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import time
import uuid
from jinja2 import PackageLoader, Environment
from app.api.engine import work_dir, pdf_folder, template_folder
from app.service.portfolio_diagnose import PortfolioDiagnose
from app.service.result_service_v2 import UserCustomerResultAdaptor
import numpy as np
from concurrent import futures
import os
# 准备数据
from app.utils.draw import draw_month_return_chart, draw_contribution_chart, draw_combination_chart, \
draw_old_combination_chart
from app.utils.html_to_pdf import html_to_pdf
from app.utils.radar_chart import gen_radar_chart
class DataIntegrate:
def __init__(self, ifa_id='USER_INFO15914346866762', customer_id='202009281545001', pdf_name=str(uuid.uuid4()) + '.pdf'):
self.user_customer = UserCustomerResultAdaptor(ifa_id, customer_id)
self.customer_name = self.user_customer.customer_real_name
self.pdf_name = pdf_name
self.df = self.user_customer.calculate_total_data()
self.d = self.user_customer.calculate_group_result_data()
# 组合数据
self.group_result = self.d["default"]
self.get_portfolio_diagnose(self.group_result["fund_id_list"])
# 投资总览
self.get_summarize()
# 月度回报
self.get_month_return()
# 月度回报表格
self.get_month_table_return()
# 旧持仓组合点评
self.comments_on_position_portfolio()
# 贡献分解
self.contribution_deco()
# 目标与业绩
self.objectives_performance(self.group_result)
# 个基点评
self.single_fund_comment()
# 旧收益比较
self.get_old_compare_pic()
# 旧相关性
self.get_old_correlation()
# # 新增基金
# self.propose_fund()
# # 新收益比较
# self.get_transfer_suggestions()
# # 新相关性
# self.get_new_correlation()
# 渲染模版
self.render_data()
def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000):
self.portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=portfolio, invest_amount=invest_amount)
self.portfolio_diagnose.optimize()
def get_summarize(self):
"""投资总览."""
self.total_cost = round(self.df["total_cost"], 2) # 投资成本
self.now_yield = round((self.df['cumulative_return']-1)*100, 2) # 成立以来累计收益率
self.now_annualised_return = round(self.df["return_ratio_year"] * 100, 2) # 年化收益率
self.index_yield = round((self.df["index_result"]["return_ratio"]-1)*100, 2) # 指数收益率
self.now_withdrawal = round(self.df["max_drawdown"][0]*100, 2) # 最大回撤
self.index_withdrawal = round(self.df["index_result"]["max_drawdown"][0]*100, 2) # 指数最大回撤
self.now_month_income = int(self.df["cur_month_profit"]) # 本月收益
self.month_rise = round(self.df["cur_month_profit_ratio"] * 100, 2) # 本月涨幅
self.year_totoal_rate_of_return = round(self.df["cur_year_profit_ratio"] * 100, 2) # 今年累计收益率
self.now_year_income = int(self.df["cur_year_profit"]) # 今年累计收益
self.final_balance = int(self.df["total_cost"] + self.df["cumulative_profit"]) # 期末资产
self.total_profit = int(self.df["cumulative_profit"]) # 累计盈利
def get_month_return(self):
"""月度回报."""
xlabels, product_list, cumulative = self.user_customer.get_month_return_chart()
self.monthly_return_performance_pic = draw_month_return_chart(xlabels, product_list, cumulative)
def get_month_table_return(self):
self.monthly_table_return = self.df["month_return_data_dict"]
def comments_on_position_portfolio(self):
"""旧持仓组合点评. 旧贡献分解数据"""
self.old_evaluation, self.old_return_compare_data, self.old_indicator_compare = self.portfolio_diagnose.old_evaluation('default', self.d, self.user_customer)
def contribution_deco(self):
"""贡献分解."""
g_data = self.group_result["contribution_decomposition"]
self.contribution_decomposition = draw_contribution_chart(g_data['xlabels'], g_data['product_list'], g_data['cumulative'])
def single_fund_comment(self):
"""个基点评."""
self.single_fund_data_list = []
portfolio_evaluation = self.portfolio_diagnose.old_portfolio_evaluation()
radar_chart_data = self.portfolio_diagnose.single_fund_radar()
with futures.ProcessPoolExecutor(os.cpu_count()) as executor:
res = executor.map(gen_radar_chart, radar_chart_data)
res = list(res)
for i in range(len(portfolio_evaluation)):
if portfolio_evaluation[i]['status'] == '保留':
portfolio_evaluation[i]['status'] = '<div class="self_type fl">保留</div>'
elif portfolio_evaluation[i]['status'] == '增仓':
portfolio_evaluation[i]['status'] = '<div class="self_type fl red">增仓</div>'
elif portfolio_evaluation[i]['status'] == '换仓':
portfolio_evaluation[i]['status'] = '<div class="self_type fl green">换仓</div>'
elif portfolio_evaluation[i]['status'] == '减仓':
portfolio_evaluation[i]['status'] = '<div class="self_type fl green">减仓</div>'
self.single_fund_data_list.append({
'fund_name': portfolio_evaluation[i]['name'],
'status': portfolio_evaluation[i]['status'],
'evaluation': portfolio_evaluation[i]['data'],
'radar_chart_path': res[i]
})
def get_old_compare_pic(self):
"""旧收益比较"""
self.old_return_compare_pic = draw_old_combination_chart(self.old_return_compare_data["xlabels"], self.old_return_compare_data["origin_combination"], self.old_return_compare_data["index"])
def get_transfer_suggestions(self):
"""新收益比较，调仓建议"""
self.suggestions_result, self.suggestions_result_asset, self.return_compare_data, \
self.indicator_compare, self.new_group_evaluation = self.portfolio_diagnose.new_evaluation("default", self.d,
self.user_customer)
self.return_compare_pic = draw_combination_chart(self.return_compare_data["xlabels"], self.return_compare_data["new_combination"],
self.return_compare_data["origin_combination"], self.return_compare_data["index"])
def get_old_correlation(self):
"""旧相关性分析."""
old_correlation = self.portfolio_diagnose.old_correlation
old_correlation_columns = old_correlation.columns.tolist()
old_correlation_values = old_correlation.values.tolist()
self.old_correlation = list(zip(range(1, len(old_correlation_columns)+1), old_correlation_columns, old_correlation_values))
def get_new_correlation(self):
"""新相关性分析."""
new_correlation = self.portfolio_diagnose.new_correlation
new_correlation_columns = new_correlation.columns.tolist()
new_correlation_values = new_correlation.values.tolist()
self.new_correlation = list(zip(range(1, len(new_correlation_columns)+1), new_correlation_columns, new_correlation_values))
def propose_fund(self):
"""新增基金"""
# 优化组合建议1 -- 新增基金
self.propose_fund_data_list = []
propose_fund_evaluation = self.portfolio_diagnose.propose_fund_evaluation()
propose_radar_chart_data = self.portfolio_diagnose.propose_fund_radar()
with futures.ProcessPoolExecutor(os.cpu_count()) as executor:
res = executor.map(gen_radar_chart, propose_radar_chart_data)
res = list(res)
for i in range(len(propose_fund_evaluation)):
self.propose_fund_data_list.append({
'fund_name': propose_fund_evaluation[i]['name'],
'status': '增仓',
'evaluation': propose_fund_evaluation[i]['data'],
'radar_chart_path': res[i]
})
# for i in range(len(propose_fund_evaluation)):
# self.propose_fund_data_list.append({
# 'fund_name': propose_fund_evaluation[i]['name'],
# 'status': '增仓',
# 'evaluation': propose_fund_evaluation[i]['data'],
# 'radar_chart_path': gen_radar_chart(propose_radar_chart_data[i])
# })
def objectives_performance(self, group_result):
"""目标与业绩"""
self.totoal_rate_of_return = round((group_result['cumulative_return']-1)*100, 2) # 成立以来累计收益率
self.annualised_return = round(group_result["return_ratio_year"]*100, 2) # 年化收益率
self.volatility = round(group_result["volatility"]*100, 2)
self.max_withdrawal = round(group_result["max_drawdown"][0]*100, 2)
self.sharpe_ratio = round(group_result["sharpe"], 2)
self.cost_of_investment = round(group_result["total_cost"]/10000.0, 2) # 投资成本
self.index_section_return = round((group_result["index_result"]["return_ratio"]-1)*100, 2)
self.index_annualised_return = round(group_result["index_result"]["return_ratio_year"]*100, 2) # 年化收益率
self.index_volatility = round(group_result["index_result"]["volatility"]*100, 2)
self.index_max_withdrawal = round(group_result["index_result"]["max_drawdown"][0]*100, 2)
self.index_sharpe_ratio = round(group_result["index_result"]["sharpe"], 2)
self.group_nav_info = group_result["group_nav_info"]
self.group_hoding_info = group_result["group_hoding_info"]
self.group_hoding_info_total = group_result["group_hoding_info_total"]
def render_data(self):
# 全部数据
data = {
# 封面 值为None不不显示，为block显示
'box0': 'block',
# 目录
'box1': 'block',
# 投资总览
'box2': 'block',
# 目标与业绩
'box3': 'block',
# 业绩的明细
'box4': 'block',
# 个基点评
'box5': 'block',
# 优化组合建议
'box6': None,
# 新增基金
'box7': None,
# 结尾
'box8': 'block',
'cover_back': template_folder + '/v2/img/cover-back.png',
'logo': template_folder + '/v2/img/logo.png',
'scene': template_folder + '/v2/img/scene.png',
'team': template_folder + '/v2/img/team.png',
'customer_name': self.customer_name,
'customer_gender': '女',
'year_month': self.user_customer.month_start_date.strftime("%Y-%m"),
'month': self.user_customer.month_start_date.strftime("%m"),
'start_date': self.user_customer.start_date.strftime("%Y-%m-%d"),
'ifa_company': '飞度工作室',
'title': '10月综述',
'brand_name': '资产管<br>理中心',
'customer_old': 42, 'customer_level': '平衡型',
# 'new_evaluation': self.new_evaluation,
'position_years': '5年', 'planned_allocation_amount': 2000.00,
'now_allocation_amount': self.total_cost, 'now_yield': self.now_yield, 'index_yield': self.index_yield,
'expected_yield': 20, 'now_annualised_return': self.now_annualised_return,
'now_withdrawal': self.now_withdrawal, 'index_withdrawal': self.index_withdrawal, 'expected_withdrawal': 20,
'now_year_income': self.now_year_income, 'now_month_income': self.now_month_income,
'totoal_rate_of_return': self.totoal_rate_of_return,
'month_rise': self.month_rise, 'year_totoal_rate_of_return': self.year_totoal_rate_of_return,
'annualised_return': self.annualised_return, 'cost_of_investment': self.cost_of_investment,
'final_balance': self.final_balance, 'total_profit': self.total_profit,
'latest_worth_day': self.user_customer.last_nav_date,
'index_comparison': {'section_return': self.totoal_rate_of_return, 'annualized_returns': self.annualised_return,
'volatility': self.volatility, 'max_withdrawal': self.max_withdrawal,
'sharpe_ratio': self.sharpe_ratio},
'index_comparison_500': {'section_return': self.index_section_return,
'annualized_returns': self.index_annualised_return,
'volatility': self.index_volatility, 'max_withdrawal': self.index_max_withdrawal,
'sharpe_ratio': self.index_sharpe_ratio},
'monthly_return_performance_pic': self.monthly_return_performance_pic,
'monthly_table_return': self.monthly_table_return,
'group_nav_info': self.group_nav_info,
'group_hoding_info': self.group_hoding_info,
'group_hoding_info_total': self.group_hoding_info_total,
'old_evaluation': self.old_evaluation,
'old_indicator_compare': self.old_indicator_compare,
'contribution_decomposition': self.contribution_decomposition,
'single_fund_data_list': self.single_fund_data_list,
'old_correlation': self.old_correlation,
'old_return_compare_pic': self.old_return_compare_pic,
# 'new_correlation': self.new_correlation,
# 'propose_fund_data_list': self.propose_fund_data_list,
# 'suggestions_result': self.suggestions_result,
# 'suggestions_result_asset': self.suggestions_result_asset,
# 'return_compare_pic': self.return_compare_pic,
# 'indicator_compare': self.indicator_compare,
# 'new_group_evaluation': self.new_group_evaluation
'new_correlation': [],
'propose_fund_data_list': [],
'suggestions_result': {},
'suggestions_result_asset': {},
'return_compare_pic': [],
'indicator_compare': [],
'new_group_evaluation': []
}
# 开始渲染html模板
env = Environment(loader=PackageLoader('app', 'templates')) # 创建一个包加载器对象
# template = env.get_template('monthReport.html') # 获取一个模板文件
template = env.get_template('/v2/monthReportV2.html') # 获取一个模板文件
monthReport_html = template.render(data) # 渲染
# 保存 monthReport_html
# save_file = "app/html/monthReport.html"
# with open(save_file, 'w', encoding="utf-8") as f:
# f.write(monthReport_html)
# save_file = "app/html/v2/monthReportV2.html"
# with open(save_file, 'w', encoding="utf-8") as f:
# f.write(monthReport_html)
html_to_pdf(monthReport_html, pdf_folder + self.pdf_name)
if __name__ == '__main__':
start = time.time()
DataIntegrate(ifa_id='USER_INFO15916072577875', customer_id='6716613804966817792')
print('耗时{}秒'.format(round(time.time()-start, 2)))