diff --git a/core/db/db_astock.py b/core/db/db_astock.py
index 26eed5a..797b5fc 100644
--- a/core/db/db_astock.py
+++ b/core/db/db_astock.py
@@ -54,7 +54,6 @@ class DBAStockData:
     def query_market_data_by_symbol_bar(
         self,
         symbol: str,
-        bar: str,
         fields: list = None,
         start: str = None,
         end: str = None,
@@ -123,3 +122,21 @@ class DBAStockData:
                     """
                 condition_dict = {"symbol": symbol, "end": end}
         return self.query_data(sql, condition_dict, return_multi=True)
+    
+    def query_index_data(self):
+        sql = f"""
+            SELECT * FROM all_index a
+            order by ts_code
+            """
+        condition_dict = {}
+        data = self.query_data(sql, condition_dict, return_multi=True)
+        return pd.DataFrame(data)
+    
+    def query_stock_data(self):
+        sql = f"""
+            SELECT * FROM all_stock a
+            order by ts_code
+            """
+        condition_dict = {}
+        data = self.query_data(sql, condition_dict, return_multi=True)
+        return pd.DataFrame(data)
diff --git a/core/statistics/similar_pattern_stocks.py b/core/statistics/similar_pattern_stocks.py
new file mode 100644
index 0000000..098025f
--- /dev/null
+++ b/core/statistics/similar_pattern_stocks.py
@@ -0,0 +1,381 @@
+import core.logger as logging
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import mplfinance as mpf
+from datetime import datetime, timedelta, timezone
+from core.utils import get_current_date_time
+import re
+import json
+import math
+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 (
+    A_MYSQL_CONFIG,
+)
+from core.db.db_astock import DBAStockData
+
+# seaborn支持中文
+plt.rcParams["font.family"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+# 统一Seaborn外观
+sns.set_theme(style="whitegrid")
+
+logger = logging.logger
+
+
+class SimilarPatternStocks:
+    def __init__(self):
+        mysql_user = A_MYSQL_CONFIG.get("user", "root")
+        mysql_password = A_MYSQL_CONFIG.get("password", "")
+        if not mysql_password:
+            raise ValueError("MySQL password is not set")
+        mysql_host = A_MYSQL_CONFIG.get("host", "localhost")
+        mysql_port = A_MYSQL_CONFIG.get("port", 3306)
+        mysql_database = A_MYSQL_CONFIG.get("database", "astock")
+        self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
+        self.db_astock = DBAStockData(self.db_url)
+        self.output_dir = r"./output/similar_pattern_stocks/"
+        os.makedirs(self.output_dir, exist_ok=True)
+
+    def get_stock_list(self):
+        stock_data = self.db_astock.query_stock_data()
+        # 仅获取market为主板，创业板与科创板的股票
+        stock_data = stock_data[stock_data["market"].isin(["主板", "创业板", "科创板"])]
+        return stock_data
+
+    def get_stock_market_data(
+        self,
+        symbol: str,
+        bar: str,
+        start_date: str,
+        end_date: str,
+        compare_record_amount: int = 100,
+    ):
+        fields = [
+            "a.ts_code as symbol",
+            "b.name as symbol_name",
+            f"'{bar}' as bar",
+            "trade_date as date_time",
+            "open",
+            "high",
+            "low",
+            "close",
+            "vol as volume",
+            "MA5 as ma5",
+            "MA10 as ma10",
+            "MA20 as ma20",
+            "MA30 as ma30",
+            "均线交叉 as ma_cross",
+            "DIF as dif",
+            "DEA as dea",
+            "MACD as macd",
+        ]
+        if bar == "1W":
+            table_name = "stock_weekly_price_from_2020"
+        elif bar == "1M":
+            table_name = "stock_monthly_price_from_2015"
+        else:
+            table_name = "stock_daily_price_from_2021"
+        data = self.db_astock.query_market_data_by_symbol_bar(
+            symbol=symbol,
+            fields=fields,
+            start=start_date,
+            end=end_date,
+            table_name=table_name,
+        )
+        data = pd.DataFrame(data)
+        data.sort_values(by=["date_time"], inplace=True)
+        # 获取最后一百条数据
+        data = data.tail(compare_record_amount)
+        data.reset_index(drop=True, inplace=True)
+        return data
+
+    def get_stock_market_data_similar_pattern(
+        self,
+        target_symbol: str,
+        bar: str,
+        start_date: str = None,
+        end_date: str = None,
+        compare_record_amount: int = 100,
+    ):
+        """
+        1. 获取目标股票的market数据
+        2. 根据end_date, 获取目标股票的最后一百条数据
+        3. 遍历所有股票，获取所有股票的最后一百条数据
+        4. 计算目标股票与所有股票的最后一百条数据的相似度
+        5. 返回相似度最高的十只股票
+        6. 绘制目标股票与相似度最高的十只股票的对比图, 通过K线图展示
+        """
+        logger.info(f"获取目标股票{target_symbol}的{bar}数据, 截止日期{end_date}")
+        target_stock_data = self.get_stock_market_data(
+            target_symbol, bar, start_date, end_date, compare_record_amount
+        )
+        if len(target_stock_data) == 0:
+            logger.error(f"目标股票{target_symbol}的{bar}数据为空")
+            return []
+        compare_record_amount = len(target_stock_data)
+        stock_list_data = self.get_stock_list()
+        if len(stock_list_data) == 0:
+            logger.error("所有股票数据为空")
+            return []
+        stock_list_data = stock_list_data[stock_list_data["ts_code"] != target_symbol]
+        now_date = datetime.now().strftime("%Y%m%d")
+        similarity_data = []
+        for index, row in stock_list_data.iterrows():
+            logger.info(f"获取第{index+1}只股票{row['ts_code']} {row['name']} 的{bar}数据, 截止日期{now_date}")
+            stock_data = self.get_stock_market_data(
+                symbol=row["ts_code"],
+                bar=bar,
+                start_date=None,
+                end_date=now_date,
+                compare_record_amount=compare_record_amount,
+            )
+            if len(stock_data) == 0:
+                logger.error(f"股票{row['ts_code']}的{bar}数据为空")
+                continue
+            if len(stock_data) != compare_record_amount:
+                logger.error(f"股票{row['ts_code']}的数据不足{compare_record_amount}条")
+                continue
+            similarity = self.calculate_similarity(target_stock_data, stock_data)
+            similarity_data.append(
+                {
+                    "symbol": row["ts_code"],
+                    "symbol_name": row["name"],
+                    "similarity_distance": similarity,
+                    "stock_data": stock_data,
+                }
+            )
+        # 按照similarity_distance升序排序
+        similarity_data.sort(key=lambda x: x["similarity_distance"])
+
+        pure_data = []
+        for item in similarity_data:
+            pure_data.append(
+                {
+                    "symbol": item["symbol"],
+                    "symbol_name": item["symbol_name"],
+                    "similarity_distance": item["similarity_distance"],
+                }
+            )
+        pure_data = pd.DataFrame(pure_data)
+        # 去除similarity_distance为空或nan的数据
+        pure_data = pure_data[pure_data["similarity_distance"].notna()]
+        pure_data.sort_values(by=["similarity_distance"], inplace=True)
+        pure_data.reset_index(drop=True, inplace=True)
+        target_stock_symbol = str(target_stock_data["symbol"].iloc[0]).split(".")[0]
+        target_stock_name = str(target_stock_data["symbol_name"].iloc[0])
+        if end_date is not None and len(end_date) > 0:
+            target_stock_folder = os.path.join(
+                self.output_dir, f"{target_stock_symbol}_{target_stock_name}_{bar}_{end_date}"
+            )
+        else:
+            target_stock_folder = os.path.join(
+                self.output_dir, f"{target_stock_symbol}_{target_stock_name}_{bar}_{now_date}"
+            )
+        os.makedirs(target_stock_folder, exist_ok=True)
+        excel_file_path = os.path.join(
+            target_stock_folder,
+            f"{target_stock_symbol}_{target_stock_name}_{bar}_similar_stocks.xlsx",
+        )
+        with pd.ExcelWriter(excel_file_path) as writer:
+            pure_data.to_excel(writer, sheet_name="股票形态相似度", index=False)
+
+        similar_stocks_chart_folder = os.path.join(
+            target_stock_folder, f"similar_stocks_chart"
+        )
+        os.makedirs(similar_stocks_chart_folder, exist_ok=True)
+        chart_list = []
+        chart_info = self.draw_similar_stocks_chart(
+            target_stock_symbol,
+            target_stock_name,
+            bar,
+            "0",
+            target_stock_data,
+            similar_stocks_chart_folder,
+        )
+        chart_list.append(chart_info)
+        for index, row in pure_data.iterrows():
+            symbol = row["symbol"]
+            symbol_name = row["symbol_name"]
+            similarity_distance = row["similarity_distance"]
+            for item in similarity_data:
+                if item["symbol"] == symbol:
+                    stock_data = item["stock_data"]
+                    break
+            chart_info = self.draw_similar_stocks_chart(
+                symbol,
+                symbol_name,
+                bar,
+                similarity_distance,
+                stock_data,
+                similar_stocks_chart_folder,
+            )
+            chart_list.append(chart_info)
+            if index >= 9:
+                break
+        self.output_chart_to_excel(excel_file_path, chart_list)
+        return chart_list
+    
+    def output_chart_to_excel(self, excel_file_path: str, charts_list: list):
+        """
+        输出Excel文件，包含所有图表
+        charts_list: 图表数据列表，格式为：
+        {
+            "chart_path": "chart_path",
+            "symbol": "symbol",
+            "stock_name": "stock_name",
+            "bar": "bar",
+            "similarity_distance": "similarity_distance",
+        }
+        """
+        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)
+        ws = wb.create_sheet(title="股票K线图")
+        row_offset = 1
+        for chart_info in charts_list:
+            chart_path = chart_info["chart_path"]
+            chart_name = chart_info["symbol"] + " " + chart_info["stock_name"] + " " + chart_info["bar"] + " 距离：" + str(chart_info["similarity_distance"])
+            # 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
+        # Save Excel file
+        wb.save(excel_file_path)
+        logger.info(f"Chart saved as {excel_file_path}")
+
+    def draw_similar_stocks_chart(
+        self,
+        symbol: str,
+        stock_name: str,
+        bar: str,
+        similarity_distance: float,
+        stock_data: pd.DataFrame,
+        similar_stocks_chart_folder: str,
+    ):
+        """
+        绘制股票K线图, 并保存到similar_stocks_chart_folder
+        """
+        pure_stock_name = stock_name.replace("*", "")
+        chart_path = os.path.join(
+            similar_stocks_chart_folder, f"{symbol}_{pure_stock_name}_{bar}_chart.png"
+        )
+
+        # 准备数据：确保日期列名为Date，并转换为datetime
+        df = stock_data.copy()
+
+        # 检查并重命名日期列
+        if "trade_date" in df.columns:
+            df = df.rename(columns={"trade_date": "Date"})
+        elif "date_time" in df.columns:
+            df = df.rename(columns={"date_time": "Date"})
+
+        # 确保Date列为datetime类型
+        df["Date"] = pd.to_datetime(df["Date"])
+        df = df.set_index("Date")
+
+        # 确保OHLC列名正确
+        df = df.rename(
+            columns={"open": "Open", "high": "High", "low": "Low", "close": "Close"}
+        )
+
+        # 使用mplfinance绘制K线图（接近Seaborn风格，红涨绿跌）
+        mc = mpf.make_marketcolors(
+            up="#e41a1c",   # 红色-上涨（A股常用）
+            down="#4daf4a", # 绿色-下跌（A股常用）
+            edge="inherit",
+            wick="inherit",
+            volume="inherit",
+        )
+        mpf_style = mpf.make_mpf_style(
+            base_mpf_style="yahoo",
+            marketcolors=mc,
+            facecolor="#FAFAFA",
+            edgecolor="#EAEAEA",
+            gridcolor="#E6E6E6",
+            gridstyle="--",
+            rc={"font.family": "SimHei", "axes.unicode_minus": False},
+        )
+        mpf.plot(
+            df,
+            type="candle",
+            style=mpf_style,
+            title=f"{symbol} {stock_name} {bar} 距离：{similarity_distance}",
+            ylabel="价格",
+            figsize=(12, 6),
+            savefig={"fname": chart_path, "dpi": 150, "bbox_inches": "tight"},
+        )
+        chart_info = {
+            "chart_path": chart_path,
+            "symbol": symbol,
+            "stock_name": stock_name,
+            "bar": bar,
+            "similarity_distance": similarity_distance,
+        }
+        return chart_info
+
+    def calculate_similarity(
+        self,
+        target_stock_data: pd.DataFrame,
+        stock_data: pd.DataFrame,
+        close_weight=0.8,
+        volume_weight=0.2,
+    ):
+        """
+        通过股价归一化以及欧氏距离，计算目标股票与股票的相似度
+        """
+        target_stock_close = target_stock_data["close"]
+        stock_close = stock_data["close"]
+        target_stock_close = (target_stock_close - target_stock_close.min()) / (
+            target_stock_close.max() - target_stock_close.min()
+        )
+        stock_close = (stock_close - stock_close.min()) / (
+            stock_close.max() - stock_close.min()
+        )
+        close_distance = np.linalg.norm(target_stock_close - stock_close)
+
+        target_stock_volume = target_stock_data["volume"]
+        stock_volume = stock_data["volume"]
+        target_stock_volume = (target_stock_volume - target_stock_volume.min()) / (
+            target_stock_volume.max() - target_stock_volume.min()
+        )
+        stock_volume = (stock_volume - stock_volume.min()) / (
+            stock_volume.max() - stock_volume.min()
+        )
+        volume_distance = np.linalg.norm(target_stock_volume - stock_volume)
+
+        similarity_distance = (
+            close_weight * close_distance + volume_weight * volume_distance
+        )
+        return float(similarity_distance)
diff --git a/core/trade/ma_break_statistics.py b/core/trade/ma_break_statistics.py
index a374334..ca14451 100644
--- a/core/trade/ma_break_statistics.py
+++ b/core/trade/ma_break_statistics.py
@@ -864,7 +864,6 @@ class MaBreakStatistics:
             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,
@@ -944,7 +943,7 @@ class MaBreakStatistics:
             "MACD as macd",
         ]
         data = self.db_market_data.query_market_data_by_symbol_bar(
-            symbol, bar, fields, start=last_date, end=end_date, table_name=table_name
+            symbol, fields, start=last_date, end=end_date, table_name=table_name
         )
         if data is not None and len(data) > 0:
             data = pd.DataFrame(data)
diff --git a/similar_pattern_stocks_main.py b/similar_pattern_stocks_main.py
new file mode 100644
index 0000000..1dae3b9
--- /dev/null
+++ b/similar_pattern_stocks_main.py
@@ -0,0 +1,45 @@
+import core.logger as logging
+from core.statistics.similar_pattern_stocks import SimilarPatternStocks
+import os
+
+logger = logging.logger
+
+def main():
+    similar_pattern_stocks = SimilarPatternStocks()
+
+    target_stock_list = [
+        {
+            "symbol": "600111.SH",
+            "bar": "1W",
+            "start_date": None,
+            "end_date": "20250711",
+            "compare_record_amount": 100,
+        },
+        {
+            "symbol": "600111.SH",
+            "bar": "1M",
+            "start_date": None,
+            "end_date": "20250630",
+            "compare_record_amount": 100,
+        },
+        {
+            "symbol": "601398.SH",
+            "bar": "1M",
+            "start_date": None,
+            "end_date": "20230430",
+            "compare_record_amount": 100,
+        }
+    ]
+
+    for target_stock in target_stock_list:
+        similar_pattern_stocks.get_stock_market_data_similar_pattern(
+            target_symbol=target_stock["symbol"],
+            bar=target_stock["bar"],
+            start_date=target_stock["start_date"],
+            end_date=target_stock["end_date"],
+            compare_record_amount=target_stock["compare_record_amount"],
+        )
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file