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时间校准

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zhangkun9038@dingtalk.com 2026-02-13 12:38:39 +08:00
parent e9ccc8f059
commit 8bac46aaaf
1 changed files with 41 additions and 15 deletions
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56
freqtrade/templates/freqaiprimer.py
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@ -977,17 +977,30 @@ class FreqaiPrimer(IStrategy):
display_latest = display_latest.replace(tzinfo=timezone.utc)
display_latest = display_latest.astimezone(UTC_PLUS_8)
latest_time_str = display_latest.strftime('%H:%M:%S')
# 计算数据新鲜度（修正时区差异）
# 计算数据新鲜度（与交易所最新数据比较）
latest_naive = display_latest.replace(tzinfo=None)
current_time_utc8 = datetime.now().replace(tzinfo=UTC_PLUS_8).replace(tzinfo=None)
data_freshness = (current_time_utc8 - latest_naive).total_seconds() / 60
# 修正时区差异后的判断
if data_freshness <= 10:
fresh_icon = "✅"
elif data_freshness <= 30:
fresh_icon = "⚠️"
# 获取交易所的最新数据时间
exchange_latest_candle, exchange_latest_date = self.get_latest_candle(
metadata['pair'], self.timeframe, dataframe
)
if exchange_latest_date:
# 转换为UTC+8时间进行比较
if exchange_latest_date.tzinfo is None:
exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
data_freshness = (exchange_latest_utc8 - latest_naive).total_seconds() / 60
else:
# 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
data_freshness = 3.0
# 数据新鲜度判断
if data_freshness <= 3: # 3分钟内为新鲜数据
fresh_icon = "✅"
elif data_freshness <= 10: # 3-10分钟为可接受延迟
fresh_icon = "⚠️"
else: # 超过10分钟为过期数据
fresh_icon = "❌"
self.strategy_log(
f"[{metadata['pair']}] 数据新鲜度: {fresh_icon} {data_freshness:.1f}min | "
@ -1002,15 +1015,28 @@ class FreqaiPrimer(IStrategy):
display_latest = display_latest.astimezone(UTC_PLUS_8)
latest_time_str = display_latest.strftime('%H:%M:%S')
latest_naive = display_latest.replace(tzinfo=None)
current_time_utc8 = datetime.now().replace(tzinfo=UTC_PLUS_8).replace(tzinfo=None)
data_freshness = (current_time_utc8 - latest_naive).total_seconds() / 60
# 修正时区差异后的判断
if data_freshness <= 10:
fresh_icon = "✅"
elif data_freshness <= 30:
fresh_icon = "⚠️"
# 获取交易所的最新数据时间
exchange_latest_candle, exchange_latest_date = self.get_latest_candle(
metadata['pair'], self.timeframe, dataframe
)
if exchange_latest_date:
# 转换为UTC+8时间进行比较
if exchange_latest_date.tzinfo is None:
exchange_latest_date = exchange_latest_date.replace(tzinfo=timezone.utc)
exchange_latest_utc8 = exchange_latest_date.astimezone(UTC_PLUS_8).replace(tzinfo=None)
data_freshness = (exchange_latest_utc8 - latest_naive).total_seconds() / 60
else:
# 如果无法获取交易所最新时间，使用保守估计（3分钟延迟）
data_freshness = 3.0
# 数据新鲜度判断
if data_freshness <= 3: # 3分钟内为新鲜数据
fresh_icon = "✅"
elif data_freshness <= 10: # 3-10分钟为可接受延迟
fresh_icon = "⚠️"
else: # 超过10分钟为过期数据
fresh_icon = "❌"
self.strategy_log(
f"[{metadata['pair']}] 数据新鲜度: {fresh_icon} {data_freshness:.1f}min | "