feat: 温区平滑+周期记录——滞回确认K=5(数据选参:112周期/20.7天/无1天噪音), regime_cycles表, router v3读平滑温区(当前trend_down→v_oversold/s2_panic主导0.8,v_weak观察0.24,避免被2天choppy误判)
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""regime_tracker.py — 温区平滑跟踪 + 周期记录（2026-08-13）
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设计（老莫确认方向 + 数据选参）：
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- 实时温区判定：滞回确认 K=5（连续5天同温区才确认，滞后约5天，宁慢勿错）
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数据依据：K=5 → 112周期/平均20.7天/无1天噪音（vs 原始457周期/151个1天）
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- 温区周期记录：regime_cycles 表（start/end/regime/days），供策略评估归因
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- 温度（rsi）：不滞后，实时反映恐慌/亢奋（与温区互补：温区滞后、温度实时）
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写表: regime_cycles(date_start, date_end, regime, days)
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输出: market_regime_smoothed.json（当前平滑温区 + 温度）
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"""
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import sys
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import json
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import sqlite3
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from pathlib import Path
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from datetime import datetime
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_SCRIPT_DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(_SCRIPT_DIR))
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sys.path.insert(0, "/home/hmo/MoFin")
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DB = "/home/hmo/MoFin/data/mofin.db"
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OUT = "/home/hmo/MoFin/data/market_regime_smoothed.json"
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# 滞回确认天数（数据选参：K=5 甜区）
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CONFIRM_DAYS = 5
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def load_daily_regime():
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"""读取 market_regime 逐日数据（时间正序）"""
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conn = sqlite3.connect(DB, timeout=5)
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rows = conn.execute(
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"SELECT date, above_ma20, adx, regime FROM market_regime ORDER BY date ASC"
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).fetchall()
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conn.close()
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return rows
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def classify_day(above, adx):
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"""单日温区（与 market_regime 同逻辑）"""
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if above == 1 and adx is not None and adx >= 20:
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return "trend_up"
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if adx is not None and adx < 20:
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return "choppy"
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return "trend_down"
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def smooth_states(rows, k=CONFIRM_DAYS):
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"""滞回确认：连续 K 天同温区才确认切换。返回 (states, cycles)"""
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dates = [r[0] for r in rows]
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raw = [classify_day(r[1], r[2]) for r in rows]
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n = len(dates)
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# 状态机：current 确认态；每 K 天窗口看是否一致
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states = [None] * n
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current = None
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for i in range(n):
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if i < k - 1:
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continue
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window = raw[i - k + 1:i + 1]
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if len(set(window)) == 1:
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# 连续 K 天同温区 → 确认（切换）
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current = window[0]
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states[i] = current if current is not None else raw[i]
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# 开头填补（前 K-1 天用原始值）
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for i in range(min(k - 1, n)):
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states[i] = raw[i]
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# 聚合周期
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cycles = []
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cur = None
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for i in range(n):
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s = states[i]
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if cur is None or s != cur["regime"]:
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if cur:
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cycles.append(cur)
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cur = {"regime": s, "start": dates[i], "end": dates[i], "days": 1}
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else:
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cur["end"] = dates[i]
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cur["days"] += 1
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if cur:
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cycles.append(cur)
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return states, cycles, dates
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def save_cycles(cycles):
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"""写入 regime_cycles 表"""
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conn = sqlite3.connect(DB, timeout=30)
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conn.execute("PRAGMA busy_timeout=30000")
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conn.execute("""
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CREATE TABLE IF NOT EXISTS regime_cycles (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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regime TEXT,
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start_date TEXT,
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end_date TEXT,
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days INTEGER,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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""")
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# 清空重建（保持与 market_regime 同步）
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conn.execute("DELETE FROM regime_cycles")
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for cy in cycles:
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conn.execute(
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"INSERT INTO regime_cycles (regime, start_date, end_date, days) VALUES (?,?,?,?)",
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(cy["regime"], cy["start"], cy["end"], cy["days"])
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)
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conn.commit()
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conn.close()
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return len(cycles)
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def get_temp():
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"""实时温度（rsi 档位，不滞后）"""
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try:
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from temp_band import get_market_temp
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return get_market_temp()
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except Exception:
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return {"band": "unknown", "rsi": None}
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def main():
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rows = load_daily_regime()
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if len(rows) < CONFIRM_DAYS + 1:
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print(f"数据不足: {len(rows)} 条")
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return
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states, cycles, dates = smooth_states(rows)
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n_cycles = save_cycles(cycles)
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# 当前平滑温区（最新确认态）
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current_regime = states[-1]
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current_date = dates[-1]
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temp = get_temp()
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# 最近周期列表
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recent = cycles[-8:]
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out = {
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"current_regime": current_regime,
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"current_date": current_date,
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"confirm_days": CONFIRM_DAYS,
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"temp": temp,
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"total_cycles": n_cycles,
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"recent_cycles": recent,
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"updated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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}
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Path(OUT).write_text(json.dumps(out, ensure_ascii=False, indent=1), encoding="utf-8")
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print(f"平滑温区: {current_regime} (确认期{CONFIRM_DAYS}天, 至{current_date})")
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print(f"温度: {temp.get('band')} (rsi={temp.get('rsi')})")
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print(f"周期总数: {n_cycles}")
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print("最近周期:")
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for cy in recent:
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print(f" {cy['regime']:<12} {cy['start']} ~ {cy['end']} ({cy['days']}天)")
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if __name__ == "__main__":
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main()
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@@ -29,7 +29,18 @@ OUT = Path("/home/hmo/MoFin/data/strategy_weights.json")
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def load_regime():
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"""读取最新 market_regime（三态）"""
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"""读取平滑温区（regime_tracker 输出，K=5 滞回确认）。
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优先用平滑结果（避免单日噪音误判）；regime_tracker 不可用时回退原始 market_regime。"""
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try:
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p = Path("/home/hmo/MoFin/data/market_regime_smoothed.json")
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if p.exists():
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d = json.loads(p.read_text(encoding="utf-8"))
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return {"regime": d.get("current_regime", "unknown"),
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"date": d.get("current_date", ""),
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"temp_band": (d.get("temp") or {}).get("band"),
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"temp_rsi": (d.get("temp") or {}).get("rsi")}
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except Exception:
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pass
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try:
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from market_regime import load_market_regime
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return load_market_regime()
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@@ -38,9 +49,9 @@ def load_regime():
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def get_temp():
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"""读取市场温度（rsi 档位）"""
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"""读取市场温度（rsi 档位）。regime_tracker 已含温度则直接用，否则实时算。"""
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try:
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from temp_band import get_market_temp, temp_multiplier
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from temp_band import get_market_temp
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return get_market_temp()
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except Exception as e:
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print(f" [router] 温度获取失败: {e}", file=sys.stderr)
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@@ -205,3 +205,48 @@
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| strategy_alert.py | 三振出局失效预警 |
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当前：choppy×neutral → v_weak/v_lurk_v3 weight=0.8，v_next4/v8.1 观察。
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---
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## 七、温区平滑与周期记录（2026-08-13 老莫补充设计）
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### 7.1 设计原则（老莫定）
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1. **策略内部不放温区门控因子**（v_next4 的 行业ADX>25 是反例，应去除）——让策略在所有温区都能发信号，才能测出全温区表现
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2. **策略带"适用温区"属性**——记录温区测试结果，不是内部硬门控
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3. **适用温区是动态的**——常态化测试，记录策略在不同温区随时间的变化
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4. **系统监控温区变化、记录温区周期**——以温区周期为锚点，触发策略周期性评估
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### 7.2 平滑手段（数据选参）
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**问题**：日级 regime 切换太频繁（原始 457 周期/151个1天周期），无法直接作为评估锚点。
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**方案**：滞回确认（hysteresis）——连续 K 天同温区才确认切换。
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**数据选参**：
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| K（确认天数） | 周期数 | 平均长度 | 效果 |
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| K=1（原始） | 457 | 5.1天 | 太碎，151个1天 |
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| K=3 | 177 | 13.1天 | 无1天，仍偏碎 |
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| **K=5** | **112** | **20.7天** | **甜区：无1天噪音，周期合理** |
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| K=10 | 49 | 47.3天 | 过度平滑，choppy 只剩391天 |
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**选 K=5**：滞后约5天（宁慢勿错），事后评估与实时判断统一口径。
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### 7.3 实时 vs 事后
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- **实时温区**：K=5 滞回确认（滞后5天，确认才切换策略权重）
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- **温度（rsi）**：不滞后（连续量，实时反映恐慌/亢奋，用于仓位乘数）
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- **事后评估**：用 K=5 平滑后的周期做策略-温区归因
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**温区与温度互补**：温区滞后、温度实时。例：当前平滑温区=trend_down（7/17起19天）但温度=neutral（rsi 58）——阴跌状态，v_oversold/s2_panic 主导（weight 0.8），v_weak 观察（0.24）。
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### 7.4 落地文件
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| 文件 | 功能 |
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| regime_tracker.py | 滞回平滑（K=5）+ 周期记录 regime_cycles 表 + 输出平滑温区 |
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| strategy_router.py v3 | 读平滑温区 + 温度 → strategy_weights.json |
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**cron 顺序**（每日）：16:50 market_regime → 16:52 regime_tracker（平滑）→ 16:55 strategy_router（路由）
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