hmo
21401f91cf
- 新增 market_regime.py: sh000001 的 above_ma20/ma20_slope/roc/adx 计算+regime分类(trend_up/choppy/trend_down)+market_regime表 - market_watch.py: 每30分钟调度时同步更新 market_regime - strategy_lifecycle.py: enrich_timing_signal 新增市场阶段因子+降级逻辑(震荡/下跌市买入信号降级为关注), regenerate_all/reassess_with_context 加载regime
260 lines
9.1 KiB
Python
260 lines
9.1 KiB
Python
#!/usr/bin/env python3
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"""market_regime.py — 大盘市场阶段判断（实盘版）
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解决实盘"无法判断当前是什么市"的断层：
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回测用 _load_index_ctx('sh000001') 算 above_ma20/ma20_slope/roc/adx 做市场过滤
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（v_next4 要求 above_ma20=True + adx>=20 才出手，v_mr 用 mkt_mode='any'），
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但实盘 strategy_lifecycle 只有当日涨跌幅 mood（±1%/1.5%→仓位乘数 0.8~1.1），
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没有 MA20+ADX 的市场阶段判断 —— 本模块把回测算法原样搬到实盘。
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算法与 /home/hmo/MoFin/strategy_lab.py 的 _load_index_ctx 完全一致：
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- above_ma20 : 收盘价 > MA20
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- ma20_slope : MA20 相对 5 日前 MA20 的变化率(%)
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- roc : 10 日 Rate of Change(%)
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- adx : 简易趋势强度 (DI+ - DI-)/(DI+ + DI-) 归一化，14 日窗口
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（calc_trend_strength，backtest_framework.py 87 行）
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regime 分类（与回测 v_next4/v_mr 分工对齐）：
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- trend_up : above_ma20=True 且 adx>=20 → 趋势市，v_next4 主战场（追涨有效）
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- choppy : adx<20 → 震荡市，v_mr 主战场（超跌反弹）
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- trend_down : above_ma20=False 且 adx>=20 → 下跌趋势，v_mr 主战场（深超跌）
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用法：
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python3 market_regime.py # 计算并写 market_regime 表（供定时调度）
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python3 market_regime.py --print # 只打印当前市场状态
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作为库: from market_regime import compute_regime, load_market_regime
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写表: market_regime(date PK, above_ma20, ma20_slope, roc, adx, regime, close, created_at)
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数据源: mofin.db stock_daily 的 sh000001（import_full_stocks 每日收盘后更新，
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盘中用最新可得日线，未收盘日不计入最终判断，adx 用真实历史）。
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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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# ── 路径注入：可被 deploy/profile-scripts 下脚本直接 import ──
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_SCRIPT_DIR = Path(__file__).resolve().parent
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_MOFIN_ROOT = _SCRIPT_DIR.parent.parent # deploy/profile-scripts → MoFin
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for _p in (str(_SCRIPT_DIR), str(_MOFIN_ROOT)):
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if _p not in sys.path:
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sys.path.insert(0, _p)
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DB_PATH = Path(_MOFIN_ROOT) / "data" / "mofin.db"
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INDEX_CODE = "sh000001" # 上证指数（A股）
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INDEX_CODE_HK = "hkHSI" # 恒生指数（港股）
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# ADX 阈值与回测 v_next4 的 mkt_adx_min=20 对齐
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ADX_TREND_MIN = 20.0
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def calc_trend_strength(highs, lows, closes, n=14):
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"""简易趋势强度（替代 ADX）: (DI+ - DI-) / (DI+ + DI-) 归一化
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与 backtest_framework.calc_trend_strength 完全同算法（内联，避免跨目录依赖）"""
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tr = []
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for i in range(len(highs)):
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if i == 0:
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tr.append(highs[i] - lows[i])
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else:
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tr.append(max(highs[i] - lows[i],
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abs(highs[i] - closes[i - 1]),
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abs(lows[i] - closes[i - 1])))
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up = [highs[i] - highs[i - 1] for i in range(1, len(highs))]
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down = [lows[i - 1] - lows[i] for i in range(1, len(lows))]
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di_plus_raw = [0.0] * len(up)
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di_minus_raw = [0.0] * len(up)
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for i in range(len(up)):
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if up[i] > down[i] and up[i] > 0:
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di_plus_raw[i] = up[i]
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if down[i] > up[i] and down[i] > 0:
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di_minus_raw[i] = down[i]
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tr_val = tr[i + 1] if (i + 1) < len(tr) else tr[-1]
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if tr_val > 0:
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di_plus_raw[i] = di_plus_raw[i] / tr_val * 100
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di_minus_raw[i] = di_minus_raw[i] / tr_val * 100
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result = []
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for i in range(len(di_plus_raw)):
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if i < n - 1:
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result.append(None)
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else:
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avg_plus = sum(di_plus_raw[i - n + 1:i + 1]) / n
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avg_minus = sum(di_minus_raw[i - n + 1:i + 1]) / n
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if avg_plus + avg_minus > 0:
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dx = abs(avg_plus - avg_minus) / (avg_plus + avg_minus) * 100
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else:
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dx = 0
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result.append(dx)
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return [None] * (len(highs) - len(result)) + result
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def calc_ma(series, n):
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result = []
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for i in range(len(series)):
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if i < n - 1:
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result.append(None)
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else:
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result.append(sum(series[i - n + 1:i + 1]) / n)
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return result
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def calc_roc(series, n=10):
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result = []
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for i in range(len(series)):
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if i < n:
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result.append(None)
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else:
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result.append((series[i] - series[i - n]) / series[i - n] * 100 if series[i - n] != 0 else 0)
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return result
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def compute_regime(index_code=INDEX_CODE, db_path=None, lookback_days=120):
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"""计算大盘市场状态（最近一个完整交易日）。
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返回 dict：
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{date, close, above_ma20, ma20_slope, roc, adx, regime, computed_at}
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数据不足(<30行)或取数失败时返回 None。
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"""
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db = db_path or DB_PATH
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conn = sqlite3.connect(str(db), timeout=5)
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try:
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rows = conn.execute(
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"SELECT date, close, high, low FROM stock_daily "
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"WHERE code=? ORDER BY date DESC LIMIT ?",
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(index_code, lookback_days)).fetchall()
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finally:
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conn.close()
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if not rows or len(rows) < 30:
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return None
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# 升序处理（与回测 prepare_bars 一致）
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rows = list(reversed(rows))
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dates = [r[0] for r in rows]
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closes = [r[1] for r in rows]
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highs = [r[2] for r in rows]
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lows = [r[3] for r in rows]
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ma20 = calc_ma(closes, 20)
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trend = calc_trend_strength(highs, lows, closes)
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roc = calc_roc(closes)
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i = len(closes) - 1 # 最新一日
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close = closes[i]
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m20 = ma20[i]
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above_ma20 = (close > m20) if m20 else None
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# ma20_slope: 相对 5 日前 MA20 的变化率
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slope = None
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if i >= 5 and ma20[i - 5] and ma20[i - 5] > 0 and m20:
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slope = round((m20 - ma20[i - 5]) / ma20[i - 5] * 100, 3)
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adx = trend[i] if i < len(trend) else None
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roc_v = roc[i] if i < len(roc) else None
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# regime 分类
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if above_ma20 is True and adx is not None and adx >= ADX_TREND_MIN:
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regime = "trend_up"
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elif adx is not None and adx < ADX_TREND_MIN:
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regime = "choppy"
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else:
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regime = "trend_down"
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return {
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"date": dates[i],
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"close": close,
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"above_ma20": above_ma20,
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"ma20_slope": slope,
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"roc": round(roc_v, 3) if roc_v is not None else None,
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"adx": round(adx, 2) if adx is not None else None,
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"regime": regime,
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"computed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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}
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def save_regime(regime, db_path=None):
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"""写入 market_regime 表（按 date 去重，同日覆盖）"""
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if not regime:
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return False
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db = db_path or DB_PATH
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conn = sqlite3.connect(str(db), timeout=5)
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try:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS market_regime (
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date TEXT PRIMARY KEY,
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above_ma20 INTEGER,
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ma20_slope REAL,
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roc REAL,
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adx REAL,
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regime TEXT,
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close REAL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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""")
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conn.execute("""
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INSERT OR REPLACE INTO market_regime
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(date, above_ma20, ma20_slope, roc, adx, regime, close, created_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
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""", (
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regime["date"],
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1 if regime["above_ma20"] else 0,
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regime.get("ma20_slope"),
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regime.get("roc"),
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regime.get("adx"),
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regime["regime"],
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regime.get("close"),
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))
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conn.commit()
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finally:
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conn.close()
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return True
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def load_market_regime(db_path=None):
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"""读取最新 market_regime（供 strategy_lifecycle 等消费方调用）"""
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db = db_path or DB_PATH
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conn = sqlite3.connect(str(db), timeout=5)
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try:
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row = conn.execute(
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"SELECT date, above_ma20, ma20_slope, roc, adx, regime, close "
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"FROM market_regime ORDER BY date DESC LIMIT 1").fetchone()
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finally:
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conn.close()
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if not row:
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return None
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return {
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"date": row[0],
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"above_ma20": bool(row[1]),
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"ma20_slope": row[2],
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"roc": row[3],
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"adx": row[4],
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"regime": row[5],
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"close": row[6],
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}
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REGIME_DESC = {
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"trend_up": "趋势市（大盘MA20上方+ADX≥20）：v_next4 追涨主战场",
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"choppy": "震荡市（ADX<20）：v_mr 超跌反弹主战场",
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"trend_down": "下跌趋势（大盘MA20下方+ADX≥20）：v_mr 深超跌主战场",
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}
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def main():
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regime = compute_regime()
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if "--print" in sys.argv or not regime:
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if not regime:
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print("sh000001 数据不足，无法计算市场状态")
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return 1
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print(json.dumps(regime, ensure_ascii=False, indent=2))
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print(f"判断: {REGIME_DESC.get(regime['regime'], '')}")
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return 0
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ok = save_regime(regime)
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if ok:
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print(f"[market_regime] {regime['date']} → {regime['regime']} "
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f"(above_ma20={regime['above_ma20']} adx={regime['adx']} "
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f"slope={regime['ma20_slope']} roc={regime['roc']})")
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else:
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print("[market_regime] 写入失败")
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return 1
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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