返回基金点评和雷达图

app/service/fund_service.py

+# -*- coding: UTF-8 -*-
+"""
+@author:Zongxi.Li
+@file:test.py
+@time:2021/01/15
+"""
 # -*- encoding: utf-8 -*-
 # -----------------------------------------------------------------------------
 # @File Name  : fund_service.py
@@ -6,8 +12,173 @@
 # @Email      : acepengxiong@163.com
 # @Software   : PyCharm
 # -----------------------------------------------------------------------------
+from app.service.portfolio_diagnose import *
+
+
+def get_tamp_nav(fund, start_date, rollback=False, invest_type=2):
+    """获取基金ID为fund, 起始日期为start_date, 终止日期为当前日期的基金净值表
+
+    Args:
+        fund[str]:基金ID
+        start_date[date]:起始日期
+        rollback[bool]:当起始日期不在净值公布日历中，是否往前取最近的净值公布日
+        invest_type[num]:0:公募 1:私募 2:优选
+
+    Returns:df[DataFrame]: 索引为净值公布日， 列为复权净值的净值表; 查询失败则返回None
+
+    """
+    with TAMP_SQL(tamp_product_engine) as tamp_product, TAMP_SQL(tamp_fund_engine) as tamp_fund:
+        tamp_product_session = tamp_product.session
+        tamp_fund_session = tamp_fund.session
+        # if invest_type == "private":
+        #     sql = "SELECT fund_id, price_date, cumulative_nav FROM fund_nav " \
+        #           "WHERE fund_id='{}'".format(fund)
+        #     # 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(
+                fund)
+            cur = tamp_fund_session.execute(sql)
+        elif invest_type == 1:
+            sql = """select distinct `fund_id`, `price_date`,`cumulative_nav` from `fund_nav` where `fund_id`='{}'  order by `price_date` ASC""".format(
+                fund)
+            cur = tamp_fund_session.execute(sql)
+        elif invest_type == 2:
+            sql = """select distinct `fund_id`,`price_date`,`cumulative_nav` from `fund_nav` where `fund_id`='{}'  order by `price_date` ASC""".format(
+                fund)
+            cur = tamp_product_session.execute(sql)
+
+        data = cur.fetchall()
+        df = pd.DataFrame(data, columns=['fund_id', 'price_date', 'cumulative_nav']).dropna(how='any')
+        df.rename({'price_date': 'end_date', 'cumulative_nav': 'adj_nav'}, axis=1, inplace=True)
+        df['end_date'] = pd.to_datetime(df['end_date'])
+
+        if rollback and df['end_date'].min() < start_date < df['end_date'].max():
+            while start_date not in list(df['end_date']):
+                start_date -= datetime.timedelta(days=1)
+
+        df = df[df['end_date'] >= start_date]
+        df.drop_duplicates(subset='end_date', inplace=True, keep='first')
+        df.set_index('end_date', inplace=True)
+        df.sort_index(inplace=True, ascending=True)
+    return df
+
+
+def single_evaluation(fund_id, invest_type=2, index_id='000905.SH'):
+    """
+       1、该基金整体表现优秀/良好/一般，收益能力优秀/良好/合格/较差，回撤控制能力优秀/良好/合格/较差，风险收益比例较高/一般/较低；
+       2、在收益方面，该基金年化收益能力高于/持平/低于同类基金平均水平，有x%区间跑赢大盘/指数，绝对收益能力优秀/一般；
+       3、在风险方面，该基金抵御风险能力优秀/良好/一般，在同类基金中处于高/中/低等水平，最大回撤为x%，高于/持平/低于同类基金平均水平；
+       4、该基金收益较好/较差的同时回撤较大/较小，也就是说，该基金在用较大/较小风险换取较大/较小收益，存在较高/较低风险；
+       5、基金经理，投资年限5.23年，经验丰富；投资能力较强，生涯中共管理过X只基金，历任的X只基金平均业绩在同类中处于上游水平，其中x只排名在前x%；生涯年化回报率x%，同期大盘只有x%；
+    Args:
+        fund_id:
+
+    Returns:
+    """
+    end_date = datetime.datetime(datetime.date.today().year,
+                                 datetime.date.today().month, 1) - datetime.timedelta(1)
+    start_date = cal_date(end_date, 'Y', 1)
+
+    z_score = search_rank(fund_rank, fund_id, metric='z_score')
+    total_level = np.select([z_score >= 80,
+                             70 <= z_score < 80,
+                             z_score < 70], [0, 1, 2]).item()
+
+    index_return_monthly = get_index_monthly(index_id, start_date)
+    fund_nav = get_tamp_nav(fund_id, start_date, invest_type=invest_type)
+    fund_nav_monthly = fund_nav.groupby([fund_nav.index.year, fund_nav.index.month]).tail(1)
+    fund_nav_monthly = rename_col(fund_nav_monthly, fund_id)
+    fund_return_monthly = simple_return(fund_nav_monthly[fund_id].astype(float))
+    index_return_monthly.index = index_return_monthly.index.strftime('%Y-%m')
+    fund_return_monthly.index = fund_return_monthly.index.strftime('%Y-%m')
+    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_level = np.select([return_rank >= 0.8,
+                              0.7 <= return_rank < 0.8,
+                              0.6 <= return_rank < 0.7,
+                              return_rank < 0.6], [0, 1, 2, 3]).item()
+    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_level = np.select([drawdown_rank >= 0.8,
+                                0.7 <= drawdown_rank < 0.8,
+                                0.6 <= drawdown_rank < 0.7,
+                                drawdown_rank < 0.6], [0, 1, 2, 3]).item()
+    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_level = np.select([sharp_rank >= 0.8,
+                             0.6 <= sharp_rank < 0.8,
+                             sharp_rank < 0.6], [0, 1, 2]).item()
+
+    data = {1: [total_level, return_level, drawdown_level, sharp_level],
+            2: [return_triple, format(fund_win_rate, '.2%'), return_bool],
+            3: [drawdown_triple, drawdown_triple, format(drawdown_value, '.2%'), drawdown_triple],
+            4: [return_bool, drawdown_bool, drawdown_bool, return_bool, drawdown_bool]}
+
+    x = '30%'
+    content = {
+        # 第一个评价
+        1: [["优秀", "良好", "一般"],
+            ["优秀", "良好", "合格", "较差"],
+            ["优秀", "良好", "合格", "较差"],
+            ["高", "一般", "较低"]],
+        # 第二个评价
+        2: [["高于", "持平", "低于"],
+            x,
+            ["优秀", "一般"]],
+        # 第三个评价
+        3: [["优秀", "良好", "一般"],
+            ["高", "中", "低"], x,
+            ["高于", "持平", "低于"]],
+        # 第四个评价
+        4: [["较好", "较差"],
+            ["较小", "较大"],
+            ["较小", "较小"],
+            ["较大", "较小"],
+            ["较低", "较高"]]}
+
+    sentence = {
+        1: "该基金整体表现%s，收益能力%s，回撤控制能力%s，风险收益比例%s；\n",
+        2: "在收益方面，该基金年化收益能力%s同类基金平均水平，有%s区间跑赢指数，绝对收益能力%s；\n",
+        3: "在风险方面，该基金抵御风险能力%s，在同类基金中处于%s等水平，最大回撤为%s，%s同类基金平均水平；\n",
+        4: "该基金收益%s的同时回撤%s，也就是说，该基金在用%s风险换取%s收益，存在%s风险；\n"}
+
+    ret = []
+    fund_name = get_fund_name(fund_id).values[0][0]
+
+    i = 1
+    for k, v in data.items():
+        single_sentence = str(i) + "、" + sentence[k] % translate_single(content, k, v)
+        ret.append(single_sentence)
+        i += 1
+
+    evaluation_dict = {'name': fund_name, 'data': ret}
+
+    # if objective:
+    #     if fund_id in self.abandon_fund_score + self.abandon_fund_corr:
+    #         evaluation_dict['status'] = "换仓"
+    #     elif fund_id in self.portfolio:
+    #         evaluation_dict['status'] = "保留"
+    # else:
+    #     evaluation_dict['status'] = ""
+    return evaluation_dict
+
 
 def get_fund_evaluation(args):
     """返回个基点评和雷达图"""
     fund_id = args.get('fund_id')
-    single_evaluation(self, fund_id, objective=False)
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+    invest_type = args.get('invest_type')
+    print(single_evaluation(fund_id='HF00005AFK'))
+    print(get_radar_data('HF00005AFK'))
+
+
+if __name__ == '__main__':
+    print(single_evaluation(fund_id='HF00005AFK'))
+    print(get_radar_data('HF00005AFK'))
\ No newline at end of file