optimize strategy statistics

core/trade/ma_break_statistics.py

@@ -63,21 +63,39 @@ class MaBreakStatistics:
         return trade_strategy_config
 
     def batch_statistics(self, strategy_name: str = "全均线策略"):
-        self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/excel/{strategy_name}/"
+        self.stats_output_dir = (
+            f"./output/trade_sandbox/ma_strategy/excel/{strategy_name}/"
+        )
         os.makedirs(self.stats_output_dir, exist_ok=True)
-        self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/chart/{strategy_name}/"
+        self.stats_chart_dir = (
+            f"./output/trade_sandbox/ma_strategy/chart/{strategy_name}/"
+        )
         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}")
+                logger.info(
-                ma_break_market_data = self.trade_simulate(symbol, bar, strategy_name)
+                    f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}"
-                if ma_break_market_data is not None:
+                )
+                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
             pct_chg_df = (
                 ma_break_market_data.groupby(["symbol", "bar"])["pct_chg"]
@@ -92,6 +110,66 @@ class MaBreakStatistics:
                 )
                 .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)
+                    ]
+                    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()
+                        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"]
@@ -132,6 +210,7 @@ class MaBreakStatistics:
 
             chart_dict = self.draw_pct_chg_mean_chart(pct_chg_df, strategy_name)
             self.output_chart_to_excel(output_file_path, chart_dict)
+            return pct_chg_df
         else:
             return None
 
@@ -179,7 +258,7 @@ class MaBreakStatistics:
         )
         if market_data is None or len(market_data) == 0:
             logger.warning(f"获取{symbol} {bar} 数据失败")
-            return
+            return None, None
         else:
             market_data = pd.DataFrame(market_data)
             market_data.sort_values(by="timestamp", ascending=True, inplace=True)
@@ -291,10 +370,20 @@ class MaBreakStatistics:
 
             if len(ma_break_market_data_pair_list) > 0:
                 ma_break_market_data = pd.DataFrame(ma_break_market_data_pair_list)
-                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)}")
+                logger.info(
-                return ma_break_market_data
+                    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
+                pct_chg = (
+                    (market_data["close"].iloc[-1] - market_data["open"].iloc[0])
+                    / market_data["open"].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
+                return None, None
 
     def fit_strategy(
         self,
@@ -438,10 +527,7 @@ 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 = {
+        column_name_dict = {"pct_chg_total": "涨跌总和", "pct_chg_mean": "涨跌均值"}
-            "pct_chg_sum": "涨跌总和",
-            "pct_chg_mean": "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
@@ -453,7 +539,9 @@ class MaBreakStatistics:
                 bar_data.reset_index(drop=True, inplace=True)
 
                 plt.figure(figsize=(10, 6))
-                ax = sns.barplot(x="symbol", y=column_name_text, data=bar_data, palette="Blues_d")
+                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)
@@ -461,12 +549,21 @@ class MaBreakStatistics:
 
                 # 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')
+                    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}_ma_break_{column_name}_{strategy_name}.png"
+                    self.stats_chart_dir,
+                    f"{bar}_ma_break_{column_name}_{strategy_name}.png",
                 )
                 plt.savefig(save_path, dpi=150)
                 plt.close()

trade_ma_strategy_main.py

@@ -34,8 +34,28 @@ class TradeMaStrategyMain:
         """
         logger.info("开始批量计算MA突破统计")
         strategy_dict = self.ma_break_statistics.main_strategy
+        pct_chg_df_list = []
         for strategy_name, strategy_info in strategy_dict.items():
-            self.ma_break_statistics.batch_statistics(strategy_name=strategy_name)
+            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):
+        """
+        1. 将各个symbol, 各个bar, 各个策略的pct_chg_total构建为新的数据结构,如：
+        symbol, bar, stratege_name_1, stratege_name_2, stratege_name_3, ...
+        stratege_name_1的值, 为该策略的pct_chg_total的值
+        2. 构建新的数据结构: symbol, bar, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name
+        如: BCT-USDT, 15m, 均线macd结合策略2, 全均线策略
+        3. 构建新的数据结构, bar, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name
+        如: 15m, 均线macd结合策略2, 全均线策略
+        4. 构建新的数据结构, symbol, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name
+        如: BCT-USDT, 均线macd结合策略2, 全均线策略
+        """
+        logger.info("开始统计pct_chg")
+        
+    
+
 
 if __name__ == "__main__":
     trade_ma_strategy_main = TradeMaStrategyMain()