optimize chart output

core/trade/ma_break_statistics.py

@@ -93,10 +93,13 @@ class MaBreakStatistics:
                         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(
@@ -127,7 +130,7 @@ class MaBreakStatistics:
                     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
@@ -136,6 +139,14 @@ class MaBreakStatistics:
                         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[
@@ -208,7 +219,9 @@ class MaBreakStatistics:
                     writer, sheet_name="买卖时间间隔统计", index=False
                 )
 
-            chart_dict = self.draw_pct_chg_mean_chart(pct_chg_df, strategy_name)
+            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:
@@ -370,13 +383,17 @@ class MaBreakStatistics:
 
             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)}"
                 )
-                # 将market_data(最后一条数据的close - 第一条数据的open) / 第一条数据的open * 100
+                # 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 = (
-                    (market_data["close"].iloc[-1] - market_data["open"].iloc[0])
+                    (ma_break_market_data["end_close"].iloc[-1] - ma_break_market_data["begin_close"].iloc[0])
-                    / market_data["open"].iloc[0]
+                    / ma_break_market_data["begin_close"].iloc[0]
                     * 100
                 )
                 pct_chg = round(pct_chg, 4)
@@ -511,7 +528,7 @@ class MaBreakStatistics:
                     pass
         return condition
 
-    def draw_pct_chg_mean_chart(
+    def draw_quant_pct_chg_bar_chart(
         self, data: pd.DataFrame, strategy_name: str = "全均线策略"
     ):
         """
@@ -527,18 +544,66 @@ class MaBreakStatistics:
         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": "涨跌总和", "pct_chg_mean": "涨跌均值"}
+        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"
                     )
@@ -563,12 +628,120 @@ class MaBreakStatistics:
 
                 save_path = os.path.join(
                     self.stats_chart_dir,
-                    f"{bar}_ma_break_{column_name}_{strategy_name}.png",
+                    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}分布图表_{strategy_name}"
+                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('%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('%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
 

trade_ma_strategy_main.py

@@ -38,6 +38,7 @@ class TradeMaStrategyMain:
         for strategy_name, strategy_info in strategy_dict.items():
             pct_chg_df = self.ma_break_statistics.batch_statistics(strategy_name=strategy_name)
             pct_chg_df_list.append(pct_chg_df)
+            
         pct_chg_df = pd.concat(pct_chg_df_list)
     
     def statistics_pct_chg(self, pct_chg_df: pd.DataFrame):