begin to research trade strategy

core/db/db_merge_market_huge_volume.py

@@ -0,0 +1,130 @@
+import pandas as pd
+
+from core.db.db_market_data import DBMarketData
+from core.db.db_huge_volume_data import DBHugeVolumeData
+
+
+class DBMergeMarketHugeVolume:
+    def __init__(self, db_url: str):
+        self.db_url = db_url
+        self.db_market_data = DBMarketData(self.db_url)
+        self.db_huge_volume_data = DBHugeVolumeData(self.db_url)
+
+    def merge_market_huge_volume(
+        self, symbol: str, bar: str, window_size: int, start: str, end: str
+    ):
+        market_data = self.db_market_data.query_market_data_by_symbol_bar(
+            symbol, bar, start, end
+        )
+        huge_volume_data = (
+            self.db_huge_volume_data.query_huge_volume_data_by_symbol_bar_window_size(
+                symbol, bar, window_size, start, end
+            )
+        )
+        if market_data is None or huge_volume_data is None:
+            return None
+        market_data = pd.DataFrame(market_data)
+        huge_volume_data = pd.DataFrame(huge_volume_data)
+        market_data = market_data.merge(
+            huge_volume_data, on=["symbol", "bar", "timestamp"], how="left"
+        )
+       
+        # drop id_x, date_time_x, open_x, high_x, low_x, close_x,
+        # volume_x, volCcy_x, volCCyQuote_x, buy_sz, sell_sz, create_time_x
+        market_data.drop(
+            columns=[
+                "id_x",
+                "date_time_x",
+                "open_x",
+                "high_x",
+                "low_x",
+                "close_x",
+                "volume_x",
+                "volCcy_x",
+                "volCCyQuote_x",
+                "buy_sz",
+                "sell_sz",
+                "create_time_x",
+            ],
+            inplace=True,
+        )
+        market_data.rename(
+            columns={
+                "id_y": "id",
+                "date_time_y": "date_time",
+                "open_y": "open",
+                "high_y": "high",
+                "low_y": "low",
+                "close_y": "close",
+                "volume_y": "volume",
+                "volCcy_y": "volCcy",
+                "volCCyQuote_y": "volCCyQuote",
+                "create_time_y": "create_time",
+            },
+            inplace=True,
+        )
+
+        # keep below columns: id, symbol, bar, timestamp, date_time, window_size, open, high, low, close, pct_chg, volume, volCcy, volCCyQuote, volume_ma, huge_volume, volume_ratio,
+        # macd, macd_signal, macd_divergence, kdj_k, kdj_d, kdj_j, kdj_signal, kdj_pattern, ma5, ma10, ma20, ma30, ma_cross, ma_long_short, ma_divergence, rsi_14, rsi_signal,
+        # boll_upper, boll_middle, boll_lower, boll_signal, boll_pattern, k_length, k_shape, k_up_down,
+        # close_80_high, close_90_high, close_20_low, close_10_low,
+        # high_80_high, high_90_high, low_20_low, low_10_low
+        # create_time
+        market_data = market_data[
+            [
+                "id",
+                "symbol",
+                "bar",
+                "timestamp",
+                "date_time",
+                "window_size",
+                "open",
+                "high",
+                "low",
+                "close",
+                "pct_chg",
+                "volume",
+                "volCcy",
+                "volCCyQuote",
+                "volume_ma",
+                "huge_volume",
+                "volume_ratio",
+                "macd",
+                "macd_signal",
+                "macd_divergence",
+                "kdj_k",
+                "kdj_d",
+                "kdj_j",
+                "kdj_signal",
+                "kdj_pattern",
+                "ma5",
+                "ma10",
+                "ma20",
+                "ma30",
+                "ma_cross",
+                "ma_long_short",
+                "ma_divergence",
+                "rsi_14",
+                "rsi_signal",
+                "boll_upper",
+                "boll_middle",
+                "boll_lower",
+                "boll_signal",
+                "boll_pattern",
+                "k_length",
+                "k_shape",
+                "k_up_down",
+                "close_80_high",
+                "close_90_high",
+                "close_20_low",
+                "close_10_low",
+                "high_80_high",
+                "high_90_high",
+                "low_20_low",
+                "low_10_low",
+                "create_time",
+            ]
+        ]
+        market_data.sort_values(by="timestamp", ascending=True, inplace=True)
+        market_data.reset_index(drop=True, inplace=True)
+        return market_data

core/statistics/ma_break_statistics.py

@@ -121,12 +121,25 @@ class MaBreakStatistics:
             (market_data["ma20"].notna()) & 
             (market_data["ma30"].notna())]
             logger.info(f"ma5, ma10, ma20, ma30不为空的行，数据条数: {len(market_data)}")
-            # 获得5上穿10且ma5 > ma10 > ma20 > ma30且close > ma20的行
+            # 计算volume_ma5
-            long_market_data = market_data[(market_data["ma_cross"] == "5上穿10") & (market_data["ma5"] > market_data["ma10"]) & 
+            market_data["volume_ma5"] = market_data["volume"].rolling(window=5).mean()
-            (market_data["ma10"] > market_data["ma20"]) & 
+            # 获得5上穿10且ma5 > ma10 > ma20 > ma30且close > ma20的行,成交量较前5日均量放大20%以上
-            (market_data["ma20"] > market_data["ma30"]) &
+            market_data["volume_pct_chg"] = (market_data["volume"] - market_data["volume_ma5"]) / market_data["volume_ma5"]
-            (market_data["close"] > market_data["ma20"])]
+            market_data["volume_pct_chg"] = market_data["volume_pct_chg"].fillna(0)
-            logger.info(f"5上穿10, 且ma5 > ma10 > ma20 > ma30，并且close > ma20的行，数据条数: {len(long_market_data)}")
+
+            if all_change:
+                long_market_data = market_data[(market_data["ma_cross"] == "5上穿10") & (market_data["ma5"] > market_data["ma10"]) & 
+                (market_data["ma10"] > market_data["ma20"]) & 
+                (market_data["ma20"] > market_data["ma30"]) &
+                (market_data["close"] > market_data["ma20"]) &
+                (market_data["volume_pct_chg"] > 0.2)]
+                logger.info(f"5上穿10, 且ma5 > ma10 > ma20 > ma30，并且close > ma20，并且成交量较前5日均量放大20%以上的行，数据条数: {len(long_market_data)}")
+            else:
+                long_market_data = market_data[(market_data["ma_cross"] == "5上穿10") & (market_data["ma5"] > market_data["ma10"]) & 
+                (market_data["volume_pct_chg"] > 0.2)]
+                logger.info(f"5上穿10, 且ma5 > ma10，并且成交量较前5日均量放大20%以上的行，数据条数: {len(long_market_data)}")
+            if len(long_market_data) == 0:
+                return None
             if all_change:
                 # 获得ma5 < ma10 < ma20 < ma30的行
                 short_market_data = market_data[(market_data["ma5"] < market_data["ma10"]) & 
@@ -134,10 +147,10 @@ class MaBreakStatistics:
                 (market_data["ma20"] < market_data["ma30"])]
                 logger.info(f"ma5 < ma10 < ma20 < ma30的行，数据条数: {len(short_market_data)}")
             else:
-                # ma5 < ma10 and close < ma20
+                # ma5 < ma10 or close < ma20
-                short_market_data = market_data[(market_data["ma5"] < market_data["ma10"]) &
+                short_market_data = market_data[(market_data["ma5"] < market_data["ma10"]) |
                 (market_data["close"] < market_data["ma20"])]
-                logger.info(f"ma5 < ma10 and close < ma20的行，数据条数: {len(short_market_data)}")
+                logger.info(f"ma5 < ma10 or close < ma20的行，数据条数: {len(short_market_data)}")
             # concat long_market_data和short_market_data
             ma_break_market_data = pd.concat([long_market_data, short_market_data])
             # 按照timestamp排序
@@ -156,7 +169,12 @@ class MaBreakStatistics:
                 ma30 = row["ma30"]
                 if pd.notna(ma_cross) and ma_cross is not None:
                     ma_cross = str(ma_cross)
-                    if ma_cross == "5上穿10" and (ma5 > ma10 and ma10 > ma20 and ma20 > ma30) and (close > ma20):
+                    buy_condition = False
+                    if all_change:
+                        buy_condition = (ma5 > ma10 and ma10 > ma20 and ma20 > ma30) and (close > ma20)
+                    else:
+                        buy_condition = ma_cross == "5上穿10" and (ma5 > ma10)
+                    if buy_condition:
                         ma_break_market_data_pair = {}
                         ma_break_market_data_pair["symbol"] = symbol
                         ma_break_market_data_pair["bar"] = bar
@@ -172,7 +190,7 @@ class MaBreakStatistics:
                         change_condition = (ma5 < ma10 and ma10 < ma20 and ma20 < ma30)
                     else:
                         # change_condition = (ma5 < ma10 or ma10 < ma20 or ma20 < ma30)
-                        change_condition = (ma5 < ma10) and (close < ma20)
+                        change_condition = (ma5 < ma10) or (close < ma20)
 
                     if change_condition:
                         if ma_break_market_data_pair.get("begin_timestamp", None) is None:
@@ -232,7 +250,10 @@ class MaBreakStatistics:
             plt.xticks(rotation=45, ha="right")
             plt.tight_layout()
 
-            save_path = os.path.join(self.stats_chart_dir, f"{bar}_ma_break_pct_chg_mean.png")
+            if all_change:
+                save_path = os.path.join(self.stats_chart_dir, f"{bar}_ma_break_pct_chg_mean_all_change.png")
+            else:
+                save_path = os.path.join(self.stats_chart_dir, f"{bar}_ma_break_pct_chg_mean_part_change.png")
             plt.savefig(save_path, dpi=150)
             plt.close()
 

core/trade/mean_reversion_sandbox.py

@@ -0,0 +1,381 @@
+import json
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+import re
+from openpyxl import Workbook
+from openpyxl.drawing.image import Image
+import openpyxl
+from openpyxl.styles import Font
+from PIL import Image as PILImage
+from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+import core.logger as logging
+from core.db.db_merge_market_huge_volume import DBMergeMarketHugeVolume
+from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
+
+# seaborn支持中文
+plt.rcParams["font.family"] = ["SimHei"]
+
+logger = logging.logger
+
+
+class MeanReversionSandbox:
+    def __init__(self, solution: str):
+        mysql_user = MYSQL_CONFIG.get("user", "xch")
+        mysql_password = MYSQL_CONFIG.get("password", "")
+        if not mysql_password:
+            raise ValueError("MySQL password is not set")
+        mysql_host = MYSQL_CONFIG.get("host", "localhost")
+        mysql_port = MYSQL_CONFIG.get("port", 3306)
+        mysql_database = MYSQL_CONFIG.get("database", "okx")
+
+        self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
+        self.db_merge_market_huge_volume = DBMergeMarketHugeVolume(self.db_url)
+        self.peak_valley_data = self.get_peak_valley_data()
+
+        self.solution = solution
+        self.save_path = f"./output/trade_sandbox/mean_reversion/{self.solution}/"
+        os.makedirs(self.save_path, exist_ok=True)
+        self.strategy_description = self.get_startegy_description()
+
+    def get_startegy_description(self):
+        desc_dict = {
+            "买入": [
+                "1. 窗口周期为100, 即100个K线",
+                "2. 当前low_10_low为1, 即当前最低价格在窗口周期的10分位以下",
+                "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",
+                "4. 当前K线为阳线, 即close > open",
+            ],
+            "止损": ["跌幅超过下跌周期跌幅中位数, 即down_median后卖出"],
+            "止盈": {
+                "solution_1": [
+                    "高位放量止盈 - 简易版",
+                    "1. 当前high_80_high为1或者high_90_high为1",
+                    "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",
+                ],
+                "solution_2": [
+                    "高位放量止盈 - 复杂版",
+                    "前提条件"
+                    "1. 当前high_80_high为1或者high_90_high为1",
+                    "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",
+                    "以下两个条件, 任一满足即可",
+                    "1. K线为阴线, 即close < open",
+                    "2. K线为阳线, 即close >= open, 且k_shape满足:",
+                    "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",
+                ],
+                "solution_3": [
+                    "上涨波段盈利中位数止盈法",
+                    "1. 超过波段中位数涨幅, 即up_median后, 记录当前价格, 继续持仓",
+                    "2. 之后一个周期, 如果价格上涨, 则记录该价格继续持仓",
+                    "3. 之后一个周期, 如果价格跌到记录价格之下, 则卖出",
+                ],
+            },
+        }
+        buy_list = desc_dict.get("买入", [])
+        stop_loss_list = desc_dict.get("止损", [])
+        take_profit_list = desc_dict.get("止盈", {}).get(self.solution, [])
+        if len(take_profit_list) == 0:
+            self.solution = "solution_1"
+            take_profit_list = desc_dict.get("止盈", {}).get(self.solution, [])
+        desc = f"策略名称: {self.solution}\n\n"
+        buy_desc = "\n".join(buy_list)
+        stop_loss_desc = "\n".join(stop_loss_list)
+        take_profit_desc = "\n".join(take_profit_list)
+        desc += f"买入策略\n {buy_desc}\n\n"
+        desc += f"止损策略\n {stop_loss_desc}\n\n"
+        desc += f"止盈策略\n {take_profit_desc}\n\n"
+        with open(f"{self.save_path}/策略描述.txt", "w", encoding="utf-8") as f:
+            f.write(desc)
+        return desc
+
+    def get_peak_valley_data(self):
+        os.makedirs("./json/", exist_ok=True)
+        json_file_path = "./json/peak_valley_data.json"
+        if not os.path.exists(json_file_path):
+            excel_file_path = "./output/statistics/excel/price_volume_stats_window_size_100_from_20250515000000_to_20250819110500.xlsx"
+            if not os.path.exists(excel_file_path):
+                raise FileNotFoundError(f"Excel file not found: {excel_file_path}")
+            sheet_name = "波峰波谷统计"
+            df = pd.read_excel(excel_file_path, sheet_name=sheet_name)
+            if df is None or len(df) == 0:
+                raise ValueError("Excel file is empty")
+            data_list = []
+            for index, row in df.iterrows():
+                data_list.append(
+                    {
+                        "symbol": row["symbol"],
+                        "bar": row["bar"],
+                        "up_mean": row["up_mean"],
+                        "up_median": row["up_median"],
+                        "down_mean": row["down_mean"],
+                        "down_median": row["down_median"],
+                    }
+                )
+            with open(json_file_path, "w", encoding="utf-8") as f:
+                json.dump(data_list, f, ensure_ascii=False, indent=4)
+            peak_valley_data = pd.DataFrame(data_list)
+        else:
+            with open(json_file_path, "r", encoding="utf-8") as f:
+                peak_valley_data = json.load(f)
+        return pd.DataFrame(peak_valley_data)
+
+    def trade_sandbox(
+        self, symbol: str, bar: str, window_size: int, start: str, end: str
+    ):
+        logger.info(f"策略描述: {self.strategy_description}")
+        logger.info(
+            f"开始获取{symbol} {bar} 的{window_size}分钟窗口大小的数据, 开始时间: {start}, 结束时间: {end}"
+        )
+        market_data = self.db_merge_market_huge_volume.merge_market_huge_volume(
+            symbol, bar, window_size, start, end
+        )
+        if market_data is None:
+            return None
+        logger.info(f"数据条数: {len(market_data)}")
+
+        trade_list = []
+        trade_pair_dict = {}
+        for index, row in market_data.iterrows():
+            # check buy condition
+            if trade_pair_dict.get("buy_timestamp", None) is None:
+                buy_condition = self.check_buy_condition(market_data, row, index)
+            else:
+                buy_condition = False
+            if buy_condition:
+                trade_pair_dict = {}
+                trade_pair_dict["solution"] = self.solution
+                trade_pair_dict["symbol"] = symbol
+                trade_pair_dict["bar"] = bar
+                trade_pair_dict["window_size"] = window_size
+                trade_pair_dict["buy_timestamp"] = row["timestamp"]
+                trade_pair_dict["buy_date_time"] = timestamp_to_datetime(
+                    row["timestamp"]
+                )
+                trade_pair_dict["buy_close"] = row["close"]
+                trade_pair_dict["buy_pct_chg"] = row["pct_chg"]
+                trade_pair_dict["buy_volume"] = row["volume"]
+                trade_pair_dict["buy_huge_volume"] = row["huge_volume"]
+                trade_pair_dict["buy_volume_ratio"] = row["volume_ratio"]
+                trade_pair_dict["buy_k_shape"] = row["k_shape"]
+                trade_pair_dict["buy_low_10_low"] = row["low_10_low"]
+                continue
+
+            if trade_pair_dict.get("buy_timestamp", None) is not None:
+                sell_condition = False
+                # check stop loss condition
+                sell_condition = self.check_stop_loss_condition(trade_pair_dict, row)
+                if sell_condition:
+                    trade_pair_dict["sell_type"] = "止损"
+                else:
+                    # check take profit condition
+                    sell_condition = self.check_take_profit_condition(
+                        trade_pair_dict, market_data, row, index
+                    )
+                    if sell_condition:
+                        trade_pair_dict["sell_type"] = "止盈"
+
+                if sell_condition:
+                    trade_pair_dict["sell_timestamp"] = row["timestamp"]
+                    trade_pair_dict["sell_date_time"] = timestamp_to_datetime(
+                        row["timestamp"]
+                    )
+                    trade_pair_dict["sell_close"] = row["close"]
+                    trade_pair_dict["sell_pct_chg"] = row["pct_chg"]
+                    trade_pair_dict["sell_volume"] = row["volume"]
+                    trade_pair_dict["sell_huge_volume"] = row["huge_volume"]
+                    trade_pair_dict["sell_volume_ratio"] = row["volume_ratio"]
+                    trade_pair_dict["sell_k_shape"] = row["k_shape"]
+                    trade_pair_dict["sell_high_80_high"] = row["high_80_high"]
+                    trade_pair_dict["sell_high_90_high"] = row["high_90_high"]
+                    trade_pair_dict["sell_low_10_low"] = row["low_10_low"]
+                    trade_pair_dict["sell_low_20_low"] = row["low_20_low"]
+                    trade_pair_dict["profit_pct"] = round(
+                        (trade_pair_dict["sell_close"] - trade_pair_dict["buy_close"])
+                        / trade_pair_dict["buy_close"]
+                        * 100,
+                        4,
+                    )
+                    if trade_pair_dict["sell_type"] == "止盈" and trade_pair_dict["profit_pct"] < 0:
+                        trade_pair_dict["sell_type"] = "止损"
+                    if trade_pair_dict.get("last_max_close", None) is not None:
+                        # remove last_max_close
+                        trade_pair_dict.pop("last_max_close")
+
+                    trade_list.append(trade_pair_dict)
+                    trade_pair_dict = {}
+
+        if len(trade_list) == 0:
+            return None
+        trade_data = pd.DataFrame(trade_list)
+        trade_data.sort_values(by="buy_timestamp", inplace=True)
+        trade_data.reset_index(drop=True, inplace=True)
+        return trade_data
+
+    def check_buy_condition(
+        self, market_data: pd.DataFrame, row: pd.Series, index: int
+    ):
+        """
+        买入条件
+        1. 窗口周期为100, 即100个K线
+        2. 当前low_10_low为1, 即当前最低价格在窗口周期的10分位以下
+        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
+        4. 当前K线为阳线, 即close > open
+        5. TODO: 考虑K线形态
+        """
+        if index < 2:
+            return False
+        if row["close"] <= row["open"]:
+            return False
+
+        if row["low_10_low"] != 1:
+            return False
+
+        # 如果当前与前两个K线，huge_volume都不为1，则返回False
+        if (
+            row["huge_volume"] != 1
+            and market_data.loc[index - 1, "huge_volume"] != 1
+            and market_data.loc[index - 2, "huge_volume"] != 1
+        ):
+            return False
+        logger.info(f"符合买入条件")
+        return True
+
+    def check_stop_loss_condition(self, trade_pair_dict: dict, row: pd.Series):
+        symbol = trade_pair_dict["symbol"]
+        bar = trade_pair_dict["bar"]
+        # 获取下跌周期跌幅中位数, 为百分比
+        down_median = (
+            self.peak_valley_data.loc[
+                (self.peak_valley_data["symbol"] == symbol)
+                & (self.peak_valley_data["bar"] == bar),
+                "down_median",
+            ].values[0]
+            / 100
+        )
+        buy_close = trade_pair_dict["buy_close"]
+        current_close = row["close"]
+        if (
+            current_close < buy_close
+            and (current_close - buy_close) / buy_close < down_median
+        ):
+            logger.info(f"符合止损条件")
+            return True
+        return False
+
+    def check_take_profit_condition(
+        self,
+        trade_pair_dict: dict,
+        market_data: pd.DataFrame,
+        row: pd.Series,
+        index: int,
+    ):
+        try:
+            if self.solution == "solution_1":
+                return self.check_take_profit_condition_solution_1(
+                    market_data, row, index
+                )
+            elif self.solution == "solution_2":
+                return self.check_take_profit_condition_solution_2(
+                    market_data, row, index
+                )
+            elif self.solution == "solution_3":
+                return self.check_take_profit_condition_solution_3(
+                    trade_pair_dict, row
+                )
+            else:
+                raise ValueError(f"Invalid strategy name: {self.solution}")
+        except Exception as e:
+            logger.error(f"检查止盈条件时发生错误: {e}")
+            return False
+
+    def check_take_profit_condition_solution_1(
+        self,
+        market_data: pd.DataFrame,
+        row: pd.Series,
+        index: int,
+    ):
+        """
+        高位放量止盈 - 简易版
+        1. 当前high_80_high为1或者high_90_high为1
+        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
+        """
+        if row["high_80_high"] != 1 and row["high_90_high"] != 1:
+            return False
+        if (
+            row["huge_volume"] != 1
+            and market_data.loc[index - 1, "huge_volume"] != 1
+            and market_data.loc[index - 2, "huge_volume"] != 1
+        ):
+            return False
+        logger.info(f"符合高位放量止盈 - 简易版条件")
+        return True
+
+    def check_take_profit_condition_solution_2(
+        self,
+        market_data: pd.DataFrame,
+        row: pd.Series,
+        index: int,
+    ):
+        """
+        高位放量止盈 - 复杂版
+        前提条件
+        1. 当前high_80_high为1或者high_90_high为1
+        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
+        以下两个条件, 任一满足即可
+        1. K线为阴线, 即close < open
+        2. K线为阳线, 即close >= open, 且k_shape满足:
+        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
+        """
+        if not self.check_take_profit_condition_solution_1(market_data, row, index):
+            return False
+        if row["close"] < row["open"]:
+            logger.info(f"符合高位放量止盈 - 复杂版条件")
+            return True
+        elif row["k_shape"] in ["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"]:
+            logger.info(f"符合高位放量止盈 - 复杂版条件")
+            return True
+        else:
+            return False
+
+    def check_take_profit_condition_solution_3(
+        self,
+        trade_pair_dict: dict,
+        row: pd.Series
+    ):
+        """
+        上涨波段盈利中位数止盈法
+        1. 超过波段中位数涨幅, 即up_median后, 记录当前价格, 继续持仓
+        2. 之后一个周期, 如果价格上涨, 则记录该价格继续持仓
+        3. 之后一个周期, 如果价格跌到记录价格之下, 则卖出
+        """
+        current_close = row["close"]
+        last_max_close = trade_pair_dict.get("last_max_close", None)
+        if last_max_close is not None:
+            if current_close >= last_max_close:
+                logger.info(f"价格上涨, 继续持仓")
+                trade_pair_dict["last_max_close"] = current_close
+                return False
+            else:
+                logger.info(f"符合上涨波段盈利中位数止盈法条件")
+                return True
+        else:
+            symbol = trade_pair_dict["symbol"]
+            bar = trade_pair_dict["bar"]
+            up_median = (
+                self.peak_valley_data.loc[
+                    (self.peak_valley_data["symbol"] == symbol)
+                    & (self.peak_valley_data["bar"] == bar),
+                    "up_median",
+                ].values[0]
+                / 100
+            )
+            
+            buy_close = trade_pair_dict["buy_close"]
+            price_chg = (current_close - buy_close) / buy_close
+            if price_chg > up_median:
+                logger.info(f"当前价格上涨超过波段中位数涨幅, 记录当前价格")
+                trade_pair_dict["last_max_close"] = current_close
+            return False

trade_sandbox_main.py

@@ -0,0 +1,277 @@
+import core.logger as logging
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.ticker import PercentFormatter
+from datetime import datetime
+import re
+from openpyxl import Workbook
+from openpyxl.drawing.image import Image
+import openpyxl
+from openpyxl.styles import Font
+from PIL import Image as PILImage
+from config import MONITOR_CONFIG
+from core.trade.mean_reversion_sandbox import MeanReversionSandbox
+from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
+
+# seaborn支持中文
+plt.rcParams["font.family"] = ["SimHei"]
+
+logger = logging.logger
+
+
+class MeanReversionSandboxMain:
+    def __init__(self, start_date: str, end_date: str, window_size: int):
+        self.symbols = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            "symbols", ["XCH-USDT"]
+        )
+        self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            "bars", ["5m", "15m", "30m", "1H"]
+        )
+        self.solution_list = ["solution_1", "solution_2", "solution_3"]
+        self.start_date = start_date
+        self.end_date = end_date
+        self.window_size = window_size
+        self.save_path = f"./output/trade_sandbox/mean_reversion/"
+        os.makedirs(self.save_path, exist_ok=True)
+
+    def batch_mean_reversion_sandbox(self):
+        """
+        批量计算均值回归
+        """
+        logger.info("开始批量计算均值回归交易策略")
+        logger.info(
+            f"开始时间: {self.start_date}, 结束时间: {self.end_date}, 窗口大小: {self.window_size}"
+        )
+
+        for solution in self.solution_list:
+            data_list = []
+            for symbol in self.symbols:
+                for bar in self.bars:
+                    data = self.mean_reversion(symbol, bar, solution)
+                    if data is not None and len(data) > 0:
+                        data_list.append(data)
+            if len(data_list) == 0:
+                return None
+            total_data = pd.concat(data_list)
+            total_data.sort_values(by="buy_timestamp", ascending=True, inplace=True)
+            total_data.reset_index(drop=True, inplace=True)
+            stat_data = self.statistic_data(total_data)
+            excel_save_path = os.path.join(self.save_path, solution, "excel")
+            os.makedirs(excel_save_path, exist_ok=True)
+            date_time_str = datetime.now().strftime("%Y%m%d%H%M%S")
+            excel_file_path = os.path.join(
+                excel_save_path, f"{solution}_{date_time_str}.xlsx"
+            )
+            with pd.ExcelWriter(excel_file_path) as writer:
+                total_data.to_excel(writer, sheet_name="total_data", index=False)
+                stat_data.to_excel(writer, sheet_name="stat_data", index=False)
+            chart_dict = {}
+            self.draw_chart(stat_data, chart_dict)
+            self.output_chart_to_excel(excel_file_path, chart_dict)
+
+    def mean_reversion(self, symbol: str, bar: str, solution: str):
+        """
+        均值回归交易策略
+        """
+        mean_reversion_sandbox = MeanReversionSandbox(solution)
+        data = mean_reversion_sandbox.trade_sandbox(
+            symbol, bar, self.window_size, self.start_date, self.end_date
+        )
+        return data
+
+    def statistic_data(self, data: pd.DataFrame):
+        """
+        统计数据
+        """
+        data_list = []
+        # 以symbol, bar分组，统计data的profit_pct>0的次数，并且获得：
+        # profit_pct的最大值，最小值，平均值，profit_pct>0的平均值，以及profit_pct<0的平均值
+        data_grouped = data.groupby(["symbol", "bar"])
+        for symbol, bar in data_grouped:
+            solution = bar["solution"].iloc[0]
+            # 止盈次数
+            take_profit_count = len(bar[bar["sell_type"] == "止盈"])
+            take_profit_ratio = round((take_profit_count / len(bar)) * 100, 4)
+            # 止损次数
+            stop_loss_count = len(bar[bar["sell_type"] == "止损"])
+            stop_loss_ratio = round((stop_loss_count / len(bar)) * 100, 4)
+            profit_pct_gt_0_count = len(bar[bar["profit_pct"] > 0])
+            profit_pct_gt_0_ratio = round((profit_pct_gt_0_count / len(bar)) * 100, 4)
+            profit_pct_lt_0_count = len(bar[bar["profit_pct"] < 0])
+            profit_pct_lt_0_ratio = round((profit_pct_lt_0_count / len(bar)) * 100, 4)
+            profit_pct_max = bar["profit_pct"].max()
+            profit_pct_min = bar["profit_pct"].min()
+            profit_pct_mean = bar["profit_pct"].mean()
+            profit_pct_gt_0_mean = bar[bar["profit_pct"] > 0]["profit_pct"].mean()
+            profit_pct_lt_0_mean = bar[bar["profit_pct"] < 0]["profit_pct"].mean()
+
+            symbol_name = bar["symbol"].iloc[0]
+            bar_name = bar["bar"].iloc[0]
+            logger.info(
+                f"策略: {solution}, symbol: {symbol_name}, bar: {bar_name}, profit_pct>0的次数: {profit_pct_gt_0_count}, profit_pct<0的次数: {profit_pct_lt_0_count}, profit_pct最大值: {profit_pct_max}, profit_pct最小值: {profit_pct_min}, profit_pct平均值: {profit_pct_mean}, profit_pct>0的平均值: {profit_pct_gt_0_mean}, profit_pct<0的平均值: {profit_pct_lt_0_mean}"
+            )
+            data_list.append(
+                {
+                    "solution": solution,
+                    "symbol": symbol_name,
+                    "bar": bar_name,
+                    "take_profit_count": take_profit_count,
+                    "take_profit_ratio": take_profit_ratio,
+                    "stop_loss_count": stop_loss_count,
+                    "stop_loss_ratio": stop_loss_ratio,
+                    "profit_pct_gt_0_count": profit_pct_gt_0_count,
+                    "profit_pct_gt_0_ratio": profit_pct_gt_0_ratio,
+                    "profit_pct_lt_0_count": profit_pct_lt_0_count,
+                    "profit_pct_lt_0_ratio": profit_pct_lt_0_ratio,
+                    "profit_pct_max": profit_pct_max,
+                    "profit_pct_min": profit_pct_min,
+                    "profit_pct_mean": profit_pct_mean,
+                    "profit_pct_gt_0_mean": profit_pct_gt_0_mean,
+                    "profit_pct_lt_0_mean": profit_pct_lt_0_mean,
+                }
+            )
+        stat_data = pd.DataFrame(data_list)
+        stat_data.sort_values(by=["bar", "symbol"], inplace=True)
+        stat_data.reset_index(drop=True, inplace=True)
+        return stat_data
+
+    def draw_chart(self, stat_data: pd.DataFrame, chart_dict: dict):
+        """
+        绘制图表
+        """
+        sns.set_theme(style="whitegrid")
+        plt.rcParams["font.sans-serif"] = ["SimHei"]  # 也可直接用字体名
+        plt.rcParams["font.size"] = 11  # 设置字体大小
+        plt.rcParams["axes.unicode_minus"] = False
+        plt.rcParams["figure.dpi"] = 150
+        plt.rcParams["savefig.dpi"] = 150
+        # 绘制各个solution的profit_pct_gt_0_ratio的柱状图
+        # bar为5m, 15, 30m, 1H，共计四个分类，
+        # 每一个bar为一张chart，构成2x2的画布
+        # 要求y轴为百分比，x轴为symbol
+        # 使用蓝色渐变色
+        # 每一个solution保存为一张chart图片，保存到output/trade_sandbox/mean_reversion/chart/
+
+        solution = stat_data["solution"].iloc[0]
+        save_path = os.path.join(self.save_path, solution, "chart")
+        os.makedirs(save_path, exist_ok=True)
+
+        bars_in_order = [
+            b for b in getattr(self, "bars", []) if b in stat_data["bar"].unique()
+        ]
+        if not bars_in_order:
+            bars_in_order = list(stat_data["bar"].unique())
+        palette_name = "Blues_d"
+
+        y_axis_fields = [
+            "take_profit_ratio",
+            "stop_loss_ratio",
+            "profit_pct_mean",
+            "profit_pct_gt_0_mean",
+            "profit_pct_lt_0_mean",
+        ]
+        sheet_name = f"{solution}_chart"
+        chart_dict[sheet_name] = {}
+        for y_axis_field in y_axis_fields:
+            # 绘制2x2的画布
+            fig, axs = plt.subplots(2, 2, figsize=(10, 10))
+            for j, bar in enumerate(bars_in_order):
+                ax = axs[j // 2, j % 2]
+                bar_data = stat_data[stat_data["bar"] == bar].copy()
+                bar_data.sort_values(by=y_axis_field, ascending=False, inplace=True)
+                bar_data.reset_index(drop=True, inplace=True)
+                colors = sns.color_palette(palette_name, n_colors=len(bar_data))
+                sns.barplot(
+                    x="symbol",
+                    y=y_axis_field,
+                    data=bar_data,
+                    palette=colors,
+                    ax=ax,
+                )
+                ax.set_ylabel(y_axis_field)
+                ax.set_xlabel("symbol")
+                ax.set_title(f"{solution} {bar}")
+                if "ratio" in y_axis_field:
+                    ax.yaxis.set_major_formatter(PercentFormatter(100))
+                    ax.set_ylim(0, 100)
+
+                for label in ax.get_xticklabels():
+                    label.set_rotation(45)
+                    label.set_horizontalalignment("right")
+            # 隐藏未使用的subplot
+            total_used = len(bars_in_order)
+            for k in range(total_used, 4):
+                ax = axs[k // 2, k % 2]
+                ax.axis("off")
+            fig.tight_layout()
+            file_name = f"{solution}_{y_axis_field}.png"
+            fig.savefig(os.path.join(save_path, file_name))
+            plt.close(fig)
+            chart_dict[sheet_name][y_axis_field] = os.path.join(save_path, file_name)
+
+    def output_chart_to_excel(self, excel_file_path: str, charts_dict: dict):
+        """
+        输出Excel文件，包含所有图表
+        charts_dict: 图表数据字典，格式为：
+        {
+            "sheet_name": {
+                "chart_name": "chart_path"
+            }
+        }
+        """
+        logger.info(f"将图表输出到{excel_file_path}")
+
+        # 打开已经存在的Excel文件
+        wb = openpyxl.load_workbook(excel_file_path)
+
+        for sheet_name, chart_data_dict in charts_dict.items():
+            try:
+                ws = wb.create_sheet(title=sheet_name)
+                row_offset = 1
+                for chart_name, chart_path in chart_data_dict.items():
+                    # Load image to get dimensions
+                    with PILImage.open(chart_path) as img:
+                        width_px, height_px = img.size
+
+                    # Convert pixel height to Excel row height (approximate: 1 point = 1.333 pixels, 1 row ≈ 15 points for 20 pixels)
+                    pixels_per_point = 1.333
+                    points_per_row = 15  # Default row height in points
+                    pixels_per_row = (
+                        points_per_row * pixels_per_point
+                    )  # ≈ 20 pixels per row
+                    chart_rows = max(
+                        10, int(height_px / pixels_per_row)
+                    )  # Minimum 10 rows for small charts
+
+                    # Add chart title
+                    # 支持中文标题
+                    ws[f"A{row_offset}"] = chart_name.encode("utf-8").decode("utf-8")
+                    ws[f"A{row_offset}"].font = openpyxl.styles.Font(bold=True, size=12)
+                    row_offset += 2  # Add 2 rows for title and spacing
+
+                    # Insert chart image
+                    img = Image(chart_path)
+                    ws.add_image(img, f"A{row_offset}")
+
+                    # Update row offset (chart height + padding)
+                    row_offset += (
+                        chart_rows + 5
+                    )  # Add 5 rows for padding between charts
+            except Exception as e:
+                logger.error(f"输出Excel Sheet {sheet_name} 失败: {e}")
+                continue
+        # Save Excel file
+        wb.save(excel_file_path)
+        print(f"Chart saved as {excel_file_path}")
+
+
+if __name__ == "__main__":
+    start_date = "2025-05-15 00:00:00"
+    end_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    mean_reversion_sandbox_main = MeanReversionSandboxMain(
+        start_date=start_date, end_date=end_date, window_size=100
+    )
+    mean_reversion_sandbox_main.batch_mean_reversion_sandbox()