201 lines
7.8 KiB
Python
201 lines
7.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""regime_perf_by_period.py — 策略-温区表现按周期预计算（2026-08-15 数据加工层落地）
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背景（老莫指正）：温区数据是数据加工层，必须预计算存储，不能每次请求现场重算。
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本脚本把每个策略×市场×周期(1y/2y/5y/10y)×温区的表现预先算好落库，
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server 直接读表返回（毫秒级，无需内存缓存/现场计算）。
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与 regime_perf.py 的区别：
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- regime_perf.py 只算最长窗口（全量），写 strategy_regime_perf
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- 本脚本按周期分窗，写 strategy_regime_perf_by_period（新增 period_tag 维度）
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- 年化用【线性放大】：温区组合总收益 × (窗口交易日数 / 该温区窗口内天数)
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（替代 portfolio_sim 的复利年化——复利在温区 trades 跨度短时会爆炸）
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用法：
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python3 regime_perf_by_period.py --market=a --periods="1y 2y 5y 10y"
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python3 regime_perf_by_period.py --market=hk --periods="2y"
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"""
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import sys
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import json
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import math
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import sqlite3
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from datetime import datetime
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from collections import defaultdict
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sys.path.insert(0, "/home/hmo/MoFin")
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sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
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DB = "/home/hmo/MoFin/data/mofin.db"
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# 周期 → 目标 period_tag（strategy_research 里的记录标签）
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PERIOD_TAGS = ["1y", "2y", "5y", "10y"]
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def get_conn():
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conn = sqlite3.connect(DB, timeout=60)
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conn.execute("PRAGMA busy_timeout=60000")
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return conn
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def create_table(conn):
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conn.execute("""
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CREATE TABLE IF NOT EXISTS strategy_regime_perf_by_period (
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strategy TEXT,
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market TEXT NOT NULL DEFAULT 'a',
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regime TEXT,
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period_tag TEXT NOT NULL DEFAULT '2y',
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trades INTEGER,
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win_rate REAL,
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avg_pnl REAL,
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avg_hold_days REAL,
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total_return_pct REAL,
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cagr_pct REAL,
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portfolio_max_dd_pct REAL,
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capital_final REAL,
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positions_taken INTEGER,
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sharpe_ratio REAL,
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profit_factor REAL,
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updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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PRIMARY KEY (strategy, market, regime, period_tag)
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)
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""")
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def calc_extra(trades):
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"""从 trades 算温区级 win_rate/avg_pnl/avg_hold/sharpe/profit_factor"""
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if not trades:
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return {}
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profits = [t.get("profit_pct", 0) for t in trades]
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wins = [p for p in profits if p > 0]
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losses = [p for p in profits if p <= 0]
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win_rate = len(wins) / len(profits) * 100 if profits else 0
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avg_p = sum(profits) / len(profits) if profits else 0
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avg_w = sum(wins) / len(wins) if wins else 0
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avg_l = abs(sum(losses) / len(losses)) if losses else 1
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pf = avg_w / avg_l if avg_l > 0 else 0
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mean_r = avg_p / 100
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std_r = math.sqrt(sum((p / 100 - mean_r) ** 2 for p in profits) / (len(profits) - 1)) if len(profits) > 1 else 0
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sharpe = mean_r / std_r * math.sqrt(252) if std_r > 0 else 0
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holds = [t.get("hold_days", 0) for t in trades if t.get("hold_days")]
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avg_hold = sum(holds) / len(holds) if holds else 0
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return {"win_rate": round(win_rate, 1), "avg_pnl": round(avg_p, 2),
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"avg_hold_days": round(avg_hold, 1), "sharpe_ratio": round(sharpe, 2),
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"profit_factor": round(pf, 2)}
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def portfolio_sim_wrap(trades, capital=1000000, max_positions=10):
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"""温区 trades → 组合模拟（复用 strategy_lab.portfolio_sim）"""
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if not trades:
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return {}
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try:
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from strategy_lab import portfolio_sim
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return portfolio_sim(trades, capital=capital, max_positions=max_positions, cost=True)
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except Exception:
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return {}
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def regime_days_in_window(conn, market, d_min, d_max):
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"""窗口内各温区天数 + 总天数（线性年化用）"""
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rows = conn.execute(
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"SELECT regime, COUNT(*) n FROM market_regime WHERE market=? AND date>=? AND date<=? GROUP BY regime",
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(market, d_min, d_max)).fetchall()
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total = sum(r[1] for r in rows)
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return {r[0]: r[1] for r in rows}, total
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def process_period(conn, market, period_tag):
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"""处理单个周期：所有策略的温区表现，写入 strategy_regime_perf_by_period"""
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# 温区映射
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rmap = dict(conn.execute(
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"SELECT date, regime FROM market_regime WHERE market=?", (market,)).fetchall())
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# 该周期所有策略记录
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rows = conn.execute(
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"SELECT version, results_json FROM strategy_research "
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"WHERE COALESCE(market,'a')=? AND period_tag=? ORDER BY version",
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(market, period_tag)).fetchall()
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written = 0
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now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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for v, results_json in rows:
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if not results_json:
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continue
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try:
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trades = json.loads(results_json).get("trades", [])
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except Exception:
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continue
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if not trades:
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continue
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# 温区归因
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by_regime = defaultdict(list)
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for t in trades:
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ed = t.get("entry_date", "")
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if ed in rmap:
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by_regime[rmap[ed]].append(t)
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if not by_regime:
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continue
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# 窗口天数（该策略 trades 的 entry_date 范围）
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eds = [t.get("entry_date") for t in trades if t.get("entry_date") and t.get("entry_date") in rmap]
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if not eds:
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continue
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reg_days, total_days = regime_days_in_window(conn, market, min(eds), max(eds))
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for reg, reg_trades in by_regime.items():
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if len(reg_trades) < 2:
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continue
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extra = calc_extra(reg_trades)
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sim = portfolio_sim_wrap(reg_trades)
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if not sim:
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continue
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ret = sim.get("total_return_pct")
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# 复利年化（2026-08-16 修正）：按该温区 trades 实际时间跨度
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# 原线性放大 ret×(total_days/rd) 对高频复利策略失真（b_td1 388%×4=1547%）
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_eds = [t.get("entry_date") for t in reg_trades if t.get("entry_date")]
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if ret is not None and _eds:
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from datetime import datetime as _dt
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_d0 = _dt.strptime(min(_eds), "%Y-%m-%d")
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_d1 = _dt.strptime(max(_eds), "%Y-%m-%d")
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span_days = max((_d1 - _d0).days, 30)
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cagr = round(((1 + ret / 100) ** (365 / span_days) - 1) * 100, 1)
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else:
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cagr = None
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conn.execute(
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"""INSERT OR REPLACE INTO strategy_regime_perf_by_period
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(strategy, market, regime, period_tag, trades, win_rate, avg_pnl, avg_hold_days,
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total_return_pct, cagr_pct, portfolio_max_dd_pct, capital_final,
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positions_taken, sharpe_ratio, profit_factor, updated_at)
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VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)""",
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(v, market, reg, period_tag, len(reg_trades),
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extra.get("win_rate"), extra.get("avg_pnl"), extra.get("avg_hold_days"),
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ret, cagr, sim.get("portfolio_max_dd_pct"),
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sim.get("capital_final"), sim.get("positions_taken"),
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extra.get("sharpe_ratio"), extra.get("profit_factor"), now))
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written += 1
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return written
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def main():
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market = "a"
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periods = PERIOD_TAGS
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for a in sys.argv[1:]:
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if a.startswith("--market="):
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market = a.split("=", 1)[1]
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elif a.startswith("--periods="):
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periods = a.split("=", 1)[1].split()
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conn = get_conn()
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create_table(conn)
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# 先清该市场旧数据
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conn.execute("DELETE FROM strategy_regime_perf_by_period WHERE market=?", (market,))
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total = 0
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for pt in periods:
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n = process_period(conn, market, pt)
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print(f"[{market}][{pt}] 写入 {n} 条", flush=True)
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total += n
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conn.commit()
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conn.close()
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print(f"完成: market={market} periods={periods} 共 {total} 条")
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if __name__ == "__main__":
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main()
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