Merge branch 'manage-public' of http://47.100.44.39:10001/pengxiong/fund_report into manage-public

app/controller/statement.py

+# -*- coding: UTF-8 -*-
+"""
+@author:Zongxi.Li
+@file:statement.py
+@time:2021/02/02
+"""
+import json
+from datetime import datetime, date
+from decimal import Decimal
+
+from flask_restful import Resource, reqparse
+from app.service.statement_service import Statement
+from app.utils.auth import login_require
+from flask import g
+
+
+class DateEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, (datetime, date)):
+            return obj.__str__()
+        if isinstance(obj, Decimal):
+            return obj.__float__()
+        return json.JSONEncoder.default(self, obj)
+
+
+class StatementController(Resource):
+    def __init__(self):
+        """."""
+        self.parser = reqparse.RequestParser()
+
+    @login_require
+    def get(self):
+        """"""
+        # self.parser.add_argument('user_id', type=str, required=True, help='用户id不能为空')
+        self.parser.add_argument('statement_time', required=False, help='对账单时间戳')
+        args = self.parser.parse_args()
+        print(args.get('statement_time'))
+        # user_id = args.get('user_id')
+        user_id = g.ifa_id
+        print(user_id)
+        if args.get('statement_time') != '':
+            year = datetime.fromtimestamp(int(args.get('statement_time')) / 1000).year
+            month = datetime.fromtimestamp(int(args.get('statement_time')) / 1000).month
+        else:
+            year = datetime.now().year
+            month = datetime.now().month - 1
+        data = Statement(user_id=user_id, year=year, month=month).summary()
+        resp = {
+            "statusCode": "0000",
+            "message": "成功",
+            "attributes": data
+        }
+        return json.loads(json.dumps(resp, cls=DateEncoder, ensure_ascii=False))

app/router/version1.py

@@ -10,6 +10,7 @@ from app.controller.customer import CustomerList
 from app.controller.fund import FundEvaluation
 from app.controller.report import *
 from app.controller.template_manage import *
+from app.controller.statement import StatementController
 
 def add_route(api):
     """注册路由"""
@@ -20,4 +21,5 @@ def add_route(api):
     api.add_resource(TemplateList, '/api/template_list')
     api.add_resource(CustomerList, '/api/customer_list')
     api.add_resource(FundEvaluation, '/api/fund_evaluation')
+    api.add_resource(StatementController, '/api/statement')
 

app/service/portfolio_diagnose.py

@@ -14,6 +14,7 @@ from app.pypfopt import risk_models
 from app.pypfopt import expected_returns
 from app.pypfopt import EfficientFrontier
 from app.api.engine import tamp_product_engine, tamp_fund_engine, TAMP_SQL
+from app.service.substrategy_dict import get_substrategy_name
 
 
 def cal_correlation(prod):
@@ -155,25 +156,41 @@ def choose_bad_evaluation(evaluation):
     return evaluation
 
 
-def get_fund_rank():
+def get_fund_rank(fund_type=1):
     """获取基金指标排名
 
     :return: 基金指标排名表
     """
-    with TAMP_SQL(tamp_fund_engine) as tamp_fund:
-        tamp_fund_session = tamp_fund.session
-        sql = "SELECT * FROM new_fund_rank"
+    if fund_type == 1:
+        with TAMP_SQL(tamp_fund_engine) as tamp_fund:
+            tamp_fund_session = tamp_fund.session
+            sql = "SELECT * FROM new_fund_rank"
 
-        # df = pd.read_sql(sql, con)
-        # df = pd.read_csv('fund_rank.csv', encoding='gbk')
-        cur = tamp_fund_session.execute(sql)
-        data = cur.fetchall()
-        df = pd.DataFrame(list(data), columns=['index', 'fund_id', 'range_return', 'annual_return', 'max_drawdown',
-                                               'sharp_ratio', 'volatility', 'sortino_ratio', 'downside_risk',
-                                               'substrategy', 'manager', 'annual_return_rank', 'downside_risk_rank',
-                                               'max_drawdown_rank', 'sharp_ratio_rank', 'z_score'])
-        df.drop('index', axis=1, inplace=True)
-        return df
+            # df = pd.read_sql(sql, con)
+            # df = pd.read_csv('fund_rank.csv', encoding='gbk')
+            cur = tamp_fund_session.execute(sql)
+            data = cur.fetchall()
+            df = pd.DataFrame(list(data), columns=['index', 'fund_id', 'range_return', 'annual_return', 'max_drawdown',
+                                                   'sharp_ratio', 'volatility', 'sortino_ratio', 'downside_risk',
+                                                   'substrategy', 'manager', 'annual_return_rank', 'downside_risk_rank',
+                                                   'max_drawdown_rank', 'sharp_ratio_rank', 'z_score'])
+            df.drop('index', axis=1, inplace=True)
+            return df
+    else:
+        with TAMP_SQL(tamp_fund_engine) as tamp_fund:
+            tamp_fund_session = tamp_fund.session
+            sql = "SELECT * FROM tx_fund_rank"
+
+            # df = pd.read_sql(sql, con)
+            # df = pd.read_csv('fund_rank.csv', encoding='gbk')
+            cur = tamp_fund_session.execute(sql)
+            data = cur.fetchall()
+            df = pd.DataFrame(list(data), columns=['index', 'fund_id', 'range_return', 'annual_return', 'max_drawdown',
+                                                   'sharp_ratio', 'volatility', 'sortino_ratio', 'downside_risk',
+                                                   'substrategy', 'manager', 'annual_return_rank', 'downside_risk_rank',
+                                                   'max_drawdown_rank', 'sharp_ratio_rank', 'z_score'])
+            df.drop('index', axis=1, inplace=True)
+            return df
 
 
 def get_index_daily(index_id, start_date):
@@ -267,7 +284,7 @@ def get_tamp_nav(fund, start_date, rollback=False, invest_type=2):
         #     # df = pd.read_sql(sql, con).dropna(how='any')
         #     cur = tamp_product_session.execute(sql)
         if invest_type == 0:
-            sql = """select distinct `id`, `end_date`, `accum_nav` from `public_fund_nav` where `id`='{}'  order by `end_date` ASC""".format(
+            sql = """select distinct `fund_id`, `price_date`, `cumulative_nav` from `tx_fund_nav` where `fund_id`='{}' and `delete_tag`=0 order by `price_date` ASC""".format(
                 fund)
             cur = tamp_fund_session.execute(sql)
         elif invest_type == 1:
@@ -388,7 +405,7 @@ def get_fund_name(fund, fund_type=1):
     if fund_type == 0:
         with TAMP_SQL(tamp_fund_engine) as tamp_fund:
             tamp_fund_session = tamp_fund.session
-            sql = "SELECT name FROM public_fund_basic WHERE ts_code='{}'".format(fund)
+            sql = "SELECT fund_short_name FROM tx_fund_info WHERE `id`='{}'".format(fund)
             # df = pd.read_sql(sql, con)
             cur = tamp_fund_session.execute(sql)
             data = cur.fetchall()
@@ -425,6 +442,7 @@ def get_fund_name(fund, fund_type=1):
 
 # 获取排名信息
 fund_rank = get_fund_rank()
+tx_fund_rank = get_fund_rank(0)
 # 获取探普产品池
 tamp_fund = get_tamp_fund()
 
@@ -848,7 +866,7 @@ class PortfolioDiagnose(object):
             data_adaptor.user_customer_order_df["folio_name"] == group_name]
         strategy_list = group_order_df["substrategy"]
         uniqe_strategy = list(strategy_list.unique())
-        uniqe_strategy_name = [dict_substrategy[int(x)] + "、" for x in uniqe_strategy]
+        uniqe_strategy_name = [get_substrategy_name(x) + "、" for x in uniqe_strategy]
         # 覆盖的基金名称
         strategy_name_evaluate = "".join(uniqe_strategy_name)[:-1]
 
@@ -1012,7 +1030,7 @@ class PortfolioDiagnose(object):
             # 基金名称，策略分级
             propose_fund_id_name_list = [propose_fund_df[propose_fund_df["fund_id"] == fund_id]["fund_name"].values[0] for
                                          fund_id in propose_fund_id_list]
-            propose_fund_id_strategy_name_list = [dict_substrategy[int(propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0])] for
+            propose_fund_id_strategy_name_list = [dict_substrategy(propose_fund_df[propose_fund_df["fund_id"] == fund_id]["substrategy"].values[0]) for
                                          fund_id in propose_fund_id_list]
             propose_fund_asset = [round(self.new_weights[i] * total_asset, 2) for i in range(len(propose_fund_id_name_list))]
 
@@ -1174,7 +1192,12 @@ class PortfolioDiagnose(object):
 
         Returns:
         """
-        z_score = search_rank(fund_rank, fund_id, metric='z_score')
+        if fund_id_type == 0:
+            rank_df = tx_fund_rank
+        else:
+            rank_df = tx_fund_rank
+
+        z_score = search_rank(rank_df, fund_id, metric='z_score')
         total_level = np.select([z_score >= 80,
                                  70 <= z_score < 80,
                                  z_score < 70], [0, 1, 2]).item()
@@ -1192,7 +1215,7 @@ class PortfolioDiagnose(object):
         compare = pd.merge(index_return_monthly, fund_return_monthly, how='inner', left_index=True, right_index=True)
         fund_win_rate = ((compare[fund_id] - compare['pct_chg']) > 0).sum() / compare[fund_id].count()
 
-        return_rank = search_rank(fund_rank, fund_id, metric='annual_return_rank')
+        return_rank = search_rank(rank_df, fund_id, metric='annual_return_rank')
         return_level = np.select([return_rank >= 0.8,
                                   0.7 <= return_rank < 0.8,
                                   0.6 <= return_rank < 0.7,
@@ -1200,8 +1223,8 @@ class PortfolioDiagnose(object):
         return_bool = 1 if return_level > 2 else 0
         return_triple = return_level - 1 if return_level >= 2 else return_level
 
-        drawdown_rank = search_rank(fund_rank, fund_id, metric='max_drawdown_rank')
-        drawdown_value = search_rank(fund_rank, fund_id, metric='max_drawdown')
+        drawdown_rank = search_rank(rank_df, fund_id, metric='max_drawdown_rank')
+        drawdown_value = search_rank(rank_df, fund_id, metric='max_drawdown')
         drawdown_level = np.select([drawdown_rank >= 0.8,
                                     0.7 <= drawdown_rank < 0.8,
                                     0.6 <= drawdown_rank < 0.7,
@@ -1209,7 +1232,7 @@ class PortfolioDiagnose(object):
         drawdown_bool = 1 if drawdown_level > 2 else 0
         drawdown_triple = drawdown_level - 1 if drawdown_level >= 2 else drawdown_level
 
-        sharp_rank = search_rank(fund_rank, fund_id, metric='sharp_ratio_rank')
+        sharp_rank = search_rank(rank_df, fund_id, metric='sharp_ratio_rank')
         sharp_level = np.select([sharp_rank >= 0.8,
                                  0.6 <= sharp_rank < 0.8,
                                  sharp_rank < 0.6], [0, 1, 2]).item()
@@ -1311,7 +1334,7 @@ class PortfolioDiagnose(object):
             result = []
             for fund in self.portfolio:
                 try:
-                    result.append(self.single_evaluation(fund, objective))
+                    result.append(self.single_evaluation(fund, self.portfolio_dict.get(fund, 1), objective))
                 except IndexError:
                     continue
             return result

app/service/substrategy_dict.py

+dict_substrategy = {
+    "-1": "其他策略",
+    "1010": '主观多头',
+    "1020": '股票多空',
+    "1030": '量化多头',
+    "2010": '宏观策略',
+    "3010": '主观趋势',
+    "3020": '主观套利',
+    "3030": '量化趋势',
+    "3040": '量化套利',
+    "3050": 'CTA策略',
+    "4010": '并购重组',
+    "4020": '定向增发',
+    "4030": '大宗交易',
+    "4040": '事件驱动复合',
+    "5010": '市场中性',
+    "5020": '套利策略',
+    "5030": '相对价值复合',
+    "6010": '纯债策略',
+    "6020": '强债策略',
+    "6030": '债券策略',
+    "7010": 'MOM',
+    "7020": 'FOF',
+    "8010": '主观多策略',
+    "8020": '量化多策略',
+    "A": "股票型",
+    "B": "混合型",
+    "C": "债券型",
+    "D": "货币基金",
+    "E": "QDII",
+    "F": "其他",
+    "J": "商品",
+    "K": "FOF",
+    "A01": "股票->积极投资股票型",
+    "A02": "股票->指数型",
+    "B01": "混合->灵活配置型",
+    "B02": "混合->积极配置型",
+    "B03": "混合->保守配置型",
+    "B04": "混合->保本型",
+    "B05": "混合->特定策略型",
+    "C01": "债券->纯债型",
+    "C02": "债券->一级债基",
+    "C03": "债券->二级债基",
+    "C04": "债券->理财债基",
+    "D01": "货币A",
+    "D02": "货币B",
+    "D03": "货币R",
+    "E01": "QDII->股票",
+    "E02": "QDII->混合",
+    "J01": "商品ETF",
+    "J02": "商品期货",
+    "J03": "商品ETF联接",
+    "A0101": "积极股票型",
+    "A0102": "稳健股票型",
+    "A0201": "纯指数型",
+    "A0202": "增强指数型",
+    "C0101": "债券->纯债型",
+    "C0102": "债券->指数型债券",
+    "C0103": "债券->债券ETF",
+    "C0104": "债券->债券ETF联接",
+    "C0105": "债券->增强型指数型债券",
+    "E0101": "QDII->股票被动型",
+    "E0102": "QDII->股票主动型",
+    "E0201": "QDII->混合",
+    "E0202": "QDII->债券",
+    "E0203": "QDII->商品",
+    "E0204": "QDII->FOF",
+    "E0205": "QDII->其他基金",
+    "A020101": "股票->股票ETF",
+    "A020102": "股票->股票ETF联接",
+    "A020103": "股票->纯指数型",
+    "E010101": "QDII->股票被动型",
+    "E010102": "QDII->股票被动型",
+    "E010103": "QDII->股票被动型",
+    "E010104": "QDII->股票被动型",
+    "E010201": "QDII->股票主动型",
+    "E010202": "QDII->股票主动型",
+    "E010203": "QDII->股票主动型",
+    "E010204": "QDII->股票主动型",
+    "E010205": "QDII->股票主动型",
+    "E010206": "QDII->股票主动型",
+    "E020101": "QDII->全球股债混合",
+    "E020102": "QDII->亚太股债混合",
+    "E020103": "QDII->大中华市场",
+    "E020104": "QDII->新兴股债混合",
+    "E020201": "QDII->债券主动型",
+    "E020202": "QDII->债券主动型",
+    "E020203": "QDII->债券主动型",
+    "E020204": "QDII->债券主动型",
+    "E020205": "QDII->债券被动型",
+    "E020206": "QDII->债券被动型",
+    "E020207": "QDII->债券被动型",
+    "E020301": "QDII->商品实物",
+    "E020302": "QDII->商品ETF",
+    "E020303": "QDII->商品主动股票"
+}
+
+
+def get_substrategy_name(sub):
+    try:
+        sub = int(sub)
+        code = str(sub)
+        strategy_name = dict_substrategy[code]
+        return strategy_name
+    except ValueError:
+        pass
+    try:
+        code = str(sub)
+        strategy_name = dict_substrategy[code]
+        return strategy_name
+    except:
+        return dict_substrategy["-1"]
+
+

app/utils/week_evaluation.py

@@ -11,10 +11,10 @@ import calendar
 import math
 
 
-dict_substrategy = {1010: '主观多头', 1020: '股票多空', 1030: '量化多头', 2010: '宏观策略', 3010: '主观趋势', 3020: '主观套利',
-                            3030: '量化趋势', 3040: '量化套利', 3050: 'CTA策略', 4010: '并购重组', 4020: '定向增发', 4030: '大宗交易',
-                            4040: '事件驱动复合', 5010: '市场中性', 5020: '套利策略', 5030: '相对价值复合', 6010: '纯债策略', 6020: '强债策略',
-                            6030: '债券策略', 7010: 'MOM', 7020: 'FOF', 8010: '主观多策略', 8020: '量化多策略', -1: '其他策略'}
+# dict_substrategy = {1010: '主观多头', 1020: '股票多空', 1030: '量化多头', 2010: '宏观策略', 3010: '主观趋势', 3020: '主观套利',
+#                             3030: '量化趋势', 3040: '量化套利', 3050: 'CTA策略', 4010: '并购重组', 4020: '定向增发', 4030: '大宗交易',
+#                             4040: '事件驱动复合', 5010: '市场中性', 5020: '套利策略', 5030: '相对价值复合', 6010: '纯债策略', 6020: '强债策略',
+#                             6030: '债券策略', 7010: 'MOM', 7020: 'FOF', 8010: '主观多策略', 8020: '量化多策略', -1: '其他策略'}
 BANK_RATE = 0.015