "年化收益%", True)[:30]
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print_table(
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f" 全局排名 — 按年化收益 Top 30（共{len(results)}组） | 本金 {cfg['initial_capital']:,.0f} USDT",
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top_annual,
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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"],
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col_widths=[22, 10, 8, 8, 9, 9, 8, 8, 7, 7, 6, 24],
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)
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# ════════════════════════════════════════════════════════
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# 表2：按夏普比率 Top 30
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# ════════════════════════════════════════════════════════
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top_sharpe = sort_results(results, "夏普比率", True)[:30]
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print_table(
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" 全局排名 — 按夏普比率 Top 30",
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top_sharpe,
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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"],
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col_widths=[22, 10, 8, 8, 9, 8, 8, 7, 7, 6, 24],
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)
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# ════════════════════════════════════════════════════════
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# 表3：近半年+近一年（近期真实表现）按年化 Top 30
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# ════════════════════════════════════════════════════════
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recent = [r for r in results if r["数据量"] in ("近半年", "近一年")]
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recent_top = sort_results(recent, "年化收益%", True)[:30]
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print_table(
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" 近期现实表现 — 近半年+近一年 — 按年化收益 Top 30",
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recent_top,
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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"],
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col_widths=[22, 10, 8, 8, 9, 9, 8, 8, 7, 7, 6, 24],
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)
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# ════════════════════════════════════════════════════════
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# 表4：全量数据（历史长期）按年化 Top 20
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# ════════════════════════════════════════════════════════
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full_data = [r for r in results if r["数据量"] == "全量"]
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full_top = sort_results(full_data, "年化收益%", True)[:20]
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print_table(
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" 全量数据 — 按年化收益 Top 20",
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full_top,
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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"],
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col_widths=[22, 10, 8, 9, 9, 8, 8, 7, 7, 6, 24],
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)
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# ════════════════════════════════════════════════════════
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# 表5：各策略在4h+1d上的近一年表现（最实用的中长线）
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# ════════════════════════════════════════════════════════
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mid_long = [r for r in recent if r["时间级别"] in ("4h", "1d")]
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mid_sorted = sort_results(mid_long, "年化收益%", True)[:30]
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print_table(
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" 中长线（4h/1d）近期表现 — 按年化收益 Top 30",
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mid_sorted,
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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"],
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col_widths=[22, 10, 8, 8, 9, 9, 8, 8, 7, 7, 6, 24],
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)
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# ════════════════════════════════════════════════════════
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# 表6：策略×币种×时间级别 盈利能力矩阵（近一年年化）
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# ════════════════════════════════════════════════════════
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one_year = [r for r in results if r["数据量"] == "近一年"]
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print()
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print("═" * 120)
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print(" 近一年盈利能力矩阵 — 策略 × 币种 × 时间级别（年化收益%）")
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print("═" * 120)
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for tf in cfg["timeframes"]:
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tf_data = [r for r in one_year if r["时间级别"] == tf]
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if not tf_data:
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continue
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print(f"\n ▲ 时间级别: {tf}")
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print(f" {'策略名':<24}", end="")
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for sym in cfg["symbols"]:
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print(f" {sym:>12}", end="")
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print()
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print(" " + "─" * 80)
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strategies_seen = set()
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for r in sort_results(tf_data, "年化收益%", True):
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if r["策略名"] not in strategies_seen:
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strategies_seen.add(r["策略名"])
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print(f" {r['策略名']:<24}", end="")
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for sym in cfg["symbols"]:
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match = [x for x in tf_data if x["策略名"] == r["策略名"] and x["币种"] == sym]
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if match:
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val = match[0]["年化收益%"]
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color = "+" if val > 0 else ""
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print(f" {color}{val:>11.1f}%", end="")
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else:
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print(f" {'—':>12}", end="")
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print()
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print()
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# ════════════════════════════════════════════════════════
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# 表7：每组(时间级别+数据量)下的最佳策略
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# ════════════════════════════════════════════════════════
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print("═" * 160)
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print(" 每组(时间级别 + 数据量)下的最佳策略 — 按年化收益")
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print("═" * 160)
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print()
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for tf in cfg["timeframes"]:
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for period in cfg["periods"]:
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subset = [r for r in results if r["时间级别"] == tf and r["数据量"] == period]
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if not subset:
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continue
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subset_sorted = sort_results(subset, "年化收益%", True)
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print(f" ▲ {tf} | {period}")
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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}")
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print(" " + "─" * 155)
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for i, r in enumerate(subset_sorted[:8]):
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rank = ["🥇1", "🥈2", "🥉3", " 4", " 5", " 6", " 7", " 8"][i]
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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}")
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print()
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# ════════════════════════════════════════════════════════
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# 表8：策略总览 — 每个策略在所有组合中的盈利比例
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# ════════════════════════════════════════════════════════
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print("═" * 120)
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print(" 策略胜率统计 — 每个策略在所有回测组合中的盈利比例")
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print("═" * 120)
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print()
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strategy_stats = {}
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for r in results:
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sn = r["策略名"]
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if sn not in strategy_stats:
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strategy_stats[sn] = {"total": 0, "positive": 0, "positive_annual": 0, "sum_return": 0, "sum_sharpe": 0}
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strategy_stats[sn]["total"] += 1
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if r["总收益%"] > 0:
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strategy_stats[sn]["positive"] += 1
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if r["年化收益%"] > 0:
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strategy_stats[sn]["positive_annual"] += 1
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strategy_stats[sn]["sum_return"] += r["年化收益%"]
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strategy_stats[sn]["sum_sharpe"] += r["夏普比率"]
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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}")
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print(" " + "─" * 75)
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for sn, st in sorted(strategy_stats.items(), key=lambda x: x[1]["positive_annual"] / x[1]["total"], reverse=True):
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avg_ret = st["sum_return"] / st["total"]
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avg_sharpe = st["sum_sharpe"] / st["total"]
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pos_pct = st["positive_annual"] / st["total"] * 100
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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}")
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print()
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print("═" * 120)
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