新版不需要经过计算推荐的客观持仓报告

app/service/fund_performance.py

+#!/usr/bin/python3.6
+# -*- coding: utf-8 -*-
+# @Time    : 2020/12/25 13:16
+# @Author  : Jie. Z
+# @Email   : zhaojiestudy@163.com
+# @File    : fund_performance.py
+# @Software: PyCharm
+import pandas as pd
+
+
+# 数据处理 入库操作
+def dataprepare(fund_id , fav_freq , index_code, enddate, strategy, data_source, status):
+
+    #银行利率，暂时写死，后期改为动态获取
+    BANK_RATE = 0.015
+    # 同时读取基金净值和指数，条件是日期同时存在
+    '''
+    global  trade_df
+    if trade_df.empty:
+        trade_df = gettradecal('2001-01-01', datetime.date.strftime(datetime.date.today(), '%Y-%m-%d'))
+    '''
+
+    df1 = gettradecal('2001-01-01', datetime.date.strftime(datetime.date.today(), '%Y-%m-%d'))
+
+
+    global current_env ,origion_env
+    pool = PooledDB(pymysql,  maxconnections=100, blocking= True,  host=origion_env[current_env]['host'], port=int(origion_env[current_env]['port']) ,user=origion_env[current_env]['user'], password=origion_env[current_env]['password'],database=origion_env[current_env]['database'], charset="utf8")  # 5为连接池里的最少连接数
+    conn = pool.connection()  # 以后每次需要数据库连接就是用connection（）函数获取连接就好了
+    cur = conn.cursor()
+
+    if index_code == '000000':
+        handle_indexes = True
+    else:
+        handle_indexes = False
+
+    if handle_indexes:
+        #待修改
+        sql = """SELECT distinct a.price_date as datetime,a.close as net_worth, a.close as benchmark, a.close as net FROM fund_market_indexes a WHERE  a.index_id= '%s' and a.price_date<= '%s' order by a.price_date ASC""" % (fund_id, enddate)
+    else:
+        sql = """SELECT distinct a.price_date as datetime,a.cumulative_nav as net_worth,b.close as benchmark, a.nav as net FROM fund_nav a ,fund_market_indexes b WHERE a.price_date=b.price_date and b.index_id= '%s' and a.fund_id= '%s' and a.price_date<= '%s' order by a.price_date ASC""" % (index_code, fund_id, enddate)
+    print(sql)
+    cur.execute(sql)
+
+    # 5.获取数据
+    data = cur.fetchall()
+
+    if len(data)==0:
+        return
+    # 处理数据
+    col_result = cur.description
+    columns = []
+    for i in range(len(col_result)):
+        columns.append(col_result[i][0])
+
+    # 设置dataframe的columns
+    df = pd.DataFrame(columns=columns)
+
+    # 插入数据，把查询结果存入dataframe
+
+    for i in range(len(data)):
+        df.loc[i] = list(data[i])
+    df['datetime'] = pd.to_datetime(df['datetime'], format='%Y/%m/%d')
+
+    # 6.关闭cursor
+    cur.close()
+
+    # 7.关闭connection
+    conn.close()
+
+    # 字符串转化为float
+    df['net_worth'] = handle_data(df['net_worth'])
+    df['net'] = handle_data(df['net'])
+    df['benchmark'] = handle_data(df['benchmark'])
+
+    if len(df) < 1:
+        return
+
+    print('重采样前：',len(df))
+    print(df)
+    # 判断基金净值更新频率，进行dataframe处理对齐
+    # 如果是日更，则按照交易日补齐，没有数据的用昨日数据；如果是周更，取当周最后一个更新，如果没有数据则取前一周数据；如果是月更，取当月最后一个数据，如果没有数据则取前一个月数据；
+    # 收益率类型，1: 天；2: 周；3: 半月；4: 月；5: 季【fund_profit_rate，天/周/月】
+    df = df.drop_duplicates(['datetime'])
+    df.set_index('datetime', inplace=True)
+    df.dropna(axis=0, how='any', inplace=True)  # 删除表中含有任何NaN的行
+
+
+    #把df重采样
+    #df = resample(df=df, trading_cal=df1, freq=fav_freq)
+    print('重采样后：',len(df))
+    print(df)
+
+    #保留原始dataframe
+    origin_df = df
+
+    # 获取序列中的开始和结束时间
+    datestart = df.index[0]
+    dateend = df.index[-1]
+    strstartdate = datestart.strftime("%Y%m%d")
+    strenddate = dateend.strftime("%Y%m%d")
+
+    #避免出现指数还没更新，但净值已更新的情况
+    if dateend < enddate:
+        return
+
+    print('---Step1: 开始准备数据---')
+    #最近净值日期、最近净值
+    price_date = origin_df.index[-1]
+    cumulative_nav = origin_df.iloc[-1,0]
+    cumulative_index = origin_df.iloc[-1,1]
+    cumulative_ori_nav = origin_df.iloc[-1,2]
+    print('最近净值日期/值: %s and %s ' %(price_date,cumulative_nav))
+
+
+    # 根据基金更新频率产生对应的N,数据类型， 周（52）， 月（12）， 日（250)
+    if fav_freq == 1:
+        N = 250  # 日更新
+    elif fav_freq == 2:
+        N = 52  # 周更新
+    elif fav_freq == 3:
+        N = 24  # 半周更新
+    elif fav_freq == 4:
+        N = 12  # 月更新
+    elif fav_freq == 5:
+        N = 3  # 季度更新
+    else:
+        N = 250  #没有设置freq默认是天更
+    print(N)
+
+    print('开始计算:')
+    # 区间收益
+    # 前1/3/6收益率
+    print('当前日期', dateend)
+    m = [1, 3, 6]
+    range_return_m = []
+    b_range_return_m = []
+    cum_returns_m = []
+    b_cum_returns_m = []
+
+    for i in range(len(m)):
+        pre_result , pre_date = dfmonth_minus(df,dateend, m[i])
+        print('pre_date:' , pre_date, 'nowdate:' , dateend)
+        print('.....近N月时间.', pre_date)
+
+        if pre_result == 1:
+            now_value = df.loc[df.index == dateend]['net_worth'][0]
+            old_value = df.loc[df.index == pre_date]['net_worth'][0]
+            r = '%.6f' % (((now_value/old_value)-1))
+            range_return_m.append(r)
+            print (now_value,old_value,r)
+
+
+            now_value = df.loc[df.index == dateend]['benchmark'][0]
+            old_value = df.loc[df.index == pre_date]['benchmark'][0]
+            b_r = '%.6f' % (((now_value/old_value)-1))
+            print (now_value,old_value,b_r)
+            b_range_return_m.append(b_r)
+
+
+            # 简单收益率
+            # print(y_time['net_worth'])
+            y_time = df.loc[(df.index >= pre_date) & (df.index <= dateend)]
+            y_time.loc[:, 'returns'] = simple_return(y_time.loc[:, 'net_worth'])
+            y_time.loc[:, 'b_returns'] = simple_return(y_time.loc[:, 'benchmark'])
+
+            # 计算累积收益率
+            y_time.loc[:, 'cum_returns'] = np.cumprod(1 + y_time.loc[:, 'returns'].astype("float64")) - 1
+            y_time.loc[:, 'b_cum_returns'] = np.cumprod(1 + y_time.loc[:, 'b_returns'].astype("float64")) - 1
+            print(y_time)
+
+
+            cum_returns_m.append(y_time['cum_returns'].iloc[-1])
+            b_cum_returns_m.append(y_time['b_cum_returns'].iloc[-1])
+
+
+    # 最近1/2/3/4/5/10年、今年以来、成立以来、收益率，波动率，夏普，回撤
+    y = [1, 2, 3, 4, 5, 10]
+    range_return_y = []  #收益率
+    b_range_return_y = []
+    annual_return_y = []  #年化收益率
+    b_annual_return_y = []
+    date_y = [] #日期
+    range_md_y = []  #回撤
+    b_range_md_y = []
+    range_md_y_begin = []
+    range_md_y_end = []
+    b_range_md_y_begin = []
+    b_range_md_y_end = []
+    sigma_y = []
+    b_sigma_y = []
+    sharpe_y = []
+    b_sharpe_y = []
+    alpha_y = []
+    beta_y = []
+    sortino_y = []
+    b_sortino_y = []
+    sortion_ratio_y = []
+    b_sortion_ratio_y = []
+
+    cum_returns_y = []
+    b_cum_returns_y = []
+
+    for i in range(len(y)):
+        pre_result , pre_date = dfyear_minus(df,dateend, y[i])
+        print('.....近N年时间.', 'pre_date:' , pre_date, 'nowdate:' , dateend)
+
+        if pre_result == -1:
+            r = b_r = np.nan
+            n_m = b_m = np.nan
+        else:
+            y_time = df.loc[(df.index >= pre_date) & (df.index <= dateend)]
+
+            #基础收益率、基准收益率
+            r , b_r = simple_2days_return(y_time , pre_date ,dateend )
+            print ('base 基准率：' , pre_date,dateend,r, b_r)
+
+            range_return_y.append(r)
+            b_range_return_y.append(b_r)
+
+            #最大回撤、基准最大回撤（回撤值，开始位置，结束位置）
+            n_m, n_m_begin, n_m_end = max_drawdown(list(y_time['net_worth']))
+            range_md_y.append('%.6f' % (n_m))
+            range_md_y_begin.append(y_time.index[n_m_begin])
+            range_md_y_end.append(y_time.index[n_m_end])
+
+            b_m, b_n_m_begin, b_n_m_end = max_drawdown(list(y_time['benchmark']))
+            b_range_md_y.append('%.6f' % (b_m ))
+            b_range_md_y_begin.append(y_time.index[b_n_m_begin])
+            b_range_md_y_end.append(y_time.index[b_n_m_end])
+
+
+            date_y.append(pre_date)
+
+            # 简单收益率
+            # print(y_time['net_worth'])
+            y_time.loc[:, 'returns'] = simple_return(y_time.loc[:, 'net_worth'])
+            y_time.loc[:, 'b_returns'] = simple_return(y_time.loc[:, 'benchmark'])
+
+            # 计算累积收益率
+            y_time.loc[:, 'cum_returns'] = np.cumprod(1 + y_time.loc[:, 'returns'].astype("float64")) - 1
+            y_time.loc[:, 'b_cum_returns'] = np.cumprod(1 + y_time.loc[:, 'b_returns'].astype("float64")) - 1
+            cum_returns_y.append(y_time['cum_returns'].iloc[-1])
+            b_cum_returns_y.append(y_time['b_cum_returns'].iloc[-1])
+
+
+            #计算超额收益率
+            y_time.loc[:, 'excess_returns'] = excess_return(y_time.loc[:, 'returns'].astype("float64"), BANK_RATE / N)
+            y_time.loc[:, 'b_excess_returns'] = excess_return(y_time.loc[:, 'b_returns'].astype("float64"), BANK_RATE / N)
+
+
+            #（年化）区间收益率、基准收益率
+            print('test_0:', r ,b_r)
+            an_r = annual_return(float(r) , y_time ,N)
+            b_an_r = annual_return(float(b_r) , y_time , N)
+            print('test:', an_r , b_an_r)
+            annual_return_y.append('%.6f' % (an_r))
+            b_annual_return_y.append('%.6f' % (b_an_r))
+
+            # 夏普比率
+            sharpe_y.append('%.6f' %sharpe_ratio(y_time.loc[:, 'excess_returns'], y_time.loc[:, 'returns'].astype("float64"), N))
+            b_sharpe_y.append('%.6f' %sharpe_ratio(y_time.loc[:, 'b_excess_returns'], y_time.loc[:, 'b_returns'].astype("float64"), N))
+            # 波动率
+            sigma_y.append('%.6f' %volatility(y_time.loc[:, 'returns'].astype("float64"), N))
+            b_sigma_y.append('%.6f' %volatility(y_time.loc[:, 'b_returns'].astype("float64"), N))
+            print(sharpe_y)
+            print(sigma_y)
+            #alpha/beta
+            alpha , beta = alpha_beta (y_time.loc[:, 'returns'].astype("float64") , y_time.loc[:, 'b_returns'].astype("float64") , N)
+            alpha_y.append('%.6f' %alpha)
+            beta_y.append(('%.6f' % beta))
+            #索提诺比率
+
+            n_downside = downside_risk(y_time.loc[:, 'returns'].astype("float64"), BANK_RATE, N)
+            b_downside = downside_risk(y_time.loc[:, 'b_returns'].astype("float64"), BANK_RATE, N)
+            n_sortino_ratio = sortino_ratio(y_time.loc[:, 'excess_returns'].astype("float64"), n_downside, N)
+            b_sortino_ratio = sortino_ratio(y_time.loc[:, 'b_excess_returns'].astype("float64"), b_downside, N)
+            sortino_y.append('%.6f' %n_downside)
+            b_sortino_y.append('%.6f' %b_downside)
+            sortion_ratio_y.append('%.6f' %n_sortino_ratio)
+            b_sortion_ratio_y.append('%.6f' % b_sortino_ratio)
+
+
+    #日/周、收益率/基准收益率
+    y_time = df
+    print(y_time.loc[:, 'net_worth'])
+    y_time.loc[:, 'returns'] = simple_return(y_time.loc[:, 'net_worth'])
+    y_time.loc[:, 'b_returns'] = simple_return(y_time.loc[:, 'benchmark'])
+    print('len(y_time):')
+    print(fav_freq,len(y_time))
+    if fav_freq in [1, 2] and len(y_time)>0 :
+        return_1 = '%.6f' % (y_time['returns'].iloc[-1])  # 日收益
+        b_return_1 = '%.6f' % (y_time['b_returns'].iloc[-1] )  #日基准收益
+
+        if fav_freq == 1:
+            return_d = return_1
+            b_return_d = b_return_1
+
+            #计算周收益
+            pre_result, pre_date = dfweek_minus(df, dateend, 1)
+
+            if pre_result == 1:
+                now_value = df.loc[df.index == dateend]['net_worth'][0]
+                old_value = df.loc[df.index == pre_date]['net_worth'][0]
+                return_w = '%.6f' % (((now_value / old_value) - 1) )
+                print(now_value, old_value, r)
+                now_value = df.loc[df.index == dateend]['benchmark'][0]
+                old_value = df.loc[df.index == pre_date]['benchmark'][0]
+                b_return_w = '%.6f' % (((now_value / old_value) - 1) )
+            else:
+                return_w = np.nan
+                b_return_w = np.nan
+        else:
+            return_d = np.nan
+            b_return_d = np.nan
+            return_w = return_1
+            b_return_w = b_return_1
+    else:
+        return_d = np.nan
+        b_return_d = np.nan
+        return_w = np.nan
+        b_return_w = np.nan
+
+    #今年以来收益率/基准收益率、成立以来收益率/基准收益率/年化收益率/年化基准收益率
+
+    #今年以来
+
+    pre_date = datetime.datetime(dateend.year, 1, 1)
+    private_count = len(origin_df.loc[origin_df.index>=pre_date])
+    location = len(origin_df)  - private_count
+    if location<0:
+        pre_date = origin_df.index[0]
+    else:
+        pre_date = origin_df.index[location]
+
+    print(pre_date,dateend)
+    short_r, b_short_r = simple_2days_return(origin_df, pre_date, dateend)
+
+    y_time = df.loc[(df.index >= pre_date) & (df.index <= dateend)]
+    # 简单收益率
+    # print(y_time['net_worth'])
+    y_time.loc[:, 'returns'] = simple_return(y_time.loc[:, 'net_worth'])
+    y_time.loc[:, 'b_returns'] = simple_return(y_time.loc[:, 'benchmark'])
+
+    # 计算累积收益率
+    y_time.loc[:, 'cum_returns'] = np.cumprod(1 + y_time.loc[:, 'returns'].astype("float64")) - 1
+    y_time.loc[:, 'b_cum_returns'] = np.cumprod(1 + y_time.loc[:, 'b_returns'].astype("float64")) - 1
+
+    short_cum_returns = y_time['cum_returns'].iloc[-1]
+    short_cum_returns_b = y_time['b_cum_returns'].iloc[-1]
+
+    #波动率
+    short_sigma = '%.6f' % volatility(origin_df.loc[pre_date:origin_df.index[-1], 'returns'].astype("float64"), N)
+    #alpha、beta
+    if len(origin_df.loc[pre_date:origin_df.index[-1]]) > 1 :
+        short_alpha, short_beta = alpha_beta(origin_df.loc[pre_date:origin_df.index[-1],'returns'].astype("float64"), origin_df.loc[pre_date:origin_df.index[-1],'b_returns'].astype("float64"),N)
+    else:
+        short_alpha = 0
+        short_beta = 0
+
+
+    #成立以来
+    y_time = origin_df
+    long_r, b_long_r = simple_2days_return(y_time, y_time.index[0], y_time.index[-1])
+    # （年化）基础收益率、基准收益率
+    an_long_r = annual_return(float(long_r), y_time, N)
+    b_an_long_r = annual_return(float(b_long_r), y_time, N)
+    #波动率
+    long_sigma = '%.6f' % volatility(y_time.loc[:, 'returns'].astype("float64"), N)
+    #alpha、beta
+    long_alpha, long_beta = alpha_beta (y_time.loc[:, 'returns'].astype("float64") , y_time.loc[:, 'b_returns'].astype("float64") , N)
+
+    # 简单收益率
+    # print(y_time['net_worth'])
+    y_time.loc[:, 'returns'] = simple_return(y_time.loc[:, 'net_worth'])
+    y_time.loc[:, 'b_returns'] = simple_return(y_time.loc[:, 'benchmark'])
+
+    # 计算累积收益率
+    y_time.loc[:, 'cum_returns'] = np.cumprod(1 + y_time.loc[:, 'returns'].astype("float64")) - 1
+    y_time.loc[:, 'b_cum_returns'] = np.cumprod(1 + y_time.loc[:, 'b_returns'].astype("float64")) - 1
+
+    long_cum_returns =  y_time['cum_returns'].iloc[-1]
+    long_cum_returns_b = y_time['b_cum_returns'].iloc[-1]
+
+    #sharperatio_incep  fund_riskadjret_stats  成立以来夏普比例
+    y_time.loc[:, 'returns'] = simple_return(y_time.loc[:, 'net_worth'])
+    y_time.loc[:, 'excess_returns'] = excess_return(y_time.loc[:, 'returns'].astype("float64"), BANK_RATE / N)
+    long_sharpe = '%.6f'%sharpe_ratio(y_time.loc[:, 'excess_returns'].astype("float64"), y_time.loc[:, 'returns'].astype("float64"), N)
+
+    #
+    long_downside = downside_risk(y_time.loc[:, 'returns'].astype("float64"), BANK_RATE, N)
+    #b_downside = downside_risk(y_time.loc[:, 'b_returns'], BANK_RATE, N)
+    long_sortino_ratio = sortino_ratio(y_time.loc[:, 'excess_returns'].astype("float64"), long_downside, N)
+    #b_sortino_ratio = sortino_ratio(y_time.loc[:, 'b_excess_returns'], b_downside, N)
+
+    #成立以来最大回撤
+    long_n_m, long_n_m_begin, long_n_m_end = max_drawdown(list(y_time['net_worth']))
+    long_n_m_begin_date = y_time.index[long_n_m_begin]
+    long_n_m_end_date = y_time.index[long_n_m_end]
+
+
+    print('---Step2：数据准备结束，把数据存入DB---')
+
+
+    #结果存入fund_performance
+
+    print('结果存入fund_performance')
+    #str_id = str(int(time.mktime(datetime.datetime.now().timetuple())))  #Table ID，时间戳，10位
+    #str_id = now_to_timestamp(13)
+    str_id = xuehuaid()
+    str_fund_id = fund_id  #基金ID
+    dt_end_date = dateend.strftime('%Y-%m-%d')  #截至日期
+    dt_price_date = dateend.strftime('%Y-%m-%d')   #最近累计净值日期
+    print(dt_end_date)
+    dec_cumulative_nav = cumulative_nav  #最近累计净值
+
+
+    ts_create_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(ts_create_time)
+
+    #开始数据库操作
+
+    pool = PooledDB(pymysql, maxconnections=100, blocking=True, host=origion_env[current_env]['host'],
+                    port=int(origion_env[current_env]['port']), user=origion_env[current_env]['user'],
+                    password=origion_env[current_env]['password'], database=origion_env[current_env]['database'],
+                    charset="utf8")  # 5为连接池里的最少连接数
+    conn = pool.connection()  # 以后每次需要数据库连接就是用connection（）函数获取连接就好了
+    cur = conn.cursor()
+
+    #如果是指数，则把数据插入fund_nav
+    sql ="select id from fund_nav where fund_id='%s' and price_date='%s'" % (fund_id, enddate)
+    fetch_count = cur.execute(sql)
+    data = cur.fetchall()
+    if fetch_count == 0:
+        str_id = xuehuaid()
+        sql = "INSERT INTO fund_nav(id, fund_id, price_date, nav, cumulative_nav, cumulative_nav_withdrawal, create_time,delete_tag) VALUES ('%s',  '%s', str_to_date('%s','%%Y-%%m-%%d'), %s, %s, %s , '%s', %d)" % (
+        str_id, str_fund_id, dt_end_date, dec_cumulative_nav, dec_cumulative_nav,dec_cumulative_nav, ts_create_time, 0)
+        print(sql)
+        try:
+            cur.execute(sql)
+            conn.commit()
+
+        except Exception as e:
+            print(e.message)
+    else:
+        #获取id
+        str_id = data[0][0]
+        #sql = "update fund_nav set fund_id='%s',  price_date='%s', nav='%s',cumulative_nav='%s', cumulative_nav_withdrawal='%s' where id='%s'" % (str_fund_id, dt_price_date, dec_cumulative_nav, dec_cumulative_nav,dec_cumulative_nav, str_id)
+
+
+
+
+    #更新fund_performance
+    sql ="select id from fund_performance where fund_id='%s' and end_date='%s'" % (fund_id, enddate)
+    print(sql)
+    fetch_count = cur.execute(sql)
+    data = cur.fetchall()
+
+    if fetch_count == 0:
+        str_id = xuehuaid()
+        sql = "INSERT INTO fund_performance(id, fund_id, end_date, price_date, cumulative_nav, ret_ytd, ret_ytd_bm1 , ret_incep, ret_incep_bm1, ret_incep_a, ret_incep_bm1_a, ret_cum_ytd, ret_cum_ytd_bm1, ret_cum_incep, ret_cum_incep_bm1, create_time, delete_tag) VALUES ('%s',  '%s', str_to_date('%s','%%Y-%%m-%%d'), str_to_date('%s','%%Y-%%m-%%d'), %s, %s, %s, %s, %s,  %s, %s, %s, %s, %s,  %s, '%s', %d)" % (str_id, str_fund_id, dt_end_date, dt_price_date, dec_cumulative_nav, short_r, b_short_r, long_r, b_long_r,an_long_r, b_an_long_r, short_cum_returns, short_cum_returns_b, long_cum_returns, long_cum_returns_b, ts_create_time, 0)
+
+    else:
+        #获取id
+        str_id = data[0][0]
+        sql = "update fund_performance set fund_id='%s', end_date='%s', price_date='%s', cumulative_nav='%s', ret_ytd='%s', ret_ytd_bm1='%s' , ret_incep='%s', ret_incep_bm1='%s', ret_incep_a='%s', ret_incep_bm1_a='%s',ret_cum_ytd='%s',ret_cum_ytd_bm1='%s',ret_cum_incep='%s',ret_cum_incep_bm1='%s' where id='%s'" % (str_fund_id, dt_end_date, dt_price_date, dec_cumulative_nav, short_r, b_short_r, long_r, b_long_r,an_long_r, b_an_long_r, short_cum_returns, short_cum_returns_b, long_cum_returns, long_cum_returns_b,str_id)
+
+
+
+
+    print(sql)
+
+    try:
+        cur.execute(sql)
+        conn.commit()
+
+    except Exception as e:
+        print(e.message)
+
+    #更新日数据和周数据
+
+    if fav_freq == 1:
+        sql = "update fund_performance set ret_1day='%s', ret_1day_bm1 = '%s', ret_1w='%s', ret_1w_bm1='%s' where id='%s'" % (return_d, b_return_d, return_w, b_return_w, str_id)
+        print(sql)
+        cur.execute(sql)
+        conn.commit()
+    elif fav_freq == 2:
+        sql = "update fund_performance set ret_1w='%s',ret_1w_bm1='%s' where id='%s'" % (return_w, b_return_w, str_id)
+        print(sql)
+        cur.execute(sql)
+        conn.commit()
+
+    #处理月份数据
+    print('开始月份数据处理：',range_return_m, cum_returns_m)
+    if len(range_return_m) >= 1:
+        sql = "update fund_performance set ret_1m='%s', ret_1m_bm1= '%s',ret_cum_1m ='%s', ret_cum_1m_bm1='%s' where id='%s'" % (range_return_m[0],b_range_return_m[0],cum_returns_m[0],b_cum_returns_m[0],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(range_return_m) >= 2:
+        sql = "update fund_performance set ret_3m='%s', ret_3m_bm1= '%s' ,ret_cum_3m ='%s', ret_cum_3m_bm1='%s' where id='%s'" % (range_return_m[1],b_range_return_m[1],cum_returns_m[1],b_cum_returns_m[1], str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(range_return_m) >= 3:
+        sql = "update fund_performance set ret_6m=%s, ret_6m_bm1= %s ,ret_cum_6m ='%s', ret_cum_6m_bm1='%s' where id='%s'" % (range_return_m[2], b_range_return_m[2],cum_returns_m[2],b_cum_returns_m[2], str_id)
+        print(sql)
+        cur.execute(sql)
+
+    #处理年份数据
+    if len(date_y) >= 1:
+        sql = "update fund_performance set ret_1y='%s', ret_1y_bm1= '%s', ret_cum_1y='%s', ret_cum_1y_bm1='%s' where id='%s'" % (range_return_y[0],b_range_return_y[0],cum_returns_y[0],b_cum_returns_y[0],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(date_y) >= 2:
+        sql = "update fund_performance set ret_2y='%s', ret_2y_bm1= '%s' , ret_2y_a='%s' , ret_2y_bm1_a= '%s',ret_cum_2y='%s', ret_cum_2y_bm1='%s' where id='%s'" % (range_return_y[1],b_range_return_y[1], annual_return_y[1], b_annual_return_y[1], cum_returns_m[1],b_cum_returns_m[1],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(date_y) >= 3:
+        sql = "update fund_performance set ret_3y='%s', ret_3y_bm1= '%s' , ret_3y_a='%s' , ret_3y_bm1_a= '%s',ret_cum_3y='%s', ret_cum_3y_bm1='%s' where id='%s'" % (range_return_y[2],b_range_return_y[2], annual_return_y[2], b_annual_return_y[2], cum_returns_m[2],b_cum_returns_m[2],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(date_y) >= 4:
+        sql = "update fund_performance set ret_4y='%s', ret_4y_bm1= '%s' , ret_4y_a='%s' , ret_4y_bm1_a= '%s',ret_cum_4y='%s', ret_cum_4y_bm1='%s'  where id='%s'" % (range_return_y[3],b_range_return_y[3], annual_return_y[3], b_annual_return_y[3], cum_returns_y[3],b_cum_returns_y[3],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(date_y) >= 5:
+        sql = "update fund_performance set ret_5y='%s', ret_5y_bm1= '%s' , ret_5y_a='%s' , ret_5y_bm1_a= '%s',ret_cum_5y='%s', ret_cum_5y_bm1='%s'  where id='%s'" % (range_return_y[4],b_range_return_y[4], annual_return_y[4], b_annual_return_y[4], cum_returns_y[4],b_cum_returns_y[4], str_id)
+        print(sql)
+        cur.execute(sql)
+
+    conn.commit()
+
+
+    #结果存入fund_risk_stats
+
+    print('结果存入fund_risk_stats')
+
+    sql ="select id from fund_risk_stats where fund_id='%s' and end_date='%s'" % (fund_id, enddate)
+    print(sql)
+    fetch_count = cur.execute(sql)
+    data = cur.fetchall()
+
+    if fetch_count == 0:
+        str_id = xuehuaid()
+        sql = "INSERT INTO fund_risk_stats(id, fund_id, end_date, create_time, delete_tag) VALUES ('%s',  '%s', str_to_date('%s','%%Y-%%m-%%d'),  '%s', %d)" % (str_id, str_fund_id, dt_end_date, ts_create_time, 0)
+
+    else:
+        #获取id
+        str_id = data[0][0]
+        sql = "update fund_risk_stats set fund_id='%s', end_date='%s' where id='%s'" % (str_fund_id, dt_end_date, str_id)
+
+    print(sql)
+
+    try:
+        cur.execute(sql)
+        conn.commit()
+
+    except Exception as e:
+        print(e.message)
+
+    #处理年份数据
+
+    if len(sigma_y) >= 1:
+        int_range_month = month_differ(range_md_y_begin[0],range_md_y_end[0])
+        sql = "update fund_risk_stats set stddev_1y='%s', alpha_1y = '%s' , beta_1y = '%s' , maxdrawdown_1y= '%s' , maxdrawdown_months_1y = %s, maxdrawdown_peakdate_1y = '%s' , maxdrawdown_valleydate_1y = '%s' where id='%s'" % (sigma_y[0],alpha_y[0],beta_y[0], range_md_y[0],int_range_month,range_md_y_begin[0], range_md_y_end[0],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sigma_y) >= 2:
+        int_range_month = month_differ(range_md_y_begin[1],range_md_y_end[1])
+        sql = "update fund_risk_stats set stddev_2y='%s', alpha_2y = '%s' , beta_2y = '%s' , maxdrawdown_2y= '%s' , maxdrawdown_months_2y = %s, maxdrawdown_peakdate_2y = '%s' , maxdrawdown_valleydate_2y = '%s' where id='%s'" % (sigma_y[1],alpha_y[1],beta_y[1], range_md_y[1],int_range_month,range_md_y_begin[1], range_md_y_end[1],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sigma_y) >= 3:
+        int_range_month = month_differ(range_md_y_begin[2],range_md_y_end[2])
+        sql = "update fund_risk_stats set stddev_3y='%s', alpha_3y = '%s' , beta_3y = '%s' , maxdrawdown_3y= '%s' , maxdrawdown_months_3y = %s, maxdrawdown_peakdate_3y = '%s' , maxdrawdown_valleydate_3y = '%s' where id='%s'" % (sigma_y[2],alpha_y[2],beta_y[2], range_md_y[2],int_range_month,range_md_y_begin[2], range_md_y_end[2],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sigma_y) >= 4:
+        int_range_month = month_differ(range_md_y_begin[3],range_md_y_end[3])
+        sql = "update fund_risk_stats set stddev_4y='%s', alpha_4y = '%s' , beta_4y = '%s' , maxdrawdown_4y= '%s' , maxdrawdown_months_4y = %s, maxdrawdown_peakdate_4y = '%s' , maxdrawdown_valleydate_4y = '%s' where id='%s'" % (sigma_y[3],alpha_y[3],beta_y[3], range_md_y[3],int_range_month,range_md_y_begin[3], range_md_y_end[3],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sigma_y) >= 5:
+        int_range_month = month_differ(range_md_y_begin[4],range_md_y_end[4])
+        sql = "update fund_risk_stats set stddev_5y='%s', alpha_5y = '%s' , beta_5y = '%s' , maxdrawdown_5y= '%s' , maxdrawdown_months_5y = %s, maxdrawdown_peakdate_5y = '%s' , maxdrawdown_valleydate_5y = '%s' where id='%s'" % (sigma_y[4],alpha_y[4],beta_y[4], range_md_y[4],int_range_month,range_md_y_begin[4], range_md_y_end[4],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sigma_y) >= 6:
+        int_range_month = month_differ(range_md_y_begin[5],range_md_y_end[5])
+        sql = "update fund_risk_stats set stddev_10y='%s', alpha_10y = '%s' , beta_10y = '%s' , maxdrawdown_10y= '%s' , maxdrawdown_months_10y = %s, maxdrawdown_peakdate_10y = '%s' , maxdrawdown_valleydate_10y = '%s' where id='%s'" % (sigma_y[5],alpha_y[5],beta_y[5], range_md_y[5],int_range_month,range_md_y_begin[5], range_md_y_end[5],str_id)
+        print(sql)
+        cur.execute(sql)
+
+    #成立以来最大回撤
+    int_range_month = month_differ(long_n_m_begin_date, long_n_m_end_date)
+    sql = "update fund_risk_stats set maxdrawdown_incep= '%s', maxdrawdown_months_incep = '%s' , maxdrawdown_peakdate_incep = '%s', maxdrawdown_valleydate_incep = '%s'  where id='%s'" % (
+    long_n_m, int_range_month, long_n_m_begin_date, long_n_m_end_date, str_id)
+    print(sql)
+    cur.execute(sql)
+
+    #成立以来/今年回来，波动率
+    sql = "update fund_risk_stats set stddev_ytd= '%s', stddev_incep = '%s'  where id='%s'" % (
+    short_sigma, long_sigma, str_id)
+    print(sql)
+    cur.execute(sql)
+
+    #成立以来/今年以来，alpha/beta
+    sql = "update fund_risk_stats set alpha_ytd= '%s', alpha_incep = '%s', beta_ytd= '%s', beta_incep = '%s'  where id='%s'" % (
+    short_alpha, long_alpha, short_beta, long_beta, str_id)
+    print(sql)
+    cur.execute(sql)
+
+
+    conn.commit()
+
+    #结果存入fund_riskadjret_stats
+
+    print('结果存入fund_riskadjret_stats')
+
+    sql ="select id from fund_riskadjret_stats where fund_id='%s' and end_date='%s'" % (fund_id, enddate)
+    fetch_count = cur.execute(sql)
+    data = cur.fetchall()
+
+    if fetch_count == 0:
+        str_id = xuehuaid()
+        sql = "INSERT INTO fund_riskadjret_stats(id, fund_id, end_date, sharperatio_incep, create_time, delete_tag) VALUES ('%s',  '%s', str_to_date('%s','%%Y-%%m-%%d'),  '%s' ,'%s', %d)" % (str_id, str_fund_id, dt_end_date, long_sharpe, ts_create_time, 0)
+
+    else:
+        #获取id
+        str_id = data[0][0]
+        sql = "update fund_riskadjret_stats set fund_id='%s', end_date='%s' ,sharperatio_incep ='%s' where id='%s'" % (str_fund_id, dt_end_date, long_sharpe , str_id)
+
+    print(sql)
+
+    try:
+        cur.execute(sql)
+        conn.commit()
+
+    except Exception as e:
+        print(e.message)
+
+    #处理年份数据
+
+    if len(sharpe_y) >= 1:
+        sql = "update fund_riskadjret_stats set sharperatio_1y='%s', sortinoratio_1y='%s' , sortinoratio_MAR_1y='%s' where id='%s'" % (sharpe_y[0],sortino_y[0],sortion_ratio_y[0],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sharpe_y) >= 2:
+        sql = "update fund_riskadjret_stats set sharperatio_2y='%s' , sortinoratio_2y='%s', sortinoratio_MAR_2y='%s' where id='%s'" % (sharpe_y[1],sortino_y[1],sortion_ratio_y[1],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sharpe_y) >= 3:
+        sql = "update fund_riskadjret_stats set sharperatio_3y='%s', sortinoratio_3y='%s' ,sortinoratio_MAR_3y='%s' where id='%s'" % (sharpe_y[2],sortino_y[2],sortion_ratio_y[2],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sharpe_y) >= 4:
+        sql = "update fund_riskadjret_stats set sharperatio_4y='%s', sortinoratio_4y='%s', sortinoratio_MAR_4y='%s' where id='%s'" % (sharpe_y[3],sortino_y[3],sortion_ratio_y[3],str_id)
+        print(sql)
+        cur.execute(sql)
+    if len(sharpe_y) >= 5:
+        sql = "update fund_riskadjret_stats set sharperatio_5y='%s', sortinoratio_5y='%s', sortinoratio_MAR_5y='%s' where id='%s'" % (sharpe_y[4],sortino_y[4],sortion_ratio_y[4],str_id)
+        print(sql)
+        cur.execute(sql)
+
+    #成立以来夏普，索提诺
+    int_range_month = month_differ(long_n_m_begin_date, long_n_m_end_date)
+    sql = "update fund_riskadjret_stats set sharperatio_incep= '%s', sortinoratio_incep = '%s' , sortinoratio_MAR_incep = '%s'  where id='%s'" % (long_sharpe, long_downside, long_sortino_ratio, str_id)
+    print(sql)
+    cur.execute(sql)
+
+    conn.commit()
+
+    #更新fund_count
+    sql ="select id , price_date from fund_count where fund_id='%s' " % (fund_id)
+    fetch_count = cur.execute(sql)
+
+    data = cur.fetchall()
+
+    if fetch_count == 0:
+        str_id = xuehuaid()
+        sql = "insert into fund_count(id, fund_id , strategy, data_sources, status, create_time, delete_tag) values('%s','%s', '%s', '%s', '%s', '%s', 0)" % (str_id, str_fund_id, strategy, data_source, status, ts_create_time)
+        print(sql)
+        cur.execute(sql)
+        conn.commit()
+        fund_count_date = datetime.datetime.strptime("1900-01-01", "%Y-%m-%d").date()
+    else:
+    #更新收益率
+        fund_count_date = data[0][1]
+
+    if fund_count_date is None:
+        fund_count_date = datetime.datetime.strptime("1900-01-01", "%Y-%m-%d").date()
+    print('当前日期：',enddate)
+    print('之前日期：',fund_count_date)
+
+    dt_end_date = datetime.datetime.strptime(strenddate, "%Y%m%d").date()
+    if dt_end_date >= fund_count_date:
+
+        #更新单位净值
+        sql = "update fund_count set net_nav='%s' , data_sources ='%s', end_date = '%s', price_date='%s',status ='%s'  where fund_id = '%s'" % (cumulative_ori_nav, data_source, enddate, enddate, status, fund_id )
+        print(sql)
+
+        sql1 ="update fund_count a inner join fund_performance b  on a.fund_id=b.fund_id set a.end_date=b.end_date, a.price_date=b.price_date, a.cumulative_nav=b.cumulative_nav,a.ret_1day=b.ret_1day, a.ret_1day_bm1=b.ret_1day_bm1, a.ret_1w=b.ret_1w, a.ret_1w_bm1=b.ret_1w_bm1, a.ret_1m=b.ret_1m, a.ret_1m_bm1=b.ret_1m_bm1, a.ret_3m=b.ret_3m, a.ret_3m_bm1=b.ret_3m_bm1, a.ret_6m=b.ret_6m, a.ret_6m_bm1=b.ret_6m_bm1, a.ret_1y=b.ret_1y, a.ret_1y_bm1=b.ret_1y_bm1, a.ret_2y=b.ret_2y, a.ret_2y_bm1=b.ret_2y_bm1, a.ret_2y_a=b.ret_2y_a, a.ret_2y_bm1_a=b.ret_2y_bm1_a, a.ret_3y=b.ret_3y, a.ret_3y_bm1=b.ret_3y_bm1, a.ret_3y_a=b.ret_3y_a, a.ret_3y_bm1_a=b.ret_3y_bm1_a,a.ret_4y=b.ret_4y, a.ret_4y_bm1=b.ret_4y_bm1, a.ret_4y_a=b.ret_4y_a, a.ret_4y_bm1_a=b.ret_4y_bm1_a,a.ret_5y=b.ret_5y, a.ret_5y_bm1=b.ret_5y_bm1, a.ret_5y_a=b.ret_5y_a, a.ret_5y_bm1_a=b.ret_5y_bm1_a, a.ret_ytd=b.ret_ytd, a.ret_ytd_bm1=b.ret_ytd_bm1, a.ret_incep=b.ret_incep, a.ret_incep_bm1=b.ret_incep_bm1, a.ret_incep_a=b.ret_incep_a, a.ret_incep_bm1_a=b.ret_incep_bm1_a  where b.fund_id ='%s' and b.price_date='%s'" % (fund_id, enddate)
+        print(sql1)
+
+
+        try:
+            cur.execute(sql)
+            cur.execute(sql1)
+            conn.commit()
+
+        except Exception as e:
+            print(e.message)
+
+        #更新年化波动和最大回撤
+        sql ="UPDATE fund_count fc inner join fund_risk_stats t1 on fc.fund_id=t1.fund_id and fc.end_date=t1.end_date and t1.fund_id='%s' and t1.end_date='%s' SET fc.stddev_1y = t1.stddev_1y, fc.stddev_2y = t1.stddev_2y, fc.stddev_3y = t1.stddev_3y, fc.stddev_4y = t1.stddev_4y, fc.stddev_5y = t1.stddev_5y, fc.stddev_10y = t1.stddev_10y, fc.stddev_ytd = t1.stddev_ytd, fc.stddev_incep = t1.stddev_incep, fc.maxdrawdown_1y = t1.maxdrawdown_1y, fc.maxdrawdown_2y = t1.maxdrawdown_2y, fc.maxdrawdown_3y = t1.maxdrawdown_3y, fc.maxdrawdown_4y = t1.maxdrawdown_4y, fc.maxdrawdown_5y = t1.maxdrawdown_5y, fc.maxdrawdown_10y = t1.maxdrawdown_10y, fc.maxdrawdown_incep = t1.maxdrawdown_incep" % (fund_id, enddate)
+
+        print(sql)
+        #更新夏普比例
+        sql1 = "UPDATE fund_count fc inner join fund_riskadjret_stats t2 on fc.fund_id=t2.fund_id and fc.end_date=t2.end_date and t2.fund_id='%s' and t2.end_date='%s' SET fc.sharperatio_1y=t2.sharperatio_1y, fc.sharperatio_2y=t2.sharperatio_2y, fc.sharperatio_3y=t2.sharperatio_3y , fc.sharperatio_4y=t2.sharperatio_4y, fc.sharperatio_5y=t2.sharperatio_5y , fc.sharperatio_incep=t2.sharperatio_incep " % (fund_id, enddate)
+        print(sql1)
+
+        try:
+            cur.execute(sql)
+            cur.execute(sql1)
+            conn.commit()
+            print("1条已更新,ID:", cur.lastrowid)
+
+        except Exception as e:
+            print(e.message)
+
+    cur.close()
+    conn.close()

app/service/result_service_v2.py

@@ -11,6 +11,7 @@ import numpy as np
 import datetime
 from decimal import Decimal
 from app.service.data_service_v2 import UserCustomerDataAdaptor
+from app.service.portfolio_diagnose import cal_correlation, get_fund_name
 from app.utils.week_evaluation import *
 
 
@@ -173,6 +174,10 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
                                                           "index")
             folio_report_data["index_result"] = index_result
             folio_report_data["return_df"] = resample_df
+
+            # 对应组合相关性
+            correlation = self.old_correlation(self.fund_cnav_total[fund_id_list])
+            folio_report_data["correlation"] = correlation
             self.group_result_data[folio] = folio_report_data
 
         return self.group_result_data
@@ -562,12 +567,9 @@ class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
     def get_group_data(self):
         return self.group_result_data
 
-    # def __month_return(self, folio_cnav_data):
-    #     # 月度回报
-    #     def year_month(x):
-    #         a = x.year
-    #         b = x.month
-    #         return str(a) + "-" + str(b)
-    #     p_folio_cnav_data = folio_cnav_data.copy()
-    #     p_folio_cnav_data["date"] = p_folio_cnav_data.index
-    #     grouped_data = p_folio_cnav_data.groupby(p_folio_cnav_data["date"].apply(year_month))
+    def old_correlation(self, folio_cnav_data):
+        old_correlation = cal_correlation(folio_cnav_data)
+        old_correlation = old_correlation.fillna(1).round(2)
+        old_correlation.columns = old_correlation.columns.map(lambda x: get_fund_name(x).values[0][0])
+        old_correlation.index = old_correlation.index.map(lambda x: get_fund_name(x).values[0][0])
+        return old_correlation

app/templates/v2/monthReportV2.1.html

@@ -959,7 +959,7 @@
 
           </span>
         </div>
-        <img class="cover_img" src={{red-rect}} >
+        <img class="cover_img" src={{red_rect}} >
         <div class="cover_title_text dcell">
           资产<br>
           存续报告

app/utils/jinjia2html_v2.py

@@ -54,7 +54,8 @@ class DataIntegrate:
                 'suggestions_result_asset': {},
                 'return_compare_pic': [],
                 'indicator_compare': [],
-                'new_group_evaluation': []
+                'new_group_evaluation': [],
+                "correlation": group_result["correlation"]
             }
 
             # 旧持仓组合点评
@@ -64,11 +65,11 @@ class DataIntegrate:
             # 目标与业绩
             self.objectives_performance(group_result, cur_group_portfolio_result)
             # 个基点评
-            self.single_fund_comment(portfolio_diagnose, cur_group_portfolio_result)
+            # self.single_fund_comment(portfolio_diagnose, cur_group_portfolio_result)
             # 旧收益比较
             self.get_old_compare_pic(cur_group_portfolio_result)
             # 旧相关性
-            self.get_old_correlation(portfolio_diagnose, cur_group_portfolio_result)
+            self.get_old_correlation(cur_group_portfolio_result)
             # # 新增基金
             # self.propose_fund(portfolio_diagnose, cur_group_portfolio_result)
             # # 新收益比较
@@ -81,7 +82,7 @@ class DataIntegrate:
     def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000):
         portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=portfolio, invest_amount=float(invest_amount),
                                                start_date=self.user_customer.start_date)
-        portfolio_diagnose.optimize()
+        # portfolio_diagnose.optimize()
         return portfolio_diagnose
 
     # 全部数据综述结果
@@ -169,9 +170,9 @@ class DataIntegrate:
                                                                                   cur_group_portfolio_result["return_compare_data"]["origin_combination"],
                                                                                   cur_group_portfolio_result["return_compare_data"]["index"])
 
-    def get_old_correlation(self, portfolio_diagnose, cur_group_portfolio_result):
+    def get_old_correlation(self, cur_group_portfolio_result):
         """旧相关性分析."""
-        old_correlation = portfolio_diagnose.old_correlation
+        old_correlation = cur_group_portfolio_result["correlation"]
         old_correlation_columns = old_correlation.columns.tolist()
         old_correlation_values = old_correlation.values.tolist()
         cur_group_portfolio_result["old_correlation"] = list(zip(range(1, len(old_correlation_columns)+1), old_correlation_columns, old_correlation_values))
@@ -246,7 +247,7 @@ class DataIntegrate:
                 'logo': template_folder + '/v2/img/logo.png',
                 'scene': template_folder + '/v2/img/scene.png',
                 'team': template_folder + '/v2/img/default-user.png',
-                'red-rect': template_folder + '/v2/img/red-rect.png',
+                'red_rect': template_folder + '/v2/img/red-rect.png',
                 'sh': template_folder + '/v2/img/sh.png',
                 # 全局数据
                 'customer_name': self.customer_name,