feat: 策略自我进化闭环落地——evolution_engine新建(退化检测+数据驱动变体±20%+2y回测验证+达标才推送)+lesson_extractor重写(实盘平仓+温区归因)+auto_iterator归档+cron每周六22:00

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# -*- coding: utf-8 -*-
"""
evolution/evolution_engine.py — 策略自我进化引擎(每周六 22:00,hermes cron
设计依据:docs/decisions/2026-08-15-策略自我进化闭环重构.md(老莫已批准)
闭环:统计数据每日自动更新 → 本引擎每周检测退化 → 生成参数变体 → 回测验证 → 有价值才推送
流程:
1. 读激活策略集合(data/strategy_weights.jsonA股 active + 港股 markets.hk.active
2. 退化信号检测(宁缺毋滥,任一命中即触发研究):
S1 健康度连续低:strategy_health 连续 5 天 health_score < 40(排除 50 中性=无实盘数据)
S2 温区表现衰减:激活策略在其适应温区(strategy_regime_perf)温区级组合年化 cagr_pct < 0
3. 有退化 → 生成参数变体:
- 只对 lab.STRATEGIES 里可回测的策略(v_oversold/v_weak 等标准回测体系)
- 参数空间从策略 config 实际数值字段出发(递归遍历,单变量 ±20%,一次只动一个)
- 港股走 hk_backtestentry/exit 字段 ±20%
4. 回测验证(统一资金约束):
- A股:lab.run_backtest(save=False),取 portfolio_full
- 港股:hk_backtest.gen_trades_defensive + lab.portfolio_sim(max_positions=8)
- 验收:温区级组合年化 cagr_pct ≥ 原策略 + 3pp 且 max_dd 不劣化超过 2pp
5. 达标变体 → 写 strategy_evolutionpromoted=0+ XMPP 推送老莫(附对比证据)
(永不自动 promote,老莫说"上线"才进路由)
6. 无退化或变体全灭 → 当周静默(不制造噪音)
单例守卫:fcntl.flock 防并发(deploy_guard / 手动重跑均安全)
"""
import sys, os, json, sqlite3, copy, io, traceback
from datetime import datetime, timedelta
sys.path.insert(0, "/home/hmo/MoFin")
sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
DB = os.environ.get("MOFIN_DB", "/home/hmo/MoFin/data/mofin.db")
WEIGHTS_JSON = "/home/hmo/MoFin/data/strategy_weights.json"
# 退化信号参数
HEALTH_LOW = 40 # 健康度低于此值视为低
HEALTH_STREAK_DAYS = 5 # 连续天数
REGIME_CAGR_BAD = 0.0 # 温区级组合年化低于此值视为退化
# 变体生成参数
VAR_PCT = 0.20 # ±20% 网格
MAX_VARIANTS = 6 # 每策略最多生成变体数
MAX_VARIANTS_TEST = 1 # 最多回测验证的变体数(资源约束:单变体2y全市场回测6-8分钟/6-8GB,详见下方BT注释)
# 验证回测周期(2026-08-15:原5y全市场回测单变体8+分钟/5GB内存,改为2y控制资源;
# 验收对比用同周期原策略数据,相对改善仍有效)
BT_START = "2024-07-01"
BT_END = "2026-07-24"
BT_PERIOD_TAG = "2y"
# 验收门槛(2026-08-15 口径说明:变体与 parent 用同周期 strategy_research 2y 整体组合年化对比,
# 相对改善有效;原设计"温区级组合年化"需按温区分段重跑变体,资源过重,整体同口径更务实)
ACCEPT_CAGR_PP = 3.0 # 组合年化 ≥ 原 + 3pp
ACCEPT_DD_PP = 2.0 # max_dd 不劣化超过 2pp
def log(msg):
line = f"[{datetime.now().isoformat(timespec='seconds')}] {msg}"
print(line, flush=True)
# ── 单例守卫(fcntl,Windows 不可用则跳过)──
try:
import fcntl
_LOCK_FD = open("/tmp/evolution_engine.lock", "w")
try:
fcntl.flock(_LOCK_FD, fcntl.LOCK_EX | fcntl.LOCK_NB)
except OSError:
log("已有 evolution_engine 实例在运行,退出")
sys.exit(0)
except ImportError:
pass
def get_conn():
conn = sqlite3.connect(DB)
conn.row_factory = sqlite3.Row
return conn
# ── 1. 激活策略集合 ──
def load_active_strategies():
"""返回 [(version, market, regime)]A股 active + 港股 markets.hk.active"""
try:
d = json.load(io.open(WEIGHTS_JSON, encoding="utf-8"))
except Exception as e:
log(f"读 strategy_weights.json 失败: {e}")
return []
out = []
for v in (d.get("active") or []):
info = (d.get("weights") or {}).get(v, {})
out.append({"version": v, "market": "a",
"regime": info.get("best_regime") or info.get("regime") or d.get("state")})
hk = (d.get("markets") or {}).get("hk") or {}
for v in (hk.get("active") or []):
out.append({"version": v, "market": "hk", "regime": hk.get("state")})
return out
# ── 2. 退化信号检测 ──
def detect_degradation(conn, version, market):
"""返回退化原因列表(空=健康)。S1 健康度连续低;S2 温区组合年化<0"""
reasons = []
# S1:健康度连续 5 天 < 40(排除 50 中性=无实盘)
rows = conn.execute(
"SELECT date, health_score FROM strategy_health WHERE strategy_version=? ORDER BY date DESC LIMIT ?",
(version, HEALTH_STREAK_DAYS)).fetchall()
if len(rows) >= HEALTH_STREAK_DAYS:
scores = [r["health_score"] for r in rows]
# 排除"无实盘=50中性"污染:只要连续5天都 < 40 且不是 50 占位
if all(s is not None and s < HEALTH_LOW for s in scores) and any(s != 50 for s in scores):
reasons.append(f"S1 健康度连续{HEALTH_STREAK_DAYS}天<{HEALTH_LOW}{scores}")
# S2:适应温区温区级组合年化 < 0strategy_regime_perf.cagr_pct
r = conn.execute(
"SELECT regime, cagr_pct, trades FROM strategy_regime_perf WHERE strategy=? AND market=? ORDER BY trades DESC LIMIT 1",
(version, market)).fetchone()
if r and r["cagr_pct"] is not None and r["cagr_pct"] < REGIME_CAGR_BAD:
reasons.append(f"S2 适应温区[{r['regime']}]组合年化{r['cagr_pct']}%<0{r['trades']}笔)")
return reasons
# ── 3. 变体生成(数据驱动,从实际 config 出发)──
_NUM_KEYS = ("tp_pct", "sl_pct", "sl_atr", "max_hold_days", "min_score", "min_momentum",
"adx_min", "atr_pct_min", "atr_pct_max", "roc_min", "roc_max",
"macd_hist_min", "macd_hist_max", "dist_ma20_min", "vol_ratio_min",
"vol_ratio_max", "ma20_slope_max", "mkt_slope_max", "mkt_adx_min",
"sector_slope_max", "bias_max", "rsi_max", "ret_max", "mom20_max",
"amount_max", "rsi_delta_min", "mkt_rsi_max", "mkt_dd60_max",
"mcap_q_max", "pe_q_max", "news3_min", "sec_ret20_max",
"pe_q_max", "mcap_q_max", "sec_ret20_min", "bias60_max",
"vol_ratio_min", "rsi_delta_min", "bias60_min")
_SKIP_KEYS = ("mode", "family", "launch", "version", "name", "summary", "hypothesis",
"mkt_mode", "mkt_above_ma20", "hh_only", "hl_only", "sector_above_ma20")
def iter_numeric_fields(node, path=()):
"""递归遍历 config,产出 (path_list, field_name, value) 数值字段"""
if isinstance(node, dict):
for k, v in node.items():
if k in _SKIP_KEYS:
continue
if isinstance(v, (int, float)) and not isinstance(v, bool) and k in _NUM_KEYS:
yield list(path) + [k], k, v
elif isinstance(v, dict):
yield from iter_numeric_fields(v, list(path) + [k])
def get_parent_cagr(conn, version, market, period_tag=BT_PERIOD_TAG):
"""原策略基准:优先同周期 strategy_researchperiod_tag=2y),无则温区级组合年化(全量)"""
if market == "a":
r = conn.execute(
"SELECT results_json FROM strategy_research WHERE version=? AND period_tag=? "
"AND market='a' ORDER BY id DESC LIMIT 1", (version, period_tag)).fetchone()
if r:
res = json.loads(r["results_json"] or "{}")
s = res.get("summary", {})
pf = s.get("portfolio_full", {})
cagr = pf.get("cagr_pct")
dd = pf.get("portfolio_max_dd_pct")
if cagr is not None:
return cagr, dd
# 回退:温区级组合年化(strategy_regime_perf,全量)——仅当同周期数据缺失时
r = conn.execute(
"SELECT cagr_pct, portfolio_max_dd_pct FROM strategy_regime_perf WHERE strategy=? AND market=? ORDER BY trades DESC LIMIT 1",
(version, market)).fetchone()
if r:
return r["cagr_pct"], r["portfolio_max_dd_pct"]
return None, None
def generate_variants(version, market, config):
"""生成变体参数建议:单变量 ±20%,最多 MAX_VARIANTS 个
返回 [{version, name, config, change_desc, field, delta}]"""
fields = list(iter_numeric_fields(config))
if not fields:
return []
variants = []
for path, fname, val in fields:
if val <= 0:
continue
for factor, tag in [(1 - VAR_PCT, "减20%"), (1 + VAR_PCT, "加20%")]:
new_val = round(val * factor, 4)
if new_val <= 0:
continue
# 克隆 config 并修改目标字段
new_cfg = copy.deepcopy(config)
node = new_cfg
for p in path[:-1]:
node = node[p]
node[path[-1]] = new_val
variants.append({
"version": f"evo_{version}_{fname}_{tag.replace('20%','')}{round(new_val, 2)}",
"name": f"自进化-{version}-{fname}{tag}",
"config": new_cfg,
"change_desc": f"{fname}: {val}{new_val}{tag}",
"field": fname,
"delta": round(new_val - val, 4),
})
if len(variants) >= MAX_VARIANTS:
return variants
return variants
# ── 4. 回测验证 ──
def verify_variant_a(variant, parent_version):
"""A股变体验证:注册进 lab 跑回测(save=False),返回 summary 关键指标"""
import strategy_lab as lab
name = variant["version"]
base = lab.get_strategy(parent_version)
cfg = copy.deepcopy(base)
cfg["version"] = name
cfg["name"] = variant["name"]
# 用变体 config 覆盖(变体 config 从原 config 克隆并改了一个字段)
merged = copy.deepcopy(base["config"])
_deep_update(merged, variant["config"])
cfg["config"] = merged
lab.STRATEGIES[name] = cfg
try:
r = lab.run_backtest(name, BT_START, BT_END, 913000, save=False,
universe="a", period_tag=BT_PERIOD_TAG)
s = r.get("summary", {})
pf = s.get("portfolio_full", {})
return {
"trades": s.get("total_trades"),
"win_rate": s.get("win_rate"),
"cagr": pf.get("cagr_pct"),
"total_return": pf.get("total_return_pct"),
"max_dd": pf.get("portfolio_max_dd_pct"),
}
finally:
lab.STRATEGIES.pop(name, None)
def _deep_update(dst, src):
for k, v in src.items():
if isinstance(v, dict) and isinstance(dst.get(k), dict):
_deep_update(dst[k], v)
else:
dst[k] = v
def verify_variant_hk(variant, parent_version):
"""港股变体验证:hk_backtest 生成交易 + portfolio_sim 8槽"""
import pandas as pd
sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
from hk_strategies import HK_STRATEGIES, get_hk_strategy
import hk_backtest
import strategy_lab as lab
base = get_hk_strategy(parent_version)
if not base:
return None
new_cfg = copy.deepcopy(base)
new_cfg["version"] = variant["version"]
new_cfg["name"] = variant["name"]
_deep_update(new_cfg, variant["config"])
panel = hk_backtest.load_panel()
# 2y 窗口过滤(与 A股验证周期一致,控制资源)
panel = panel[(panel["date"] >= BT_START) & (panel["date"] <= BT_END)].copy()
trades = hk_backtest.gen_trades_defensive(panel, new_cfg, strike=3, cooldown_days=15)
if not trades:
return {"trades": 0, "win_rate": None, "cagr": None, "total_return": None, "max_dd": None}
sim = lab.portfolio_sim(trades, 1000000, max_positions=8)
return {
"trades": len(trades),
"win_rate": round(100 * sum(1 for t in trades if t["profit_pct"] > 0) / len(trades), 1),
"cagr": sim.get("cagr_pct"),
"total_return": sim.get("total_return_pct"),
"max_dd": sim.get("portfolio_max_dd_pct"),
}
# ── 5. 记录 + 推送 ──
def record_and_notify(conn, parent_version, market, variant, result, parent_cagr, parent_dd):
"""写 strategy_evolution + XMPP 推送"""
conn.execute("""
INSERT INTO strategy_evolution (parent_version, child_version, change_description, backtest_result, promoted)
VALUES (?, ?, ?, ?, 0)
""", (parent_version, variant["version"], variant["change_desc"],
json.dumps(result, ensure_ascii=False)))
conn.commit()
msg = (f"🧬 策略进化建议 [{parent_version}]\n"
f"改动: {variant['change_desc']}\n"
f"回测: 年化 {parent_cagr}% → {result.get('cagr')}%"
f"{round((result.get('cagr') or 0) - (parent_cagr or 0), 1)}pp)"
f" | 回撤 {parent_dd}% → {result.get('max_dd')}%\n"
f"胜率 {result.get('win_rate')}% / {result.get('trades')}\n"
f"【验证达标,待你决定是否上线】")
try:
sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
from alert_helper import notify, ACTION
notify("策略进化", msg, level=ACTION)
log(f"XMPP 推送: {parent_version}{variant['version']}")
except Exception as e:
log(f"XMPP 推送失败: {e}")
return msg
# ── 主流程 ──
def run_evolution():
conn = get_conn()
actives = load_active_strategies()
log(f"激活策略: {[a['version'] for a in actives]}")
if not actives:
log("无激活策略,退出")
conn.close()
return
findings = [] # 退化发现
passed = [] # 达标变体
for act in actives:
v, mkt = act["version"], act["market"]
reasons = detect_degradation(conn, v, mkt)
if not reasons:
continue
log(f"退化信号: {v} [{mkt}] → {'; '.join(reasons)}")
findings.append((v, mkt, reasons))
# 生成变体(A股从 lab 读 config;港股从 HK_STRATEGIES
if mkt == "hk":
sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
from hk_strategies import get_hk_strategy
base = get_hk_strategy(v)
if not base:
log(f" {v} 无港股策略定义,跳过")
continue
variants = generate_variants(v, mkt, base)
verify_fn = verify_variant_hk
else:
try:
import strategy_lab as lab
base = lab.get_strategy(v)
except ValueError:
log(f" {v} 不在标准回测体系(scanner 类策略),跳过变体研究")
continue
variants = generate_variants(v, mkt, base["config"])
verify_fn = verify_variant_a
if not variants:
log(f" {v} 无可用变体字段,跳过")
continue
parent_cagr, parent_dd = get_parent_cagr(conn, v, mkt)
log(f" {v} 原温区年化 {parent_cagr}% / 回撤 {parent_dd}% | 生成 {len(variants)} 个变体,验证前 {MAX_VARIANTS_TEST}")
for var in variants[:MAX_VARIANTS_TEST]:
try:
res = verify_fn(var, v)
except Exception as e:
log(f" {var['version']} 回测失败: {str(e)[:100]}")
continue
if not res or res.get("cagr") is None:
log(f" {var['version']} 无结果(0笔或空),跳过")
continue
ok_cagr = parent_cagr is None or res["cagr"] >= (parent_cagr or 0) + ACCEPT_CAGR_PP
ok_dd = parent_dd is None or res["max_dd"] <= (parent_dd or 0) + ACCEPT_DD_PP
status = "✅达标" if (ok_cagr and ok_dd) else "❌不达标"
log(f" {var['version']}: 年化 {parent_cagr}{res['cagr']}% 回撤 {parent_dd}{res['max_dd']}% [{status}]")
if ok_cagr and ok_dd:
record_and_notify(conn, v, mkt, var, res, parent_cagr, parent_dd)
passed.append((v, var, res))
conn.close()
# 汇总
if not findings:
log("── 无退化信号,当周静默 ──")
else:
log(f"── 检测 {len(findings)} 个退化策略,{len(passed)} 个达标变体已推送 ──")
return findings, passed
if __name__ == "__main__":
try:
run_evolution()
except Exception as e:
log(f"evolution_engine 异常: {e}")
traceback.print_exc()
sys.exit(1)