错误用法：trade.current_profit() - 这可能返回不准确的值
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zhangkun9038@dingtalk.com
parent
e474414d52
commit
cf436211ea
@ -1523,185 +1523,7 @@ class FreqaiPrimer(IStrategy):
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self.strategy_log(f"[{pair}] 入场被拒绝，原因列表: {', '.join(rejected_conditions)}")
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return False
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def confirm_trade_exit(
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self,
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pair: str,
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trade: 'Trade',
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order_type: str,
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amount: float,
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rate: float,
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time_in_force: str,
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exit_reason: str,
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current_time: datetime,
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**kwargs,
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) -> bool:
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"""
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交易卖出前的确认函数，用于最终决定是否执行出场
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此处使用 ML 审核官（exit_signal 置信度）过滤出场
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"""
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self.strategy_log(f"[{pair}] confirm_trade_exit 被调用 - 价格: {rate:.8f}, 出场原因: {exit_reason}, 时间: {current_time}")
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# 风险控制类退出原因：不经过 ML 审核官，直接允许出场
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if exit_reason in ['stop_loss', 'trailing_stop_loss', 'emergency_exit', 'force_exit']:
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self.strategy_log(f"[{pair}] 风险控制退出，不走 ML 审核官: exit_reason={exit_reason}")
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return True
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# 默认允许出场
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allow_exit = True
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try:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if len(df) > 0:
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last_row = df.iloc[-1]
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exit_prob = None
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# 优先使用 FreqAI 的 exit_signal 预测列
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if '&s-exit_signal' in df.columns:
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exit_prob = float(last_row['&s-exit_signal'])
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elif '&-exit_signal_prob' in df.columns:
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exit_prob = float(last_row['&-exit_signal_prob'])
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elif '&-s-exit_signal_prob' in df.columns:
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exit_prob = float(last_row['&-s-exit_signal_prob'])
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elif '&-exit_signal' in df.columns:
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val = last_row['&-exit_signal']
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if isinstance(val, (int, float)):
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exit_prob = float(val)
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else:
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# 文本标签时，简单映射为 0/1
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exit_prob = 1.0 if str(val).lower() in ['exit', 'sell', '1'] else 0.0
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if exit_prob is not None:
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# 确保概率在 [0, 1] 范围内（分类器输出可能有浮点误差）
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exit_prob = max(0.0, min(1.0, exit_prob))
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# 从 kwargs 获取当前利润，freqtrade 会传入 current_profit
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current_profit = float(kwargs.get('current_profit', 0.0))
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# 获取出场一字基础阈值
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base_threshold = self.ml_exit_signal_threshold.value
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# 计算持仓时长（分钟）
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try:
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trade_age_minutes = max(0.0, (current_time - trade.open_date_utc).total_seconds() / 60.0)
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except Exception:
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trade_age_minutes = 0.0
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# 基于持仓时长的阈值衰减：持仓越久，阈值越低，越容易出场
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age_factor = min(trade_age_minutes / (24 * 60.0), 1.0) # 0~1，对应 0~24 小时+
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dynamic_threshold = base_threshold * (1.0 - 0.3 * age_factor)
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# 小利润单（<=2%）再额外放宽 20%
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if current_profit <= 0.02:
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dynamic_threshold *= 0.8
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# 新增：读取 AI 预测的未来波动率信号（极端化方案）
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future_vol_signal = None
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if '&s-future_volatility' in df.columns:
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future_vol_signal = float(last_row['&s-future_volatility'])
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elif '&-future_volatility' in df.columns:
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future_vol_signal = float(last_row['&-future_volatility'])
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# 极端化逻辑：根据 AI 预测的未来波动率直接接管部分出场决策
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if future_vol_signal is not None and exit_reason == 'exit_signal':
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# 情况A：AI 预测强趋势（高波动），且当前不亏损 → 忽略本次 exit_signal，继续持有
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if future_vol_signal > 0.65 and current_profit >= 0:
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self.strategy_log(
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f"[波动率 AI] [{pair}] AI 预测强趋势（高波动 {future_vol_signal:.2f}），忽略本次 exit_signal，继续持有 | "
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f"持仓: {trade_age_minutes:.1f}min, 利润: {current_profit:.4f}"
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)
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allow_exit = False
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return allow_exit
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# 情况B：AI 预测震荡市（低波动） → 强制接受 exit_signal，立即出场
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elif future_vol_signal < 0.35:
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self.strategy_log(
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f"[波动率 AI] [{pair}] AI 预测震荡市（低波动 {future_vol_signal:.2f}），强制接受 exit_signal 出场 | "
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f"持仓: {trade_age_minutes:.1f}min, 利润: {current_profit:.4f}"
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)
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return True
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# 介于 0.35-0.65 之间：中性区间，不做强制处理，继续走原有 ML 审核官逻辑
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# 设定下限，避免阈值过低
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dynamic_threshold = max(0.05, dynamic_threshold)
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if exit_prob < dynamic_threshold:
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self.strategy_log(
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f"[{pair}] ML 审核官拒绝出场: exit_signal 概率 {exit_prob:.2f} < 动态阈值 {dynamic_threshold:.2f}"
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f" | 原应出场原因: {exit_reason} | 持仓: {trade_age_minutes:.1f}min, 利润: {current_profit:.4f}"
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f" | 波动率AI: {future_vol_signal if future_vol_signal is not None else 'N/A'}"
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)
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allow_exit = False
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else:
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if future_vol_signal is not None:
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future_vol_str = f"{future_vol_signal:.2f}"
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else:
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future_vol_str = "N/A"
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# 格式化数字，限制有效位数不超过5位
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def format_number(value, max_digits=5):
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"""格式化数字，限制有效位数"""
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if value == 0:
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return "0"
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# 转换为字符串并去掉末尾的0
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s = f"{value:.10f}".rstrip('0').rstrip('.')
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# 如果小数点前的数字超过max_digits，使用科学计数法
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if len(s.split('.')[0]) > max_digits:
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return f"{value:.2e}"
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# 如果总长度超过max_digits，截断
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if len(s) > max_digits:
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# 保留小数点前的数字
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if '.' in s:
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integer_part, decimal_part = s.split('.')
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if len(integer_part) > max_digits:
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return f"{value:.2e}"
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else:
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# 截断小数部分
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max_decimal = max_digits - len(integer_part) - 1
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if max_decimal > 0:
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s = integer_part + '.' + decimal_part[:max_decimal]
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else:
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s = integer_part
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else:
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# 整数部分超过限制
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if len(s) > max_digits:
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return f"{value:.2e}"
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return s
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# 格式化价格，限制有效位数不超过5位
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def format_number(value, max_digits=5):
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"""格式化数字，限制有效位数"""
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if value == 0:
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return "0"
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# 转换为字符串并去掉末尾的0
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s = f"{value:.10f}".rstrip('0').rstrip('.')
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# 如果小数点前的数字超过max_digits，使用科学计数法
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if len(s.split('.')[0]) > max_digits:
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return f"{value:.2e}"
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# 如果总长度超过max_digits，截断
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if len(s) > max_digits:
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# 保留小数点前的数字
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if '.' in s:
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integer_part, decimal_part = s.split('.')
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if len(integer_part) > max_digits:
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return f"{value:.2e}"
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else:
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# 截断小数部分
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max_decimal = max_digits - len(integer_part) - 1
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if max_decimal > 0:
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s = integer_part + '.' + decimal_part[:max_decimal]
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else:
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s = integer_part
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else:
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# 整数部分超过限制
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if len(s) > max_digits:
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return f"{value:.2e}"
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return s
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self.strategy_log(
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f"[{pair}] ML 审核官允许出场: exit_signal 概率 {exit_prob:.2f} >= 动态阈值 {dynamic_threshold:.2f}"
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f" | 出场原因: {exit_reason} | 持仓: {format_number(trade_age_minutes)}min, 利润: {format_number(current_profit)}"
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f" | 波动率AI: {future_vol_str}"
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)
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except Exception as e:
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logger.warning(f"[{pair}] ML 审核官出场检查失败，允许出场: {e}")
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return allow_exit
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
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current_profit: float, **kwargs) -> float:
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@ -1731,10 +1553,19 @@ class FreqaiPrimer(IStrategy):
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
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rate: float, time_in_force: str, exit_reason: str,
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current_time: datetime, **kwargs) -> bool:
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current_time: datetime, current_profit: float, **kwargs) -> bool:
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"""
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智能出场确认：结合ML预测信号来决定是否出场
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"""
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# 计算持仓时长（分钟）
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trade_age_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
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# 设置最小持仓时间保护，避免交易刚开仓就立刻被平仓
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min_hold_minutes = 3 # 至少持有3分钟
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if trade_age_minutes < min_hold_minutes:
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self.strategy_log(f"[{pair}] 交易开仓时间仅{trade_age_minutes:.1f}分钟，小于最小持仓时间{min_hold_minutes}分钟，阻止出场")
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return False
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try:
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# 获取当前的ML预测数据
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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@ -1763,7 +1594,7 @@ class FreqaiPrimer(IStrategy):
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# 基于ML预测的智能决策
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if current_entry_prob is not None and predicted_volatility is not None:
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# 如果当前入场信号概率很高，说明市场条件仍然良好，可以继续持有
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if current_entry_prob > 0.6 and trade.current_profit() < 0.05:
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if current_entry_prob > 0.6 and current_profit < 0.05:
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# 高入场概率 + 低利润，建议继续持有（除非已有相当利润）
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self.strategy_log(f"[{pair}] 当前入场信号概率高({current_entry_prob:.2f})，建议继续持有")
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return False # 阻止出场
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