✨ feat(analyzer): 添加资金流分析器，实现主力资金指标对行业后续表现影响的分析与可视化功能

main.py

@@ -1,8 +1,9 @@
 from data_fetcher import DataFetcher
+from money_flow_analyzer import MoneyflowAnalyzer
 
 if __name__ == "__main__":
     # 指定日期范围
-    start_date = '20250401'
+    start_date = '20230912'
     end_date = None
 
     # 创建数据获取器实例
@@ -10,4 +11,7 @@ if __name__ == "__main__":
 
     # 获取板块资金流向数据
     # 可以通过force_update=True参数强制更新指定日期范围的数据
-    df = data_fetcher.get_moneyflow_ind_dc(start_date, end_date, force_update=False)
+    df = data_fetcher.get_moneyflow_ind_dc(start_date, end_date, force_update=False)
+
+    analyzer = MoneyflowAnalyzer()
+    analyzer.main_flow_analyze(days_forward=2,use_consistent_samples=True)

money_flow_analyzer.py

@@ -0,0 +1,246 @@
+import os
+import traceback
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from config_manager import get_config_manager
+from database_manager import DatabaseManager
+from logger import get_logger
+
+
+class MoneyflowAnalyzer:
+    """
+    市场资金流分析器，分析各类资金流向指标对行业在随后1-N天表现的影响
+    """
+
+    def __init__(self):
+        self.db_manager = DatabaseManager()
+        self.config = get_config_manager()
+        self.logger = get_logger()
+
+    def main_flow_analyze(self, days_forward=10, use_consistent_samples=False, max_days=20):
+        """
+        分析各类资金流向指标对行业在随后1-N天表现的影响
+
+        参数:
+            days_forward (int): 要分析的未来天数，默认为10天
+        """
+        self.logger.info(f"开始分析资金流数据，未来{days_forward}天的表现...")
+        self.logger.info(f"使用一致样本集: {use_consistent_samples}")
+
+        # 读取资金流数据
+        try:
+            df = self.db_manager.load_df_from_db('moneyflow_ind_dc')
+            self.logger.info(f"成功从数据库加载资金流数据，共计{len(df)}条记录")
+        except Exception as e:
+            self.logger.error(f"从数据库读取数据失败: {e}")
+            self.logger.debug(f"完整的错误追踪信息:\n{traceback.format_exc()}")
+            return False
+
+        # 检查是否成功获取数据
+        if df.empty:
+            self.logger.info("获取的资金流数据为空，无法进行分析")
+            return False
+
+        # 数据预处理 - 计算主力资金
+        df['main_force_amount'] = df['buy_elg_amount'] + df['buy_lg_amount']
+
+        # 将日期格式转换为datetime
+        df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d', errors='coerce')
+        df = df[~df['trade_date'].isna()]
+
+        # 按日期排序
+        df = df.sort_values('trade_date')
+
+        # 获取所有交易日期
+        all_dates = df['trade_date'].unique()
+
+        # 确定样本范围
+        if use_consistent_samples:
+            # 使用固定的最大预测天数来确保所有分析使用相同的样本集
+            sample_limit = len(all_dates) - max_days
+            self.logger.info(f"使用统一样本集进行分析，样本量: {sample_limit}")
+        else:
+            # 使用当前分析所需的预测天数
+            sample_limit = len(all_dates) - days_forward
+            self.logger.info(f"使用动态样本集进行分析，样本量: {sample_limit}")
+
+        # 定义要分析的资金流指标
+        # 格式: (指标名, 排序方向, 关联性)
+        # 关联性: 正相关=1, 负相关=-1 (用于确定是取最高还是最低)
+        flow_indicators = [
+            ('main_force_amount', 1, '主力净额')
+        ]
+
+        # 确保结果目录存在
+        os.makedirs('result', exist_ok=True)
+
+        # 为每个指标进行分析
+        for indicator, correlation, indicator_name in flow_indicators:
+            self.logger.info(f"分析 {indicator_name} 与未来指数关系...")
+
+            # 创建结果数据结构
+            results = []
+
+            # 遍历每个交易日期（固定样本集大小）
+            for i in range(sample_limit):
+                current_date = all_dates[i]
+
+                # 获取当前日期的数据
+                current_day_data = df[df['trade_date'] == current_date]
+
+                # 找出该指标排名靠前的行业
+                if correlation > 0:
+                    # 正相关，找最高值
+                    top_sectors = current_day_data.sort_values(indicator, ascending=False).head(1)['name'].tolist()
+                else:
+                    # 负相关，找最低值
+                    top_sectors = current_day_data.sort_values(indicator, ascending=True).head(1)['name'].tolist()
+
+                # 分析每个行业在随后1-N天的表现
+                for sector in top_sectors:
+                    # 获取该行业当天的指数变化和指标值
+                    sector_current = current_day_data[current_day_data['name'] == sector]
+                    if sector_current.empty:
+                        continue
+
+                    current_pct_change = sector_current['pct_change'].values[0]
+                    current_indicator_value = sector_current[indicator].values[0]
+
+                    # 分析随后1-N天的表现
+                    future_changes = []
+                    for day_offset in range(1, days_forward + 1):
+                        if i + day_offset < len(all_dates):
+                            future_date = all_dates[i + day_offset]
+                            future_data = df[(df['trade_date'] == future_date) & (df['name'] == sector)]
+                            if not future_data.empty:
+                                future_changes.append(future_data['pct_change'].values[0])
+                            else:
+                                future_changes.append(None)
+                        else:
+                            future_changes.append(None)
+
+                    # 如果至少有一个未来日期有数据
+                    if any(x is not None for x in future_changes):
+                        result_entry = {
+                            'date': current_date.strftime('%Y%m%d'),  # 将日期格式化为YYYYMMDD字符串
+                            'sector': sector,
+                            f'{indicator}': current_indicator_value,
+                            'current_pct_change': current_pct_change,
+                        }
+
+                        # 添加1-N天的变化
+                        for day in range(1, days_forward + 1):
+                            result_entry[f'day{day}_change'] = future_changes[day - 1]
+
+                        # 计算平均变化
+                        result_entry[f'avg_{days_forward}day_change'] = np.nanmean(
+                            [x for x in future_changes if x is not None])
+                        results.append(result_entry)
+
+            # 转换为DataFrame
+            results_df = pd.DataFrame(results)
+            if results_df.empty:
+                self.logger.info(f"没有足够的数据来分析{indicator_name}与后续表现的关系")
+                continue
+
+            # 保存结果
+            os.makedirs('result', exist_ok=True)
+            output_file = f'result/{indicator}_performance_{days_forward}days.xlsx'
+            results_df.to_excel(output_file, index=False)
+            self.logger.info(f"{indicator_name}表现分析已保存至{output_file}")
+
+            # 分析整体表现
+            avg_performance = {}
+            success_rates = {}
+            for day in range(1, days_forward + 1):
+                # 计算每一天的变化均值
+                avg_performance[f'day{day}'] = results_df[f'day{day}_change'].mean()
+                # 计算每一天的胜率（正收益的概率）
+                success_rates[f'day{day}'] = (results_df[f'day{day}_change'] > 0).mean() * 100
+
+            avg_performance[f'avg_{days_forward}day'] = results_df[f'avg_{days_forward}day_change'].mean()
+
+            # 输出每天的表现数据
+            self.logger.info(f"{indicator_name}极值行业在未来{days_forward}天的表现分析：")
+            self.logger.info("日期\t\t平均收益率\t\t胜率")
+            for day in range(1, days_forward + 1):
+                self.logger.info(f"T+{day}\t\t{avg_performance[f'day{day}']:.5f}%\t\t{success_rates[f'day{day}']:.2f}%")
+            self.logger.info(f"{days_forward}天平均: {avg_performance[f'avg_{days_forward}day']:.5f}%")
+
+            # 绘制折线图显示未来N天的平均表现和胜率
+            plt.figure(figsize=(14, 8))
+            # 设置中文字体
+            try:
+                plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'WenQuanYi Micro Hei']
+                plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+                days_labels = ['当天'] + [f'第{i}天' for i in range(1, days_forward + 1)]
+                direction = "最高" if correlation > 0 else "最低"
+                title = f'{indicator_name}{direction}后的平均表现 ({days_forward}天)'
+                xlabel = '时间'
+                ylabel = '指数变化率 (%)'
+            except:
+                # 如果没有中文字体，使用英文
+                days_labels = ['Current'] + [f'Day+{i}' for i in range(1, days_forward + 1)]
+                direction = "Highest" if correlation > 0 else "Lowest"
+                title = f'Average Performance After {indicator_name} {direction} ({days_forward} Days)'
+                xlabel = 'Time'
+                ylabel = 'Index Change Rate (%)'
+
+            # 创建双y轴图表
+            fig, ax1 = plt.subplots(figsize=(14, 8))
+
+            # 设置收益率曲线 (左y轴)
+            values = [results_df['current_pct_change'].mean()] + [avg_performance[f'day{i}'] for i in
+                                                                  range(1, days_forward + 1)]
+            ax1.plot(days_labels, values, marker='o', linewidth=2, color='blue', label='平均收益率')
+            ax1.axhline(y=0, color='r', linestyle='--')
+            ax1.set_xlabel(xlabel)
+            ax1.set_ylabel('收益率 (%)', color='blue')
+            ax1.tick_params(axis='y', labelcolor='blue')
+
+            # 设置胜率曲线 (右y轴)
+            ax2 = ax1.twinx()
+            win_rates = [50]  # 当天默认为50%
+            for i in range(1, days_forward + 1):
+                win_rates.append(success_rates[f'day{i}'])
+            ax2.plot(days_labels, win_rates, marker='s', linewidth=2, color='green', label='胜率')
+            ax2.set_ylabel('胜率 (%)', color='green')
+            ax2.tick_params(axis='y', labelcolor='green')
+            ax2.axhline(y=50, color='green', linestyle='--', alpha=0.5)
+
+            # 添加图例
+            lines1, labels1 = ax1.get_legend_handles_labels()
+            lines2, labels2 = ax2.get_legend_handles_labels()
+            ax1.legend(lines1 + lines2, labels1 + labels2, loc='best')
+
+            plt.title(title, fontsize=14)
+            plt.grid(True)
+            plt.xticks(rotation=45)  # 旋转x轴标签以避免重叠
+
+            # 保存图表
+            output_image = f'result/{indicator}_performance_{days_forward}days.png'
+            plt.savefig(output_image, dpi=300, bbox_inches='tight')
+            self.logger.info(f"{indicator_name}表现图表已保存至{output_image}")
+
+            # 额外保存胜率图表
+            plt.figure(figsize=(14, 8))
+            plt.bar(range(1, days_forward + 1), [success_rates[f'day{i}'] for i in range(1, days_forward + 1)],
+                    color='green', alpha=0.7)
+            plt.axhline(y=50, color='r', linestyle='--')
+            plt.title(f'{indicator_name}{direction}后的未来胜率分布 ({days_forward}天)', fontsize=14)
+            plt.xlabel('未来天数')
+            plt.ylabel('胜率 (%)')
+            plt.xticks(range(1, days_forward + 1), [f'T+{i}' for i in range(1, days_forward + 1)])
+            plt.grid(True, axis='y')
+
+            # 在柱状图上标注具体数值
+            for i in range(1, days_forward + 1):
+                plt.text(i, success_rates[f'day{i}'] + 1, f"{success_rates[f'day{i}']:.1f}%",
+                         ha='center', va='bottom', fontsize=9)
+
+            win_rate_image = f'result/{indicator}_win_rate_{days_forward}days.png'
+            plt.savefig(win_rate_image, dpi=300, bbox_inches='tight')
+            self.logger.info(f"{indicator_name}胜率分布图已保存至{win_rate_image}")
+
+        return True