动态调整UARC

portfolios/builder.py

@@ -95,6 +95,7 @@ class PoemPortfoliosBuilder(MptPortfoliosBuilder):
         portfolios = {}
         for risk in PortfoliosRisk:
             solver = self._factory.create_solver(risk, type)
+            self.__day = day
             navs_group = solver.reset_navs(day)
             for category, navs in navs_group.items():
                 solver.set_navs(navs)
@@ -222,5 +223,3 @@ class RiskParityARCPortfoliosBuilder(MptPortfoliosBuilder):
             'solve': SolveType.INFEASIBLE
         }
         return result
-
-

portfolios/solver.py

 import math
 import os
+import statistics
 import sys
 from logging import DEBUG, getLogger
 
@@ -196,7 +197,7 @@ class DefaultSolver(Solver):
         model = self.create_model()
         model.objective = Objective(expr=sum(
             [(model.z[i] * model.w[i] * (self.risk_parity_sigma.iloc[i] @ model.w) - model.z[j] * model.w[j] * (
-                        self.risk_parity_sigma.iloc[j] @ model.w)) ** 2
+                    self.risk_parity_sigma.iloc[j] @ model.w)) ** 2
              for i in model.indices for j in model.indices]), sense=minimize)
         self._solver.solve(model)
         return self.calc_port_weight(model)
@@ -344,6 +345,26 @@ class ARCSolver(DefaultSolver):
         count = self.get_config('asset-count')
         return min(count[0] if isinstance(count, list) else count, len(self.rtn_annualized))
 
+    @property
+    def LARC(self):
+        return self._config['LARC']
+
+    @property
+    def UARC(self):
+        ecos = self._datum.get_datums(ticker=['CPI YOY Index', 'FDTR Index'])
+        cpi_id = [data['id'] for data in ecos if data['bloombergTicker'] == 'CPI YOY Index']
+        fdtr_id = [data['id'] for data in ecos if data['bloombergTicker'] == 'FDTR Index']
+        cpi = self._navs.get_last_eco_values(max_date=self.date, datum_id=cpi_id, count=2, by_release_date=True)
+        cpi = statistics.mean([c['indicator'] for c in cpi])
+        fdtr = self._navs.get_last_eco_values(max_date=self.date, datum_id=fdtr_id, by_release_date=True)['indicator']
+        cash_uarc = round(
+            self._config['fix-w'] + self._config['fdtr-w'] * fdtr + abs(cpi - self._config['cpi-expect']) *
+            self._config['cpiyoy-w'], 2)
+        cash_uarc = self._config['max-w'] if cash_uarc > self._config['max-w'] else cash_uarc
+        UARC = self._config['UARC']
+        UARC[self._config['uarc-index']] = cash_uarc
+        return UARC
+
     def create_model(self):
         low_weight = self.get_config('mpt.low-weight')
         high_weight = self.get_config('mpt.high-weight')
@@ -361,8 +382,8 @@ class ARCSolver(DefaultSolver):
         model.cons_bounds_low = Constraint(model.indices, rule=lambda m, i: m.z[i] * low_weight <= m.w[i])
         model.cons_bounds_up = Constraint(model.indices, rule=lambda m, i: m.z[i] * high_weight >= m.w[i])
         if self._config['arc']:
-            LARC = self._config['LARC']
-            UARC = self._config['UARC']
+            LARC = self.LARC
+            UARC = self.UARC
             numARC = len(LARC)  # this is the M in the doc
             numAsset = len(self.navs.columns)