新增价差回测、宏观数据管理和交易信号框架
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
vipg
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"""
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跨期价差均值回归回测：支持任意合约月份组合。
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"""
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import sqlite3
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from collections import defaultdict
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from statistics import mean, stdev
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DB_PATH = "/Users/vipg/Documents/futures-data-warehouse/db/futures.db"
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conn = sqlite3.connect(DB_PATH)
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daily = conn.execute(
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"SELECT ts_code, trade_date, close FROM daily WHERE close IS NOT NULL AND close != '' ORDER BY trade_date"
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).fetchall()
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conn.close()
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by_date = defaultdict(dict)
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for ts_code, d, close in daily:
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by_date[d][ts_code] = float(close)
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dates = sorted(by_date.keys())
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def build_spread(month_a, month_b, cross_year=False):
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"""
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构建两合约月份的价差序列。
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month_a, month_b: '01','05','09'
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cross_year: True 表示 year_b = year_a + 1（如 09-01 跨年）
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返回 [(date, ts_a, ts_b, p_a, p_b, spread)]
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"""
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data = []
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for d in dates:
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items = by_date[d]
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for ts_code in list(items.keys()):
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if not ts_code.endswith(".ZCE") or not ts_code.startswith("FG"):
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continue
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yr, mon = ts_code[2:4], ts_code[4:6]
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if mon == month_a:
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yr_b = str(int(yr) + 1) if cross_year else yr
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ts_b = f"FG{yr_b}{month_b}.ZCE"
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if ts_b in items:
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p_a = items[ts_code]
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p_b = items[ts_b]
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spread = p_b - p_a
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data.append((d, ts_code, ts_b, p_a, p_b, spread))
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return data
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def backtest_spread(spread_data, window=60, entry_z=2.0, exit_z=0.5, max_hold=20):
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equity = 1.0
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position = 0
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entry_spread = 0
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entry_p_a = 0
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entry_date = ""
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hold_days = 0
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trades = []
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daily_eq = []
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for i, (d, ts_a, ts_b, p_a, p_b, spread_val) in enumerate(spread_data):
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if i < window:
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daily_eq.append((d, equity))
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continue
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hist = [spread_data[j][5] for j in range(i - window, i)]
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roll_mean = mean(hist)
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roll_std = stdev(hist)
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signal = 0
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if spread_val > roll_mean + entry_z * roll_std:
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signal = -1 # 价差过大 → 空价差（空远月、多近月）
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elif spread_val < roll_mean - entry_z * roll_std:
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signal = 1
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should_exit = False
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if position != 0:
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hold_days += 1
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if hold_days >= max_hold:
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should_exit = True
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if position == 1 and spread_val >= roll_mean - exit_z * roll_std:
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should_exit = True
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elif position == -1 and spread_val <= roll_mean + exit_z * roll_std:
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should_exit = True
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if position != 0 and should_exit:
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if position == 1:
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pnl = (spread_val - entry_spread) / entry_p_a
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else:
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pnl = (entry_spread - spread_val) / entry_p_a
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equity *= (1 + pnl)
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trades.append((entry_date, d, entry_spread, spread_val, pnl, position))
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position = 0
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hold_days = 0
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if position == 0 and signal != 0:
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position = signal
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entry_spread = spread_val
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entry_p_a = p_a
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entry_date = d
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hold_days = 0
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daily_eq.append((d, equity))
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if position != 0 and spread_data:
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d, _, _, p_a, _, spread_val = spread_data[-1]
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if position == 1:
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pnl = (spread_val - entry_spread) / entry_p_a
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else:
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pnl = (entry_spread - spread_val) / entry_p_a
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equity *= (1 + pnl)
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trades.append((entry_date, d, entry_spread, spread_val, pnl, position))
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return trades, daily_eq
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def print_results(trades, daily_eq, label):
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if not trades:
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print(f"\n{label}: 无交易")
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return
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wins = [t for t in trades if t[4] > 0]
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losses = [t for t in trades if t[4] <= 0]
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total_ret = daily_eq[-1][1] - 1.0 if daily_eq else 0
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wr = len(wins) / len(trades) if trades else 0
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returns = []
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for i in range(1, len(daily_eq)):
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if daily_eq[i-1][1] > 0:
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returns.append(daily_eq[i][1] / daily_eq[i-1][1] - 1)
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avg_ret = mean(returns) if returns else 0
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std_ret = stdev(returns) if len(returns) > 1 else 1
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sharpe = (avg_ret / std_ret) * (252 ** 0.5) if std_ret > 0 else 0
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peak = 1.0
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mdd = 0.0
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for _, e in daily_eq:
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if e > peak: peak = e
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dd = (peak - e) / peak
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if dd > mdd: mdd = dd
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print(f"\n{label}")
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print(f" {'' if trades else '无'}交易次数: {len(trades)}")
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print(f" 总收益率: {total_ret:+.2%}")
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print(f" 夏普比率: {sharpe:.2f}")
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print(f" 最大回撤: {mdd:.2%}")
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print(f" 胜率: {wr:.0%}")
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if losses:
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avg_w = mean(t[4] for t in wins)
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avg_l = mean(t[4] for t in losses)
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print(f" 盈亏比: {abs(avg_w/avg_l):.2f}")
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# ── 分析所有有效组合 ──────────────────────────
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pairs = [
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("01", "05", False, "同一年 01-05"),
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("05", "09", False, "同一年 05-09"),
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("09", "01", True, "跨年 09-01"),
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]
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# 先看各组合的统计特征
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print("=" * 60)
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print("各合约组合价差统计")
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print("=" * 60)
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for ma, mb, cross, label in pairs:
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data = build_spread(ma, mb, cross)
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n = len(data)
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vals = [r[5] for r in data]
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pos = sum(1 for v in vals if v > 0)
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avg_s = mean(vals)
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std_s = stdev(vals)
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print(f"\n{label:>12} {n:>5}天 均值{avg_s:>+7.1f} σ{std_s:>6.1f} 正{pos:>4}({pos/n:.0%})")
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# 回测各组合
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print("\n" + "=" * 60)
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print("均值回归回测 (window=60 entry=2σ exit=0.5σ max_hold=20)")
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print("=" * 60)
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for ma, mb, cross, label in pairs:
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data = build_spread(ma, mb, cross)
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trades, eq = backtest_spread(data, window=60, entry_z=2.0, exit_z=0.5, max_hold=20)
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print_results(trades, eq, label)
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# 当前可交易组合 (202607)
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print("\n" + "=" * 60)
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print("当前可交易组合分析 (FG2609, FG2701, FG2705)")
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print("=" * 60)
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today = "20260717"
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items = by_date.get(today, {})
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for ts in ["FG2609.ZCE", "FG2701.ZCE", "FG2705.ZCE"]:
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if ts in items:
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print(f" {ts}: {items[ts]}")
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else:
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print(f" {ts}: 无当日数据")
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# 当前配对及统计
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pairs_now = [
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("FG2609.ZCE", "FG2701.ZCE", "09-01(跨年)"),
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("FG2701.ZCE", "FG2705.ZCE", "01-05(同一年)"),
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("FG2609.ZCE", "FG2705.ZCE", "09-05(跨年)"),
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]
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for ts_a, ts_b, label in pairs_now:
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if ts_a not in items or ts_b not in items:
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print(f"\n{label}: {ts_a} 或 {ts_b} 无数据")
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continue
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spread_now = items[ts_b] - items[ts_a]
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print(f"\n{label}: {ts_a} vs {ts_b}")
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print(f" 当前价差: {spread_now:+.1f}")
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# 找历史均值
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for ma, mb, cross, _ in pairs:
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if (ts_a[4:6] == ma and ts_b[4:6] == mb) or \
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(ts_a[4:6] == mb and ts_b[4:6] == ma and cross):
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data = build_spread(ma, mb, cross)
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if len(data) < 60:
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continue
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vals = [r[5] for r in data]
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# 近 60 天
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recent = vals[-60:]
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avg_r = mean(recent)
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std_r = stdev(recent)
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z = (spread_now - avg_r) / std_r if std_r else 0
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print(f" 近60日均值: {avg_r:.1f} σ: {std_r:.1f} z值: {z:.2f}")
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if abs(z) >= 2:
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print(f" → 偏离 {z:.0f}σ，触发信号！")
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else:
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print(f" → 未触发 (需 |z|>=2)")
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