回测平台调整：持有基金自身配息不再下次调仓后拿来再投资

api.py

@@ -645,3 +645,12 @@ class RebalanceSignal(ABC):
         :param risk: 指定的风险等级
         :return: 如果有信号，则返回信号数据，否则返回None
         '''
+
+    @abstractmethod
+    def clear(self, min_date=None, risk: PortfoliosRisk = None):
+        '''
+        清理指定的数据
+        :param min_date: 指定的起始时间
+        :param risk: 指定的风险等级
+        '''
+        pass
\ No newline at end of file

config-svrobo6.yml

@@ -231,15 +231,15 @@ reports: # 报告模块相关
         subject: "SVROBO6-实盘版-每日监测_{today}"
         content: "Dear All: 附件是今天生成的监测数据，請驗收，謝謝！ 注>：該郵件為自動發送，如有問題請聯繫矽谷團隊 telan_qian@chifufund.com"
 robo-executor: # 执行器相关
-  use: ${ROBO_EXECUTOR:real} # 执行哪个执行器，优先取系统环境变量ROBO_EXECUTOR的值，默认backtest
-  sync-data: ${SYNC_DATA:on} # 是否开启同步资料数据
+  use: ${ROBO_EXECUTOR:backtest} # 执行哪个执行器，优先取系统环境变量ROBO_EXECUTOR的值，默认backtest
+  sync-data: ${SYNC_DATA:off} # 是否开启同步资料数据
   backtest: # 回测执行器相关
-    start-date: 2022-09-30 # 回测起始日期
+    start-date: 2022-09-22 # 回测起始日期
     end-date: 2023-07-03 # 回测截止日期
     sealing-period: 10 #调仓封闭期
     start-step: ${BACKTEST_START_STEP:3} # 回测从哪一步开始执行 1:计算资产池；2:计算最优投组：3:计算再平衡信号以及持仓投组
     end-step: ${BACKTEST_END_STEP:3} # 回测从哪一步执行完成后结束执行 1:计算资产池；2:计算最优投组：3:计算再平衡信号以及持仓投组
-    clean-up: true
+    clean-up: off
   real: # 实盘执行器
     export: ${EXPORT_ENABLE:off} # 是否开启报告
     start-date: 2023-05-08 # 实盘开始时间

portfolios/holder.py

@@ -237,11 +237,11 @@ class InvTrustPortfoliosHolder(DividendPortfoliosHolder):
             fund_div_tuple = self.get_navs_and_div(fund_ids=tuple(set(weight) | set(share)), day=day)
             navs = fund_div_tuple[0]
             fund_dividend = fund_div_tuple[1]
-            fund_dividend = sum(
+            fund_dividend = last_nav['fund_div'] + sum(
                 map(lambda k: share[k] * fund_dividend[k], filter(lambda k: k in fund_dividend, share.keys())))
             dividend_acc = last_nav['div_acc']
             fund_av = round(sum([navs[x] * y for x, y in share.items()]), 4) + last_nav['fund_div']
-            asset_nav = fund_av + fund_dividend
+            asset_nav = fund_av
             nav = last_nav['nav'] * asset_nav / last_nav['asset_nav']
             share = {x: fund_av * w / navs[x] for x, w in weight.items()}
             if self.is_first_workday(day):
@@ -313,7 +313,7 @@ class InvTrustPortfoliosHolder(DividendPortfoliosHolder):
         weight = format_weight(weight)
         fund_dividend = last_nav['fund_div'] + sum(
             map(lambda k: share[k] * fund_dividend[k], filter(lambda k: k in fund_dividend, share.keys())))
-        asset_nav = fund_av + fund_dividend
+        asset_nav = fund_av
         nav = last_nav['nav'] * asset_nav / last_nav['asset_nav']
         div_forecast = last_nav['div_forecast']
         if self.is_first_workday(day):

rebalance/base_signal.py

@@ -73,6 +73,9 @@ class BaseRebalanceSignal(RebalanceSignal, ABC):
         last_re = rrs.get_last_one(max_date=day, risk=risk, effective=True)
         return last_re
 
+    def clear(self, min_date=None, risk: PortfoliosRisk = None):
+        rrs.delete(min_date=min_date, risk=risk)
+
 
 @component(bean_name='signal-report')
 class SignalReportor(RoboReportor):

robo_executor.py

@@ -24,27 +24,27 @@ class BacktestExecutor(RoboExecutor):
     @autowired(names={'datum': 'hk-datum'})
     def __init__(self, datum: Datum = None, pool: AssetPool = None,
                  syncs: List[DataSync] = None, export: RoboExportor = None,
-                 builder: PortfoliosBuilder = None, hold: PortfoliosHolder = None):
+                 builder: PortfoliosBuilder = None, hold: PortfoliosHolder = None,
+                 signal: RebalanceSignal = None):
         self._datum = datum
         self._pool = pool
         self._builder = builder
         self._hold = hold
         self._syncs = syncs
         self._export = export
+        self._signal = signal
         self._config = get_config(__name__)['backtest']
 
     @staticmethod
     def get_last_business_day(start_date, end_date):
         # 生成日期范围并转换为DataFrame
-        dates = pd.date_range(start_date, end_date, freq='MS')
+        dates = pd.date_range(start_date, end_date, freq='M')
+        if dates[0] != start_date:
+            dates = dates.insert(0, start_date)
         df = pd.DataFrame({'dates': dates})
-        df['last_business_day'] = df['dates'].map(
-            lambda date: pd.date_range(start=date, periods=1, freq='BM')[-1]
-        )
-        # 每隔n个月提取第一个工作日
         result = []
         for i in range(0, len(df), get_config('portfolios')['holder']['warehouse-frequency']):
-            result.append(df.iloc[i]['last_business_day'])
+            result.append(df.iloc[i]['dates'])
         delta = workday_range(result[0], result[1])
         period = get_config(__name__)['backtest']['sealing-period']
         if len(delta) <= period:
@@ -83,6 +83,7 @@ class BacktestExecutor(RoboExecutor):
                 BacktestStep.NORMAL_PORTFOLIO):
             logger.info('start to clear normal portfolios'.center(50, '-'))
             self._builder.clear()
+            self._signal.clear()
         if self.start_step.within(BacktestStep.HOLD_PORTFOLIO) and self.end_step.without(BacktestStep.HOLD_PORTFOLIO):
             logger.info('start to clear hold portfolios'.center(50, '-'))
             self._hold.clear()
@@ -199,5 +200,3 @@ class RealExecutor(RoboExecutor):
                     f'export email for date[{format_date(date)}] send success, use[{(dt.now() - now).seconds}s]')
         else:
             logger.info(f'today[{format_date(date)}] is a rest day, do not execute the daily real robo.')
-
-