将读取metadata.json文件的逻辑抽离为load_metadata_stats函数
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zhangkun9038@dingtalk.com
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36735067ae
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e3719c7c48
@ -397,49 +397,18 @@ class FreqaiPrimer(IStrategy):
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def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float,
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min_roi: float, max_profit: float, **kwargs):
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pair = trade.pair
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hold_time = (current_time - trade.open_date_utc).total_seconds() / 60
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# 确定市场状态
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labels_mean = self.pair_stats.get(trade.pair, {}).get("labels_mean", {"&-future_adx": 25})
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labels_std = self.pair_stats.get(trade.pair, {}).get("labels_std", {"&-future_adx": 1})
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k_adx = 0.5 if labels_std["&-future_adx"] <= 9 and labels_std["&-future_adx"] > 5 else (0.3 if labels_std["&-future_adx"] > 9 else 0.7)
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future_adx_threshold = labels_mean["&-future_adx"] + k_adx * labels_std["&-future_adx"]
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future_adx_threshold = max(future_adx_threshold, 20)
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future_adx_threshold = min(future_adx_threshold, 35)
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market_state = "trending" if ((trade.dataframe["adx"].iloc[-1] > 25) or (trade.dataframe["&-future_adx"].iloc[-1] > future_adx_threshold)) and \
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(trade.dataframe["ema200_slope"].iloc[-1] > 0) and \
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(trade.dataframe["atr"].iloc[-1] > trade.dataframe["atr_median"].iloc[-1]) else "ranging"
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# 最小持仓时间
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if hold_time < 15:
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logger.info(f"[{trade.pair}] 持仓时间 {hold_time:.1f} 分钟，未达到最小持仓时间 15 分钟，暂不退出")
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# 获取当前币对的 dataframe
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dataframe = self.dp.get_pair_dataframe(pair=pair, timeframe=self.timeframe)
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if dataframe.empty:
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logger.warning(f"[{pair}] 无法获取 dataframe，跳过调整持仓")
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return None
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# Trailing Stop
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profit_ratio = (current_rate - trade.open_rate) / trade.open_rate
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if profit_ratio >= self.trailing_stop_start and not self.trailing_stop_enabled:
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self.trailing_stop_enabled = True
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trade.adjust_max_rate(current_rate)
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logger.info(f"[{trade.pair}] 价格上涨超过 {self.trailing_stop_start*100:.1f}%，启动 Trailing Stop")
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# 确保 dataframe 已包含所需指标
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dataframe = self.populate_indicators(dataframe, {"pair": pair})
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if self.trailing_stop_enabled:
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max_rate = trade.max_rate
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trailing_stop_price = max_rate * (1 - self.trailing_stop_distance)
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if current_rate < trailing_stop_price:
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logger.info(f"[{trade.pair}] 价格回落至 Trailing Stop 点 {trailing_stop_price:.6f}，触发卖出")
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return -1
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trade.adjust_max_rate(current_rate)
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# 根据市场状态调整持仓时间限制
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max_hold_time = 60 if market_state == "trending" else 30
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if hold_time > max_hold_time:
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logger.info(f"[{trade.pair}] 持仓时间超过{max_hold_time}分钟（市场状态：{market_state}），强制平仓")
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return -1
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return None
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, **kwargs) -> bool:
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# 确定市场状态
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labels_mean = self.pair_stats.get(pair, {}).get("labels_mean", {"&-future_adx": 25})
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labels_std = self.pair_stats.get(pair, {}).get("labels_std", {"&-future_adx": 1})
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@ -448,9 +417,59 @@ class FreqaiPrimer(IStrategy):
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future_adx_threshold = max(future_adx_threshold, 20)
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future_adx_threshold = min(future_adx_threshold, 35)
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market_state = "trending" if ((self.dataframe["adx"].iloc[-1] > 25) or (self.dataframe["&-future_adx"].iloc[-1] > future_adx_threshold)) and \
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(self.dataframe["ema200_slope"].iloc[-1] > 0) and \
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(self.dataframe["atr"].iloc[-1] > self.dataframe["atr_median"].iloc[-1]) else "ranging"
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market_state = "trending" if ((dataframe["adx"].iloc[-1] > 25) or (dataframe["&-future_adx"].iloc[-1] > future_adx_threshold)) and \
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(dataframe["ema200_slope"].iloc[-1] > 0) and \
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(dataframe["atr"].iloc[-1] > dataframe["atr_median"].iloc[-1]) else "ranging"
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# 最小持仓时间
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if hold_time < 15:
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logger.info(f"[{pair}] 持仓时间 {hold_time:.1f} 分钟，未达到最小持仓时间 15 分钟，暂不退出")
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return None
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# Trailing Stop
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profit_ratio = (current_rate - trade.open_rate) / trade.open_rate
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if profit_ratio >= self.trailing_stop_start and not self.trailing_stop_enabled:
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self.trailing_stop_enabled = True
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trade.adjust_max_rate(current_rate)
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logger.info(f"[{pair}] 价格上涨超过 {self.trailing_stop_start*100:.1f}%，启动 Trailing Stop")
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if self.trailing_stop_enabled:
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max_rate = trade.max_rate
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trailing_stop_price = max_rate * (1 - self.trailing_stop_distance)
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if current_rate < trailing_stop_price:
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logger.info(f"[{pair}] 价格回落至 Trailing Stop 点 {trailing_stop_price:.6f}，触发卖出")
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return -1
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trade.adjust_max_rate(current_rate)
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# 根据市场状态调整持仓时间限制
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max_hold_time = 60 if market_state == "trending" else 30
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if hold_time > max_hold_time:
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logger.info(f"[{pair}] 持仓时间超过{max_hold_time}分钟（市场状态：{market_state}），强制平仓")
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return -1
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return None
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, **kwargs) -> bool:
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# 获取当前币对的 dataframe
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dataframe = self.dp.get_pair_dataframe(pair=pair, timeframe=self.timeframe)
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if dataframe.empty:
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logger.warning(f"[{pair}] 无法获取 dataframe，跳过交易确认")
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return False
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# 确保 dataframe 已包含所需指标
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dataframe = self.populate_indicators(dataframe, {"pair": pair})
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# 确定市场状态
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labels_mean = self.pair_stats.get(pair, {}).get("labels_mean", {"&-future_adx": 25})
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labels_std = self.pair_stats.get(pair, {}).get("labels_std", {"&-future_adx": 1})
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k_adx = 0.5 if labels_std["&-future_adx"] <= 9 and labels_std["&-future_adx"] > 5 else (0.3 if labels_std["&-future_adx"] > 9 else 0.7)
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future_adx_threshold = labels_mean["&-future_adx"] + k_adx * labels_std["&-future_adx"]
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future_adx_threshold = max(future_adx_threshold, 20)
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future_adx_threshold = min(future_adx_threshold, 35)
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market_state = "trending" if ((dataframe["adx"].iloc[-1] > 25) or (dataframe["&-future_adx"].iloc[-1] > future_adx_threshold)) and \
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(dataframe["ema200_slope"].iloc[-1] > 0) and \
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(dataframe["atr"].iloc[-1] > dataframe["atr_median"].iloc[-1]) else "ranging"
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# 根据市场状态调整冷却期
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cooldown_minutes = 3 if market_state == "trending" else 5
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@ -465,11 +484,21 @@ class FreqaiPrimer(IStrategy):
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return False
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self.trailing_stop_enabled = False
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logger.info(f"[{pair}] 交易确认通过，市场状态：{market_state}")
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return True
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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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# 获取当前币对的 dataframe
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dataframe = self.dp.get_pair_dataframe(pair=pair, timeframe=self.timeframe)
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if dataframe.empty:
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logger.warning(f"[{pair}] 无法获取 dataframe，默认允许退出")
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return True
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# 确保 dataframe 已包含所需指标
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dataframe = self.populate_indicators(dataframe, {"pair": pair})
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# 确定市场状态
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labels_mean = self.pair_stats.get(pair, {}).get("labels_mean", {"&-future_adx": 25})
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labels_std = self.pair_stats.get(pair, {}).get("labels_std", {"&-future_adx": 1})
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@ -478,9 +507,9 @@ class FreqaiPrimer(IStrategy):
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future_adx_threshold = max(future_adx_threshold, 20)
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future_adx_threshold = min(future_adx_threshold, 35)
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market_state = "trending" if ((trade.dataframe["adx"].iloc[-1] > 25) or (trade.dataframe["&-future_adx"].iloc[-1] > future_adx_threshold)) and \
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(trade.dataframe["ema200_slope"].iloc[-1] > 0) and \
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(trade.dataframe["atr"].iloc[-1] > trade.dataframe["atr_median"].iloc[-1]) else "ranging"
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market_state = "trending" if ((dataframe["adx"].iloc[-1] > 25) or (dataframe["&-future_adx"].iloc[-1] > future_adx_threshold)) and \
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(dataframe["ema200_slope"].iloc[-1] > 0) and \
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(dataframe["atr"].iloc[-1] > dataframe["atr_median"].iloc[-1]) else "ranging"
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# 调整卖出价格
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adjusted_rate = rate * (1 + 0.0025)
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@ -492,7 +521,7 @@ class FreqaiPrimer(IStrategy):
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drop_percentage = (max_rate - rate) / max_rate * 100
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additional_info = f"最大价格：{max_rate:.6f}，回落：{drop_percentage:.2f}%"
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elif market_state == "ranging" and "rsi" in exit_reason.lower():
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price_value_divergence = trade.dataframe["&-price_value_divergence"].iloc[-1]
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price_value_divergence = dataframe["&-price_value_divergence"].iloc[-1]
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additional_info = f"&-price_value_divergence：{price_value_divergence:.6f}"
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logger.info(f"[{pair}] 退出交易，原因：{exit_reason}, 市场状态：{market_state}, "
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