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禁用入场间隔最小限制机制

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zhangkun9038@dingtalk.com 2026-02-03 21:08:37 +08:00
parent cd7dc94d45
commit 49270fbe02
1 changed files with 36 additions and 25 deletions
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61
freqtrade/templates/freqaiprimer.py
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@ -1216,12 +1216,13 @@ class FreqaiPrimer(IStrategy):
# 仅对多头交易进行检查
if side == 'long':
# 检查1：入场间隔控制（使用hyperopt参数）
if pair in self._last_entry_time:
last_entry = self._last_entry_time[pair]
time_diff = (current_time - last_entry).total_seconds() * 0.0166666667 # 转换为分钟（使用乘法避免除法）
if time_diff < self.entry_interval_minutes.value:
self.strategy_log(f"[{pair}] 入场间隔不足: 距离上次入场 {time_diff:.1f}分钟 < {self.entry_interval_minutes.value}分钟，取消本次入场")
allow_trade = False
# 暂时禁用入场间隔限制以进行验证
# if pair in self._last_entry_time:
# last_entry = self._last_entry_time[pair]
# time_diff = (current_time - last_entry).total_seconds() * 0.0166666667 # 转换为分钟（使用乘法避免除法）
# if time_diff < self.entry_interval_minutes.value:
# self.strategy_log(f"[{pair}] 入场间隔不足: 距离上次入场 {time_diff:.1f}分钟 < {self.entry_interval_minutes.value}分钟，取消本次入场")
# allow_trade = False
# 检查2：检查是否处于剧烈拉升的不稳固区域
if allow_trade:
@ -1401,22 +1402,27 @@ class FreqaiPrimer(IStrategy):
# 识别入场类型并生成标签
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if len(df) > 0:
entry_info = self.identify_entry_type(df, -1)
# 获取全面的市场环境信息
market_context = self.get_comprehensive_market_context(df, pair)
# 生成标签字符串，包含入场类型信息和市场环境信息
tag_string = f"type:{entry_info['type']},duration:{entry_info['duration']},risk:{entry_info['risk']},confidence:{entry_info['confidence']},name:{entry_info['name']},market_state:{market_context['market_state']},trend_strength:{market_context['trend_strength']:.2f}"
self.strategy_log(
f"[{pair}] 入场类型识别: {entry_info['name']} (类型{entry_info['type']}), "
f"建议持仓: {entry_info['duration']}分钟, 风险等级: {entry_info['risk']}, "
f"市场状态: {market_context['market_state']}, 趋势强度: {market_context['trend_strength']:.2f}"
)
# 返回带有标签的字典，Freqtrade会自动处理
return {'enter_tag': tag_string}
try:
entry_info = self.identify_entry_type(df, -1)
# 获取全面的市场环境信息
market_context = self.get_comprehensive_market_context(df, pair)
# 生成标签字符串，包含入场类型信息和市场环境信息
tag_string = f"type:{entry_info['type']},duration:{entry_info['duration']},risk:{entry_info['risk']},confidence:{entry_info['confidence']},name:{entry_info['name']},market_state:{market_context['market_state']},trend_strength:{market_context['trend_strength']:.2f}"
self.strategy_log(
f"[{pair}] 入场类型识别: {entry_info['name']} (类型{entry_info['type']}), "
f"建议持仓: {entry_info['duration']}分钟, 风险等级: {entry_info['risk']}, "
f"市场状态: {market_context['market_state']}, 趋势强度: {market_context['trend_strength']:.2f}"
)
# 返回带有标签的字典，Freqtrade会自动处理
return {'enter_tag': tag_string}
except Exception as e:
# 如果在生成标签过程中出现异常，仍允许入场，但不带标签
self.strategy_log(f"[{pair}] 生成入场标签时出错: {str(e)}, 允许无标签入场")
return {} # 允许入场，但不带标签
return allow_trade
@ -1884,12 +1890,17 @@ class FreqaiPrimer(IStrategy):
)
# 返回ROI字典，格式为 {时间: ROI阈值}
# 这里我们返回一个简单的ROI表，从0时间开始直到计算出的时间，然后维持最终的ROI值
# ROI字典应该从最短时间到最长时间，ROI值逐渐降低（或保持）
# 为了确保交易能够持续，我们需要设置一个合理的ROI曲线
roi_dict = {}
roi_dict[0] = adjusted_roi # 立即止盈
roi_dict[0] = adjusted_roi # 立即生效的ROI
roi_dict[int(adjusted_time)] = adjusted_roi # 在指定时间后保持该ROI水平
roi_dict[1440] = 0.01 # 24小时后至少1%的ROI（安全网）
return roi_dict
# 确保字典按照时间升序排列
sorted_roi_dict = dict(sorted(roi_dict.items()))
return sorted_roi_dict
def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float,