crypto_quant/core/trade/ma_break_statistics.py

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 datetime import datetime, timedelta, timezone
from core.utils import get_current_date_time
import re
import json
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 OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE, BINANCE_MONITOR_CONFIG
from core.db.db_market_data import DBMarketData
from core.db.db_binance_data import DBBinanceData
from core.db.db_huge_volume_data import DBHugeVolumeData
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 MaBreakStatistics:
    """
    统计MA突破之后的涨跌幅
    MA向上突破的点位周期K线：5 > 10 > 20 > 30
    统计MA向上突破的点位周期K线，突破之后，到：
    下一个MA向下突破的点位周期K线：30 > 20 > 10 > 5
    之间的涨跌幅
    """

    def __init__(self, is_us_stock: bool = False, is_binance: bool = False):
        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_huge_volume_data = DBHugeVolumeData(self.db_url)
        self.is_us_stock = is_us_stock
        self.is_binance = is_binance
        if is_us_stock:
            self.symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
                "symbols", ["QQQ"]
            )
            self.bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
                "bars", ["5m"]
            )
            self.initial_date = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
                "initial_date", "2014-11-30 00:00:00"
            )
            self.db_market_data = DBMarketData(self.db_url)
        else:
            if is_binance:
                self.symbols = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
                    "symbols", ["BTC-USDT"]
                )
                self.bars = ["30m", "1H"]
                self.initial_date = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
                    "initial_date", "2017-08-16 00:00:00"
                )
                self.db_market_data = DBBinanceData(self.db_url)
            else:
                self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                "symbols", ["XCH-USDT"]
                )
                self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                "bars", ["5m", "15m", "30m", "1H"]
                )
                self.initial_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                    "initial_date", "2025-05-15 00:00:00"
                )
                self.db_market_data = DBMarketData(self.db_url)
        if len(self.initial_date) > 10:
            self.initial_date = self.initial_date[:10]
        self.end_date = get_current_date_time()
        self.trade_strategy_config = self.get_trade_strategy_config()
        self.main_strategy = self.trade_strategy_config.get("均线系统策略", None)

    def get_trade_strategy_config(self):
        with open("./json/trade_strategy.json", "r", encoding="utf-8") as f:
            trade_strategy_config = json.load(f)
        return trade_strategy_config

    def batch_statistics(self, strategy_name: str = "全均线策略"):
        if self.is_us_stock:
            self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/"
            self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/"
        elif self.is_binance:
            self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/"
            self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/"
        else:
            self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/"
            self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/"
        os.makedirs(self.stats_output_dir, exist_ok=True)
        os.makedirs(self.stats_chart_dir, exist_ok=True)

        ma_break_market_data_list = []
        market_data_pct_chg_list = []
        if strategy_name not in self.main_strategy.keys() or strategy_name is None:
            strategy_name = "全均线策略"
        for symbol in self.symbols:
            for bar in self.bars:
                logger.info(
                    f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}"
                )
                ma_break_market_data, market_data_pct_chg = self.trade_simulate(
                    symbol, bar, strategy_name
                )
                if (
                    ma_break_market_data is not None
                    and len(ma_break_market_data) > 0
                    and market_data_pct_chg is not None
                ):
                    ma_break_market_data_list.append(ma_break_market_data)
                    logger.info(
                        f"{symbol} {bar} 的市场价格变化, {market_data_pct_chg.get('pct_chg', 0)}%"
                    )
                    market_data_pct_chg_list.append(market_data_pct_chg)
            
        if len(ma_break_market_data_list) > 0:
            ma_break_market_data = pd.concat(ma_break_market_data_list)
            market_data_pct_chg_df = pd.DataFrame(market_data_pct_chg_list)
            # 依据symbol和bar分组，统计每个symbol和bar的pct_chg的max, min, mean, std, median, count
            ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
            ma_break_market_data.reset_index(drop=True, inplace=True)
            pct_chg_df = (
                ma_break_market_data.groupby(["symbol", "bar"])["pct_chg"]
                .agg(
                    pct_chg_sum="sum",
                    pct_chg_max="max",
                    pct_chg_min="min",
                    pct_chg_mean="mean",
                    pct_chg_std="std",
                    pct_chg_median="median",
                    pct_chg_count="count",
                )
                .reset_index()
            )
            pct_chg_df["strategy_name"] = strategy_name
            pct_chg_df["pct_chg_total"] = 0
            pct_chg_df["market_pct_chg"] = 0
            # 将pct_chg_total与market_pct_chg的值类型转换为float
            pct_chg_df["pct_chg_total"] = pct_chg_df["pct_chg_total"].astype(float)
            pct_chg_df["market_pct_chg"] = pct_chg_df["market_pct_chg"].astype(float)
            # 统计pct_chg_total
            # 算法要求，ma_break_market_data，然后pct_chg/100 + 1
            ma_break_market_data["pct_chg_total"] = (
                ma_break_market_data["pct_chg"] / 100 + 1
            )
            # 遍历symbol和bar，按照end_timestamp排序，计算pct_chg_total的值，然后相乘
            for symbol in pct_chg_df["symbol"].unique():
                for bar in pct_chg_df["bar"].unique():
                    symbol_bar_data = ma_break_market_data[
                        (ma_break_market_data["symbol"] == symbol)
                        & (ma_break_market_data["bar"] == bar)
                    ].copy()  # 创建副本避免SettingWithCopyWarning
                    if len(symbol_bar_data) > 0:
                        symbol_bar_data.sort_values(
                            by="end_timestamp", ascending=True, inplace=True
                        )
                        symbol_bar_data.reset_index(drop=True, inplace=True)
                        symbol_bar_data["pct_chg_total"] = symbol_bar_data[
                            "pct_chg_total"
                        ].cumprod()
                        
                        # 将更新后的pct_chg_total数据同步更新到ma_break_market_data的对应数据行中
                        for idx, row in symbol_bar_data.iterrows():
                            mask = (ma_break_market_data["symbol"] == symbol) & \
                                   (ma_break_market_data["bar"] == bar) & \
                                   (ma_break_market_data["end_timestamp"] == row["end_timestamp"])
                            ma_break_market_data.loc[mask, "pct_chg_total"] = row["pct_chg_total"]
                        
                        last_pct_chg_total = symbol_bar_data["pct_chg_total"].iloc[-1]
                        last_pct_chg_total = (last_pct_chg_total - 1) * 100
                        pct_chg_df.loc[
                            (pct_chg_df["symbol"] == symbol)
                            & (pct_chg_df["bar"] == bar),
                            "pct_chg_total",
                        ] = last_pct_chg_total
                        market_pct_chg = market_data_pct_chg_df.loc[
                            (market_data_pct_chg_df["symbol"] == symbol)
                            & (market_data_pct_chg_df["bar"] == bar),
                            "pct_chg",
                        ].values[0]
                        pct_chg_df.loc[
                            (pct_chg_df["symbol"] == symbol)
                            & (pct_chg_df["bar"] == bar),
                            "market_pct_chg",
                        ] = market_pct_chg

            pct_chg_df = pct_chg_df[
                [
                    "strategy_name",
                    "symbol",
                    "bar",
                    "market_pct_chg",
                    "pct_chg_total",
                    "pct_chg_sum",
                    "pct_chg_max",
                    "pct_chg_min",
                    "pct_chg_mean",
                    "pct_chg_std",
                    "pct_chg_median",
                    "pct_chg_count",
                ]
            ]
            # 依据symbol和bar分组，统计每个symbol和bar的interval_minutes的max, min, mean, std, median, count
            interval_minutes_df = (
                ma_break_market_data.groupby(["symbol", "bar"])["interval_minutes"]
                .agg(
                    interval_minutes_max="max",
                    interval_minutes_min="min",
                    interval_minutes_mean="mean",
                    interval_minutes_std="std",
                    interval_minutes_median="median",
                    interval_minutes_count="count",
                )
                .reset_index()
            )

            earliest_market_date_time = ma_break_market_data["begin_date_time"].min()
            earliest_market_date_time = re.sub(
                r"[\:\-\s]", "", str(earliest_market_date_time)
            )
            latest_market_date_time = ma_break_market_data["end_date_time"].max()
            if latest_market_date_time is None:
                latest_market_date_time = get_current_date_time(format="%Y%m%d%H%M%S")
            latest_market_date_time = re.sub(
                r"[\:\-\s]", "", str(latest_market_date_time)
            )
            output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}.xlsx"
            output_file_path = os.path.join(self.stats_output_dir, output_file_name)
            logger.info(f"导出{output_file_path}")
            strategy_info_df = self.get_strategy_info(strategy_name)
            with pd.ExcelWriter(output_file_path) as writer:
                strategy_info_df.to_excel(writer, sheet_name="策略信息", index=False)
                ma_break_market_data.to_excel(
                    writer, sheet_name="买卖记录明细", index=False
                )
                pct_chg_df.to_excel(writer, sheet_name="买卖涨跌幅统计", index=False)
                interval_minutes_df.to_excel(
                    writer, sheet_name="买卖时间间隔统计", index=False
                )

            chart_dict = self.draw_quant_pct_chg_bar_chart(pct_chg_df, strategy_name)
            self.output_chart_to_excel(output_file_path, chart_dict)
            chart_dict = self.draw_quant_line_chart(ma_break_market_data, strategy_name)
            self.output_chart_to_excel(output_file_path, chart_dict)
            return pct_chg_df
        else:
            return None

    def get_strategy_info(self, strategy_name: str = "全均线策略"):
        strategy_config = self.main_strategy.get(strategy_name, None)
        if strategy_config is None:
            logger.error(f"策略{strategy_name}不存在")
            return None
        strategy_info = {"策略名称": strategy_name, "买入策略": "", "卖出策略": ""}
        buy_dict = strategy_config.get("buy", {})
        buy_and_list = buy_dict.get("and", [])
        buy_or_list = buy_dict.get("or", [])
        buy_and_text = ""
        buy_or_text = ""
        for and_condition in buy_and_list:
            buy_and_text += f"{and_condition}, \n"
        if len(buy_or_list) > 0:
            for or_condition in buy_or_list:
                buy_or_text += f"{or_condition}, \n"
        if len(buy_or_text) > 0:
            strategy_info["买入策略"] = buy_and_text + " 或者 \n" + buy_or_text
        else:
            strategy_info["买入策略"] = buy_and_text
        sell_dict = strategy_config.get("sell", {})
        sell_and_list = sell_dict.get("and", [])
        sell_or_list = sell_dict.get("or", [])
        sell_and_text = ""
        sell_or_text = ""
        for and_condition in sell_and_list:
            sell_and_text += f"{and_condition}, \n"
        if len(sell_or_list) > 0:
            for or_condition in sell_or_list:
                sell_or_text += f"{or_condition}, \n"
        if len(sell_or_text) > 0:
            strategy_info["卖出策略"] = sell_and_text + " 或者 \n" + sell_or_text
        else:
            strategy_info["卖出策略"] = sell_and_text
        # 将strategy_info转换为pd.DataFrame
        strategy_info_df = pd.DataFrame([strategy_info])
        return strategy_info_df

    def trade_simulate(self, symbol: str, bar: str, strategy_name: str = "全均线策略"):
        market_data = self.get_full_data(symbol, bar)
        if market_data is None or len(market_data) == 0:
            logger.warning(f"获取{symbol} {bar} 数据失败")
            return None, None
        else:
            logger.info(f"获取{symbol} {bar} 数据成功，数据条数: {len(market_data)}")
            # 获得ma5, ma10, ma20, ma30不为空的行
            market_data = market_data[
                (market_data["ma5"].notna())
                & (market_data["ma10"].notna())
                & (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)}"
            )
            # 计算volume_ma5
            market_data["volume_ma5"] = market_data["volume"].rolling(window=5).mean()

            market_data["volume_pct_chg"] = (
                market_data["volume"] - market_data["volume_ma5"]
            ) / market_data["volume_ma5"]
            market_data["volume_pct_chg"] = market_data["volume_pct_chg"].fillna(0)

            # 按照timestamp排序
            market_data = market_data.sort_values(by="timestamp", ascending=True)
            # 获得ma_break_market_data的close列
            market_data.reset_index(drop=True, inplace=True)
            ma_break_market_data_pair_list = []
            ma_break_market_data_pair = {}
            if self.is_us_stock:
                date_time_field = "date_time_us"
            else:
                date_time_field = "date_time"
            for index, row in market_data.iterrows():
                ma_cross = row["ma_cross"]
                timestamp = row["timestamp"]
                date_time = row[date_time_field]
                close = row["close"]
                ma5 = row["ma5"]
                ma10 = row["ma10"]
                ma20 = row["ma20"]
                ma30 = row["ma30"]
                macd_diff = float(row["dif"])
                macd_dea = float(row["dea"])
                macd = float(row["macd"])

                if ma_break_market_data_pair.get("begin_timestamp", None) is None:
                    buy_condition = self.fit_strategy(
                        strategy_name=strategy_name,
                        market_data=market_data,
                        row=row,
                        behavior="buy",
                    )

                    if buy_condition:
                        ma_break_market_data_pair = {}
                        ma_break_market_data_pair["symbol"] = symbol
                        ma_break_market_data_pair["bar"] = bar
                        ma_break_market_data_pair["begin_timestamp"] = timestamp
                        ma_break_market_data_pair["begin_date_time"] = date_time
                        ma_break_market_data_pair["begin_close"] = close
                        ma_break_market_data_pair["begin_ma5"] = ma5
                        ma_break_market_data_pair["begin_ma10"] = ma10
                        ma_break_market_data_pair["begin_ma20"] = ma20
                        ma_break_market_data_pair["begin_ma30"] = ma30
                        ma_break_market_data_pair["begin_macd_diff"] = macd_diff
                        ma_break_market_data_pair["begin_macd_dea"] = macd_dea
                        ma_break_market_data_pair["begin_macd"] = macd
                        continue
                else:
                    sell_condition = self.fit_strategy(
                        strategy_name=strategy_name,
                        market_data=market_data,
                        row=row,
                        behavior="sell",
                    )

                    if sell_condition:
                        ma_break_market_data_pair["end_timestamp"] = timestamp
                        ma_break_market_data_pair["end_date_time"] = date_time
                        ma_break_market_data_pair["end_close"] = close
                        ma_break_market_data_pair["end_ma5"] = ma5
                        ma_break_market_data_pair["end_ma10"] = ma10
                        ma_break_market_data_pair["end_ma20"] = ma20
                        ma_break_market_data_pair["end_ma30"] = ma30
                        ma_break_market_data_pair["end_macd_diff"] = macd_diff
                        ma_break_market_data_pair["end_macd_dea"] = macd_dea
                        ma_break_market_data_pair["end_macd"] = macd
                        ma_break_market_data_pair["pct_chg"] = (
                            close - ma_break_market_data_pair["begin_close"]
                        ) / ma_break_market_data_pair["begin_close"]
                        ma_break_market_data_pair["pct_chg"] = round(
                            ma_break_market_data_pair["pct_chg"] * 100, 4
                        )
                        ma_break_market_data_pair["interval_seconds"] = (
                            timestamp - ma_break_market_data_pair["begin_timestamp"]
                        ) / 1000
                        # 将interval转换为分钟
                        ma_break_market_data_pair["interval_minutes"] = (
                            ma_break_market_data_pair["interval_seconds"] / 60
                        )
                        ma_break_market_data_pair["interval_hours"] = (
                            ma_break_market_data_pair["interval_seconds"] / 3600
                        )
                        ma_break_market_data_pair["interval_days"] = (
                            ma_break_market_data_pair["interval_seconds"] / 86400
                        )
                        ma_break_market_data_pair_list.append(ma_break_market_data_pair)
                        ma_break_market_data_pair = {}

            if len(ma_break_market_data_pair_list) > 0:
                ma_break_market_data = pd.DataFrame(ma_break_market_data_pair_list)
                # sort by end_timestamp
                ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
                ma_break_market_data.reset_index(drop=True, inplace=True)
                logger.info(
                    f"获取{symbol} {bar} 的买卖记录明细成功, 买卖次数: {len(ma_break_market_data)}"
                )
                # 量化期间，市场的波动率：
                # ma_break_market_data(最后一条数据的end_close - 第一条数据的begin_close) / 第一条数据的begin_close * 100
                pct_chg = (
                    (ma_break_market_data["end_close"].iloc[-1] - ma_break_market_data["begin_close"].iloc[0])
                    / ma_break_market_data["begin_close"].iloc[0]
                    * 100
                )
                pct_chg = round(pct_chg, 4)
                market_data_pct_chg = {"symbol": symbol, "bar": bar, "pct_chg": pct_chg}
                return ma_break_market_data, market_data_pct_chg
            else:
                return None, None
    
    def get_full_data(self, symbol: str, bar: str = "5m"):
        """
        分段获取数据，并将数据合并为完整数据
        分段依据：如果end_date与start_date相差超过一年，则每次取一年数据
        """
        data = pd.DataFrame()
        start_date = datetime.strptime(self.initial_date, "%Y-%m-%d")
        end_date = datetime.strptime(self.end_date, "%Y-%m-%d") + timedelta(days=1)
        fields = [
            "symbol",
            "bar",
            "timestamp",
            "date_time",
            "date_time_us",
            "open",
            "high",
            "low",
            "close",
            "volume",
            "sar_signal",
            "ma5",
            "ma10",
            "ma20",
            "ma30",
            "ma_cross",
            "dif",
            "dea",
            "macd",
        ]
        while start_date < end_date:
            current_end_date = min(start_date + timedelta(days=180), end_date)
            start_date_str = start_date.strftime("%Y-%m-%d")
            current_end_date_str = current_end_date.strftime("%Y-%m-%d")
            logger.info(
                f"获取{symbol}数据：{start_date_str}至{current_end_date_str}"
            )
            current_data = self.db_market_data.query_market_data_by_symbol_bar(
                symbol, bar, fields, start=start_date_str, end=current_end_date_str
            )
            if current_data is not None and len(current_data) > 0:
                current_data = pd.DataFrame(current_data)
                data = pd.concat([data, current_data])
            start_date = current_end_date
        data.drop_duplicates(inplace=True)
        if self.is_us_stock:
            date_time_field = "date_time_us"
        else:
            date_time_field = "date_time"
        data.sort_values(by=date_time_field, inplace=True)
        data.reset_index(drop=True, inplace=True)
        return data


    def fit_strategy(
        self,
        strategy_name: str = "全均线策略",
        market_data: pd.DataFrame = None,
        row: pd.Series = None,
        behavior: str = "buy",
    ):
        strategy_config = self.main_strategy.get(strategy_name, None)
        if strategy_config is None:
            logger.error(f"策略{strategy_name}不存在")
            return False
        condition_dict = strategy_config.get(behavior, None)
        if condition_dict is None:
            logger.error(f"策略{strategy_name}的{behavior}条件不存在")
            return False
        ma_cross = row["ma_cross"]
        if pd.isna(ma_cross) or ma_cross is None:
            ma_cross = ""
        ma_cross = str(ma_cross)
        ma5 = float(row["ma5"])
        ma10 = float(row["ma10"])
        ma20 = float(row["ma20"])
        ma30 = float(row["ma30"])
        close = float(row["close"])
        volume_pct_chg = float(row["volume_pct_chg"])
        macd_diff = float(row["dif"])
        macd_dea = float(row["dea"])
        macd = float(row["macd"])

        and_list = condition_dict.get("and", [])

        condition = True
        for and_condition in and_list:
            if and_condition == "5上穿10":
                condition = condition and ("5上穿10" in ma_cross)
            elif and_condition == "10上穿20":
                condition = condition and ("10上穿20" in ma_cross)
            elif and_condition == "20上穿30":
                condition = condition and ("20上穿30" in ma_cross)
            elif and_condition == "ma5>ma10":
                condition = condition and (ma5 > ma10)
            elif and_condition == "ma10>ma20":
                condition = condition and (ma10 > ma20)
            elif and_condition == "ma20>ma30":
                condition = condition and (ma20 > ma30)
            elif and_condition == "close>ma20":
                condition = condition and (close > ma20)
            elif and_condition == "volume_pct_chg>0.2":
                condition = condition and (volume_pct_chg > 0.2)
            elif and_condition == "macd_diff>0":
                condition = condition and (macd_diff > 0)
            elif and_condition == "macd_dea>0":
                condition = condition and (macd_dea > 0)
            elif and_condition == "macd>0":
                condition = condition and (macd > 0)
            elif and_condition == "10下穿5":
                condition = condition and ("10下穿5" in ma_cross)
            elif and_condition == "20下穿10":
                condition = condition and ("20下穿10" in ma_cross)
            elif and_condition == "30下穿20":
                condition = condition and ("30下穿20" in ma_cross)
            elif and_condition == "ma5<ma10":
                condition = condition and (ma5 < ma10)
            elif and_condition == "ma10<ma20":
                condition = condition and (ma10 < ma20)
            elif and_condition == "ma20<ma30":
                condition = condition and (ma20 < ma30)
            elif and_condition == "close<ma20":
                condition = condition and (close < ma20)
            elif and_condition == "macd_diff<0":
                condition = condition and (macd_diff < 0)
            elif and_condition == "macd_dea<0":
                condition = condition and (macd_dea < 0)
            elif and_condition == "macd<0":
                condition = condition and (macd < 0)
            else:
                pass
        if not condition:
            or_list = condition_dict.get("or", [])
            for or_condition in or_list:
                if or_condition == "5上穿10":
                    condition = condition or ("5上穿10" in ma_cross)
                elif or_condition == "10上穿20":
                    condition = condition or ("10上穿20" in ma_cross)
                elif or_condition == "20上穿30":
                    condition = condition or ("20上穿30" in ma_cross)
                elif or_condition == "ma5>ma10":
                    condition = condition or (ma5 > ma10)
                elif or_condition == "ma10>ma20":
                    condition = condition or (ma10 > ma20)
                elif or_condition == "ma20>ma30":
                    condition = condition or (ma20 > ma30)
                elif or_condition == "close>ma20":
                    condition = condition or (close > ma20)
                elif or_condition == "volume_pct_chg>0.2":
                    condition = condition or (volume_pct_chg > 0.2)
                elif or_condition == "macd_diff>0":
                    condition = condition or (macd_diff > 0)
                elif or_condition == "macd_dea>0":
                    condition = condition or (macd_dea > 0)
                elif or_condition == "macd>0":
                    condition = condition or (macd > 0)
                elif or_condition == "10下穿5":
                    condition = condition or ("10下穿5" in ma_cross)
                elif or_condition == "20下穿10":
                    condition = condition or ("20下穿10" in ma_cross)
                elif or_condition == "30下穿20":
                    condition = condition or ("30下穿20" in ma_cross)
                elif or_condition == "ma5<ma10":
                    condition = condition or (ma5 < ma10)
                elif or_condition == "ma10<ma20":
                    condition = condition or (ma10 < ma20)
                elif or_condition == "ma20<ma30":
                    condition = condition or (ma20 < ma30)
                elif or_condition == "close<ma20":
                    condition = condition or (close < ma20)
                elif or_condition == "macd_diff<0":
                    condition = condition or (macd_diff < 0)
                elif or_condition == "macd_dea<0":
                    condition = condition or (macd_dea < 0)
                elif or_condition == "macd<0":
                    condition = condition or (macd < 0)
                else:
                    pass
        return condition

    def draw_quant_pct_chg_bar_chart(
        self, data: pd.DataFrame, strategy_name: str = "全均线策略"
    ):
        """
        绘制pct_chg mean的柱状图表（美观，保存到self.stats_chart_dir）
        :param data: 波段pct_chg_mean的数据
        :return: None
        """
        if data is None or data.empty:
            return None
        # seaborn风格设置
        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  # 解决负号显示问题
        chart_dict = {}
        column_name_dict = {"pct_chg_total": "量化策略涨跌", "pct_chg_mean": "量化策略涨跌均值"}
        for column_name, column_name_text in column_name_dict.items():
            for bar in data["bar"].unique():
                bar_data = data[data["bar"] == bar].copy()  # 一次筛选即可
                if bar_data.empty:
                    continue
                bar_data.rename(columns={column_name: column_name_text}, inplace=True)
                if column_name == "pct_chg_total":
                    bar_data.rename(columns={"market_pct_chg": "市场自然涨跌"}, inplace=True)
                # 可选：按均值排序
                bar_data.sort_values(by=column_name_text, ascending=False, inplace=True)
                bar_data.reset_index(drop=True, inplace=True)

                # 如果column_name_text是"量化策略涨跌"，则柱状图，同时绘制量化策略涨跌与市场自然涨跌的柱状图，并绘制在同一个图表中
                if column_name == "pct_chg_total":
                    plt.figure(figsize=(12, 7))
                    
                    # 设置x轴位置，为并列柱状图做准备
                    x = np.arange(len(bar_data))
                    width = 0.35  # 柱状图宽度
                    
                    # 确保symbol列是字符串类型，避免matplotlib警告
                    bar_data["symbol"] = bar_data["symbol"].astype(str)
                    
                    # 绘制量化策略涨跌柱状图（蓝色渐变色）
                    bars1 = plt.bar(x - width/2, bar_data[column_name_text], width, 
                                   label=column_name_text, 
                                   color=plt.cm.Blues(np.linspace(0.6, 0.9, len(bar_data))))
                    
                    # 绘制市场自然涨跌柱状图（绿色渐变色）
                    bars2 = plt.bar(x + width/2, bar_data["市场自然涨跌"], width, 
                                   label="市场自然涨跌", 
                                   color=plt.cm.Greens(np.linspace(0.6, 0.9, len(bar_data))))
                    
                    # 设置图表标题和标签
                    plt.title(f"{bar}趋势{column_name_text}与市场自然涨跌对比(%)", fontsize=14, fontweight='bold')
                    plt.xlabel("Symbol", fontsize=12)
                    plt.ylabel("涨跌幅(%)", fontsize=12)
                    plt.xticks(x, bar_data['symbol'], rotation=45, ha='right')
                    plt.legend()
                    plt.grid(True, alpha=0.3)
                    
                    # 在量化策略涨跌柱状图上方添加数值标签
                    for i, (bar1, value1) in enumerate(zip(bars1, bar_data[column_name_text])):
                        plt.text(bar1.get_x() + bar1.get_width()/2, value1 + (0.01 if value1 >= 0 else -0.01),
                                f'{value1:.3f}%', ha='center', va='bottom' if value1 >= 0 else 'top',
                                fontsize=9, fontweight='bold', color='darkblue')
                    
                    # 在市场自然涨跌柱状图上方添加数值标签
                    for i, (bar2, value2) in enumerate(zip(bars2, bar_data["市场自然涨跌"])):
                        plt.text(bar2.get_x() + bar2.get_width()/2, value2 + (0.01 if value2 >= 0 else -0.01),
                                f'{value2:.3f}%', ha='center', va='bottom' if value2 >= 0 else 'top',
                                fontsize=9, fontweight='bold', color='darkgreen')

                else:
                    plt.figure(figsize=(10, 6))
                    
                    # 确保symbol列是字符串类型，避免matplotlib警告
                    bar_data["symbol"] = bar_data["symbol"].astype(str)
                    
                    ax = sns.barplot(
                        x="symbol", y=column_name_text, data=bar_data, palette="Blues_d"
                    )
                    plt.title(f"{bar}趋势{column_name_text}(%)")
                    plt.xlabel("symbol")
                    plt.ylabel(column_name_text)
                    plt.xticks(rotation=45, ha="right")

                    # 在柱状图上添加数值标签
                    for i, v in enumerate(bar_data[column_name_text]):
                        ax.text(
                            i,
                            v,
                            f"{v:.3f}",
                            ha="center",
                            va="bottom",
                            fontsize=10,
                            fontweight="bold",
                        )

                plt.tight_layout()

                save_path = os.path.join(
                    self.stats_chart_dir,
                    f"{bar}_bar_chart_{column_name}_{strategy_name}.png",
                )
                plt.savefig(save_path, dpi=150)
                plt.close()

                sheet_name = f"{bar}_趋势{column_name_text}柱状图_{strategy_name}"
                chart_dict[sheet_name] = save_path
        return chart_dict
    
    def draw_quant_line_chart(self, data: pd.DataFrame, strategy_name: str = "全均线策略"):
        """
        根据量化策略买卖明细记录，绘制量化策略涨跌与市场自然涨跌的折线图
        :param data: 量化策略买卖明细记录
        :param strategy_name: 策略名称
        :return: None
        """
        symbols = data["symbol"].unique()
        bars = data["bar"].unique()
        chart_dict = {}
        for symbol in symbols:
            for bar in bars:
                symbol_bar_data = data[(data["symbol"] == symbol) & (data["bar"] == bar)]
                if symbol_bar_data.empty:
                    continue
                    
                # 获取第一行数据作为基准
                first_row = symbol_bar_data.iloc[0].copy()
                
                # 创建初始化行，设置基准值
                init_row = first_row.copy()
                init_row.loc["pct_chg_total"] = 1.0  # 量化策略初始值为1
                init_row.loc["end_timestamp"] = first_row["begin_timestamp"]
                init_row.loc["end_date_time"] = first_row["begin_date_time"]
                init_row.loc["end_close"] = first_row["begin_close"]
                init_row.loc["end_ma5"] = first_row["begin_ma5"]
                init_row.loc["end_ma10"] = first_row["begin_ma10"]
                init_row.loc["end_ma20"] = first_row["begin_ma20"]
                init_row.loc["end_ma30"] = first_row["begin_ma30"]
                init_row.loc["end_macd_diff"] = first_row["begin_macd_diff"]
                init_row.loc["end_macd_dea"] = first_row["begin_macd_dea"]
                init_row.loc["end_macd"] = first_row["begin_macd"]
                init_row.loc["pct_chg"] = 0
                init_row.loc["interval_seconds"] = 0
                init_row.loc["interval_minutes"] = 0
                init_row.loc["interval_hours"] = 0
                init_row.loc["interval_days"] = 0
                
                # 将初始化行添加到数据开头
                symbol_bar_data = pd.concat([pd.DataFrame([init_row]), symbol_bar_data])
                symbol_bar_data.sort_values(by="end_timestamp", ascending=True, inplace=True)
                symbol_bar_data.reset_index(drop=True, inplace=True)
                
                # 确保时间列是datetime类型，避免matplotlib警告
                symbol_bar_data["end_date_time"] = pd.to_datetime(symbol_bar_data["end_date_time"])
                
                # 计算市场价位归一化数据（相对于初始价格）
                symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close"] / init_row["end_close"]
                symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close_to_1"].round(4)
                
                # 绘制折线图
                plt.figure(figsize=(12, 7))
                
                # 绘制量化策略涨跌线（蓝色）
                plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["pct_chg_total"], 
                        label="量化策略涨跌", color='blue', linewidth=2, marker='o', markersize=4)
                
                # 绘制市场自然涨跌线（绿色）
                plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["end_close_to_1"], 
                        label="市场自然涨跌", color='green', linewidth=2, marker='s', markersize=4)
                
                plt.title(f"{symbol} {bar} 量化与市场折线图_{strategy_name}", 
                         fontsize=14, fontweight='bold')
                plt.xlabel("时间", fontsize=12)
                plt.ylabel("涨跌变化", fontsize=12)
                plt.legend(fontsize=11)
                plt.grid(True, alpha=0.3)
                
                # 设置x轴标签，避免matplotlib警告
                # 选择合适的时间间隔显示标签，避免过于密集
                if len(symbol_bar_data) > 30:
                    # 如果数据点较多，选择间隔显示，但确保第一条和最后一条始终显示
                    step = max(1, len(symbol_bar_data) // 30)
                    
                    # 创建标签索引列表，确保包含首尾数据
                    label_indices = [0]  # 第一条
                    
                    # 添加中间间隔的标签
                    for i in range(step, len(symbol_bar_data) - 1, step):
                        label_indices.append(i)
                    
                    # 添加最后一条（如果还没有包含的话）
                    if len(symbol_bar_data) - 1 not in label_indices:
                        label_indices.append(len(symbol_bar_data) - 1)
                    
                    # 设置x轴标签
                    plt.xticks(symbol_bar_data["end_date_time"].iloc[label_indices], 
                              symbol_bar_data["end_date_time"].iloc[label_indices].dt.strftime('%Y%m%d %H:%M'), 
                              rotation=45, ha='right')
                else:
                    # 如果数据点较少，全部显示
                    plt.xticks(symbol_bar_data["end_date_time"], 
                              symbol_bar_data["end_date_time"].dt.strftime('%Y%m%d %H:%M'), 
                              rotation=45, ha='right')
                
                plt.tight_layout()
                
                save_path = os.path.join(
                    self.stats_chart_dir,
                    f"{symbol}_{bar}_line_chart_{strategy_name}.png",
                )
                plt.savefig(save_path, dpi=150, bbox_inches='tight')
                plt.close()
                
                sheet_name = f"{symbol}_{bar}_折线图_{strategy_name}"
                chart_dict[sheet_name] = save_path
        return chart_dict

    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_path in charts_dict.items():
            try:
                ws = wb.create_sheet(title=sheet_name)
                row_offset = 1
                # Insert chart image
                img = Image(chart_path)
                ws.add_image(img, f"A{row_offset}")

            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}")