summary新增个股权重分析

rqalpha/apis/api_rqdatac.py

@@ -1039,7 +1039,7 @@ def _futures_get_dominant_price(
     """ 获取主力合约行情数据
 
     :param underlying_symbols: 期货合约品种，可传入 underlying_symbol, underlying_symbol list
-    :param start_date: 开始日期, 最小日期为 20210104
+    :param start_date: 开始日期, 最小日期为 20100104
     :param end_date: 结束日期
     :param frequency: 历史数据的频率。 支持/日/分钟/tick 级别的历史数据，默认为'1d'。
         1m- 分钟线，1d-日线，分钟可选取不同频率，例如'5m'代表 5 分钟线

rqalpha/mod/rqalpha_mod_sys_analyser/mod.py

@@ -268,6 +268,7 @@ def _to_position_record(self, date, order_book_id, long, short):
             for direction_prefix, pos in direction_pos_iter:
                 data[direction_prefix + "_pnl"] = self._safe_convert(getattr(pos, "pnl", None))
                 data[direction_prefix + "_margin"] = self._safe_convert(pos.margin)
+                data[direction_prefix + "_market_value"] = self._safe_convert(pos.market_value)
                 data[direction_prefix + "_quantity"] = self._safe_convert(pos.quantity)
                 data[direction_prefix + "_avg_open_price"] = self._safe_convert(getattr(pos, "avg_price", None))
         return data
@@ -489,6 +490,26 @@ def tear_down(self, code, exception=None):
                 df = df.set_index("date").sort_index()
             result_dict["{}_positions".format(account_name)] = df
 
+        # 个股权重
+        df_list = []
+        need_cols = ["order_book_id", "market_value"]
+        for table_name in ["stock_positions", "future_positions"]:
+            if table_name not in result_dict:
+                continue
+            table = result_dict[table_name]
+            for field in ["market_value", "LONG_market_value", "SHORT_market_value"]:
+                if field not in table.columns:
+                    continue
+                df_list.append(table.rename(columns={field: "market_value"})[need_cols])
+        if len(df_list) > 0:
+            positions_weight_df = pd.concat(df_list).dropna()
+            positions_weight_df["total_value"] = positions_weight_df.groupby(by="date")["market_value"].sum()
+            positions_weight_df["weight"] = positions_weight_df["market_value"] / positions_weight_df["total_value"]
+            positions_weight_df = positions_weight_df.groupby(by="date")["weight"].describe()
+        else:
+            positions_weight_df = pd.DataFrame(columns=["count", "mean", "std", "min", "25%", "50%", "75%", "max"])
+        positions_weight_df = positions_weight_df.reindex(total_portfolios.index).fillna(value=0)
+        result_dict["positions_weight"] = positions_weight_df
         result_dict["yearly_risk_free_rates"] = dict(_get_yearly_risk_free_rates(data_proxy, start_date, end_date))
 
         if self._mod_config.output_file:

rqalpha/mod/rqalpha_mod_sys_analyser/report/excel_template.py

@@ -81,7 +81,8 @@ class SummaryTemplate(ExcelTemplate):
         "概览": SingleCellSchema,
         "年度指标": VerticalSeriesSchema,
         "月度收益": VerticalSeriesSchema,
-        "月度超额收益（几何）": VerticalSeriesSchema
+        "月度超额收益（几何）": VerticalSeriesSchema,
+        "个股权重": VerticalSeriesSchema,
     }
 
 

rqalpha/mod/rqalpha_mod_sys_analyser/report/report.py

@@ -106,6 +106,11 @@ def _monthly_geometric_excess_returns(p_returns: Series, b_returns: Optional[Ser
     return ChainMap({str(c): data[c] for c in data.columns}, {"year": data.index})
 
 
+def _gen_positions_weight(df):
+    rename = {"{}%".format(i): "percent_{}".format(i) for i in [25, 50, 75]}
+    return df.reset_index().rename(columns=rename).to_dict(orient="list")
+
+
 def generate_report(result_dict, output_path):
     from six import StringIO
 
@@ -128,11 +133,12 @@ def generate_report(result_dict, output_path):
         "概览": summary,
         "年度指标": _yearly_indicators(p_nav, p_returns, b_nav, b_returns, result_dict["yearly_risk_free_rates"]),
         "月度收益": _monthly_returns(p_returns),
-        "月度超额收益（几何）": _monthly_geometric_excess_returns(p_returns, b_returns)
+        "月度超额收益（几何）": _monthly_geometric_excess_returns(p_returns, b_returns),
+        "个股权重": _gen_positions_weight(result_dict["positions_weight"]),
     }, output_path)
 
     for name in ["portfolio", "stock_account", "future_account",
-                 "stock_positions", "future_positions", "trades"]:
+                 "stock_positions", "future_positions", "trades", "positions_weight"]:
         try:
             df = result_dict[name]
         except KeyError: