1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
import json
import sys
import time
import uuid
from jinja2 import PackageLoader, Environment
from app.api.engine import work_dir, pdf_folder, template_folder, pdf_save_folder
from app.config.default_template_params import hold_default_template, diagnose_default_template
from app.service.fund_service import fund_index_compare
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
from datetime import datetime
# 准备数据
from app.utils.draw import draw_month_return_chart, draw_contribution_chart, draw_combination_chart, \
draw_old_combination_chart, draw_index_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_INFO15917850824287', customer_id='6716613802534121472', pdf_name=str(uuid.uuid4()) + '.pdf', type=1):
self.user_customer = UserCustomerResultAdaptor(ifa_id, customer_id)
self.customer_name = self.user_customer.customer_real_name
self.ifa_name = self.user_customer.ifa_real_name
# self.pdf_name = self.ifa_name + "_" + self.customer_name + "_" + '.pdf'
self.pdf_name = pdf_name
# 1持仓报告2诊断报告
self.type = type
# 全部数据
self.df = self.user_customer.calculate_total_data()
# 组合结果数据
self.d = self.user_customer.calculate_group_result_data()
self.all_folio_result = {}
# 分组合拼接结果数据
self.get_group_result()
# 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()
# 分组和计算个基点评以及新增基金等结果
def get_group_result(self):
for group_name, group_result in self.d.items():
portfolio_diagnose = self.get_portfolio_diagnose(group_result["fund_id_list"], invest_amount=group_result["total_cost"])
cur_group_portfolio_result = {
'new_correlation': [],
'propose_fund_data_list': [],
'suggestions_result': {},
'suggestions_result_asset': {},
'return_compare_pic': [],
'indicator_compare': [],
'new_group_evaluation': [],
"correlation": group_result["correlation"]
}
# 旧持仓组合点评
self.comments_on_position_portfolio(portfolio_diagnose, group_name, cur_group_portfolio_result)
# 贡献分解
self.contribution_deco(group_result, cur_group_portfolio_result)
# 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)
# 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)
if self.type == 2:
# 新增基金
self.propose_fund(portfolio_diagnose, cur_group_portfolio_result)
# 新收益比较
self.get_transfer_suggestions(portfolio_diagnose, group_name, cur_group_portfolio_result)
# 新相关性
self.get_new_correlation(portfolio_diagnose, cur_group_portfolio_result)
self.all_folio_result[group_name] = cur_group_portfolio_result
def get_portfolio_diagnose(self, portfolio, client_type=1, invest_amount=10000000):
if invest_amount < 10000000:
invest_amount = 10000000
folio_fund_dict = {}
for fd in portfolio:
folio_fund_dict[fd] = self.user_customer.all_fund_type_dict[fd]
portfolio_diagnose = PortfolioDiagnose(client_type=client_type, portfolio=folio_fund_dict,
invest_amount=float(invest_amount),
start_date=self.user_customer.start_date)
if self.type == 2:
portfolio_diagnose.optimize()
return portfolio_diagnose
# 全部数据综述结果
def get_summarize(self):
"""投资总览."""
self.total_cost = int(self.df["total_cost"]) # 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."""
"""组合月度及累计回报率曲线图"""
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):
"""月度盈亏和期末资产"""
self.monthly_table_return = self.df["month_return_data_dict"]
# 旧组合持仓点评,贡献分解数据
def comments_on_position_portfolio(self, portfolio_diagnose, folio, cur_group_portfolio_result):
"""旧持仓组合点评. 旧贡献分解数据"""
cur_group_portfolio_result["old_evaluation"], cur_group_portfolio_result["old_return_compare_data"],\
cur_group_portfolio_result["old_indicator_compare"] = portfolio_diagnose.old_evaluation(folio, self.d, self.user_customer)
def contribution_deco(self, group_result, cur_group_portfolio_result):
"""贡献分解."""
g_data = group_result["contribution_decomposition"]
cur_group_portfolio_result["contribution_decomposition"] = draw_contribution_chart(g_data['xlabels'], g_data['product_list'], g_data['cumulative'])
def single_fund_comment(self, portfolio_diagnose, cur_group_portfolio_result):
"""个基点评."""
single_fund_data_list = []
portfolio_evaluation = portfolio_diagnose.old_portfolio_evaluation()
index_compare_chart_data = portfolio_diagnose.original_fund_index_compare(self.user_customer.fund_cnav_total)
# with futures.ProcessPoolExecutor(os.cpu_count()) as executor:
# res = executor.map(draw_index_combination_chart, index_compare_chart_data)
# res = list(res)
res = []
for chart_data in index_compare_chart_data:
r = draw_index_combination_chart(chart_data)
res.append(r)
for i in range(len(portfolio_evaluation)):
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]
})
cur_group_portfolio_result["single_fund_data_list"] = single_fund_data_list
def get_old_compare_pic(self, cur_group_portfolio_result):
"""旧收益比较"""
cur_group_portfolio_result["old_return_compare_pic"] = draw_old_combination_chart(cur_group_portfolio_result["old_return_compare_data"]["xlabels"],
cur_group_portfolio_result["old_return_compare_data"]["origin_combination"],
cur_group_portfolio_result["old_return_compare_data"]["index"])
def get_transfer_suggestions(self, portfolio_diagnose, folio, cur_group_portfolio_result):
"""新收益比较,调仓建议"""
cur_group_portfolio_result["suggestions_result"], cur_group_portfolio_result["suggestions_result_asset"], \
cur_group_portfolio_result["return_compare_data"], \
cur_group_portfolio_result["indicator_compare"], cur_group_portfolio_result["new_group_evaluation"] = portfolio_diagnose.new_evaluation(folio, self.d,
self.user_customer)
cur_group_portfolio_result["return_compare_pic"] = draw_combination_chart(cur_group_portfolio_result["return_compare_data"]["xlabels"],
cur_group_portfolio_result["return_compare_data"]["new_combination"],
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):
"""旧相关性分析."""
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))
del cur_group_portfolio_result["correlation"]
def get_new_correlation(self, portfolio_diagnose, cur_group_portfolio_result):
"""新相关性分析."""
new_correlation = portfolio_diagnose.new_correlation
new_correlation_columns = new_correlation.columns.tolist()
new_correlation_values = new_correlation.values.tolist()
cur_group_portfolio_result["new_correlation"] = list(zip(range(1, len(new_correlation_columns)+1), new_correlation_columns, new_correlation_values))
def propose_fund(self, portfolio_diagnose, cur_group_portfolio_result):
"""新增基金"""
# 优化组合建议1 -- 新增基金
propose_fund_data_list = []
propose_fund_evaluation = portfolio_diagnose.propose_fund_evaluation()
# propose_radar_chart_data = portfolio_diagnose.propose_fund_radar()
# with futures.ProcessPoolExecutor(os.cpu_count()) as executor:
# res = executor.map(gen_radar_chart, propose_radar_chart_data)
res = []
for fund_id in portfolio_diagnose.propose_portfolio.columns:
r = fund_index_compare(fund_id, portfolio_diagnose.portfolio_dict.get(fund_id, 2))
res.append(r)
for i in range(len(propose_fund_evaluation)):
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]
})
cur_group_portfolio_result["propose_fund_data_list"] = propose_fund_data_list
def objectives_performance(self, group_result, cur_group_portfolio_result):
"""目标与业绩"""
cur_group_portfolio_result["totoal_rate_of_return"] = "%.2f" % round((group_result['cumulative_return']-1)*100, 2) # 成立以来累计收益率
cur_group_portfolio_result["annualised_return"] = "%.2f" % round(group_result["return_ratio_year"]*100, 2) # 年化收益率
cur_group_portfolio_result["volatility"] = "%.2f" % round(group_result["volatility"]*100, 2)
cur_group_portfolio_result["max_withdrawal"] = "%.2f" % round(group_result["max_drawdown"][0]*100, 2)
cur_group_portfolio_result["sharpe_ratio"] = "%.2f" % round(group_result["sharpe"], 2)
cur_group_portfolio_result["cost_of_investment"] = "%.2f" % round(group_result["total_cost"]/10000.0, 2) # 投资成本
cur_group_portfolio_result["index_section_return"] = "%.2f" % round((group_result["index_result"]["return_ratio"]-1)*100, 2)
cur_group_portfolio_result["index_annualised_return"] = "%.2f" % round(group_result["index_result"]["return_ratio_year"]*100, 2) # 年化收益率
cur_group_portfolio_result["index_volatility"] = "%.2f" % round(group_result["index_result"]["volatility"]*100, 2)
cur_group_portfolio_result["index_max_withdrawal"] = "%.2f" % round(group_result["index_result"]["max_drawdown"][0]*100, 2)
cur_group_portfolio_result["index_sharpe_ratio"] = "%.2f" % round(group_result["index_result"]["sharpe"], 2)
cur_group_portfolio_result["group_nav_info"] = group_result["group_nav_info"]
cur_group_portfolio_result["group_hoding_info"] = group_result["group_hoding_info"]
cur_group_portfolio_result["group_hoding_info_total"] = group_result["group_hoding_info_total"]
def get_template_data(self, default_template=None):
""""""
if self.type == 1:
# 持仓报告数据
data = {
# 全局数据
'customer_name': self.customer_name,
'year_month': datetime.now().strftime("%Y-%m-%d"),
'valueSex': self.user_customer.valueSex,
'month': self.user_customer.month_start_date.strftime("%m"),
'start_date': self.user_customer.start_date.strftime("%Y-%m-%d"),
'latest_worth_day': self.user_customer.last_nav_date,
'customer_level': '平衡型',
# 综述数据
'now_allocation_amount': '{:,}'.format(self.total_cost), 'now_yield': self.now_yield,
'index_yield': self.index_yield,
'now_annualised_return': self.now_annualised_return,
'now_withdrawal': self.now_withdrawal, 'index_withdrawal': self.index_withdrawal,
'expected_withdrawal': 20,
'now_year_income': '{:,}'.format(self.now_year_income),
'now_month_income': '{:,}'.format(self.now_month_income),
'final_balance': '{:,}'.format(self.final_balance), 'total_profit': '{:,}'.format(self.total_profit),
'total_profit_temp': self.total_profit,
'now_year_income_temp': self.now_year_income, 'now_month_income_temp': self.now_month_income,
'monthly_return_performance_pic': self.monthly_return_performance_pic,
'month_rise': self.month_rise, 'year_totoal_rate_of_return': self.year_totoal_rate_of_return,
'monthly_table_return': self.monthly_table_return,
# 组合数据
'all_folio_result': self.all_folio_result,
}
if default_template:
self.data = {**default_template, **data}
else:
self.data = {**hold_default_template, **data}
elif self.type == 2:
# 诊断报告数据
data = {
# 全局数据
'customer_name': self.customer_name,
'year_month': self.user_customer.end_date.strftime("%Y-%m-%d"),
'valueSex': self.user_customer.valueSex,
'month': self.user_customer.month_start_date.strftime("%m"),
'start_date': self.user_customer.start_date.strftime("%Y-%m-%d"),
'latest_worth_day': self.user_customer.last_nav_date,
'customer_level': '平衡型',
# 综述数据
'now_allocation_amount': '{:,}'.format(self.total_cost), 'now_yield': self.now_yield,
'index_yield': self.index_yield,
'now_annualised_return': self.now_annualised_return,
'now_withdrawal': self.now_withdrawal, 'index_withdrawal': self.index_withdrawal,
'expected_withdrawal': 20,
'now_year_income': '{:,}'.format(self.now_year_income),
'now_month_income': '{:,}'.format(self.now_month_income),
'final_balance': '{:,}'.format(self.final_balance), 'total_profit': '{:,}'.format(self.total_profit),
'total_profit_temp': self.total_profit,
'now_year_income_temp': self.now_year_income, 'now_month_income_temp': self.now_month_income,
'monthly_return_performance_pic': self.monthly_return_performance_pic,
'month_rise': self.month_rise, 'year_totoal_rate_of_return': self.year_totoal_rate_of_return,
'monthly_table_return': self.monthly_table_return,
# 组合数据
'all_folio_result': self.all_folio_result,
}
if default_template:
self.data = {**default_template, **data}
else:
self.data = {**hold_default_template, **data}
return self.data
def render_data(self, data=None):
# 全部数据
if data:
self.data = data
# 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/monthReportV2.1.html') # 获取一个模板文件
monthReport_html = template.render(self.data).replace('None', 'none') # 渲染
# 保存 monthReport_html
# save_file = "app/pdf/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_save_folder + self.pdf_name)
if __name__ == '__main__':
start = time.time()
dt = DataIntegrate(ifa_id='USER_INFO15955928945523', customer_id='67347292618078412802', type=2)
data = dt.get_template_data()
dt.render_data()
print('耗时{}秒'.format(round(time.time()-start, 2)))