result_service.py

#!/usr/bin/python3.6
# -*- coding: utf-8 -*-
# @Time    : 2020/11/23 15:29
# @Author  : Jie. Z
# @Email   : zhaojiestudy@163.com
# @File    : result_service.py
# @Software: PyCharm

import pandas as pd
import numpy as np
import datetime
from decimal import Decimal
from app.service.data_service import UserCustomerDataAdaptor
from app.utils.week_evaluation import *


class UserCustomerResultAdaptor(UserCustomerDataAdaptor):
    total_result_data = {}
    group_result_data = {}

    def __init__(self, user_id, customer_id, end_date=str(datetime.date.today())):
        super().__init__(user_id, customer_id, end_date)

    # 组合结果数据
    def calculate_group_result_data(self):

        for folio in self.group_data.keys():
            folio_report_data = {}

            cur_folio_result_cnav_data = self.group_data[folio]["result_cnav_data"]
            cur_folio_order_data = self.group_data[folio]["order_df"]
            freq_max = cur_folio_order_data["freq"].max()
            first_trade_date = cur_folio_order_data["confirm_share_date"].min()

            fund_id_list = list(cur_folio_order_data["fund_id"].unique())
            fund_id_list_earn = [i + "_earn" for i in fund_id_list]
            # fund_id_list_amount = [i + "_amount" for i in fund_id_list]
            profit_df = cur_folio_result_cnav_data[fund_id_list_earn]

            # 组合收益率数组
            return_ratio_df = self.combination_yield(cur_folio_result_cnav_data, fund_id_list)
            resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)

            # 总成本
            total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
                         cur_folio_order_data[cur_folio_order_data["order_type"] == 2]["confirm_amount"].sum())
            folio_report_data["total_cost"] = total_cost

            # 累积盈利
            cumulative_profit = profit_df.sum().sum()
            folio_report_data["cumulative_profit"] = float(cumulative_profit)

            # 区间年化收益率
            n_freq = freq_days(int(freq_max))
            return_ratio_year = annual_return((resample_df["cum_return_ratio"].values[-1]-1), resample_df, n_freq)
            folio_report_data["return_ratio_year"] = float(return_ratio_year)

            # 波动率
            volatility_ = volatility(resample_df["cum_return_ratio"], n_freq)
            folio_report_data["volatility"] = float(volatility_)

            # 最大回撤
            drawdown = max_drawdown(resample_df["cum_return_ratio"])
            folio_report_data["max_drawdown"] = drawdown

            # 夏普比率
            sim = simple_return(resample_df["cum_return_ratio"])
            exc = excess_return(sim, BANK_RATE, n_freq)
            sharpe = sharpe_ratio(exc, sim, n_freq)
            folio_report_data["sharpe"] = float(sharpe)

            # 期末资产
            ending_assets = cumulative_profit + total_cost
            folio_report_data["ending_assets"] = float(ending_assets)

            # 本月收益
            cur_month_profit_df = profit_df.loc[self.month_start_date:self.end_date+datetime.timedelta(days=1), fund_id_list_earn]
            cur_month_profit = cur_month_profit_df.sum().sum()
            folio_report_data["cur_month_profit"] = float(cur_month_profit)

            # 本月累积收益率
            last_profit_ratio = return_ratio_df.loc[:self.month_start_date, "cum_return_ratio"].values
            cur_profit_ratio = return_ratio_df.loc[self.month_start_date:, "cum_return_ratio"].values
            if len(last_profit_ratio) <= 0:
                cur_month_profit_ratio = cur_profit_ratio[-1] - 1
            else:
                cur_month_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
            folio_report_data["cur_month_profit_ratio"] = float(cur_month_profit_ratio)

            # 今年累积收益
            cur_year_date = pd.to_datetime(str(datetime.date(year=self.end_date.year, month=1, day=1)))
            cur_year_profit_df = profit_df.loc[cur_year_date:self.end_date + datetime.timedelta(days=1), fund_id_list_earn]
            cur_year_profit = cur_year_profit_df.sum().sum()
            folio_report_data["cur_year_profit"] = float(cur_year_profit)

            # 今年累积收益率
            last_profit_ratio = return_ratio_df.loc[:cur_year_date, "cum_return_ratio"].values
            cur_profit_ratio = return_ratio_df.loc[cur_year_date:, "cum_return_ratio"].values
            if len(last_profit_ratio) <= 0:
                cur_year_profit_ratio = cur_profit_ratio[-1] - 1
            else:
                cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
            folio_report_data["cur_year_profit_ratio"] = float(cur_year_profit_ratio)

            # 累积收益率
            cumulative_return= return_ratio_df["cum_return_ratio"].values[-1]
            folio_report_data["contribution_decomposition"] = float(cumulative_return)

            # 组合内单个基金净值数据  组合内基金持仓数据
            result_fund_nav_info, result_fund_hoding_info = self.group_fund_basic_info_data(cur_folio_order_data, cur_folio_result_cnav_data, cumulative_profit, total_cost)

            # 拼接组合以及综合结果数据
            folio_report_data["group_nav_info"] = result_fund_nav_info
            folio_report_data["group_hoding_info"] = result_fund_hoding_info

            # 对应指数数据
            index_df = self.get_customer_index_nav_data()
            index_result = self.signal_fund_profit_result(index_df[index_df.index >= pd.to_datetime(first_trade_date)],
                                                          "index")
            folio_report_data["index_result"] = index_result

            self.group_result_data[folio] = folio_report_data

        return self.group_result_data

    # 综述数据
    def calculate_total_data(self):
        report_data = {}

        cur_folio_result_cnav_data = self.total_customer_order_cnav_df
        cur_folio_order_data = self.user_customer_order_df
        freq_max = cur_folio_order_data["freq"].max()
        #
        fund_id_list = list(cur_folio_order_data["fund_id"].unique())
        fund_id_list_earn = [i + "_earn" for i in fund_id_list]
        profit_df = cur_folio_result_cnav_data[fund_id_list_earn]

        # 持仓周期
        first_trade_date = cur_folio_order_data["confirm_share_date"].min()
        hold_days = (self.end_date - pd.to_datetime(first_trade_date)).days
        report_data["hold_days"] = hold_days

        # 组合收益率数组
        return_ratio_df = self.combination_yield(cur_folio_result_cnav_data, fund_id_list)
        resample_df = resample(return_ratio_df, self.trade_cal_date, freq_max)

        # 总成本
        total_cost = float(cur_folio_order_data[cur_folio_order_data["order_type"] == 1]["confirm_amount"].sum() - \
                           cur_folio_order_data[cur_folio_order_data["order_type"] == 2]["confirm_amount"].sum())
        report_data["total_cost"] = total_cost
        #
        # # 累积盈利
        # cumulative_profit = profit_df.sum().sum()
        # report_data["cumulative_profit"] = float(cumulative_profit)
        #
        # # 区间年化收益
        # n_freq = freq_days(int(freq_max))
        # return_ratio_year = annual_return((resample_df["cum_return_ratio"].values[-1] - 1), resample_df, n_freq)
        # report_data["return_ratio_year"] = float(return_ratio_year)
        #
        # # 波动率
        # volatility_ = volatility(resample_df["cum_return_ratio"], n_freq)
        # report_data["volatility"] = float(volatility_)

        # 最大回撤
        drawdown = max_drawdown(resample_df["cum_return_ratio"])
        report_data["max_drawdown"] = drawdown
        #
        # # 夏普比率
        # sim = simple_return(resample_df["cum_return_ratio"])
        # exc = excess_return(sim, BANK_RATE, n_freq)
        # sharpe = sharpe_ratio(exc, sim, n_freq)
        # report_data["sharpe"] = float(sharpe)
        #
        # # 期末资产
        # ending_assets = cumulative_profit + total_cost
        # report_data["ending_assets"] = float(ending_assets)
        #
        # # 本月收益
        # cur_month_profit_df = profit_df.loc[self.month_start_date:self.end_date + datetime.timedelta(days=1),
        #                       fund_id_list_earn]
        # cur_month_profit = cur_month_profit_df.sum().sum()
        # report_data["cur_month_profit"] = float(cur_month_profit)
        #
        # # 本月累积收益率
        # last_profit_ratio = return_ratio_df.loc[:self.month_start_date, "cum_return_ratio"].values
        # cur_profit_ratio = return_ratio_df.loc[self.month_start_date:, "cum_return_ratio"].values
        # if len(last_profit_ratio) <= 0:
        #     cur_month_profit_ratio = cur_profit_ratio[-1] - 1
        # else:
        #     cur_month_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
        # report_data["cur_month_profit_ratio"] = float(cur_month_profit_ratio)
        #
        # # 今年累积收益
        # cur_year_date = pd.to_datetime(str(datetime.date(year=self.end_date.year, month=1, day=1)))
        # cur_year_profit_df = profit_df.loc[cur_year_date:self.end_date + datetime.timedelta(days=1), fund_id_list_earn]
        # cur_year_profit = cur_year_profit_df.sum().sum()
        # report_data["cur_year_profit"] = float(cur_year_profit)
        #
        # # 今年累积收益率
        # last_profit_ratio = return_ratio_df.loc[:cur_year_date, "cum_return_ratio"].values
        # cur_profit_ratio = return_ratio_df.loc[cur_year_date:, "cum_return_ratio"].values
        # if len(last_profit_ratio) <= 0:
        #     cur_year_profit_ratio = cur_profit_ratio[-1] - 1
        # else:
        #     cur_year_profit_ratio = (cur_profit_ratio[-1] - last_profit_ratio[-1]) / last_profit_ratio[-1]
        # report_data["cur_year_profit_ratio"] = float(cur_year_profit_ratio)

        # 月度回报
        def year_month(x):
            a = x.year
            b = x.month
            return str(a) + "/" + str(b)

        profit_df_cp = profit_df.copy()
        profit_df_cp["date"] = profit_df_cp.index
        grouped = profit_df_cp.groupby(profit_df_cp["date"].apply(year_month))
        sum_group = grouped.agg(np.sum)
        month_sum = sum_group.sum(axis=1)

        return_ratio_df["date"] = return_ratio_df.index
        return_group = return_ratio_df.groupby(return_ratio_df["date"].apply(year_month))
        month_last_return_ratio = return_group.last()["cum_return_ratio"]
        month_result = pd.DataFrame({"date": month_sum.index, "profit": month_sum.values, "ratio": month_last_return_ratio.values})
        month_result["datetime"] = pd.to_datetime(month_result["date"])
        month_result.sort_values(by="datetime", inplace=True)
        report_data["month_return"] = month_result


        # # 贡献分解
        # month_earn = sum_group.div(month_sum, axis='rows')
        # report_data["contribution_decomposition"] = month_earn

        # 累积收益率
        cumulative_return = return_ratio_df["cum_return_ratio"].values[-1]
        report_data["contribution_decomposition"] = float(cumulative_return)

        # 对应指数数据
        index_df = self.get_customer_index_nav_data()
        index_result = self.signal_fund_profit_result(index_df[index_df.index >= pd.to_datetime(first_trade_date)], "index")
        report_data["index_result"] = index_result

        self.total_result_data = report_data
        return report_data

    # 基金净值数据，持仓数据
    def group_fund_basic_info_data(self, p_order_df, p_result_cnav_data, p_sum_profit, p_total_amount):
        group_fund_basic_info = []
        group_fund_hoding_info = []
        for index, row in p_order_df.iterrows():
            cur_fund_id = str(row["fund_id"])
            cur_fund_performance = self.all_fund_performance[cur_fund_id]
            cur_fund_info_series = cur_fund_performance.iloc[-1]
            # 基金净值数据
            fund_basic_info = {"fund_name": row["fund_name"], "confirm_nav": round(row["nav"],4)}
            fund_basic_info["cur_nav"] = round(float(self.fund_nav_total[cur_fund_id].dropna().values[-1]), 4)
            fund_basic_info["cur_cnav"] = round(float(self.fund_cnav_total[cur_fund_id].dropna().values[-1]), 4)
            fund_basic_info["ret_1w"] = round(cur_fund_info_series["ret_1w"]*100, 2) if cur_fund_info_series["ret_1w"] is not None else "-"    # 上周
            fund_basic_info["ret_cum_1m"] = round(cur_fund_info_series["ret_cum_1m"]*100, 2) if cur_fund_info_series["ret_cum_1m"] is not None else "-"  # 最近一个月
            fund_basic_info["ret_cum_6m"] = round(cur_fund_info_series["ret_cum_6m"]*100, 2) if cur_fund_info_series["ret_cum_6m"] is not None else "-"  # 最近半年
            fund_basic_info["ret_cum_1y"] = round(cur_fund_info_series["ret_cum_1y"]*100, 2) if cur_fund_info_series["ret_cum_1y"] is not None else "-"  # 最近一年
            fund_basic_info["ret_cum_ytd"] = round(cur_fund_info_series["ret_cum_ytd"]*100, 2) if cur_fund_info_series["ret_cum_ytd"] is not None else "-"    # 今年以来
            fund_basic_info["ret_cum_incep"] = round(cur_fund_info_series["ret_cum_incep"]*100, 2) if cur_fund_info_series["ret_cum_incep"] is not None else "-"    # 成立以来
            # 申购以来
            confirm_date = pd.to_datetime(row["confirm_share_date"])
            confirm_cnav = float(p_result_cnav_data.loc[confirm_date, cur_fund_id])
            fund_basic_info["ret_after_confirm"] = round((fund_basic_info["cur_cnav"] - confirm_cnav)/confirm_cnav*100, 2)
            # 分红
            distribution_df = self.all_fund_distribution[cur_fund_id]
            if distribution_df.empty:
                fund_basic_info["distribution"] = "-"
            else:
                distribution_df["price_date"] = pd.to_datetime(distribution_df["price_date"])
                distribution = float(distribution_df[distribution_df["price_date"] > confirm_date]["distribution"].sum())
                fund_basic_info["distribution"] = round(distribution, 4) if distribution != 0 else "-"

            group_fund_basic_info.append(fund_basic_info)

            # 基金持仓数据
            fund_hoding_info = {"fund_strategy_name": dict_substrategy[int(row["substrategy"])], "fund_name": row["fund_name"]}
            fund_hoding_info["confirm_date"] = row["confirm_share_date"]
            fund_hoding_info["weight"] = round(float(row["confirm_amount"]) / p_total_amount * 100, 2)
            fund_hoding_info["market_values"] = round((float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav) + float(row["confirm_amount"]))/10000, 2)
            fund_hoding_info["cost"] = round(float(row["confirm_amount"])/10000, 2)
            fund_hoding_info["profit"] = round(float(row["confirm_share"]) * (fund_basic_info["cur_cnav"] - confirm_cnav)/10000, 2)
            # fund_hoding_info["ykb"] = fund_hoding_info["profit"] / fund_hoding_info["cost"]
            try:
                fund_hoding_info["ykb"] = round(float(gain_loss_ratio(p_result_cnav_data[cur_fund_id + "_profit"].dropna()))*100, 2)
            except:
                fund_hoding_info["ykb"] = "-"
            fund_hoding_info["profit_contribution"] = round(fund_hoding_info["profit"]*10000 / p_sum_profit*100, 2)
            group_fund_hoding_info.append(fund_hoding_info)
        return group_fund_basic_info, group_fund_hoding_info

    @staticmethod
    def combination_yield(p_combina_df, fund_id_list):
        fund_id_list_amount = [i + "_amount" for i in fund_id_list]
        fund_id_list_profit_ratio = [i + "_profit_ratio" for i in fund_id_list]
        nav_amount_df = p_combina_df[fund_id_list + fund_id_list_amount+fund_id_list_profit_ratio].copy()

        nav_amount_df["sum_amount"] = nav_amount_df[fund_id_list_amount].sum(axis=1).apply(lambda x: Decimal.from_float(x))
        for amount_name in fund_id_list:
            nav_amount_df[amount_name+"_amount_ratio"] = nav_amount_df[amount_name+"_amount"]/nav_amount_df["sum_amount"]
            nav_amount_df[amount_name+"_profit_ratio_weight"] = nav_amount_df[amount_name+"_amount_ratio"] * nav_amount_df[amount_name+"_profit_ratio"]

        fund_id_list_profit_ratio_weight = [i + "_profit_ratio_weight" for i in fund_id_list]
        nav_profit_ratio_weight = nav_amount_df[fund_id_list_profit_ratio_weight].copy().fillna(method='ffill')

        # 收益率
        return_ratio = nav_profit_ratio_weight.sum(axis=1)

        # 累积收益率
        return_ratio_list = list(return_ratio.values)
        cum_return_ratio = []
        last_ratio = 0
        for i in range(len(return_ratio_list)):
            if i == 0:
                last_ratio = 1 + return_ratio_list[i]
                cum_return_ratio.append(last_ratio)
                continue

            cur_ratio = (1 + return_ratio_list[i]) * last_ratio
            cum_return_ratio.append(cur_ratio)
            last_ratio = cur_ratio

        # 收益率
        cum_return_ratio_df = pd.DataFrame(return_ratio.values, columns=["return_ratio"])
        cum_return_ratio_df["cum_return_ratio"] = cum_return_ratio
        cum_return_ratio_df.index = return_ratio.index
        return cum_return_ratio_df

    @staticmethod
    def signal_fund_profit_result(p_fund_nav_df, cur_fund_id):
        result = {"fund_id": cur_fund_id}
        fund_nav_df = p_fund_nav_df.copy()
        profit = fund_nav_df[cur_fund_id].dropna() - fund_nav_df[cur_fund_id].dropna().shift(1)
        fund_nav_df[cur_fund_id + "_profit"] = profit
        fund_nav_df[cur_fund_id + "_profit_ratio"] = profit / fund_nav_df[cur_fund_id].dropna().shift(1)

        # 累积收益率
        return_ratio_list = list(fund_nav_df[cur_fund_id + "_profit_ratio"].astype("float64").values)
        cum_return_ratio = []
        last_ratio = 0
        for i in range(len(return_ratio_list)):
            if i == 0:
                last_ratio = 1 + return_ratio_list[i] if str(return_ratio_list[0]) != 'nan' else 1
                cum_return_ratio.append(last_ratio)
                continue

            cur_ratio = (1 + return_ratio_list[i]) * last_ratio
            cum_return_ratio.append(cur_ratio)
            last_ratio = cur_ratio

        fund_nav_df['cum_return_ratio'] = cum_return_ratio

        # 区间收益率
        result["return_ratio"] = cum_return_ratio[-1]

        # 区间年化收益
        n_freq = freq_days(1)
        return_ratio_year = annual_return((fund_nav_df["cum_return_ratio"].values[-1] - 1), fund_nav_df, n_freq)
        result["return_ratio_year"] = float(return_ratio_year)

        # 波动率
        volatility_ = volatility(fund_nav_df["cum_return_ratio"], n_freq)
        result["volatility"] = float(volatility_)

        # 最大回撤
        drawdown = max_drawdown(fund_nav_df["cum_return_ratio"])
        result["max_drawdown"] = drawdown

        # 夏普比率
        sim = simple_return(fund_nav_df["cum_return_ratio"])
        exc = excess_return(sim, BANK_RATE, n_freq)
        sharpe = sharpe_ratio(exc, sim, n_freq)
        result["sharpe"] = float(sharpe)

        return result

    def get_month_return_chart(self):
        res = self.total_result_data["month_return"]
        xlabels = res["date"].values
        res["profit"] = res["profit"].apply(lambda x: round(x/100.0, 2))
        res["ratio"] = res["ratio"].apply(lambda x: round((x-1)*100, 2))
        product_list = {'name': '月度回报', 'data': res["profit"].values}
        cumulative = {'name': '累积收益', 'data': res["ratio"].values}

        return xlabels, [product_list], cumulative

    def get_total_basic_data(self):
        return self.total_result_data

    def get_group_data(self):
        return self.group_result_data