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feat: 适应度门槛95+swing_point策略+多项改进

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- 新增适应度门槛: min_backtest_fitness=95, 适应度<95暂停开仓
- 新增 SwingPointRetest 策略替代 Turtle
- 新增 monday_reset.py 周重置脚本
- exit_rules: 拖尾止损相对回撤模式
- market_state: 趋势检测优化
- position: 一票制并发锁+合约规格缓存
- optimize: Optuna 替代 DEAP 遗传算法
- realtime_trader: 适应度门槛+同向递增
- weights: 动态权重管理
- cron_optimize: PYTHONPATH 修复
- .gitignore: 排除生成文件
This commit is contained in:
silencesdg
2026-05-21 20:26:55 +08:00
parent 218efef538
commit e1691c3c41
14 changed files with 699 additions and 449 deletions
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core/risk/exit_rules.py
+10 -3
View File
@@ -54,15 +54,22 @@ class TrailingStopRule(BaseExitRule):
def check(self, ctx: ExitContext) -> tuple[str, str]:
min_profit = self.config.get("min_profit_for_trailing", 0.01)
retracement_pct = self.config.get("profit_retracement_pct", 0.10)
retracement_mode = self.config.get("retracement_mode", "absolute")
if ctx.peak_profit_pct <= min_profit:
return "none", ""
# ★ 回撤从峰值绝对值扣除（账户%，非相对%）：峰值+2.0%回撤1.0%→止损在+1.0%
stop_level = ctx.peak_profit_pct - retracement_pct
if retracement_mode == "relative":
# ★ 相对回撤：止损 = 峰值 × (1-回撤%)
# 例: 峰值+50% 回撤30% → 止损+35%（利润从50%回落到35%时平仓）
stop_level = ctx.peak_profit_pct * (1 - retracement_pct)
else:
# ★ 绝对值扣除（旧模式）：峰值+2.0%回撤1.0%→止损在+1.0%
stop_level = ctx.peak_profit_pct - retracement_pct
if ctx.current_profit_pct <= stop_level:
mode_tag = "相对" if retracement_mode == "relative" else "绝对"
return "close", (
f"追踪止损触发 "
f"追踪止损触发({mode_tag}回撤) "
f"(峰值 {ctx.peak_profit_pct:.2%} 回落至 {ctx.current_profit_pct:.2%})"
)
return "none", ""
core/risk/market_state.py
+4 -3
View File
@@ -1,6 +1,7 @@
import pandas as pd
import numpy as np
from logger import logger
import config
from config import (
MARKET_STATE_CONFIG, SYMBOL, DEFAULT_WEIGHTS,
TREND_INDICATOR_WEIGHTS, TREND_THRESHOLDS,
@@ -288,7 +289,7 @@ class MarketStateAnalyzer:
base_weights = dict(individual_weights)
else:
# 正常模式：从配置获取市场状态对应权重
base_weights = dict(MARKET_STATE_WEIGHTS.get(market_state, DEFAULT_WEIGHTS))
base_weights = dict(config.MARKET_STATE_WEIGHTS.get(market_state, config.DEFAULT_WEIGHTS))
high_conf = self.confidence_thresholds.get("high_confidence", 0.7)
medium_conf = self.confidence_thresholds.get("medium_confidence", 0.4)
@@ -297,9 +298,9 @@ class MarketStateAnalyzer:
return {k: v * confidence for k, v in base_weights.items()}
elif confidence > medium_conf:
return {
k: (v * confidence + DEFAULT_WEIGHTS.get(k, 1.0) * (1 - confidence))
k: (v * confidence + config.DEFAULT_WEIGHTS.get(k, 1.0) * (1 - confidence))
for k, v in base_weights.items()
}
else:
# 低置信度：individual_weights 优先（优化器模式），否则回退到 DEFAULT_WEIGHTS
return dict(individual_weights) if individual_weights is not None else dict(DEFAULT_WEIGHTS)
return dict(individual_weights) if individual_weights is not None else dict(config.DEFAULT_WEIGHTS)
core/risk/position.py
+73 -8
View File
@@ -3,10 +3,8 @@ import numpy as np
import json
import os
from logger import logger
from config import (
RISK_CONFIG, SYMBOL, INITIAL_CAPITAL, CAPITAL_ALLOCATION,
RISK_CONFIG_CONST, SIMULATION_CONFIG
)
import config
from config import RISK_CONFIG, SYMBOL, INITIAL_CAPITAL, CAPITAL_ALLOCATION, RISK_CONFIG_CONST, SIMULATION_CONFIG
from core.risk.exit_rules import ExitRuleEngine, ExitContext
@@ -61,6 +59,7 @@ class PositionManager:
self.min_profit_for_trailing = risk.get("min_profit_for_trailing", 0.01) * self._lev_ratio
self.take_profit_pct = risk.get("take_profit_pct", 0.20) * self._lev_ratio
self.min_profit_for_time_exit = risk.get("min_profit_for_time_exit", 0.001) * self._lev_ratio
self.retracement_mode = risk.get("retracement_mode", "absolute") # relative/absolute
# 资金管理
self.initial_capital = INITIAL_CAPITAL
@@ -89,13 +88,24 @@ class PositionManager:
"max_holding_minutes": self.max_holding_minutes,
"min_profit_for_time_exit": self.min_profit_for_time_exit,
"max_daily_loss": self.max_daily_loss,
"retracement_mode": self.retracement_mode,
}
self.exit_engine = ExitRuleEngine(exit_config)
self._exit_config = exit_config # 保存引用，供波动率自适应更新
# ★ 波动率自适应拖尾 — 保存基准值
self._base_min_profit_for_trailing = self.min_profit_for_trailing
self._base_profit_retracement_pct = self.profit_retracement_pct
self._vol_adaptive = risk.get("vol_adaptive_trailing", False)
self._trailing_atr_period = risk.get("trailing_atr_period", 14)
self._trailing_atr_baseline = risk.get("trailing_atr_baseline", 100)
self._last_atr_update = None # 节流：最多1分钟更新一次
if self._vol_adaptive:
logger.info(f"📐 波动率自适应拖尾已启用 (ATR{self._trailing_atr_period}/ATR{self._trailing_atr_baseline})")
# 峰值数据持久化（优化器中禁用文件I/O避免多进程竞争）
self.peak_data_file = "position_peaks.json"
if self._persist_peaks:
self._load_peak_data()
self._saved_peaks = self._load_peak_data() if self._persist_peaks else {}
# 对冲管理器
from config import HEDGE_CONFIG
@@ -106,6 +116,54 @@ class PositionManager:
self._pending_long = 0
self._pending_short = 0
# ── ATR 计算与波动率自适应 ──
def _compute_atr(self, period: int) -> float | None:
"""计算指定周期的 ATR（Average True Range）"""
try:
import numpy as np
rates = self.data_provider.get_historical_data(self.symbol, 1, period + 1)
if rates is None or len(rates) < period + 1:
return None
highs = np.array([r[2] for r in rates[-period-1:]]) # high
lows = np.array([r[3] for r in rates[-period-1:]]) # low
closes = np.array([r[4] for r in rates[-period-1:]]) # close
tr = np.maximum(
highs[1:] - lows[1:],
np.maximum(
np.abs(highs[1:] - closes[:-1]),
np.abs(lows[1:] - closes[:-1])
)
)
return float(np.mean(tr))
except Exception as e:
logger.debug(f"ATR 计算失败: {e}")
return None
def _update_volatility_trailing(self):
"""波动率自适应：按 ATR 比率调整拖尾激活和回撤参数"""
if not self._vol_adaptive:
return
# 节流：最多每分钟更新一次
from datetime import datetime
now = datetime.now()
if self._last_atr_update is not None:
if (now - self._last_atr_update).total_seconds() < 60:
return
short_atr = self._compute_atr(self._trailing_atr_period)
long_atr = self._compute_atr(self._trailing_atr_baseline)
if short_atr is None or long_atr is None or long_atr <= 0:
return
vol_ratio = short_atr / long_atr
# 限制极端值：0.5 ~ 2.0
vol_ratio = max(0.5, min(2.0, vol_ratio))
# 更新
self.min_profit_for_trailing = self._base_min_profit_for_trailing * vol_ratio
self.profit_retracement_pct = self._base_profit_retracement_pct * vol_ratio
self._exit_config["min_profit_for_trailing"] = self.min_profit_for_trailing
self._exit_config["profit_retracement_pct"] = self.profit_retracement_pct
self._last_atr_update = now
# ── 仓位计算 ──
def _calculate_position_size(self, capital_to_allocate, current_price):
@@ -277,6 +335,9 @@ class PositionManager:
if not self.positions:
return
# ★ 波动率自适应拖尾 — 每分钟更新一次
self._update_volatility_trailing()
current_time = current_price.get('time', pd.Timestamp.now())
positions_to_remove = []
@@ -326,7 +387,7 @@ class PositionManager:
self.cleanup_peak_data()
# ── 对冲评估 ──
if self.positions and self.hedge_manager:
if self.positions and hasattr(self, 'hedge_manager') and self.hedge_manager:
hedge_actions = self.hedge_manager.evaluate(weighted_signal, current_price)
for action, target, reason in hedge_actions:
if action == "hedge":
@@ -441,6 +502,8 @@ class PositionManager:
if live_positions is None:
return
saved_peaks = self._load_peak_data()
# 合并构造器中加载的峰值（优先已持久化）
saved_peaks = {**saved_peaks, **getattr(self, '_saved_peaks', {})}
existing_peaks = {pos['ticket']: pos.get('peak_profit_pct', 0.0) for pos in self.positions}
merged_peaks = {**existing_peaks, **saved_peaks}
self.positions.clear()
@@ -496,7 +559,9 @@ class PositionManager:
try:
if os.path.exists(self.peak_data_file):
with open(self.peak_data_file, 'r', encoding='utf-8') as f:
return json.load(f)
raw = json.load(f)
# ★ JSON 键是字符串，MT5 ticket 是整数 → 统一转 int
return {int(k): v for k, v in raw.items()}
except Exception as e:
logger.error(f"加载峰值数据失败: {e}")
return {}