optimize statistics
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@ -45,7 +45,7 @@ class MeanReversionSandbox:
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desc_dict = {
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"买入": [
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"1. 窗口周期为100, 即100个K线",
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"2. 当前close_10_low为1, 即当前收盘价在窗口周期的10分位以下",
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"2. 满足close_10_low为1, 即当前收盘价在窗口周期的10分位以下",
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"3. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量",
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"4. 当前K线为阳线, 即close > open或者K线为一字, 长倒T线, 倒T线, 长十字星, 十字星",
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# "5. 相同symbol的1H当前周期, ma5大于ma10",
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@ -143,7 +143,7 @@ class MeanReversionSandbox:
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for index, row in market_data.iterrows():
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# check buy condition
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if trade_pair_dict.get("buy_timestamp", None) is None:
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buy_condition = self.check_buy_condition(market_data, row, index)
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buy_condition = self.check_buy_condition(market_data, row, index, window_size)
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else:
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buy_condition = False
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if buy_condition:
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@ -170,17 +170,20 @@ class MeanReversionSandbox:
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sell_condition = False
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# check stop loss condition
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sell_condition = self.check_stop_loss_condition(trade_pair_dict, row)
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if sell_condition:
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sell = sell_condition["sell"]
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if sell:
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trade_pair_dict["sell_type"] = "止损"
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else:
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# check take profit condition
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sell_condition = self.check_take_profit_condition(
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trade_pair_dict, market_data, row, index
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)
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if sell_condition:
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sell = sell_condition["sell"]
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if sell:
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trade_pair_dict["sell_type"] = "止盈"
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if sell_condition:
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if sell:
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trade_pair_dict["sell_reason"] = sell_condition["reason"]
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trade_pair_dict["sell_timestamp"] = row["timestamp"]
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trade_pair_dict["sell_date_time"] = timestamp_to_datetime(
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row["timestamp"]
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@ -218,17 +221,50 @@ class MeanReversionSandbox:
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if len(trade_list) == 0:
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return None
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trade_data = pd.DataFrame(trade_list)
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trade_data = trade_data[
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[
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"solution",
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"symbol",
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"bar",
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"window_size",
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"sell_type",
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"sell_reason",
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"profit_pct",
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"buy_timestamp",
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"buy_date_time",
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"sell_timestamp",
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"sell_date_time",
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"buy_close",
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"buy_pct_chg",
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"sell_close",
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"sell_pct_chg",
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"buy_volume",
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"buy_huge_volume",
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"buy_volume_ratio",
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"buy_k_shape",
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"buy_close_10_low",
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"buy_ma5_lt_ma10",
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"sell_volume",
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"sell_huge_volume",
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"sell_volume_ratio",
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"sell_k_shape",
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"sell_close_80_high",
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"sell_close_90_high",
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"sell_close_10_low",
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"sell_close_20_low",
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]
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]
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trade_data.sort_values(by="buy_timestamp", inplace=True)
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trade_data.reset_index(drop=True, inplace=True)
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return trade_data
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def check_buy_condition(
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self, market_data: pd.DataFrame, row: pd.Series, index: int
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self, market_data: pd.DataFrame, row: pd.Series, index: int, window_size: int
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):
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"""
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买入条件
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1. 窗口周期为100, 即100个K线,
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2. 当前close_10_low为1, 即当前收盘价在窗口周期的10分位以下,
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2. 满足close_10_low为1, 即当前收盘价在窗口周期的10分位以下
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3. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量,
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4. (当前K线为阳线, 即close > open)或者K线为一字, 长倒T线, 倒T线, 长十字星, 十字星,
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"""
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@ -243,6 +279,8 @@ class MeanReversionSandbox:
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]:
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return False
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# 满足close_10_low为1, 即当前收盘价在窗口周期的10分位以下
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if row["close_10_low"] != 1:
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return False
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@ -310,13 +348,18 @@ class MeanReversionSandbox:
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)
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buy_close = trade_pair_dict["buy_close"]
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current_close = row["close"]
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result = {"sell": False, "reason": ""}
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if (
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current_close < buy_close
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and (current_close - buy_close) / buy_close < down_median
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):
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logger.info(f"符合止损条件")
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return True
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return False
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result["sell"] = True
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result["reason"] = f"亏损超过下跌波段跌幅中位数"
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return result
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result["sell"] = False
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result["reason"] = "未达到止损条件"
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return result
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def check_take_profit_condition(
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self,
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@ -353,16 +396,23 @@ class MeanReversionSandbox:
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1. 当前close_80_high为1或者close_90_high为1
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2. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量
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"""
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result = {"sell": False, "reason": ""}
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if row["close_80_high"] != 1 and row["close_90_high"] != 1:
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return False
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result["sell"] = False
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result["reason"] = "未达到止盈条件"
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return result
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if (
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row["huge_volume"] != 1
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and market_data.loc[index - 1, "huge_volume"] != 1
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and market_data.loc[index - 2, "huge_volume"] != 1
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):
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return False
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result["sell"] = False
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result["reason"] = "未达到止盈条件"
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return result
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logger.info(f"符合高位放量止盈 - 简易版条件")
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return True
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result["sell"] = True
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result["reason"] = "符合高位放量止盈 - 简易版条件"
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return result
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def check_take_profit_condition_solution_2(
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self,
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@ -380,11 +430,16 @@ class MeanReversionSandbox:
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2. K线为阳线, 即close >= open, 且k_shape满足:
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一字, 长吊锤线, 吊锤线, 长倒T线, 倒T线, 长十字星, 十字星, 长上影线纺锤体, 长下影线纺锤体
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"""
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result = {"sell": False, "reason": ""}
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if not self.check_take_profit_condition_solution_1(market_data, row, index):
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return False
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result["sell"] = False
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result["reason"] = "未达到止盈条件"
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return result
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if row["close"] < row["open"]:
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logger.info(f"符合高位放量止盈 - 复杂版条件")
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return True
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result["sell"] = True
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result["reason"] = "符合高位放量止盈 - 复杂版条件"
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return result
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elif row["k_shape"] in [
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"一字",
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"长吊锤线",
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@ -397,9 +452,13 @@ class MeanReversionSandbox:
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"长下影线纺锤体",
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]:
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logger.info(f"符合高位放量止盈 - 复杂版条件")
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return True
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result["sell"] = True
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result["reason"] = "符合高位放量止盈 - 复杂版条件"
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return result
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else:
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return False
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result["sell"] = False
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result["reason"] = "未达到止盈条件"
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return result
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def check_take_profit_condition_solution_3(
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self, trade_pair_dict: dict, row: pd.Series
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@ -413,12 +472,14 @@ class MeanReversionSandbox:
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"""
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current_close = row["close"]
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last_max_close = trade_pair_dict.get("last_max_close", None)
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result = {"sell": False, "reason": ""}
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if trade_pair_dict["buy_ma5_lt_ma10"]:
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if trade_pair_dict.get("process_ma5_gt_ma10", None):
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if row["ma5"] < row["ma10"]:
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logger.info(f"MA5小于MA10发生转势, 卖出")
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return True
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result["sell"] = True
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result["reason"] = "MA5小于MA10发生转势"
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return result
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if row["ma5"] > row["ma10"]:
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trade_pair_dict["process_ma5_gt_ma10"] = True
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@ -429,10 +490,14 @@ class MeanReversionSandbox:
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if current_close >= last_max_close:
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logger.info(f"价格上涨, 继续持仓")
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trade_pair_dict["last_max_close"] = current_close
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return False
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result["sell"] = False
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result["reason"] = "价格上涨, 继续持仓"
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return result
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else:
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logger.info(f"符合上涨波段盈利中位数止盈法条件")
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return True
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result["sell"] = True
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result["reason"] = "符合上涨波段盈利中位数止盈条件"
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return result
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else:
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symbol = trade_pair_dict["symbol"]
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bar = trade_pair_dict["bar"]
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@ -461,7 +526,9 @@ class MeanReversionSandbox:
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need_record = False
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if need_record:
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trade_pair_dict["last_max_close"] = current_close
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return False
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result["sell"] = False
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result["reason"] = "未达到止盈条件"
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return result
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def check_metrics_over_buy(self, row: pd.Series):
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"""
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@ -112,6 +112,7 @@ class MeanReversionSandboxMain:
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profit_pct_max = bar["profit_pct"].max()
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profit_pct_min = bar["profit_pct"].min()
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profit_pct_mean = bar["profit_pct"].mean()
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profit_pct_sum = bar["profit_pct"].sum()
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profit_pct_gt_0_mean = bar[bar["profit_pct"] > 0]["profit_pct"].mean()
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profit_pct_lt_0_mean = bar[bar["profit_pct"] < 0]["profit_pct"].mean()
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@ -125,6 +126,7 @@ class MeanReversionSandboxMain:
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"solution": solution,
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"symbol": symbol_name,
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"bar": bar_name,
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"profit_pct_sum": profit_pct_sum,
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"take_profit_count": take_profit_count,
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"take_profit_ratio": take_profit_ratio,
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"stop_loss_count": stop_loss_count,
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@ -133,9 +135,9 @@ class MeanReversionSandboxMain:
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"profit_pct_gt_0_ratio": profit_pct_gt_0_ratio,
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"profit_pct_lt_0_count": profit_pct_lt_0_count,
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"profit_pct_lt_0_ratio": profit_pct_lt_0_ratio,
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"profit_pct_mean": profit_pct_mean,
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"profit_pct_max": profit_pct_max,
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"profit_pct_min": profit_pct_min,
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"profit_pct_mean": profit_pct_mean,
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"profit_pct_gt_0_mean": profit_pct_gt_0_mean,
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"profit_pct_lt_0_mean": profit_pct_lt_0_mean,
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}
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@ -176,6 +178,7 @@ class MeanReversionSandboxMain:
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y_axis_fields = [
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"take_profit_ratio",
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"stop_loss_ratio",
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"profit_pct_sum",
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"profit_pct_mean",
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"profit_pct_gt_0_mean",
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"profit_pct_lt_0_mean",
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