feat: 测温机制定稿——三态骨架+温度(rsi)连续维度(数据验证) 回填market_regime 10年(2016-2026 2319条); 温度分档temp_band(panic/fear/neutral/greed/euphoria); strategy_router v2三态选策略+温度乘数; 废弃四态thermometer; 策略-适用温度表(v_weak=choppy×fear, v_oversold=trend_down×panic, v_next4=trend_up 63%)

2026-08-13 09:13:04 +08:00
parent 9310149171
commit 06fbdf5961
4 changed files with 286 additions and 232 deletions
@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""market_thermometer.py — MoFin 市场测温模块（2026-08-13 一次性落地）
-
-核心理念（市场周期测温文/霍华德·马克斯）：周期像钟摆无法预测，但可测温——
-不是预测拐点，是判断当前摆到哪（牛/熊/震荡/结构性），据此调整策略攻守。
-
-四态判定（用 market_indicators 已有字段，无拍脑袋）：
- bull_trend: above_ma20=1 + rsi>55 + adx>20（MA20上方+强势+强趋势）→ 龙头/趋势策略
- bull_osc:   above_ma20=1 + rsi>50（MA20上方+偏强+弱趋势）→ 震荡偏强，均衡配置
- bear:       above_ma20=0 + rsi<45（MA20下方+弱势）→ 弱市均值回复策略（v_weak/v_oversold）
- neutral:    其他（中性震荡）→ 观望/轻仓
-
-输出：market_state.json（当前状态+历史分态统计），供策略动态切换
-"""
-import sqlite3
-import json
-from datetime import datetime, timedelta
-from pathlib import Path
-
-DB_PATH = "/home/hmo/MoFin/data/mofin.db"
-OUT_PATH = "/home/hmo/MoFin/data/market_state.json"
-
-# ── 回填 market_indicators 历史（从 stock_daily 计算上证指数指标）──
-def backfill_market_indicators(days=500):
-    """从 stock_daily 计算上证指数（000001.SH）的 mkt_* 指标，回填到 market_indicators"""
-    c = sqlite3.connect(DB_PATH)
-    # 上证指数代码（MoFin 约定：000001 = 上证指数）
-    rows = c.execute(
-        "SELECT date, open, high, low, close, volume FROM stock_daily WHERE code='000001' ORDER BY date DESC LIMIT ?",
-        (days,)
-    ).fetchall()
-    if not rows:
-        print("stock_daily 无 000001 数据")
-        return 0
-
-    bars = list(reversed(rows))  # 时间正序
-    n = len(bars)
-    if n < 60:
-        print(f"数据不足 {n} 条，无法计算")
-        return 0
-
-    # 计算 MA20 / RSI14 / ADX14 / 60日高点回撤
-    closes = [b[4] for b in bars]
-    highs = [b[2] for b in bars]
-    lows = [b[3] for b in bars]
-    dates = [b[0] for b in bars]
-
-    # MA20
-    ma20 = [None] * n
-    for i in range(19, n):
-        ma20[i] = sum(closes[i-19:i+1]) / 20
-
-    # RSI14
-    rsi = [None] * n
-    gains, losses = [], []
-    for i in range(1, n):
-        ch = closes[i] - closes[i-1]
-        gains.append(max(ch, 0))
-        losses.append(max(-ch, 0))
-        if i >= 14:
-            avg_g = sum(gains[i-14:i]) / 14
-            avg_l = sum(losses[i-14:i]) / 14
-            rs = avg_g / avg_l if avg_l > 0 else 100
-            rsi[i] = 100 - 100 / (1 + rs)
-
-    # ADX14（简化：用 DMI 近似，实际 MoFin 有 indicators.calc_adx，这里用简化版）
-    adx = [None] * n
-    tr_list = [0.0]
-    pdm, ndm = [0.0], [0.0]
-    for i in range(1, n):
-        h, l, pc = highs[i], lows[i], closes[i-1]
-        tr = max(h - l, abs(h - pc), abs(l - pc))
-        tr_list.append(tr)
-        up_move = highs[i] - highs[i-1]
-        down_move = lows[i-1] - lows[i]
-        pdm.append(up_move if up_move > down_move and up_move > 0 else 0)
-        ndm.append(down_move if down_move > up_move and down_move > 0 else 0)
-    for i in range(14, n):
-        atr = sum(tr_list[i-14:i]) / 14
-        pdi = 100 * sum(pdm[i-14:i]) / 14 / atr if atr > 0 else 0
-        ndi = 100 * sum(ndm[i-14:i]) / 14 / atr if atr > 0 else 0
-        dx = 100 * abs(pdi - ndi) / (pdi + ndi) if (pdi + ndi) > 0 else 0
-        adx[i] = dx  # 简化：用 DX 近似 ADX（平滑需更多数据，足够测温）
-
-    # 60日高点回撤
-    dd60 = [None] * n
-    for i in range(59, n):
-        hi60 = max(closes[i-59:i+1])
-        dd60[i] = (closes[i] - hi60) / hi60 * 100 if hi60 > 0 else 0
-
-    # 写入 market_indicators（UPSERT）
-    written = 0
-    for i in range(14, n):
-        if ma20[i] is None or rsi[i] is None:
-            continue
-        above = 1 if closes[i] > ma20[i] else 0
-        # 近20日涨跌（roc）
-        roc = ((closes[i] - closes[i-20]) / closes[i-20] * 100) if i >= 20 and closes[i-20] > 0 else 0
-        # 近20日斜率（简化）
-        slope = (ma20[i] - ma20[i-5]) / ma20[i-5] * 100 if i >= 5 and ma20[i-5] else 0
-        c.execute(
-            """INSERT OR REPLACE INTO market_indicators
-               (date, mkt_rsi, mkt_dd60, mkt_adx, mkt_above_ma20, mkt_down_days, mkt_slope, mkt_roc, updated_at)
-               VALUES (?,?,?,?,?,?,?,?,?)""",
-            (dates[i], rsi[i], dd60[i], adx[i], above, 0, slope, roc, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
-        )
-        written += 1
-    c.commit()
-    c.close()
-    print(f"回填 market_indicators {written} 条（最新: {dates[-1]}）")
-    return written
-
-# ── 四态判定 ──
-def classify_state(mkt_row):
-    """判定市场状态。输入：market_indicators 行 dict"""
-    rsi = mkt_row.get("mkt_rsi", 50)
-    adx = mkt_row.get("mkt_adx", 20)
-    above = mkt_row.get("mkt_above_ma20", 0)
-    dd60 = mkt_row.get("mkt_dd60", 0)
-
-    if above and rsi > 55 and adx > 20:
-        return "bull_trend"
-    if above and rsi > 50:
-        return "bull_osc"
-    if not above and rsi < 45:
-        return "bear"
-    return "neutral"
-
-# ── 主流程 ──
-def main():
-    # 1. 回填历史（500 日 ≈ 2 年）
-    backfill_market_indicators(500)
-
-    # 2. 当前状态
-    c = sqlite3.connect(DB_PATH)
-    row = c.execute(
-        "SELECT date, mkt_rsi, mkt_dd60, mkt_adx, mkt_above_ma20, mkt_slope, mkt_roc FROM market_indicators ORDER BY date DESC LIMIT 1"
-    ).fetchone()
-    c.close()
-    if not row:
-        print("market_indicators 无数据")
-        return
-
-    current = {
-        "date": row[0], "mkt_rsi": row[1], "mkt_dd60": row[2],
-        "mkt_adx": row[3], "mkt_above_ma20": row[4], "mkt_slope": row[5], "mkt_roc": row[6],
-    }
-    state = classify_state(current)
-    current["state"] = state
-    current["state_desc"] = {
-        "bull_trend": "牛市趋势（龙头/趋势策略重仓）",
-        "bull_osc": "强势震荡（均衡配置）",
-        "bear": "熊市/下跌（弱市均值回复策略 v_weak/v_oversold）",
-        "neutral": "中性震荡（观望/轻仓）",
-    }[state]
-
-    # 3. 历史分态统计（近 250 交易日 ≈ 1 年）
-    c = sqlite3.connect(DB_PATH)
-    rows = c.execute(
-        "SELECT date, mkt_rsi, mkt_adx, mkt_above_ma20, mkt_dd60 FROM market_indicators ORDER BY date DESC LIMIT 250"
-    ).fetchall()
-    c.close()
-    from collections import Counter
-    hist = Counter(classify_state({"mkt_rsi": r[1], "mkt_adx": r[2], "mkt_above_ma20": r[3], "mkt_dd60": r[4]}) for r in rows)
-    current["hist_1y"] = dict(hist)
-    current["updated_at"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-    # 4. 写入 market_state.json
-    Path(OUT_PATH).write_text(json.dumps(current, ensure_ascii=False, indent=1), encoding="utf-8")
-    print(f"market_state.json 写入: {state} ({current['state_desc']})")
-    print(f"  近1年分态: {dict(hist)}")
-    print(f"  当前指标: rsi={current['mkt_rsi']:.1f} adx={current['mkt_adx']:.1f} above={current['mkt_above_ma20']} dd60={current['mkt_dd60']:.1f}")
-
-if __name__ == "__main__":
-    main()
@@ -1,74 +1,137 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-"""strategy_router.py — MoFin 策略动态路由（2026-08-13 一次性落地）
+"""strategy_router.py v2 — MoFin 策略动态路由（三态 + 温度维度，2026-08-13 重写）

-核心：读 market_state.json 的当前市场状态，决定各策略权重/开关
- bear: v_weak/v_oversold 降权 50%（信号质量差，28%胜率）
- bull_osc: v_weak/v_oversold 正常（60%胜率，当前状态）
- bull_trend: v_weak/v_oversold 降权 50%，启用龙头策略（待研究）
- neutral: 轻仓观望
+结论（数据验证）：三态骨架 + 温度(rsi)连续维度。
+- 三态（trend_up/choppy/trend_down）→ 选策略类型（market_regime.py 判定）
+- 温度（rsi 档位）→ 仓位乘数（temp_band.py 判定，连续量）

-输出：strategy_weights.json 供扫描器/重评脚本读取
+策略-适用温度映射（数据来源：2026-08-13 分市场状态检验，见 docs/decisions/2026-08-13-事项五）：
+  v_weak     → choppy × [fear, neutral]    （震荡市+恐慌最佳 71%）
+  v_oversold → trend_down × [panic, fear]  （下跌市+恐慌最佳 64-86%）
+  v_next4    → trend_up × [greed, euphoria]（趋势市 63%）
+  v8.1       → trend_up                    （趋势市 57%）
+  s2_panic   → trend_down × [panic]        （极端恐慌 86%）
+  v_lurk_v3  → [choppy, trend_down] × [fear, neutral]（77%/71%）
+
+输出：strategy_weights.json（各策略 weight 由 状态×温度 决定）
 """
 import json
+import sys
 from pathlib import Path
 from datetime import datetime

-MARKET_STATE = Path("/home/hmo/MoFin/data/market_state.json")
+_SCRIPT_DIR = Path(__file__).resolve().parent
+sys.path.insert(0, str(_SCRIPT_DIR))
+sys.path.insert(0, "/home/hmo/MoFin")
+
 OUT = Path("/home/hmo/MoFin/data/strategy_weights.json")

-def load_market_state():
-    if MARKET_STATE.exists():
-        return json.loads(MARKET_STATE.read_text(encoding="utf-8"))
-    return {"state": "neutral", "date": ""}

-def route(state):
-    """市场状态 → 策略权重。返回 dict"""
-    # 基准权重（等权，程飞：不确定时等权最稳健）
-    base = {"v_weak": 1.0, "v_oversold": 1.0, "leader": 0.0}  # leader 待研究，先 0
-    if state == "bear":
-        # 熊市：v_weak/v_oversold 信号质量差（近1年 bear 胜率 28%），降权
-        return {
-            "v_weak": {"weight": 0.5, "action": "降权50%", "reason": "bear 胜率28%，信号质量差"},
-            "v_oversold": {"weight": 0.5, "action": "降权50%", "reason": "bear 胜率28%"},
-            "leader": {"weight": 0.0, "action": "停用", "reason": "bear 不适用龙头"},
-            "state": state,
+def load_regime():
+    """读取最新 market_regime（三态）"""
+    try:
+        from market_regime import load_market_regime
+        return load_market_regime()
+    except Exception:
+        return None
+
+
+def get_temp():
+    """读取市场温度（rsi 档位）"""
+    try:
+        from temp_band import get_market_temp, temp_multiplier
+        return get_market_temp()
+    except Exception as e:
+        print(f"  [router] 温度获取失败: {e}", file=sys.stderr)
+        return {"band": "unknown", "rsi": None}
+
+
+# 策略-适用温度映射（基础数据，数据驱动固化）
+# weight = 状态匹配度 × 温度乘数
+STRATEGY_TEMP = {
+    "v_weak": {
+        "family": "mr",
+        "regimes": ["choppy"],
+        "desc": "震荡市超跌反弹（choppy+恐慌71%最佳）",
+    },
+    "v_oversold": {
+        "family": "mr",
+        "regimes": ["trend_down"],
+        "desc": "下跌市超跌（trend_down+恐慌64-86%）",
+    },
+    "v_next4": {
+        "family": "trend",
+        "regimes": ["trend_up"],
+        "desc": "趋势市追涨（trend_up 63%）",
+    },
+    "v8.1": {
+        "family": "trend",
+        "regimes": ["trend_up"],
+        "desc": "波段先出再进（trend_up 57%）",
+    },
+    "s2_panic": {
+        "family": "mr",
+        "regimes": ["trend_down"],
+        "desc": "极端恐慌买超跌（panic 86%）",
+    },
+    "v_lurk_v3": {
+        "family": "mr",
+        "regimes": ["choppy", "trend_down"],
+        "desc": "潜伏型（choppy/trend_down 71-77%）",
+    },
+}
+
+
+def route(regime, temp):
+    """三态 + 温度 → 各策略权重"""
+    current_regime = regime.get("regime", "unknown") if regime else "unknown"
+    band = temp.get("band", "unknown")
+    family = temp.get("family", "mr")
+
+    weights = {}
+    for name, cfg in STRATEGY_TEMP.items():
+        # 状态匹配度：适用温区包含当前状态 → 1.0，否则 0.3（保留观察）
+        matched = current_regime in cfg["regimes"]
+        base = 1.0 if matched else 0.3
+        # 温度乘数（按策略家族）
+        try:
+            from temp_band import temp_multiplier
+            mult = temp_multiplier(band, cfg["family"])
+        except Exception:
+            mult = 0.8
+        weights[name] = {
+            "weight": round(base * mult, 2),
+            "regime": current_regime,
+            "temp_band": band,
+            "family": cfg["family"],
+            "matched": matched,
+            "desc": cfg["desc"],
        }
-    if state == "bull_osc":
-        # 强势震荡：v_weak/v_oversold 最有效（60%胜率），正常
-        return {
-            "v_weak": {"weight": 1.0, "action": "正常", "reason": "bull_osc 胜率60%，最有效"},
-            "v_oversold": {"weight": 1.0, "action": "正常", "reason": "bull_osc 适用"},
-            "leader": {"weight": 0.0, "action": "停用", "reason": "bull_osc 非趋势市"},
-            "state": state,
-        }
-    if state == "bull_trend":
-        # 牛市趋势：v_weak/v_oversold 失效（30%胜率），启用龙头策略
-        return {
-            "v_weak": {"weight": 0.5, "action": "降权50%", "reason": "bull_trend 胜率30%"},
-            "v_oversold": {"weight": 0.5, "action": "降权50%", "reason": "bull_trend 失效"},
-            "leader": {"weight": 1.0, "action": "启用", "reason": "bull_trend 适用龙头"},
-            "state": state,
-        }
-    # neutral：轻仓观望
-    return {
-        "v_weak": {"weight": 0.5, "action": "轻仓", "reason": "neutral 观望"},
-        "v_oversold": {"weight": 0.5, "action": "轻仓", "reason": "neutral 观望"},
-        "leader": {"weight": 0.0, "action": "停用", "reason": "neutral 观望"},
-        "state": state,
-    }
+    return weights
+

 def main():
-    ms = load_market_state()
-    state = ms.get("state", "neutral")
-    weights = route(state)
-    weights["market_state"] = ms
-    weights["updated_at"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    OUT.write_text(json.dumps(weights, ensure_ascii=False, indent=1), encoding="utf-8")
-    print(f"strategy_weights.json 写入: {state}")
-    for k, v in weights.items():
-        if isinstance(v, dict) and "weight" in v:
-            print(f"  {k}: weight={v['weight']} ({v['action']}) - {v['reason']}")
+    regime = load_regime()
+    temp = get_temp()
+    if not regime:
+        print("  [router] market_regime 无数据", file=sys.stderr)
+        regime = {"regime": "unknown"}
+
+    weights = route(regime, temp)
+    out = {
+        "state": regime.get("regime", "unknown"),
+        "temp_band": temp.get("band", "unknown"),
+        "temp_rsi": temp.get("rsi"),
+        "weights": weights,
+        "updated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "note": "三态(trend_up/choppy/trend_down)选策略类型 + 温度(rsi)乘数决定仓位",
+    }
+    OUT.write_text(json.dumps(out, ensure_ascii=False, indent=1), encoding="utf-8")
+    print(f"strategy_weights.json: 状态={out['state']} 温度={out['temp_band']}(rsi={out['temp_rsi']})")
+    for name, w in weights.items():
+        print(f"  {name:<12} weight={w['weight']:<5} regime={w['regime']:<10} band={w['temp_band']:<8} {'✓' if w['matched'] else '观察'}")
+

 if __name__ == "__main__":
    main()
@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""temp_band.py — 市场温度分档模块（2026-08-13）
+
+结论（数据验证）：三态骨架 + 温度(rsi)连续维度。
+- 三态（trend_up/choppy/trend_down）= market_regime.py 已有（策略类型分类）
+- 温度（rsi 分档）= 本模块（力度维度，仓位乘数）
+
+数据依据（2026-08-13 分市场状态检验）：
+  v_weak choppy: 恐慌<45 -> 71%胜率 / 中性45-60 -> 46% / 亢奋>60 -> 37%
+  v_oversold trend_down: 恐慌<35 -> 64% / 偏弱35-50 -> 53% / 中性>50 -> 0%
+  s2_panic trend_down 恐慌<35: 86%胜率 +16.6%
+
+档位：panic(<35) / fear(35-45) / neutral(45-60) / greed(60-70) / euphoria(>70)
+用法：
+  from temp_band import get_market_temp, temp_multiplier
+  t = get_market_temp()      # {rsi, band, regime}
+  mult = temp_multiplier(t["band"], strategy_family="mr")
+"""
+import sqlite3
+from pathlib import Path
+
+DB = Path("/home/hmo/MoFin/data/mofin.db")
+INDEX = "sh000001"
+
+
+def calc_rsi(series, n=14):
+    result = [None] * len(series)
+    if len(series) < n + 1:
+        return result
+    gains, losses = [], []
+    for i in range(1, len(series)):
+        ch = series[i] - series[i - 1]
+        gains.append(max(ch, 0))
+        losses.append(max(-ch, 0))
+        if i >= n:
+            avg_g = sum(gains[i - n:i]) / n
+            avg_l = sum(losses[i - n:i]) / n
+            rs = avg_g / avg_l if avg_l > 0 else 100
+            result[i] = 100 - 100 / (1 + rs)
+    return result
+
+
+def temp_band(rsi):
+    """连续 rsi -> 温度档位"""
+    if rsi is None:
+        return "unknown"
+    if rsi < 35:
+        return "panic"
+    if rsi < 45:
+        return "fear"
+    if rsi < 60:
+        return "neutral"
+    if rsi < 70:
+        return "greed"
+    return "euphoria"
+
+
+def temp_multiplier(band, strategy_family="mr"):
+    """温度 -> 仓位乘数。mr=均值回复（恐慌重仓），trend=趋势（恐慌回避）"""
+    if strategy_family == "trend":
+        return {
+            "panic": 0.0, "fear": 0.0, "neutral": 0.5,
+            "greed": 1.0, "euphoria": 0.5, "unknown": 0.5,
+        }.get(band, 0.5)
+    return {
+        "panic": 1.5, "fear": 1.0, "neutral": 0.8,
+        "greed": 0.5, "euphoria": 0.3, "unknown": 0.5,
+    }.get(band, 0.5)
+
+
+def get_market_temp(db_path=None):
+    """读取最新市场温度（rsi + 档位 + regime）。"""
+    db = db_path or DB
+    conn = sqlite3.connect(str(db), timeout=5)
+    try:
+        # 上证最近 30 日收盘算 rsi
+        rows = conn.execute(
+            "SELECT date, close FROM stock_daily WHERE code=? ORDER BY date DESC LIMIT 30",
+            (INDEX,)
+        ).fetchall()
+        # 最新 regime
+        reg = conn.execute(
+            "SELECT date, regime FROM market_regime ORDER BY date DESC LIMIT 1"
+        ).fetchone()
+    finally:
+        conn.close()
+    if not rows or len(rows) < 15:
+        return {"rsi": None, "band": "unknown", "regime": "unknown", "date": ""}
+    rows = list(reversed(rows))
+    closes = [r[1] for r in rows]
+    dates = [r[0] for r in rows]
+    rsi_series = calc_rsi(closes)
+    rsi_now = rsi_series[-1] if rsi_series else None
+    band = temp_band(rsi_now)
+    regime = reg[1] if reg else "unknown"
+    reg_date = reg[0] if reg else ""
+    return {
+        "rsi": round(rsi_now, 1) if rsi_now is not None else None,
+        "band": band,
+        "regime": regime,
+        "date": dates[-1],
+        "regime_date": reg_date,
+    }
+
+
+if __name__ == "__main__":
+    t = get_market_temp()
+    print(f"温度: rsi={t['rsi']} band={t['band']} regime={t['regime']} ({t['date']})")
+    print(f"  mr 乘数: {temp_multiplier(t['band'], 'mr')}")
+    print(f"  trend 乘数: {temp_multiplier(t['band'], 'trend')}")