时间校准
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zhangkun9038@dingtalk.com
parent
723af8e6a9
commit
1bb359857c
@ -84,6 +84,39 @@ class FreqaiPrimer(IStrategy):
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else:
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logger.info(message)
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def calculate_data_freshness(self, data_timestamp: datetime, pair: str, dataframe: DataFrame) -> float:
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"""
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计算数据新鲜度（与交易所最新数据比较）
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:param data_timestamp: 数据时间戳
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:param pair: 交易对
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:param dataframe: 数据框
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:return: 数据延迟时间（分钟）
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"""
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try:
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# 转换为UTC+8时间
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if data_timestamp.tzinfo is None:
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data_timestamp = data_timestamp.replace(tzinfo=timezone.utc)
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data_timestamp_utc8 = data_timestamp.astimezone(UTC_PLUS_8).replace(tzinfo=None)
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# 获取交易所的最新数据时间
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exchange_latest_candle, exchange_latest_date = self.get_latest_candle(pair, self.timeframe, dataframe)
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if exchange_latest_date:
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# 转换为UTC+8时间进行比较
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if exchange_latest_date.tzinfo is None:
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exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
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exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
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freshness_minutes = (exchange_latest_utc8 - data_timestamp_utc8).total_seconds() / 60
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else:
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# 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
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freshness_minutes = 3.0
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return max(0.0, freshness_minutes) # 确保不会返回负数
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except Exception as e:
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self.strategy_log(f"[{pair}] 数据新鲜度计算异常: {e}", "warning")
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return 3.0 # 异常时返回保守估计
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def update_freshness_stats(self, pair: str, data_age_minutes: float) -> None:
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"""
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更新数据新鲜度统计
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@ -519,15 +552,9 @@ class FreqaiPrimer(IStrategy):
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latest_date = latest_date.to_pydatetime()
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except:
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pass
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# 计算ML数据新鲜度
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# 计算ML数据新鲜度（使用统一函数）
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if isinstance(latest_date, datetime):
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# 转换为UTC+8时间
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if latest_date.tzinfo is None:
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latest_date = latest_date.replace(tzinfo=timezone.utc)
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latest_date_utc8 = latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
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current_time_utc8 = datetime.now().replace(tzinfo=UTC_PLUS_8).replace(tzinfo=None)
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ml_age_minutes = (current_time_utc8 - latest_date_utc8).total_seconds() / 60
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ml_age_minutes = self.calculate_data_freshness(latest_date, metadata['pair'], dataframe)
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# 检查目标变量的类型和新鲜度
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target_vars = []
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@ -977,23 +1004,8 @@ class FreqaiPrimer(IStrategy):
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display_latest = display_latest.replace(tzinfo=timezone.utc)
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display_latest = display_latest.astimezone(UTC_PLUS_8)
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latest_time_str = display_latest.strftime('%H:%M:%S')
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# 计算数据新鲜度（与交易所最新数据比较）
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latest_naive = display_latest.replace(tzinfo=None)
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# 获取交易所的最新数据时间
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exchange_latest_candle, exchange_latest_date = self.get_latest_candle(
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metadata['pair'], self.timeframe, dataframe
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)
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if exchange_latest_date:
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# 转换为UTC+8时间进行比较
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if exchange_latest_date.tzinfo is None:
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exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
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exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
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data_freshness = (exchange_latest_utc8 - latest_naive).total_seconds() / 60
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else:
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# 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
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data_freshness = 3.0
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# 计算数据新鲜度（使用统一函数）
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data_freshness = self.calculate_data_freshness(latest_data_date, metadata['pair'], dataframe)
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# 数据新鲜度判断
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if data_freshness <= 3: # 3分钟内为新鲜数据
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@ -1016,20 +1028,8 @@ class FreqaiPrimer(IStrategy):
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latest_time_str = display_latest.strftime('%H:%M:%S')
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latest_naive = display_latest.replace(tzinfo=None)
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# 获取交易所的最新数据时间
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exchange_latest_candle, exchange_latest_date = self.get_latest_candle(
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metadata['pair'], self.timeframe, dataframe
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)
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if exchange_latest_date:
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# 转换为UTC+8时间进行比较
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if exchange_latest_date.tzinfo is None:
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exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
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exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
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data_freshness = (exchange_latest_utc8 - latest_naive).total_seconds() / 60
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else:
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# 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
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data_freshness = 3.0
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# 计算数据新鲜度（使用统一函数）
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data_freshness = self.calculate_data_freshness(latest_data_date, metadata['pair'], dataframe)
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# 数据新鲜度判断
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if data_freshness <= 3: # 3分钟内为新鲜数据
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@ -1311,17 +1311,11 @@ class FreqaiPrimer(IStrategy):
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except:
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pass
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# 计算数据延迟（分钟）
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# 计算数据延迟（分钟）- 使用统一函数
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if isinstance(data_timestamp, datetime):
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current_time = datetime.now()
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# FreqAI dataframe 的 date 列是 UTC 时间
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if data_timestamp.tzinfo is None:
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data_timestamp = data_timestamp.replace(tzinfo=timezone.utc)
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# 转换为 UTC+8 并移除时区信息
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data_timestamp = data_timestamp.astimezone(UTC_PLUS_8).replace(tzinfo=None)
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data_age_minutes = (current_time - data_timestamp).total_seconds() / 60
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# 获取当前数据框用于计算新鲜度
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current_df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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data_age_minutes = self.calculate_data_freshness(data_timestamp, pair, current_df)
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# 📊 更新统计数据
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self.update_freshness_stats(pair, data_age_minutes)
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