support draw chart

core/biz/huge_volume_chart.py

@@ -0,0 +1,459 @@
+from core.db.db_huge_volume_data import DBHugeVolumeData
+from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from openpyxl import Workbook
+from openpyxl.drawing.image import Image
+from PIL import Image as PILImage
+import logging
+from datetime import datetime
+import pandas as pd
+import os
+import re
+import openpyxl
+from openpyxl.styles import Font
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+sns.set_theme(style="whitegrid")
+# 设置中文
+plt.rcParams["font.sans-serif"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+
+
+class HugeVolumeChart:
+    def __init__(
+        self,
+        data: pd.DataFrame,
+        output_folder: str = "./output/huge_volume_statistics/",
+    ):
+        """
+        初始化
+        data: 数据
+        data中的列名为：
+        symbol: 币种
+        bar: 周期
+        window_size: 窗口大小
+        huge_volume: 是否巨量
+        volume_ratio_percentile_10: 10%分位数
+        volume_ratio_percentile_10_mean: 10%分位数平均值
+        price_type: 价格类型
+        next_period: 下一个周期
+        average_return: 平均回报
+        max_return: 最大回报
+        min_return: 最小回报
+        rise_count: 上涨次数
+        rise_ratio: 上涨比例
+        fall_count: 下跌次数
+        fall_ratio: 下跌比例
+        draw_count: 持平次数
+        draw_ratio: 持平比例
+        total_count: 总次数
+        output_folder: 输出文件夹
+        """
+        self.data = data
+        # remove 1D bar
+        self.data = self.data[self.data["bar"] != "1D"]
+        self.data.reset_index(drop=True, inplace=True)
+        self.output_folder = output_folder
+        os.makedirs(self.output_folder, exist_ok=True)
+        self.temp_dir = os.path.join(self.output_folder, "temp")
+        os.makedirs(self.temp_dir, exist_ok=True)
+        self.symbol_list = self.data["symbol"].unique().tolist()
+        # sort symbol_list
+        self.symbol_list.sort()
+        self.bar_list = self.data["bar"].unique().tolist()
+        self.bar_list.sort()
+        self.window_size_list = self.data["window_size"].unique().tolist()
+        self.window_size_list.sort()
+        self.next_period_list = self.data["next_period"].unique().tolist()
+        self.next_period_list.sort()
+        self.volume_ratio_percentile_10_list = (
+            self.data["volume_ratio_percentile_10"].unique().tolist()
+        )
+        self.volume_ratio_percentile_10_list.sort()
+        self.price_type_list = self.data["price_type"].unique().tolist()
+        self.price_type_list.sort()
+
+    def plot_entrance(self, include_heatmap: bool = True, include_line: bool = True):
+        """
+        绘制上涨下跌图入口
+        """
+        charts_dict = {}
+        if include_heatmap:
+            heatmap_plot_dict = self.plot_heatmap_entrance()
+        if include_line:
+            line_plot_dict = self.plot_line_chart_entrance()
+        
+        if include_line:
+            charts_dict.update(line_plot_dict)
+        if include_heatmap:
+            charts_dict.update(heatmap_plot_dict)
+        return charts_dict
+    
+    def plot_line_chart_entrance(self):
+        """
+        绘制折线图入口
+        """
+        charts_dict = {}
+        # 根据price_type_list，得到各个price_type的平均rise_ratio，平均fall_ratio，平均draw_ratio, 平均average_return
+        total_chart_path = self.plot_pice_rise_fall(data=self.data, prefix="总体")
+        charts_dict["总体"] = {"总体": total_chart_path}
+        self.plot_window_size_rise_fall(charts_dict=charts_dict)
+        self.plot_window_size_bar_rise_fall(charts_dict=charts_dict)
+        self.plot_window_size_bar_next_period_rise_fall(charts_dict=charts_dict)
+        self.plot_symbol_rise_fall(charts_dict=charts_dict)
+        self.plot_symbol_bar_rise_fall(charts_dict=charts_dict)
+        self.plot_symbol_bar_window_size_rise_fall(charts_dict=charts_dict)
+        self.plot_symbol_bar_window_size_next_period_rise_fall(charts_dict=charts_dict)
+
+        # self.plot_symbol_bar_window_size_volume_ratio_percentile_10_mean_rise_fall(charts_dict=charts_dict)
+        # self.plot_symbol_bar_window_size_volume_ratio_percentile_10_mean_next_period_rise_fall(charts_dict=charts_dict)
+
+        self.output_excel(chart_type="line_chart", charts_dict=charts_dict)
+        return charts_dict
+
+    def plot_heatmap_entrance(self):
+        """
+        绘制热力图入口
+        """
+        charts_dict = {}
+
+        self.plot_symbol_heatmap(charts_dict=charts_dict, ratio_column="rise_ratio", title=f"Rise Ratio Heatmap by Window Size and Bar")
+        self.plot_symbol_heatmap(charts_dict=charts_dict, ratio_column="fall_ratio", title=f"Fall Ratio Heatmap by Window Size and Bar")
+        self.plot_symbol_heatmap(charts_dict=charts_dict, ratio_column="average_return", title=f"Average Return Heatmap by Window Size and Bar")
+
+        self.output_excel(chart_type="heatmap_chart", charts_dict=charts_dict)
+        return charts_dict
+
+    def plot_symbol_heatmap(self, 
+        charts_dict: dict, 
+        ratio_column: str = "rise_ratio",
+        title: str = "Rise Ratio Heatmap by Window Size and Bar"
+    ):
+        """
+        绘制symbol热力图
+        """ 
+        for symbol in self.symbol_list:
+            charts_dict[f"{symbol}_{ratio_column}_heatmap"] = {}
+            for price_type in self.price_type_list:
+                logging.info(f"绘制{symbol} {price_type} {ratio_column}热力图")
+                df = self.data[(self.data["symbol"] == symbol) & (self.data["price_type"] == price_type)]
+                pivot_table = df.pivot_table(values=ratio_column, index='window_size', columns='bar', aggfunc='mean')
+                plt.figure(figsize=(10, 6))
+                # 热力图以红色渐变为主,红色表示高，绿色表示低
+                sns.heatmap(pivot_table, annot=True, cmap='RdYlGn_r', fmt='.3f')
+                plt.xlabel('Period')
+                plt.ylabel('Window Size')
+                plt.title(f"{title} {price_type}")
+                # plt.show()
+                chart_path = os.path.join(self.temp_dir, f'{symbol}_{price_type}_{ratio_column}_heatmap.png')
+                plt.savefig(chart_path, bbox_inches='tight', dpi=100)
+                plt.close()
+                charts_dict[f"{symbol}_{ratio_column}_heatmap"][f"{symbol}_{price_type}_{ratio_column}_heatmap"] = chart_path
+
+    def plot_window_size_rise_fall(self, charts_dict: dict):
+        """
+        不区分symbol, 绘制window_size上涨下跌图
+        """
+        charts_dict["window_size"] = {}
+        for window_size in self.window_size_list:
+            data = self.data[self.data["window_size"] == window_size]
+            chart_path = self.plot_pice_rise_fall(data, prefix=f"window_size_{window_size}")
+            charts_dict["window_size"][f"window_size_{window_size}"] = chart_path
+
+    def plot_window_size_bar_rise_fall(self, charts_dict: dict):
+        """
+        不区分symbol, 根据window_size绘制bar上涨下跌图
+        """
+        charts_dict["window_size_bar"] = {}
+        for window_size in self.window_size_list:
+            for bar in self.bar_list:
+                data = self.data[
+                    (self.data["window_size"] == window_size)
+                    & (self.data["bar"] == bar)
+                ]
+                chart_path = self.plot_pice_rise_fall(
+                    data, prefix=f"window_size_{window_size}_bar_{bar}"
+                )
+                charts_dict["window_size_bar"][f"window_size_{window_size}_bar_{bar}"] = chart_path
+
+    def plot_window_size_bar_next_period_rise_fall(self, charts_dict: dict):
+        """
+        不区分symbol, 根据window_size, bar, next_period上涨下跌图
+        """
+        charts_dict["window_size_bar_period"] = {}
+        for window_size in self.window_size_list:
+            for bar in self.bar_list:
+                for next_period in self.next_period_list:
+                    data = self.data[
+                        (self.data["window_size"] == window_size)
+                        & (self.data["bar"] == bar)
+                        & (self.data["next_period"] == next_period)
+                    ]
+                    chart_path = self.plot_pice_rise_fall(
+                        data,
+                        prefix=f"window_size_{window_size}_bar_{bar}_next_period_{next_period}"
+                    )
+                    charts_dict["window_size_bar_period"][f"window_size_{window_size}_bar_{bar}_next_period_{next_period}"] = chart_path
+
+    def plot_symbol_rise_fall(self, charts_dict: dict):
+        """
+        区分symbol, 绘制symbol上涨下跌图
+        """
+        for symbol in self.symbol_list:
+            charts_dict[f"{symbol}_总体"] = {}
+            data = self.data[self.data["symbol"] == symbol]
+            chart_path = self.plot_pice_rise_fall(data, prefix=symbol)
+            charts_dict[f"{symbol}_总体"][symbol] = chart_path
+
+    def plot_symbol_bar_rise_fall(self, charts_dict: dict):
+        """
+        区分symbol, bar, 绘制symbol上涨下跌图
+        """
+        for symbol in self.symbol_list:
+            charts_dict[f"{symbol}_bar"] = {}
+            for bar in self.bar_list:
+                data = self.data[
+                    (self.data["symbol"] == symbol) & (self.data["bar"] == bar)
+                ]
+                chart_path = self.plot_pice_rise_fall(data, prefix=f"{symbol}_{bar}")
+                charts_dict[f"{symbol}_bar"][f"{symbol}_{bar}"] = chart_path
+
+    def plot_symbol_bar_window_size_rise_fall(self, charts_dict: dict):
+        """
+        区分symbol, bar, window_size, 绘制symbol上涨下跌图
+        """
+        for symbol in self.symbol_list:
+            charts_dict[f"{symbol}_bar_window_size"] = {}
+            for bar in self.bar_list:
+                for window_size in self.window_size_list:
+                    data = self.data[
+                        (self.data["symbol"] == symbol)
+                        & (self.data["bar"] == bar)
+                        & (self.data["window_size"] == window_size)
+                    ]
+                    chart_path = self.plot_pice_rise_fall(
+                        data, prefix=f"{symbol}_{bar}_ws_{window_size}"
+                    )
+                    charts_dict[f"{symbol}_bar_window_size"][f"{symbol}_{bar}_ws_{window_size}"] = chart_path
+
+    def plot_symbol_bar_window_size_next_period_rise_fall(self, charts_dict: dict):
+        """
+        区分symbol, bar, window_size, next_period, 绘制symbol上涨下跌图
+        """
+        for symbol in self.symbol_list:
+            charts_dict[f"{symbol}_bar_ws_period"] = {}
+            for bar in self.bar_list:
+                for window_size in self.window_size_list:
+                    for next_period in self.next_period_list:
+                        data = self.data[
+                            (self.data["symbol"] == symbol)
+                            & (self.data["bar"] == bar)
+                            & (self.data["window_size"] == window_size)
+                            & (self.data["next_period"] == next_period)
+                        ]
+                        chart_path = self.plot_pice_rise_fall(
+                            data,
+                            prefix=f"{symbol}_{bar}_ws_{window_size}_next_period_{next_period}"
+                        )
+                        charts_dict[f"{symbol}_bar_ws_period"][f"{symbol}_{bar}_ws_{window_size}_next_period_{next_period}"] = chart_path
+
+    def plot_symbol_bar_window_size_volume_ratio_percentile_10_mean_rise_fall(self, charts_dict: dict):
+        """
+        区分symbol, bar, window_size, volume_ratio_percentile_10_mean, 绘制symbol上涨下跌图
+        """
+        for symbol in self.symbol_list:
+            charts_dict[f"{symbol}_bar_window_size_vol_per"] = {}
+            for bar in self.bar_list:
+                for window_size in self.window_size_list:
+                    for (
+                        volume_ratio_percentile_10
+                    ) in self.volume_ratio_percentile_10_list:
+                        data = self.data[
+                            (self.data["symbol"] == symbol)
+                            & (self.data["bar"] == bar)
+                            & (self.data["window_size"] == window_size)
+                            & (
+                                self.data["volume_ratio_percentile_10"]
+                                == volume_ratio_percentile_10
+                            )
+                        ]
+                        chart_path = self.plot_pice_rise_fall(
+                            data,
+                            prefix=f"{symbol}_{bar}_ws_{window_size}_vol_per_{volume_ratio_percentile_10}"
+                        )
+                        charts_dict[f"{symbol}_bar_window_size_vol_per"][f"{symbol}_{bar}_ws_{window_size}_vol_per_{volume_ratio_percentile_10}"] = chart_path
+
+    def plot_symbol_bar_window_size_volume_ratio_percentile_10_mean_next_period_rise_fall(
+        self,
+        charts_dict: dict,
+    ):
+        """
+        区分symbol, bar, window_size, volume_ratio_percentile_10_mean, next_period, 绘制symbol上涨下跌图
+        """
+        for symbol in self.symbol_list:
+            charts_dict[f"{symbol}_bar_ws_vol_period"] = {}
+            for bar in self.bar_list:
+                for window_size in self.window_size_list:
+                    for (
+                        volume_ratio_percentile_10
+                    ) in self.volume_ratio_percentile_10_list:
+                        for next_period in self.next_period_list:
+                            data = self.data[
+                                (self.data["symbol"] == symbol)
+                                & (self.data["bar"] == bar)
+                                & (self.data["window_size"] == window_size)
+                                & (
+                                    self.data["volume_ratio_percentile_10"]
+                                    == volume_ratio_percentile_10
+                                )
+                                & (self.data["next_period"] == next_period)
+                            ]
+                            chart_path = self.plot_pice_rise_fall(
+                                data,
+                                prefix=f"{symbol}_{bar}_ws_{window_size}_vol_per_{volume_ratio_percentile_10}_next_period_{next_period}"
+                            )
+                            charts_dict[f"{symbol}_bar_ws_vol_period"][f"{symbol}_{bar}_ws_{window_size}_vol_per_{volume_ratio_percentile_10}_next_period_{next_period}"] = chart_path
+
+    def plot_pice_rise_fall(self, data: pd.DataFrame, prefix: str = ""):
+        """
+        绘制价格上涨下跌图
+        """
+        logging.info(f"绘制价格上涨下跌图: {prefix}")
+        # 根据price_type_list，得到各个price_type的平均rise_ratio，平均fall_ratio，平均draw_ratio, 平均average_return
+        price_type_data_dict = {}
+        for price_type in self.price_type_list:
+            filtered_data = data[data["price_type"] == price_type]
+            average_rise_ratio = filtered_data["rise_ratio"].mean()
+            average_fall_ratio = filtered_data["fall_ratio"].mean()
+            average_draw_ratio = filtered_data["draw_ratio"].mean()
+            average_average_return = filtered_data["average_return"].mean()
+            price_type_data_dict[price_type] = {
+                "average_rise_ratio": average_rise_ratio,
+                "average_fall_ratio": average_fall_ratio,
+                "average_draw_ratio": average_draw_ratio,
+                "average_average_return": average_average_return,
+            }
+
+        # 准备数据用于绘图
+        price_types = list(price_type_data_dict.keys())
+        rise_ratios = [
+            price_type_data_dict[pt]["average_rise_ratio"] for pt in price_types
+        ]
+        fall_ratios = [
+            price_type_data_dict[pt]["average_fall_ratio"] for pt in price_types
+        ]
+        draw_ratios = [
+            price_type_data_dict[pt]["average_draw_ratio"] for pt in price_types
+        ]
+        avg_returns = [
+            price_type_data_dict[pt]["average_average_return"] for pt in price_types
+        ]
+
+        # 创建子图，保持2x2布局
+        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
+
+        # 绘制上涨比例
+        bars1 = ax1.bar(price_types, rise_ratios, color="green", alpha=0.7)
+        ax1.set_title(f"{prefix}总体平均上涨比例")
+        ax1.set_ylabel("上涨比例")
+        ax1.tick_params(axis="x", rotation=0)
+        ax1.bar_label(bars1, fmt="%.2f")
+
+        # 绘制下跌比例
+        bars2 = ax2.bar(price_types, fall_ratios, color="red", alpha=0.7)
+        ax2.set_title(f"{prefix}平均下跌比例")
+        ax2.set_ylabel("下跌比例")
+        ax2.tick_params(axis="x", rotation=0)
+        ax2.bar_label(bars2, fmt="%.2f")
+
+        # 绘制持平比例
+        bars3 = ax3.bar(price_types, draw_ratios, color="gray", alpha=0.7)
+        ax3.set_title(f"{prefix}平均持平比例")
+        ax3.set_ylabel("持平比例")
+        ax3.tick_params(axis="x", rotation=0)
+        ax3.bar_label(bars3, fmt="%.2f")
+
+        # 绘制平均回报
+        bars4 = ax4.bar(price_types, avg_returns, color="blue", alpha=0.7)
+        ax4.set_title(f"{prefix}平均回报")
+        ax4.set_ylabel("平均回报")
+        ax4.tick_params(axis="x", rotation=0)
+        ax4.bar_label(bars4, fmt="%.2f")
+
+        # 调整布局，增加底部空间和垂直间距以显示完整的x轴标签
+        plt.tight_layout()
+        plt.subplots_adjust(bottom=0.15, hspace=0.4)
+        # plt.show()
+        chart_path = os.path.join(self.temp_dir, f'{prefix}.png')
+        plt.savefig(chart_path, bbox_inches='tight', dpi=100)
+        plt.close()
+        return chart_path
+
+    def output_excel(self, chart_type: 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"
+            }
+        }
+        """
+        logging.info(f"输出Excel文件，包含所有{chart_type}图表")
+        file_name = f"huge_volume_{chart_type}_{datetime.now().strftime('%Y%m%d%H%M%S')}.xlsx"
+        file_path = os.path.join(self.output_folder, file_name)
+
+        # Create Excel file and worksheet
+        wb = Workbook()
+        wb.remove(wb.active)  # Remove default sheet
+
+        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:
+                logging.error(f"输出Excel Sheet {sheet_name} 失败: {e}")
+                continue
+
+        # Save Excel file
+        wb.save(file_path)
+        print(f"Excel file saved as {file_path}")
+
+        for sheet_name, chart_data_dict in charts_dict.items():
+            for chart_name, chart_path in chart_data_dict.items():
+                try:
+                    os.remove(chart_path)
+                except Exception as e:
+                    logging.error(f"删除临时文件失败: {e}")

huge_volume_main.py

@@ -1,4 +1,5 @@
 from core.biz.huge_volume import HugeVolume
+from core.biz.huge_volume_chart import HugeVolumeChart
 from core.db.db_market_data import DBMarketData
 from core.db.db_huge_volume_data import DBHugeVolumeData
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
@@ -35,7 +36,9 @@ class HugeVolumeMain:
         self.output_folder = "./output/huge_volume_statistics/"
         os.makedirs(self.output_folder, exist_ok=True)
 
-    def batch_initial_detect_volume_spike(self, window_size: int = 50, start: str = None):
+    def batch_initial_detect_volume_spike(
+        self, window_size: int = 50, start: str = None
+    ):
         for symbol in self.market_data_main.symbols:
             for bar in self.market_data_main.bars:
                 if start is None:
@@ -43,7 +46,12 @@ class HugeVolumeMain:
                         "initial_date", "2025-05-01 00:00:00"
                     )
                 data = self.detect_volume_spike(
-                    symbol, bar, window_size, start, only_output_huge_volume=False, is_update=False
+                    symbol,
+                    bar,
+                    window_size,
+                    start,
+                    only_output_huge_volume=False,
+                    is_update=False,
                 )
                 if data is not None and len(data) > 0:
                     logging.info(f"此次初始化巨量交易数据: {len(data)}条")
@@ -66,16 +74,22 @@ class HugeVolumeMain:
             )
         if end is None:
             end = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        logging.info(f"开始处理巨量交易数据: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}")
+        logging.info(
+            f"开始处理巨量交易数据: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}"
+        )
         data = self.db_market_data.query_market_data_by_symbol_bar(
             symbol, bar, start, end
         )
         if data is None:
-            logging.warning(f"获取行情数据失败: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}")
+            logging.warning(
+                f"获取行情数据失败: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}"
+            )
             return None
         else:
             if len(data) == 0:
-                logging.warning(f"获取行情数据为空: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}")
+                logging.warning(
+                    f"获取行情数据为空: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}"
+                )
                 return None
             else:
                 if isinstance(data, list):
@@ -93,10 +107,8 @@ class HugeVolumeMain:
         if data is not None:
             if is_update:
                 for index, row in data.iterrows():
-                    exist_huge_volume_data = (
-                        self.db_huge_volume_data.query_data_by_symbol_bar_window_size_timestamp(
-                            symbol, bar, window_size, row["timestamp"]
-                        )
+                    exist_huge_volume_data = self.db_huge_volume_data.query_data_by_symbol_bar_window_size_timestamp(
+                        symbol, bar, window_size, row["timestamp"]
                     )
                     if exist_huge_volume_data is not None:
                         # remove the exist_huge_volume_data from data
@@ -152,7 +164,9 @@ class HugeVolumeMain:
                 else:
                     logging.info(f"此次更新巨量交易数据为空")
         except Exception as e:
-            logging.error(f"更新巨量交易数据失败: {symbol} {bar} 窗口大小: {window_size} 从 {earliest_date_time} 到 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}: {e}")
+            logging.error(
+                f"更新巨量交易数据失败: {symbol} {bar} 窗口大小: {window_size} 从 {earliest_date_time} 到 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}: {e}"
+            )
 
     def get_seconds_by_bar(self, bar: str):
         """
@@ -214,26 +228,29 @@ class HugeVolumeMain:
         if end is None:
             end = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         periods_text = ", ".join([str(period) for period in periods])
-        logging.info(f"开始计算巨量出现后，之后{periods_text}个周期，上涨或下跌的比例: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}")
+        logging.info(
+            f"开始计算巨量出现后，之后{periods_text}个周期，上涨或下跌的比例: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}"
+        )
         volume_statistics_data = (
             self.db_huge_volume_data.query_huge_volume_data_by_symbol_bar_window_size(
                 symbol, bar, window_size, start, end
             )
         )
         if volume_statistics_data is None or len(volume_statistics_data) == 0:
-            logging.warning(f"获取巨量交易数据为空: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}")
+            logging.warning(
+                f"获取巨量交易数据为空: {symbol} {bar} 窗口大小: {window_size} 从 {start} 到 {end}"
+            )
             return None
         else:
             if isinstance(volume_statistics_data, list):
                 volume_statistics_data = pd.DataFrame(volume_statistics_data)
             elif isinstance(volume_statistics_data, dict):
                 volume_statistics_data = pd.DataFrame([volume_statistics_data])
-        if (
-            volume_statistics_data is not None
-            and len(volume_statistics_data) > 0
-        ):
+        if volume_statistics_data is not None and len(volume_statistics_data) > 0:
             # 根据timestamp排序
-            volume_statistics_data = volume_statistics_data.sort_values(by="timestamp", ascending=True)
+            volume_statistics_data = volume_statistics_data.sort_values(
+                by="timestamp", ascending=True
+            )
             volume_statistics_data["window_size"] = window_size
             volume_statistics_data = volume_statistics_data[
                 [
@@ -279,7 +296,11 @@ class HugeVolumeMain:
         huge_volume_data_list = []
         result_data_list = []
         window_size_list = WINDOW_SIZE.get("window_sizes", None)
-        if window_size_list is None or not isinstance(window_size_list, list) or len(window_size_list) == 0:
+        if (
+            window_size_list is None
+            or not isinstance(window_size_list, list)
+            or len(window_size_list) == 0
+        ):
             window_size_list = [50, 80, 100, 120]
 
         for symbol in self.market_data_main.symbols:
@@ -296,14 +317,14 @@ class HugeVolumeMain:
             total_huge_volume_data = total_huge_volume_data.reset_index(drop=True)
             total_result_data = total_result_data.reset_index(drop=True)
             current_date = datetime.now().strftime("%Y%m%d%H%M%S")
-            file_name = (
-                f"next_periods_rise_or_fall_{current_date}.xlsx"
-            )
+            file_name = f"next_periods_rise_or_fall_{current_date}.xlsx"
             try:
                 with pd.ExcelWriter(
                     os.path.join(self.output_folder, file_name)
                 ) as writer:
-                    total_huge_volume_data.to_excel(writer, sheet_name="details", index=False)
+                    total_huge_volume_data.to_excel(
+                        writer, sheet_name="details", index=False
+                    )
                     total_result_data.to_excel(
                         writer, sheet_name="next_periods_statistics", index=False
                     )
@@ -311,10 +332,29 @@ class HugeVolumeMain:
                 logging.error(f"导出Excel文件失败: {e}")
         return total_huge_volume_data, total_result_data
 
+    def plot_huge_volume_data(
+        self,
+        data_file_path: str,
+        sheet_name: str = "next_periods_statistics",
+        output_folder: str = "./output/huge_volume_statistics/",
+    ):
+        os.makedirs(output_folder, exist_ok=True)
+        huge_volume_data = pd.read_excel(data_file_path, sheet_name=sheet_name)
+        huge_volume_chart = HugeVolumeChart(huge_volume_data)
+        include_heatmap = True
+        include_line = False
+        huge_volume_chart.plot_entrance(
+            include_heatmap=include_heatmap, include_line=include_line
+        )
+
 
 def batch_initial_detect_volume_spike(threshold: float = 2.0):
     window_sizes = WINDOW_SIZE.get("window_sizes", None)
-    if window_sizes is None or not isinstance(window_sizes, list) or len(window_sizes) == 0:
+    if (
+        window_sizes is None
+        or not isinstance(window_sizes, list)
+        or len(window_sizes) == 0
+    ):
         window_sizes = [50, 80, 100, 120]
     huge_volume_main = HugeVolumeMain(threshold)
     for window_size in window_sizes:
@@ -326,7 +366,11 @@ def batch_initial_detect_volume_spike(threshold: float = 2.0):
 
 def batch_update_volume_spike(threshold: float = 2.0):
     window_sizes = WINDOW_SIZE.get("window_sizes", None)
-    if window_sizes is None or not isinstance(window_sizes, list) or len(window_sizes) == 0:
+    if (
+        window_sizes is None
+        or not isinstance(window_sizes, list)
+        or len(window_sizes) == 0
+    ):
         window_sizes = [50, 80, 100, 120]
     huge_volume_main = HugeVolumeMain(threshold)
     for window_size in window_sizes:
@@ -337,4 +381,12 @@ if __name__ == "__main__":
     # batch_initial_detect_volume_spike(threshold=2.0)
     # batch_update_volume_spike(threshold=2.0)
     huge_volume_main = HugeVolumeMain(threshold=2.0)
-    huge_volume_main.batch_next_periods_rise_or_fall(output_excel=True)
+    # huge_volume_main.batch_next_periods_rise_or_fall(output_excel=True)
+    data_file_path = "./output/huge_volume_statistics/next_periods_rise_or_fall_stat_20250731200304.xlsx"
+    sheet_name = "next_periods_statistics"
+    output_folder = "./output/huge_volume_statistics/"
+    huge_volume_main.plot_huge_volume_data(
+        data_file_path=data_file_path,
+        sheet_name=sheet_name,
+        output_folder=output_folder,
+    )

requirements.txt

@@ -4,3 +4,4 @@ requests>=2.25.0
 sqlalchemy >= 2.0.41
 pymysql >= 1.1.1
 wechatpy >= 1.8.18
+seaborn >= 0.13.2

sql/query/sql_playground.sql

@@ -1,18 +1,18 @@
 select * from crypto_market_data
-WHERE symbol='XCH-USDT-SWAP' and bar='5m' #and date_time > '2025-07-01'
+WHERE symbol='XCH-USDT' and bar='5m' #and date_time > '2025-07-01'
 order by timestamp desc;
 
 delete FROM crypto_market_data where symbol != 'XCH-USDT';
 
 select * from crypto_trade_data
-where symbol='BTC-USDT'
+where symbol='XCH-USDT'
 order by ts desc;
 
 select count(1) from crypto_trade_data;
 
 select * from crypto_huge_volume
-WHERE symbol='XCH-USDT' and bar='5m' and date_time > '2025-07-26'
-order by timestamp desc;
+WHERE symbol='XCH-USDT' and bar='5m' and window_size = 80 and date_time > '2025-07-26'
+order by timestamp;
 
 select * from crypto_huge_volume
 WHERE symbol='XCH-USDT-SWAP' and bar='5m' and date_time > '2025-07-26'