256 lines
11 KiB
Python
256 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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"""2026-08-16 资格评估与可用性管理（数据层）
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标准 A（老莫确认）：
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长期合格 = 适应温区 10y 年化 > 大盘10y
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近期不错 = 适应温区 2y 年化 > 大盘2y
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当下能打 = 适应温区 1y 年化 > 大盘1y
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三项全过 → 激活资格；长期+近期 → 可用；长期不过 → 历史
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可用性(manual_available)由老莫手动把关：激活只能是可用的策略。
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新策略按默认标准(A)初始化可用性；定期评估自动标记「可用性下降/回升」警告。
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存储：data/strategy_availability.json（git 可追踪，同 strategy_weights.json 模式）
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"""
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import json
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import os
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import sqlite3
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from datetime import datetime, timedelta
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from pathlib import Path
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DATA_DIR = Path("/home/hmo/MoFin/data")
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AVAIL_JSON = DATA_DIR / "strategy_availability.json"
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# 大盘基准（market_regime 指数年化，按需现算）
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BENCH_DAYS = {"1y": 365, "2y": 730, "10y": 3650}
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def get_benchmarks(market="a"):
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"""大盘年化基准：1y/2y/10y
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A股：全年大盘年化（market_regime 指数 close，复利年化）
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港股（2026-08-16 老莫确认）：下跌市时段基准——trend_down 日期的 HSI 表现，
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线性放大年化（trend_down 时段收益 × 365/td天数）。港股策略只在下跌市运行，与全年比不公平。
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"""
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out = {}
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try:
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conn = sqlite3.connect(str(DATA_DIR / "mofin.db"), timeout=5)
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for label, days in BENCH_DAYS.items():
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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last = conn.execute(
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"SELECT close FROM market_regime WHERE market=? AND close IS NOT NULL ORDER BY date DESC LIMIT 1",
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(market,)).fetchone()
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base = conn.execute(
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"SELECT close FROM market_regime WHERE market=? AND close IS NOT NULL AND date>=? ORDER BY date LIMIT 1",
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(market, cutoff)).fetchone()
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if last and base and base[0]:
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pct = (last[0] / base[0] - 1) * 100
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yrs = days / 365.0
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if pct > -100:
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out[label] = round(((1 + pct / 100) ** (1 / yrs) - 1) * 100, 1)
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# ── 港股：下跌市时段基准（trend_down 期间 HSI，按每次时段分段算再合并）──
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if market == "hk":
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out = {}
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for label, days in BENCH_DAYS.items():
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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rows = conn.execute(
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"SELECT date, regime, close FROM market_regime WHERE market='hk' AND date>=? "
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"ORDER BY date", (cutoff,)).fetchall()
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# 分段：连续 trend_down 日组成段，段内 HSI 末/首 -1
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closes = {r[0]: r[2] for r in rows}
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seg_ret = []
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in_seg = False
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seg_first = None
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seg_last_close = None
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for d, reg, c in rows:
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if reg == "trend_down" and c:
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if not in_seg:
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in_seg = True
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seg_first = c
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seg_last_close = c
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else:
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if in_seg:
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seg_ret.append((c / seg_first - 1) * 100 if c and seg_first else 0)
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in_seg = False
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seg_first = None
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seg_last_close = None
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if in_seg and seg_first:
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# 最后一段未结束（窗口尾部仍下跌市），用段内最后 close
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if seg_last_close:
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seg_ret.append((seg_last_close / seg_first - 1) * 100)
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if not seg_ret:
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continue
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# 合并：段收益加总（下跌市期间累计表现），线性年化 ×365/窗口天数
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total_ret = sum(seg_ret)
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out[label] = round(total_ret * (365.0 / max(days, 1)), 1)
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conn.close()
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except Exception:
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pass
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return out
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def evaluate_strategy(version, market="a", best_regime=None, bench=None):
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"""评估单策略资格：长期/近期/当下 三项达标
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返回 {long_ok, mid_ok, short_ok, cagr_10y, cagr_2y, cagr_1y, regime}
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"""
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bench = bench or get_benchmarks(market)
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if not bench:
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return None
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try:
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conn = sqlite3.connect(str(DATA_DIR / "mofin.db"), timeout=5)
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conn.row_factory = sqlite3.Row
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# 适应温区：best_regime 传入或用 10y 最高胜率温区
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if not best_regime:
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rows = conn.execute(
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"SELECT regime, win_rate FROM strategy_regime_perf_by_period "
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"WHERE strategy=? AND market=? AND period_tag='10y'",
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(version, market)).fetchall()
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if not rows:
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conn.close()
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return None
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best_regime = max(rows, key=lambda x: x[1] or 0)[0]
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# 各周期适应温区年化
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cagr = {}
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for pt in ["1y", "2y", "10y"]:
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r = conn.execute(
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"SELECT cagr_pct, win_rate, trades FROM strategy_regime_perf_by_period "
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"WHERE strategy=? AND market=? AND regime=? AND period_tag=?",
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(version, market, best_regime, pt)).fetchone()
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cagr[pt] = (r["cagr_pct"], r["win_rate"], r["trades"]) if r else (None, None, None)
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conn.close()
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except Exception:
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return None
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c10, w10, t10 = cagr.get("10y", (None, None, None))
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c2, w2, t2 = cagr.get("2y", (None, None, None))
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c1, w1, t1 = cagr.get("1y", (None, None, None))
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return {
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"regime": best_regime,
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"cagr_10y": c10, "cagr_2y": c2, "cagr_1y": c1,
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"trades_10y": t10, "trades_2y": t2, "trades_1y": t1,
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"long_ok": c10 is not None and c10 > bench.get("10y", 0),
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"mid_ok": c2 is not None and c2 > bench.get("2y", 0),
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"short_ok": c1 is not None and c1 > bench.get("1y", 0),
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}
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def evaluate_all_regimes(version, market="a", bench=None):
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"""评估策略在【每个有数据温区】的资格（2026-08-16 老莫：策略可适应多个温区）
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返回 {regime: {long_ok, mid_ok, short_ok, cagr_10y, cagr_2y, cagr_1y, trades_10y, trades_2y, trades_1y}}
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只包含温区数据存在（有 10y 记录）的温区
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"""
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bench = bench or get_benchmarks(market)
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if not bench:
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return {}
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out = {}
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try:
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conn = sqlite3.connect(str(DATA_DIR / "mofin.db"), timeout=5)
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conn.row_factory = sqlite3.Row
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# 该策略所有温区（有 10y 记录才算数）
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regimes = [r[0] for r in conn.execute(
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"SELECT DISTINCT regime FROM strategy_regime_perf_by_period "
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"WHERE strategy=? AND market=? AND period_tag='10y'",
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(version, market)).fetchall()]
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for rg in regimes:
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cagr = {}
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for pt in ["1y", "2y", "10y"]:
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r = conn.execute(
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"SELECT cagr_pct, win_rate, trades FROM strategy_regime_perf_by_period "
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"WHERE strategy=? AND market=? AND regime=? AND period_tag=?",
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(version, market, rg, pt)).fetchone()
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cagr[pt] = (r["cagr_pct"], r["win_rate"], r["trades"]) if r else (None, None, None)
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c10, w10, t10 = cagr.get("10y", (None, None, None))
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c2, w2, t2 = cagr.get("2y", (None, None, None))
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c1, w1, t1 = cagr.get("1y", (None, None, None))
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out[rg] = {
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"cagr_10y": c10, "cagr_2y": c2, "cagr_1y": c1,
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"trades_10y": t10, "trades_2y": t2, "trades_1y": t1,
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"long_ok": c10 is not None and c10 > bench.get("10y", 0),
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"mid_ok": c2 is not None and c2 > bench.get("2y", 0),
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"short_ok": c1 is not None and c1 > bench.get("1y", 0),
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}
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conn.close()
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except Exception:
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pass
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return out
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def load_availability():
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"""读手动可用性状态 {version: {available: bool, note, updated_at}}"""
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try:
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if AVAIL_JSON.exists():
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return json.loads(AVAIL_JSON.read_text(encoding="utf-8"))
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except Exception:
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pass
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return {}
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def save_availability(data):
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"""写手动可用性状态"""
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AVAIL_JSON.write_text(json.dumps(data, ensure_ascii=False, indent=1), encoding="utf-8")
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def _strategy_market(version):
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"""探测策略市场：strategy_research.market（'hk'=港股，否则 A股）"""
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try:
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conn = sqlite3.connect(str(DATA_DIR / "mofin.db"), timeout=5)
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r = conn.execute(
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"SELECT market FROM strategy_research WHERE version=? AND COALESCE(market,'')!='' "
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"ORDER BY id DESC LIMIT 1", (version,)).fetchone()
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conn.close()
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if r and r[0] == "hk":
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return "hk"
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except Exception:
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pass
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return "a"
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def auto_init_availability(versions, bench=None, market_by_version=None):
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"""新策略按标准 A 自动初始化可用性（一般默认可用；长期不合格则不可用）
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已存在的手动状态不动。返回新增的默认可用策略。
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market_by_version: dict {version: 'a'|'hk'}，缺省按 strategy_research.market 探测
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"""
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bench = bench or get_benchmarks()
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avail = load_availability()
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changed = []
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for v in versions:
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if v in avail:
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continue
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mkt = (market_by_version or {}).get(v) or _strategy_market(v)
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ev = evaluate_strategy(v, mkt, bench=bench)
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# 默认：长期合格即可用（A 标准）；长期不合格(10y跑不赢大盘)默认不可用
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# 无评估数据（如港股策略在 hk 也无温区数据）→ 默认可用（不误杀新策略）
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default_avail = True if ev is None else bool(ev["long_ok"])
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if ev is None:
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note = "自动初始化:无温区数据,默认可用"
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elif default_avail:
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note = "自动初始化(标准A)"
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else:
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note = "自动初始化:长期不合格(10y<大盘)"
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avail[v] = {
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"available": default_avail,
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"note": note,
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"updated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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}
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changed.append((v, default_avail))
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if changed:
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save_availability(avail)
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return changed
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def is_available(version):
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"""查询手动可用性（默认 true——新策略默认可用，除非明确标不可用）"""
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avail = load_availability()
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entry = avail.get(version)
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if entry is None:
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return True # 未标记 = 默认可用
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return bool(entry.get("available", True))
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def set_available(version, available, note=""):
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"""手动设置可用性"""
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avail = load_availability()
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avail[version] = {
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"available": bool(available),
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"note": note or ("手动可用" if available else "手动不可用"),
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"updated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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}
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save_availability(avail)
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return avail[version]
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