Merge remote-tracking branch 'origin/dev-dividend' into dev-dividend

ai/EstimateMarketTrendV20.py

+import json
 from typing import List
 
-from py_jftech import autowired
+from py_jftech import autowired, parse_date, prev_workday, format_date
 
-from ai.dao.robo_datas import get_base_info
+from ai.dao.robo_datas import get_base_info, get_index_list, get_fund_list
 from ai.data_access import DataAccess
 from ai.model_trainer import ModelTrainer
-from ai.noticer import upload_predict
 from ai.training_data_builder import TrainingDataBuilder
 from api import DataSync
 
+
 # 截止日期
-max_date = None
+# max_date = None
+# max_date = '2023-10-06'
+# max_date = '2023-10-13'
+# max_date = '2023-10-20'
+# max_date = '2023-10-27'
+# max_date = '2023-11-03'
+# max_date = '2023-11-10'
+# max_date = '2023-11-17'
+# max_date = '2023-11-24'
 # max_date = '2023-12-01'
+# max_date = '2023-12-08'
+max_date = '2023-12-15'
 
 # 待预测指数
-# PREDICT_LIST = [67]
-PREDICT_LIST = [67, 121, 122, 123]
+# PREDICT_LIST = [67, 121, 122, 123]
+PREDICT_LIST = [67, 121, 122, 123, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 170, 171, 174, 175]
 eco = [65, 66, 74, 134]
-index = [67, 68, 69, 70, 71, 72, 73, 75, 116, 117, 138, 139, 142, 143, 140, 141, 144, 145, 146]
-fund = [121, 122, 123]
+index = [67, 68, 69, 70, 71, 72, 73, 75,76,77,105,106, 116, 117, 138, 139, 142, 143, 140, 141, 144, 145, 146]
+fund = [121, 122, 123, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 170, 171, 174, 175]
 
 
 @autowired
@@ -43,49 +54,51 @@ def predictionFromMoel(the_model, scaledX_forecast, predict_item, indexDict: dic
     return prediction
 
 
+def judgement(id, type, predict):
+    from datetime import datetime
+    predict_term = 21
+    start = parse_date(max_date) if max_date else prev_workday(datetime.today())
+    navs = []
+    if type == 'INDEX':
+        navs = get_index_list(index_ids=id, min_date=start, limit=predict_term)
+        navs = [nav['rid_close'] for nav in navs]
+    elif type == 'FUND':
+        navs = get_fund_list(fund_ids=id, min_date=start, limit=predict_term)
+        navs = [nav['rfn_nav_cal'] for nav in navs]
+    if len(navs) == predict_term:
+        upper = True if navs[-1] >= navs[0] else False
+        result = {}
+        for k, v, in predict.items():
+            pred = True if v[0] > 0 else False
+            if upper == pred:
+                result[k] = True
+            else:
+                result[k] = False
+        j = {
+            'id': id,
+            'date': format_date(start),
+            'result': result
+        }
+        with open('predict.txt', 'a+') as file:
+            file.write(json.dumps(j))
+            file.write('\n')
+
+
 ########################################
 if __name__ == '__main__':
     sync()
-    toForecast = False  # False means test, True means forecast
+    toForecast = True  # False means test, True means forecast
     # define some parameters
     win1W = 5  # 1 week
     win1M = 21  # 1 Month
     win1Q = 63  # 1 Quarter
     numForecastDays = 21  # business days, 21 business days means one month
     theThreshold = 0.0
-
-    indexDict = {
-        65: "CPI_YOY",
-        66: "FDTR",
-        67: "SPX",
-        68: "USGG10YR",
-        69: "USGG2YR",
-        70: "MXWO",  # not use now
-        71: "MXWD",  # not use now
-        72: "CCMP",
-        73: "TWSE",  # not use now
-        74: "CPURNSA",
-        75: "VIX",
-        76: "US0001M",
-        77: "US0012M",
-        # FUND
-        121: "IEF_US",
-        122: "TLT_US",
-        123: "UUP_US",
-
-        139: "COI_TOTL",
-        138: "LEI_TOTL",
-        116: "MID",
-        134: "NAPMPMI",
-        142: "OE4EKLAC",
-        143: "OEA5KLAC",
-        146: "OECNKLAC",
-        145: "OEJPKLAC",
-        141: "OEOTGTAC",
-        144: "OEUSKLAC",
-        117: "SML",
-        140: "USRINDEX"
-    }
+    ids = set(PREDICT_LIST) | set(eco) | set(index) | set(fund)
+    infos = get_base_info(ids)
+    infos_type = {info['id']: info['type'] for info in infos}
+    indexDict = {info['id']: info['ticker'].replace(' Index', '').replace(' Equity', '').replace(' ', '_') for info in
+                 infos}
     ###################
     # Step 1: Prepare X and y (features and labels)
     # 准备基础数据
@@ -114,6 +127,11 @@ if __name__ == '__main__':
         gbt_model = trainer.train_GBT(X_train, y_train, X_test, y_test)
         svc_model = trainer.train_SVC(X_train, y_train, X_test, y_test)
         ensemble_model = trainer.ensemble_model(rf_model, gbt_model, svc_model, X_train, y_train, X_test, y_test)
-
-        if (toForecast):
+        model_predict = {'forest': rf_model.predict(scaledX_forecast),
+                         'gbt': gbt_model.predict(scaledX_forecast),
+                         'svc': svc_model.predict(scaledX_forecast),
+                         'ensemble': ensemble_model.predict(scaledX_forecast)}
+        print(f'预测结果：{model_predict}'.center(60, '+'))
+        judgement(pid, infos_type[pid], model_predict)
+        if toForecast:
             predictionFromMoel(ensemble_model, scaledX_forecast, indexDict[pid], indexDict)

ai/dao/robo_datas.py

@@ -2,7 +2,8 @@ from py_jftech import read, to_tuple, where
 
 
 @read
-def get_index_list(index_ids=None, min_date=None, max_date=None):
+def get_index_list(index_ids=None, min_date=None, max_date=None, limit=None):
+    limit_sql = f'limit {limit}' if limit else ''
     sqls = []
     if min_date:
         sqls.append(f"rid_date >= '{min_date}'")
@@ -10,7 +11,7 @@ def get_index_list(index_ids=None, min_date=None, max_date=None):
         sqls.append(f"rid_date <= '{max_date}'")
     return f'''
     select * from robo_index_datas 
-    {where(*sqls, rid_index_id=to_tuple(index_ids))} order by rid_index_id, rid_date
+    {where(*sqls, rid_index_id=to_tuple(index_ids))} order by rid_index_id, rid_date {limit_sql}
     '''
 
 
@@ -27,7 +28,8 @@ def get_eco_list(eco_ids=None, min_date=None, max_date=None):
     '''
 
 @read
-def get_fund_list(fund_ids=None, min_date=None, max_date=None):
+def get_fund_list(fund_ids=None, min_date=None, max_date=None, limit=None):
+    limit_sql = f'limit {limit}' if limit else ''
     sqls = []
     if min_date:
         sqls.append(f"rfn_date >= '{min_date}'")
@@ -35,7 +37,7 @@ def get_fund_list(fund_ids=None, min_date=None, max_date=None):
         sqls.append(f"rfn_date <= '{max_date}'")
     return f'''
     select * from robo_fund_navs 
-    {where(*sqls, rfn_fund_id=to_tuple(fund_ids))} order by rfn_fund_id, rfn_date
+    {where(*sqls, rfn_fund_id=to_tuple(fund_ids))} order by rfn_fund_id, rfn_date {limit_sql}
     '''
 
 @read

ai/data_access.py

@@ -21,9 +21,16 @@ class DataAccess(ABC):
         indexData = indexData[
             ["rid_index_id", "rid_date", "rid_high", "rid_open", "rid_low", "rid_close", "rid_pe", "rid_pb",
              "rid_volume", "rid_frdpe", "rid_frdpes", "rid_pc"]]
+        # 数据替换和截取以后用105 和106，（都从2022.1.3开始）然后2022年以前的部分：105前边接76的数据 106前边接77的数据
+        condition1 = ((indexData['rid_index_id'] == 76) & (indexData['rid_date'] >= '2022-01-03'))
+        condition2 = ((indexData['rid_index_id'] == 77) & (indexData['rid_date'] >= '2022-01-03'))
+        condition3 = ((indexData['rid_index_id'] == 105) & (indexData['rid_date'] < '2022-01-03'))
+        condition4 = ((indexData['rid_index_id'] == 106) & (indexData['rid_date'] < '2022-01-03'))
+        indexData.drop(indexData[condition1 | condition2 | condition3 | condition4].index, inplace=True)
+        indexData.loc[indexData['rid_index_id'] == 76, 'rid_index_id'] = 105
+        indexData.loc[indexData['rid_index_id'] == 77, 'rid_index_id'] = 106
         indexData.rename(columns={"rid_date": 'date'}, inplace=True)  # please use 'date'
         indexData["rid_index_id"] = indexData["rid_index_id"].map(self._indexDict)
-        indexData['rid_frdpe'].ffill(inplace=True)
         return indexData
 
     def get_eco_datas(self):
@@ -45,16 +52,21 @@ class DataAccess(ABC):
     def get_vix(self, indexData):
         # VIX：芝加哥期权交易所SPX波动率指
         vixData = indexData[indexData['rid_index_id'] == "VIX"].copy()
-        vixData = vixData[["date", "rid_high", "rid_open", "rid_low", "rid_close"]]
+        vixData = vixData[
+            ["date", "rid_high", "rid_open", "rid_low", "rid_close", "rid_pc", "rid_pb", "rid_pe", "rid_frdpe",
+             "rid_frdpes"]]
         vixData.rename(
-            columns={"rid_high": 'vix_high', 'rid_open': 'vix_open', "rid_low": 'vix_low', "rid_close": 'vix_close'},
+            columns={"rid_high": 'vix_high', 'rid_open': 'vix_open', "rid_low": 'vix_low', "rid_volume": 'vix_volume',
+                     "rid_close": 'vix_close', "rid_pc": 'vix_pc', "rid_pb": 'vix_pb', "rid_pe": 'vix_pe',
+                     "rid_frdpe": 'vix_frdpe', "rid_frdpes": 'vix_frdpes'},
             inplace=True)
         vixData.set_index('date', inplace=True)
         vixData.index = pd.to_datetime(vixData.index)
+        vixData.dropna(axis=1, inplace=True)
         return vixData
 
     def get_other_index(self, indexData):
-        other_index = ["USGG10YR", "USGG2YR", "CCMP", "US0001M", "US0012M", "COI_TOTL", "LEI_TOTL", "MID",
+        other_index = ["USGG10YR", "USGG2YR", "CCMP", "TSFR1M", "TSFR12M", "COI_TOTL", "LEI_TOTL", "MID",
                        "OE4EKLAC", "OEA5KLAC", "OECNKLAC", "OEJPKLAC", "OEOTGTAC", "OEUSKLAC", "USRINDEX", "SPX"]
         cols = ['date', 'rid_close', 'rid_pe', 'rid_pb', 'rid_volume', 'rid_frdpe', 'rid_frdpes', 'rid_pc']
         indexOtherData = pd.DataFrame()

ai/predict.txt

+{"id": 67, "date": "2023-10-06", "result": {"forest": false, "gbt": true, "svc": true, "ensemble": true}}
+{"id": 121, "date": "2023-10-06", "result": {"forest": true, "gbt": true, "svc": false, "ensemble": true}}
+{"id": 122, "date": "2023-10-06", "result": {"forest": true, "gbt": true, "svc": false, "ensemble": true}}
+{"id": 123, "date": "2023-10-06", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 67, "date": "2023-10-13", "result": {"forest": false, "gbt": true, "svc": true, "ensemble": true}}
+{"id": 121, "date": "2023-10-13", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 122, "date": "2023-10-13", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 123, "date": "2023-10-13", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 67, "date": "2023-10-20", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 121, "date": "2023-10-20", "result": {"forest": false, "gbt": true, "svc": false, "ensemble": true}}
+{"id": 122, "date": "2023-10-20", "result": {"forest": true, "gbt": true, "svc": false, "ensemble": true}}
+{"id": 123, "date": "2023-10-20", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 67, "date": "2023-10-27", "result": {"forest": false, "gbt": true, "svc": true, "ensemble": true}}
+{"id": 121, "date": "2023-10-27", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 122, "date": "2023-10-27", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 123, "date": "2023-10-27", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 67, "date": "2023-11-03", "result": {"forest": true, "gbt": true, "svc": true, "ensemble": true}}
+{"id": 121, "date": "2023-11-03", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 122, "date": "2023-11-03", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 123, "date": "2023-11-03", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 67, "date": "2023-11-17", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 121, "date": "2023-11-17", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 122, "date": "2023-11-17", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 123, "date": "2023-11-17", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 67, "date": "2023-11-24", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 121, "date": "2023-11-24", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 122, "date": "2023-11-24", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 123, "date": "2023-11-24", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 67, "date": "2023-12-01", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 121, "date": "2023-12-01", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 122, "date": "2023-12-01", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 123, "date": "2023-12-01", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 67, "date": "2023-12-08", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 121, "date": "2023-12-08", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 122, "date": "2023-12-08", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 123, "date": "2023-12-08", "result": {"forest": false, "gbt": false, "svc": false, "ensemble": false}}
+{"id": 67, "date": "2023-12-15", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 121, "date": "2023-12-15", "result": {"forest": true, "gbt": true, "svc": true, "ensemble": true}}
+{"id": 122, "date": "2023-12-15", "result": {"forest": false, "gbt": false, "svc": true, "ensemble": false}}
+{"id": 123, "date": "2023-12-15", "result": {"forest": true, "gbt": true, "svc": true, "ensemble": true}}

ai/training_data_builder.py

@@ -87,7 +87,7 @@ class TrainingDataBuilder(ABC):
             predictData.rename(
                 columns={"rid_high": 'high', 'rid_open': 'open', "rid_low": 'low', "rid_close": 'close',
                          'rid_volume': 'volume',
-                         "rid_pe": "SPX_pe", "rid_pb": "SPX_pb"},
+                         "rid_pe": f"{self._indexDict[pid]}_pe", "rid_pb": f"{self._indexDict[pid]}_pb"},
                 inplace=True)
         elif pid in self._fund:
             predictData = indexData[indexData['rfn_fund_id'] == self._indexDict[pid]].copy()
@@ -147,6 +147,11 @@ class TrainingDataBuilder(ABC):
             DataAll[col].bfill(inplace=True)
             DataAll[col].ffill(inplace=True)
 
+        ###### clean NaN
+        DataAll.ffill(inplace=True)
+        DataAll.dropna(inplace=True)
+        DataAll.reset_index(inplace=True, drop=True)
+
         if (self._toForecast):
             # 处理CPI_YOY:美国城镇消费物价指数同比未经季 CPURNSA:美国消费者物价指数未经季调
             DataAllCopy = DataAll.copy()
@@ -157,13 +162,10 @@ class TrainingDataBuilder(ABC):
             DataAllCopy.drop(['futureR', 'yLabel'], axis=1, inplace=True)
             forecastDayIndex = DataAllCopy.index[DataAllCopy['date'] == forecastDay]
             forecastData = DataAllCopy.iloc[forecastDayIndex.to_list(), 1:]
+            forecastData.dropna(inplace=True, axis=1)
             X_forecast = forecastData.to_numpy()
             del DataAllCopy
 
-        ###### clean NaN
-        DataAll.dropna(inplace=True)
-        DataAll.reset_index(inplace=True, drop=True)
-
         ###### get X and y
         y = DataAll['yLabel'].to_numpy(copy=True)
 

api.py

@@ -51,6 +51,7 @@ class SolveType(Enum):
     INFEASIBLE = 0
     MPT = 1
     POEM = 2
+    RISK_PARITY = 3
 
 
 @unique
@@ -365,6 +366,15 @@ class Solver(ABC):
         '''
         pass
 
+
+    @abstractmethod
+    def solve_risk_parity(self):
+        '''
+        risk_parity计算
+        :return: 投组
+        '''
+        pass
+
     @abstractmethod
     def reset_navs(self, day):
         '''

basic/datum.py

@@ -42,7 +42,7 @@ class DefaultDatum(Datum):
         if DatumType(datum['type']) is DatumType.FUND:
             return {
                 **datum,
-                'inceptDate': parse_date(datum['inceptDate'])
+                'inceptDate': parse_date(datum.get('inceptDate')) if datum.get('inceptDate') else None
             }
         return datum
 

basic/sync.py

@@ -177,7 +177,9 @@ class IndexSync(JDCDataSync):
         return next_workday(last['date']) if last else self.start_date
 
     def build_urls(self, datum, start_date, page=0) -> str:
-        return f'http://jdcprod.thiztech.com/api/datas/index-value?page={page}&size=200&sourceCode={quote(datum["bloombergTicker"])}&sourceType=BLOOMBERG&startDate={format_date(start_date)}'
+        sourceCode = quote(datum["bloombergTicker"]) if quote(datum["bloombergTicker"]) else quote(datum["thsTicker"])
+        sourceType = 'BLOOMBERG' if quote(datum["bloombergTicker"]) else 'THS'
+        return f'http://jdcprod.thiztech.com/api/datas/index-value?page={page}&size=200&sourceCode={sourceCode}&sourceType={sourceType}&startDate={format_date(start_date)}'
 
     def store_date(self, datumid, datas: List[dict]):
         # add frdpe，frdpes,erp，pc
@@ -195,7 +197,8 @@ class IndexSync(JDCDataSync):
             'frdpes': x['forwardEps'] if 'forwardEps' in x else None,
             'erp': x['erp'] if 'erp' in x else None,
             'pc': x['pcRatio'] if 'pcRatio' in x else None,
-        } for x in datas if is_workday(dt.fromtimestamp(x['date'] / 1000, tz=pytz.timezone('Asia/Shanghai'))) and 'close' in x]
+        } for x in datas if
+            is_workday(dt.fromtimestamp(x['date'] / 1000, tz=pytz.timezone('Asia/Shanghai'))) and 'close' in x]
         if save_datas:
             rid.batch_insert(save_datas)
 
@@ -270,7 +273,6 @@ class FundNavSync(JDCDataSync):
             } for x in response['body']['content'] if x['fundId'] in ft_tickers
         }}
 
-
     def store_date(self, datumid, datas: List[dict]):
         save_navs = [{
             'fund_id': datumid,

config-svrobo_Mdiv_PRR3.yml

@@ -40,7 +40,7 @@ py-jftech:
       backtest: robo_executor.BacktestExecutor
       datum: basic.datum.DefaultDatum
       hold-report: portfolios.holder.DivHoldReportor
-      mpt: portfolios.builder.PoemARCPortfoliosBuilder
+      mpt: portfolios.builder.RiskParityARCPortfoliosBuilder
       dividend-holder: portfolios.holder.InvTrustPortfoliosHolder
       navs-sync: basic.sync.FundNavSync
   email:
@@ -115,7 +115,7 @@ portfolios: # 投组模块
   checker:  #投组检测模块
     switch: on  #是否开启检查
     custom-type-priority: [3,2,1,4]  # 检测优先级
-    month-fund-filter: {2:['TEMSCFA LX Equity'],12:['TEMHYAD LX Equity','TEMFIAI LX Equity']} # 根据月份删除某几档基金
+    month-fund-filter: {} # 根据月份删除某几档基金
 reports: # 报告模块相关
   navs:
     type: FUND
@@ -243,7 +243,7 @@ robo-executor: # 执行器相关
     start-date: 2023-01-02 # 回测起始日期
     end-date: 2023-10-31 # 回测截止日期
     sealing-period: 10 #调仓封闭期
-    start-step: ${BACKTEST_START_STEP:1} # 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
+    start-step: ${BACKTEST_START_STEP:2} # 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: on
   real: # 实盘执行器

portfolios/builder.py

@@ -121,7 +121,7 @@ class MptARCPortfoliosBuilder(MptPortfoliosBuilder):
         try:
             portfolio = rmp.get_one(day, type, risk)
             if not portfolio:
-                result, detail = self.build_portfolio(day, type)
+                result = self.build_portfolio(day, type)
                 for build_risk, datas in result.items():
                     datas['portfolio'] = self._checker.check(day, json.loads(datas['portfolio']))
                     try:
@@ -141,7 +141,9 @@ class MptARCPortfoliosBuilder(MptPortfoliosBuilder):
                 return {int(x[0]): x[1] for x in result.items()}
             return None
         except Exception as e:
-            logger.exception(f"build protfolio of type[{type.name}] and risk[{risk.name}] with date[{format_date(day)}] failure.", exc_info=e)
+            logger.exception(
+                f"build protfolio of type[{type.name}] and risk[{risk.name}] with date[{format_date(day)}] failure.",
+                exc_info=e)
             raise e
 
     def build_portfolio(self, day, type: PortfoliosType):
@@ -180,6 +182,7 @@ class MptARCPortfoliosBuilder(MptPortfoliosBuilder):
 
 @component(bean_name='mpt')
 class PoemARCPortfoliosBuilder(MptARCPortfoliosBuilder):
+
     def build_portfolio(self, day, type: PortfoliosType):
         result, detail = super(PoemARCPortfoliosBuilder, self).build_portfolio(day, type)
         risk = PortfoliosRisk.FT3
@@ -200,3 +203,24 @@ class PoemARCPortfoliosBuilder(MptARCPortfoliosBuilder):
             }
             detail[risk]['mpt_cvar'] = mpt_cvar
         return result, detail
+
+
+@component(bean_name='mpt')
+class RiskParityARCPortfoliosBuilder(MptPortfoliosBuilder):
+    def build_portfolio(self, day, type: PortfoliosType):
+        result = {}
+        risk = PortfoliosRisk.FT3
+        logger.info(
+            f"start to build protfolio of type[{type.name}] and risk[{risk.name}] with date[{format_date(day)}]")
+        solver = self._factory.create_solver(risk, type)
+        solver.reset_navs(day)
+        portfolio = solver.solve_risk_parity()
+        result[risk] = {
+            'solve': SolveType.RISK_PARITY,
+            'portfolio': json.dumps(portfolio),
+        } if portfolio else {
+            'solve': SolveType.INFEASIBLE
+        }
+        return result
+
+

portfolios/solver.py

@@ -81,6 +81,10 @@ class DefaultSolver(Solver):
         rtn = (self.navs / self.navs.shift(1) - 1)[1:]
         return rtn.cov() * 252
 
+    @property
+    def risk_parity_sigma(self):
+        return self.navs.cov()
+
     @property
     def rtn_history(self):
         result = self.rtn_matrix * 12
@@ -188,6 +192,14 @@ class DefaultSolver(Solver):
         self.debug_solve_result(model)
         return self.calc_port_weight(model), self.calc_port_cvar(model)
 
+    def solve_risk_parity(self):
+        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
+             for i in model.indices for j in model.indices]), sense=minimize)
+        self._solver.solve(model)
+        return self.calc_port_weight(model)
+
     def calc_port_weight(self, model):
         id_list = self.navs.columns
         weight_list = []