994 lines
45 KiB
Python
994 lines
45 KiB
Python
import json
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import time
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import requests
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import pandas as pd
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from DBHelper.MongoHelper import MongoHelper
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from RatingData.scripts.risk_detail import associate_risk_detail, change_log_detail
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from Utils.ErrorUtil import LogConditionCheckFailed
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from CompanyData.CompanyDataObj import CompanyData, BasicInfo, ShareHolder, MainMember, ComprehensiveCreditAnalysis, \
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FinancialElementsAnalysis, BusinessElementsAnalysis, FinancialIndex, BalanceSheet, ProfitSheet, AppendixDataSheet, \
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EsgRatingAnalysis, FinancialIndexs
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from Utils.CommonUtil import sub_dict, df_iterrows
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class CompanyDataImpl(CompanyData):
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"""企业数据实现类"""
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db_tyc = MongoHelper("tyc")
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db_tfse = MongoHelper("tfse_v0.21")
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def drag_data_from_tyc_api(self):
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"""拉取天眼查接口数据"""
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url = "http://api.fecribd.com/api/tyc/drag_data"
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headers = {'token': "uzdq51N4!I0%HY4sCaQ!aeCSIDIVIdAM"}
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data = {"企业名称": self.name}
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res = requests.post(url=url, headers=headers, data=json.dumps(data))
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return True if res.status_code == 200 else False
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def prepare_company_init_data(self):
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def prepare_business_data():
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data = self.db_tyc.find_single_column(
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"公司背景",
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"基本信息",
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{"企业名称": self.name},
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"基本信息"
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)
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basic_info = BasicInfo()
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basic_info.status = data['regStatus']
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basic_info.legal_person = data['legalPersonName']
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basic_info.company_type = data['companyOrgType']
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basic_info.taxpayer_id = data['taxNumber']
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basic_info.business_scope = data['businessScope']
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basic_info.registered_capital = data['regCapital']
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basic_info.paid_capital = data['actualCapital']
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basic_info.registered_address = data['regLocation']
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basic_info.registration_authority = data['regInstitute']
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basic_info.industry = data['industry']
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basic_info.staff_size = data['staffNumRange']
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basic_info.people_insured_num = data['socialStaffNum']
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basic_info.micro_company = "是" if data['regStatus'] == 1 else "否"
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self.basic_info = basic_info
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def prepare_share_holders():
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try:
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self.share_holders = list()
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data = self.db_tyc.find_single_column(
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"公司背景",
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"企业股东",
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{"企业名称": self.name},
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"企业股东"
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)
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try:
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results = data['result']
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except KeyError:
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raise LogConditionCheckFailed("企业股东数据异常", 200)
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for result in results:
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share_holder = ShareHolder()
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share_holder.name = result['name']
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share_holder.share_holder_type = "公司" if result['type'] == 1 else (
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"个人" if result['type'] == 2 else "其他")
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if result.__contains__('capital'):
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share_holder.share_holding_ratio = None if result['capital'][0]['percent'] == '' else \
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result['capital'][0]['percent']
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share_holder.subscription_amount = None if result['capital'][0]['amomon'] == '' else \
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result['capital'][0]['amomon']
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share_holder.subscription_date = None if result['capital'][0]['time'] == '' else \
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result['capital'][0]['time']
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else:
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share_holder.share_holding_ratio = None
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share_holder.subscription_amount = None
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share_holder.subscription_date = None
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if result.__contains__('capitalActl'):
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share_holder.paid_amount = [capital_actl['amomon'] for capital_actl in result['capitalActl']]
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share_holder.payment_method = [capital_actl['paymet'] for capital_actl in result['capitalActl']]
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share_holder.payment_time = [capital_actl['time'] for capital_actl in result['capitalActl']]
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else:
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share_holder.paid_amount = None
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share_holder.payment_method = None
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share_holder.payment_time = None
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self.share_holders.append(share_holder)
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except TypeError:
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self.share_holders = list()
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data = self.db_tyc.find_single_column(
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"公司背景",
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"十大股东",
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{"企业名称": self.name},
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"十大股东"
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)
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try:
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results = data['holderList']
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except KeyError:
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raise LogConditionCheckFailed("企业股东数据异常", 200)
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for result in results:
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share_holder = ShareHolder()
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share_holder.name = result['name']
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share_holder.share_holder_type = "公司" if result['cType'] == 1 else (
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"个人" if result['cType'] == 2 else "其他")
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share_holder.share_holding_ratio = result['proportion']
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share_holder.subscription_amount = None
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share_holder.subscription_date = None
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share_holder.paid_amount = None
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share_holder.payment_method = None
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share_holder.payment_time = None
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self.share_holders.append(share_holder)
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def prepare_main_members():
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self.main_members = list()
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data = self.db_tyc.find_single_column(
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"公司背景",
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"主要人员",
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{"企业名称": self.name},
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"主要人员"
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)
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try:
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results = data['result']
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except KeyError:
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raise LogConditionCheckFailed("主要人员数据异常", 200)
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for result in results:
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main_member = MainMember()
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main_member.name = result['name']
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main_member.job_title = result['typeJoin']
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self.main_members.append(main_member)
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def prepare_industry_l1():
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"""一级行业数据"""
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db = MongoHelper("tfse_v0.21")
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tyc_industry_l2 = self.basic_info.dict_keys_toggle()['行业']
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fecr_industry_l1 = db.find_single_column(
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"模型端",
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"天眼查公司行业分类",
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{"二级行业": tyc_industry_l2},
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"远东_一级行业"
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)
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self.industry_l1 = fecr_industry_l1
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def save_to_basic_business_info():
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self.db_tfse.upsert_single_data(
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"企业数据",
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"基本工商信息",
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{"企业ID": self.cid},
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{
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"企业ID": self.cid,
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"企业名称": self.name,
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"更新日期": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
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"工商信息": self.basic_info.dict_keys_toggle(),
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"股东信息": [item.dict_keys_toggle() for item in self.share_holders],
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"主要成员": [item.dict_keys_toggle() for item in self.main_members]
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}
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)
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def save_to_update_summary():
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self.update_time = {
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"工商信息": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
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"财务信息": None,
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"综信评价": None,
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"ESG评价": None,
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}
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self.db_tfse.upsert_single_data(
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"企业数据",
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"企业数据_更新汇总",
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{"企业ID": self.cid},
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self.dict_keys_toggle()
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)
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def __main__():
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prepare_business_data()
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prepare_share_holders()
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prepare_main_members()
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prepare_industry_l1()
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save_to_basic_business_info()
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save_to_update_summary()
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__main__()
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def update_credit_data(self, rid):
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"""更新综信评价相关数据"""
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def update_rating_result():
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"""更新评价结果"""
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rating_result = self.db_tfse.find_single_data(
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'综信评价数据',
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'评价结果',
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{'评价ID': rid},
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['企业ID', '信用等级', '信用评分', '评价年度', '经营评分', '财务评分', '风险评分', '评价时间']
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)
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self.cid = rating_result['企业ID']
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del rating_result['企业ID']
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rating_result['评价ID'] = rid
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self.cc_rating_result = rating_result
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def financial_indicator():
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"""更新财务指标"""
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financial_result = self.db_tfse.find_single_data(
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'综信评价数据',
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'财指结果',
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{'企业ID': self.cid},
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['净资产收益率', '存货周转率', '已获利息倍数', '应收账款周转率', '总资产周转率', '总资产增长率', '总资产报酬率',
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'技术投入比率', '营业增长率', '资产负债率', '速动比率']
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)
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financial_index = FinancialIndex()
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financial_index.roe = financial_result['净资产收益率']
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financial_index.inventory_turnover = financial_result['存货周转率']
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financial_index.interest_multiple = financial_result['已获利息倍数']
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financial_index.accounts_receivable_turnover = financial_result['应收账款周转率']
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financial_index.total_asset_turnover = financial_result['总资产周转率']
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financial_index.total_asset_growth_rate = financial_result['总资产增长率']
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financial_index.roa = financial_result['总资产报酬率']
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financial_index.technology_investment_ratio = financial_result['技术投入比率']
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financial_index.operating_growth_rate = financial_result['营业增长率']
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financial_index.assets_and_liabilities = financial_result['资产负债率']
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financial_index.quick_ratio = financial_result['速动比率']
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self.fin_index = financial_index
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def financial_sheet():
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"""更新财务表"""
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fill_data = self.db_tfse.find_single_data(
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'综信评价数据',
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'填报数据',
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{'评价ID': rid},
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['财务填报']
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)
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def update_balance_sheet():
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"""资产负债表"""
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balance_data = fill_data['财务填报']['资产负债表'][0]
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balance = BalanceSheet()
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balance.accounts_receivable = balance_data['应收账款']
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balance.stock = balance_data['存货']
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balance.total_current_assets = balance_data['流动资产合计']
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balance.total_assets = balance_data['资产总计']
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balance.short_loan = balance_data['短期借款']
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balance.one_year_liabilities = balance_data['一年内到期非流动负债']
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balance.total_current_liabilities = balance_data['流动负债合计']
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balance.long_term_loan = balance_data['长期借款']
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balance.total_liabilities = balance_data['负债合计']
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balance.total_owners_equity = balance_data['所有者权益合计']
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self.balance_sheet = balance
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def update_profit_sheet():
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"""利润表"""
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profit_data = fill_data['财务填报']['利润表'][0]
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profit = ProfitSheet()
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profit.operating_income = profit_data['营业收入']
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profit.operating_cost = profit_data['营业成本']
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profit.total_profit = profit_data['利润总额']
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profit.net_profit = profit_data['净利润']
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self.profit_sheet = profit
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def update_appendix_sheet():
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"""补充数据表"""
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appendix_data = fill_data['财务填报']['补充数据表'][0]
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appendix = AppendixDataSheet()
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appendix.rd_expenses = appendix_data['研发费用']
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appendix.interest_disbursement = appendix_data['计入财务费的利息支出']
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appendix.interest_expense = appendix_data['资本化利息支出']
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self.appendix_sheet = appendix
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def _main_():
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update_balance_sheet()
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update_profit_sheet()
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update_appendix_sheet()
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_main_()
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def __main__():
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update_rating_result()
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financial_indicator()
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financial_sheet()
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result = self.dict_to_save()
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del result['企业ID']
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self.db_tfse.update_single_data(
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'企业数据',
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'企业数据_更新汇总',
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{'企业ID': self.cid},
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result
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)
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__main__()
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def update_esg_data(self, rid):
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"""更新ESG相关数据"""
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rating_result = self.db_tfse.find_single_data(
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'ESG评价数据',
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'评价结果',
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{'评价ID': rid},
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['企业ID', '评价等级', '评价年度', 'ESG得分', '环境得分', '社会得分', '公司治理得分', '评价时间']
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)
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cid = rating_result['企业ID']
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del rating_result['企业ID']
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rating_result['评价ID'] = rid
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self.esg_rating_result = rating_result
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result = self.dict_to_save()
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self.db_tfse.update_single_data(
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'企业数据',
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'企业数据_更新汇总',
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{'企业ID': cid},
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result
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)
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class CreditAnalysisImpl(ComprehensiveCreditAnalysis):
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"""综合信用分析类"""
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db = MongoHelper("tfse_v0.21")
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def cc_rating_etl(self):
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rating_record = self.db.find_single_data(
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'企业数据',
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'评价记录',
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{"评价ID": self.rid},
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["报告fid"]
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)
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rating_result = self.db.find_single_data(
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'综信评价数据',
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'评价结果',
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{"评价ID": self.rid},
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["企业ID", "企业名称", "经营评分", "财务评分", "风险评分", "评价年度"]
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)
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rating_results = self.db.find_all_data(
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'综信评价数据',
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'评价结果',
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{"企业ID": rating_result['企业ID']},
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['信用等级', "信用评分", '评价时间', "评价方式"]
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)
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text_model = self.db.find_single_data(
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'综信评价数据',
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'报告数据',
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{"评价ID": self.rid},
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["行业选择", "报告内容"]
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)
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df_records = pd.DataFrame(rating_results).sort_values('评价时间', ascending=False)
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def result_head():
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"""结果数据"""
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self.cid = rating_result['企业ID']
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self.name = rating_result['企业名称']
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self.update_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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def make_radar_chart():
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credit_performence = self.CreditPerformance()
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indicator_radar = credit_performence.IndicatorRadar()
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"""综合信用指标表现雷达图数据"""
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max_score = indicator_radar.MaxScore()
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max_score.environment = 10
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max_score.social_responsibility = 10
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max_score.corporate_governance = 16
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max_score.profitability = 18
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max_score.asset_quality = 18
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max_score.debt_risk = 18
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max_score.operational_growth = 18
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max_score.compliance_risk = 43
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max_score.cperational_risk = 10
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max_score.associated_risk = 10
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index_score = indicator_radar.IndexScore()
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index_score.environment = rating_result['经营评分']["环境"]
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index_score.social_responsibility = rating_result['经营评分']["社会责任"]
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index_score.corporate_governance = rating_result['经营评分']["公司治理"]
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index_score.profitability = rating_result['财务评分']["盈利能力"]['合计']
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index_score.asset_quality = rating_result['财务评分']["资产质量"]['合计']
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index_score.debt_risk = rating_result['财务评分']["债务风险"]['合计']
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index_score.operational_growth = rating_result['财务评分']["经营增长"]['合计']
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index_score.compliance_risk = round(43 - rating_result['风险评分']["合规风险"], 2)
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index_score.cperational_risk = round(10 - rating_result['风险评分']["经营风险"]['合计'], 2)
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index_score.associated_risk = round(10 - rating_result['风险评分']["关联风险"]['合计'], 2)
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indicator_radar.index_score = index_score
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indicator_radar.max_score = max_score
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credit_performence.indicator_radar = indicator_radar
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self.performance = credit_performence
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def credit_analysis_content():
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"""综合评价分析中信用分析数据"""
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def business_analysis_content():
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"""经营分析"""
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describe = text_model['报告内容'][1]['章节内容'][0]['小节内容'][1]['段落']
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return describe
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def financial_analysis_content():
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"""财务分析"""
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if text_model['行业选择'][0] == '制造业':
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describe = list()
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describe.append(text_model['报告内容'][3]['章节内容'][0]['小节内容'][1]['段落'])
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describe.append(text_model['报告内容'][3]['章节内容'][1]['小节内容'][2]['段落'])
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describe = ''.join(describe)
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else:
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describe = text_model['报告内容'][3]['章节内容'][0]['小节内容'][2]['段落']
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return describe
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def risk_analysis_content():
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"""风险分析"""
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risk_01 = text_model['报告内容'][4]['章节内容'][0]['小节内容'][0]['段落']
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list_01 = risk_01.split('，')
|
||
risk_02 = text_model['报告内容'][4]['章节内容'][1]['小节内容'][0]['段落']
|
||
list_02 = risk_02.split('，')
|
||
risk_03 = text_model['报告内容'][4]['章节内容'][2]['小节内容'][0]['段落']
|
||
list_03 = risk_03.split('，')
|
||
describe = list()
|
||
describe.append(list_01[::-1][0])
|
||
describe.append(list_02[::-1][0])
|
||
describe.append(list_03[::-1][0])
|
||
describe = ','.join(describe)
|
||
res = describe.replace('。', '', 2)
|
||
return res
|
||
|
||
def evaluation_comments_content():
|
||
"""评价意见"""
|
||
describe = list()
|
||
eva_01 = text_model['报告内容'][5]['章节内容'][0]['小节内容'][0]['段落']
|
||
eva_02 = text_model['报告内容'][5]['章节内容'][0]['小节内容'][1]['段落']
|
||
eva_03 = text_model['报告内容'][5]['章节内容'][0]['小节内容'][2]['段落']
|
||
describe.append(eva_01)
|
||
describe.append(eva_02)
|
||
describe.append(eva_03)
|
||
describe = ''.join(describe)
|
||
return describe
|
||
|
||
credit_analysis = self.CreditAnalysis()
|
||
credit_analysis.management_analysis = business_analysis_content()
|
||
credit_analysis.finance_analysis = financial_analysis_content()
|
||
credit_analysis.risk_analysis = risk_analysis_content()
|
||
credit_analysis.evaluation_opinion = evaluation_comments_content()
|
||
credit_analysis.view_report = '/file/get_cc_rating_report?file_id={}'.format(rating_record['报告fid'])
|
||
self.credit_analysis = credit_analysis
|
||
|
||
def rating_history():
|
||
"""历史级别"""
|
||
df_records['变化趋势'] = (df_records['信用评分'] - df_records['信用评分'].shift(-1)).apply(
|
||
lambda x: '下降' if x < 0 else ('上升' if x > 0 else ('维持' if x == 0 else '-')))
|
||
history_list = list(json.loads(df_records[['信用等级', "信用评分", '评价时间', "评价方式", "变化趋势"]].T.to_json()).values())
|
||
self.history_level = list()
|
||
for item in history_list:
|
||
history_level = self.HistoryLevel()
|
||
history_level.credit_rating = item['信用等级']
|
||
history_level.credit_score = item['信用评分']
|
||
history_level.evaluation_time = item['评价时间']
|
||
history_level.evaluation_method = item['评价方式']
|
||
history_level.change_trend = item['变化趋势']
|
||
self.history_level.append(history_level.dict_to_save())
|
||
|
||
def rating_index_analysis():
|
||
"""指标表格"""
|
||
index_table = self.IndexTable()
|
||
|
||
def financial():
|
||
"""财务指标"""
|
||
financial_dict = dict()
|
||
financial_dict['盈利能力'] = rating_result['财务评分']['盈利能力']['合计']
|
||
financial_dict['资产质量'] = rating_result['财务评分']['资产质量']['合计']
|
||
financial_dict['债务风险'] = rating_result['财务评分']['债务风险']['合计']
|
||
financial_dict['经营增长'] = rating_result['财务评分']['经营增长']['合计']
|
||
df_financial = pd.DataFrame(
|
||
{'实际值': financial_dict, '最大值': {"盈利能力": 16, "资产质量": 18, "债务风险": 18, "经营增长": 18}})
|
||
financial_dict = json.loads((df_financial['实际值'] / df_financial['最大值']).apply(
|
||
lambda x: '优' if x >= 1 else (
|
||
'良' if x >= 0.75 else ('中' if x >= 0.5 else ('低' if x >= 0.25 else '差')))).to_json())
|
||
financial_index = index_table.FinancialIndicators()
|
||
financial_index.profitability = financial_dict['盈利能力']
|
||
financial_index.asset_quality = financial_dict['资产质量']
|
||
financial_index.debt_risk = financial_dict['债务风险']
|
||
financial_index.operating_growth = financial_dict['经营增长']
|
||
index_table.financial_indicators = financial_index
|
||
|
||
def risks():
|
||
"""风险指标"""
|
||
risk_dict = dict()
|
||
risk_dict['合规风险'] = 43 - rating_result['风险评分']['合规风险']
|
||
risk_dict['经营风险'] = 10 - rating_result['风险评分']['经营风险']['合计']
|
||
risk_dict['关联风险'] = 10 - rating_result['风险评分']['关联风险']['合计']
|
||
df_risks = pd.DataFrame({'实际值': risk_dict, '最大值': {"合规风险": 43, "经营风险": 10, "关联风险": 10}})
|
||
risk_dict = json.loads((df_risks['实际值'] / df_risks['最大值']).apply(lambda x: '优' if x >= 1 else (
|
||
'良' if x >= 0.75 else ('中' if x >= 0.5 else ('低' if x >= 0.25 else '差')))).to_json())
|
||
risk_index = index_table.RiskIndicators()
|
||
risk_index.compliance_risk = risk_dict['合规风险']
|
||
risk_index.operational_risk = risk_dict['经营风险']
|
||
risk_index.associated_risk = risk_dict['关联风险']
|
||
index_table.risk_indicators = risk_index
|
||
|
||
def operate():
|
||
"""经营指标"""
|
||
df_operate = pd.DataFrame(
|
||
{'实际值': sub_dict(rating_result['经营评分'], ['环境', '社会责任', '公司治理']),
|
||
'最大值': {'环境': 10, '社会责任': 10, '公司治理': 10}})
|
||
operate_dict = json.loads((df_operate['实际值'] / df_operate['最大值']).apply(lambda x: '优' if x >= 1 else (
|
||
'良' if x >= 0.75 else ('中' if x >= 0.5 else ('低' if x >= 0.25 else '差')))).to_json())
|
||
operate_index = index_table.OperatingIndicators()
|
||
operate_index.environmental = operate_dict['环境']
|
||
operate_index.social_responsibility = operate_dict['社会责任']
|
||
operate_index.corporate_governance = operate_dict['公司治理']
|
||
index_table.operating_indicators = operate_index
|
||
|
||
financial()
|
||
risks()
|
||
operate()
|
||
self.index_table = index_table
|
||
|
||
def _main_():
|
||
result_head()
|
||
make_radar_chart()
|
||
credit_analysis_content()
|
||
rating_history()
|
||
rating_index_analysis()
|
||
result = self.dict_to_save()
|
||
self.db.upsert_single_data(
|
||
'企业数据',
|
||
'综合信用分析',
|
||
{'评价ID': self.rid},
|
||
result
|
||
)
|
||
|
||
_main_()
|
||
|
||
|
||
class FinancialAnalysisImpl(FinancialElementsAnalysis):
|
||
"""财务要素分析类"""
|
||
db = MongoHelper("tfse_v0.21")
|
||
|
||
def financial_analysis_etl(self):
|
||
rating_result = self.db.find_single_data(
|
||
'综信评价数据',
|
||
'评价结果',
|
||
{"评价ID": self.rid},
|
||
["企业ID", "财务评分"]
|
||
)
|
||
rating_input = self.db.find_single_data(
|
||
'综信评价数据',
|
||
'填报数据',
|
||
{"评价ID": self.rid},
|
||
["行业选择", "财务填报"]
|
||
)
|
||
industry = rating_input['行业选择']
|
||
search = ["年报期", '净资产收益率', '总资产报酬率', '总资产周转率',
|
||
'应收账款周转率', '存货周转率', '资产负债率', '已获利息倍数',
|
||
'速动比率', '营业增长率', '总资产增长率', '技术投入比率']
|
||
periods = list(
|
||
pd.DataFrame(rating_input['财务填报']['资产负债表']).sort_values('报告期', ascending=False)['报告期'][0:2].values)
|
||
df_recent_2year = pd.DataFrame(self.db.find_all_data('综信评价数据', '财指结果', {'企业ID': rating_result['企业ID']},
|
||
search)).sort_values('年报期', ascending=False)[0:2]
|
||
df_this = df_recent_2year[0:1]
|
||
df_last = df_recent_2year[1:2]
|
||
|
||
data_last = list(json.loads(df_last[['净资产收益率', '存货周转率', '已获利息倍数', '应收账款周转率', '总资产周转率', '总资产增长率', '总资产报酬率',
|
||
'技术投入比率', '营业增长率', '资产负债率', '速动比率']].T.to_json()).values())
|
||
url = "http://api.fecribd.com/tfse_rating/rating/financial_score"
|
||
headers = {'token': "X0gSlC!YE8jmr2jJr&ilcjS83j!tsoh5", "content-type": "application/json"}
|
||
data = {"财务指标": data_last, "所属行业": industry}
|
||
score_last_origin = json.loads(requests.post(url=url, headers=headers, data=json.dumps(data)).text)[
|
||
'result']
|
||
score_avg = {
|
||
"盈利能力": 8,
|
||
"资产质量": 9,
|
||
"债务风险": 9,
|
||
"经营增长": 9
|
||
}
|
||
score_actl = {
|
||
"盈利能力": rating_result['财务评分']['盈利能力']['合计'],
|
||
"资产质量": rating_result['财务评分']['资产质量']['合计'],
|
||
"债务风险": rating_result['财务评分']['债务风险']['合计'],
|
||
"经营增长": rating_result['财务评分']['经营增长']['合计']
|
||
}
|
||
score_last = {
|
||
"盈利能力": score_last_origin['盈利能力']['合计'],
|
||
"资产质量": score_last_origin['资产质量']['合计'],
|
||
"债务风险": score_last_origin['债务风险']['合计'],
|
||
"经营增长": score_last_origin['经营增长']['合计']
|
||
}
|
||
df = pd.DataFrame({'今年值': score_actl, '去年值': score_last, "平均值": score_avg})
|
||
|
||
def result_head():
|
||
"""基本信息"""
|
||
self.cid = rating_result['企业ID']
|
||
self.report_date = periods[0]
|
||
self.update_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||
|
||
def index_detail():
|
||
"""指标详情"""
|
||
index_value = json.loads(df_this[
|
||
['净资产收益率', '总资产报酬率', '总资产周转率', '应收账款周转率', '存货周转率', '资产负债率', '已获利息倍数', '速动比率',
|
||
'营业增长率', '总资产增长率', '技术投入比率']].apply(
|
||
lambda x: '{}%'.format(x.values[0]) if '率' in x.name or '倍数' in x.name else x.values[0]).T.to_json())
|
||
rate = dict()
|
||
rate['净资产收益率'] = rating_result['财务评分']['盈利能力']['净资产收益率'] / 8
|
||
rate['总资产报酬率'] = rating_result['财务评分']['盈利能力']['总资产报酬率'] / 8
|
||
rate['总资产周转率'] = rating_result['财务评分']['资产质量']['总资产周转率'] / 6
|
||
rate['存货周转率'] = rating_result['财务评分']['资产质量']['存货周转率'] / 6
|
||
rate['应收账款周转率'] = rating_result['财务评分']['资产质量']['应收账款周转率'] / 6
|
||
rate['资产负债率'] = rating_result['财务评分']['债务风险']['资产负债率'] / 8
|
||
rate['已获利息倍数'] = rating_result['财务评分']['债务风险']['已获利息倍数'] / 5
|
||
rate['速动比率'] = rating_result['财务评分']['债务风险']['速动比率'] / 5
|
||
rate['营业增长率'] = rating_result['财务评分']['经营增长']['营业增长率'] / 8
|
||
rate['总资产增长率'] = rating_result['财务评分']['经营增长']['总资产增长率'] / 5
|
||
rate['技术投入比率'] = rating_result['财务评分']['经营增长']['技术投入比率'] / 5
|
||
df_rate = pd.DataFrame([rate])
|
||
index_level = json.loads(df_rate.apply(lambda x: '优' if x.values[0] >= 1 else (
|
||
'良' if x.values[0] >= 0.75 else (
|
||
'中' if x.values[0] >= 0.5 else ('低' if x.values[0] >= 0.25 else '差')))).to_json())
|
||
df_index = pd.DataFrame({'值': index_value, "级别": index_level})
|
||
df_list = df_iterrows(df_index)
|
||
self.index_details = list()
|
||
for item in df_list:
|
||
index_details = self.IndexDetails()
|
||
index_details.index = item[0]
|
||
index_details.value = item[1]
|
||
index_details.level = item[2]
|
||
self.index_details.append(index_details.dict_to_save())
|
||
|
||
def compare_to_year():
|
||
"""财务得分年度比较"""
|
||
self.compare_to_years = list()
|
||
|
||
def score_actl_script():
|
||
"""今年财务得分"""
|
||
compare = self.CompareToYears()
|
||
score_index = FinancialIndexs()
|
||
score_index.profitability = rating_result['财务评分']['盈利能力']['合计']
|
||
score_index.asset_quality = rating_result['财务评分']['资产质量']['合计']
|
||
score_index.debt_risk = rating_result['财务评分']['债务风险']['合计']
|
||
score_index.business_growth = rating_result['财务评分']['经营增长']['合计']
|
||
compare.year = df_this['年报期'][0]
|
||
compare.index = score_index
|
||
self.compare_to_years.append(compare.dict_to_save())
|
||
|
||
def score_last_script():
|
||
"""去年财务得分"""
|
||
compare = self.CompareToYears()
|
||
score_index = FinancialIndexs()
|
||
score_index.profitability = score_last_origin['盈利能力']['合计']
|
||
score_index.asset_quality = score_last_origin['资产质量']['合计']
|
||
score_index.debt_risk = score_last_origin['债务风险']['合计']
|
||
score_index.business_growth = score_last_origin['经营增长']['合计']
|
||
compare.year = df_last['年报期'][1]
|
||
compare.index = score_index
|
||
self.compare_to_years.append(compare.dict_to_save())
|
||
|
||
score_actl_script()
|
||
score_last_script()
|
||
|
||
def compare_to_industry():
|
||
"""财务得分同行比较"""
|
||
compare_industry = self.CompareToIndustry()
|
||
|
||
def company_level():
|
||
"""公司水平"""
|
||
company = FinancialIndexs()
|
||
company.profitability = rating_result['财务评分']['盈利能力']['合计']
|
||
company.asset_quality = rating_result['财务评分']['资产质量']['合计']
|
||
company.debt_risk = rating_result['财务评分']['债务风险']['合计']
|
||
company.business_growth = rating_result['财务评分']['经营增长']['合计']
|
||
compare_industry.company_score = company
|
||
|
||
def average_level():
|
||
"""平均水平"""
|
||
average = FinancialIndexs()
|
||
average.profitability = 8
|
||
average.asset_quality = 9
|
||
average.debt_risk = 9
|
||
average.business_growth = 9
|
||
compare_industry.average_score = average
|
||
|
||
company_level()
|
||
average_level()
|
||
self.compare_to_industry = compare_industry
|
||
|
||
def delta_to_year():
|
||
"""财务得分较去年变化"""
|
||
delat_data = json.loads(
|
||
(df['今年值'] / df['去年值'] - 1).apply(lambda x: round(x * 100, 2)).T.to_json())
|
||
delat = FinancialIndexs()
|
||
delat.profitability = delat_data['盈利能力']
|
||
delat.asset_quality = delat_data['资产质量']
|
||
delat.debt_risk = delat_data['债务风险']
|
||
delat.business_growth = delat_data['经营增长']
|
||
self.delta_to_years = delat
|
||
|
||
def delta_to_industry():
|
||
"""财务扽分交同行差异"""
|
||
delat_data = json.loads(
|
||
(df['今年值'] / df['平均值'] - 1).apply(lambda x: round(x * 100, 2)).T.to_json())
|
||
delat = FinancialIndexs()
|
||
delat.profitability = delat_data['盈利能力']
|
||
delat.asset_quality = delat_data['资产质量']
|
||
delat.debt_risk = delat_data['债务风险']
|
||
delat.business_growth = delat_data['经营增长']
|
||
self.delta_to_industry = delat
|
||
|
||
def _main_():
|
||
result_head()
|
||
index_detail()
|
||
compare_to_year()
|
||
compare_to_industry()
|
||
delta_to_year()
|
||
delta_to_industry()
|
||
result = self.dict_to_save()
|
||
self.db.upsert_single_data(
|
||
'企业数据',
|
||
'财务要素分析',
|
||
{"评价ID": self.rid},
|
||
result
|
||
)
|
||
|
||
_main_()
|
||
|
||
|
||
class BusinessAnalysisImpl(BusinessElementsAnalysis):
|
||
"""经营要素分析类"""
|
||
db = MongoHelper("tfse_v0.21")
|
||
|
||
def risk_analysis_etl(self):
|
||
rating_result = self.db.find_single_data(
|
||
'综信评价数据',
|
||
'评价结果',
|
||
{"评价ID": self.rid},
|
||
["企业ID", "企业名称", "风险评分"]
|
||
)
|
||
risk_data = self.db.find_single_data(
|
||
'综信评价数据',
|
||
'风险数据',
|
||
{"评价ID": self.rid},
|
||
["合规风险", "经营风险"]
|
||
)
|
||
|
||
def head_info():
|
||
self.cid = rating_result['企业ID']
|
||
self.update_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||
|
||
def risk_relative_score():
|
||
"""风险分数"""
|
||
risk_score = rating_result['风险评分']['合计']
|
||
self.risk_score = 100 if risk_score / 43 >= 1 else round(risk_score / 43 * 100, 2)
|
||
|
||
def risk_level():
|
||
"""风险级别"""
|
||
risk_score = rating_result['风险评分']['合计']
|
||
if risk_score >= 43:
|
||
level = '高'
|
||
elif risk_score >= 33:
|
||
level = '较高'
|
||
elif risk_score >= 23:
|
||
level = '中等'
|
||
elif risk_score >= 10:
|
||
level = '警示'
|
||
else:
|
||
level = '低'
|
||
self.risk_level = level
|
||
|
||
def risk_num():
|
||
"""风险数量"""
|
||
self.risk_quantity = sum(risk_data['合规风险'].values()) + sum(risk_data['经营风险'].values())
|
||
|
||
def is_break_trust():
|
||
"""列入失信人名单"""
|
||
self.untrustworthy_list = '是' if risk_data['合规风险']['失信人'] >= 1 else '否'
|
||
|
||
def eligibility_risk():
|
||
"""合规风险"""
|
||
|
||
def degree_of_impact(param):
|
||
if param.name in ['失信人', '严重违法']:
|
||
impact = '严重'
|
||
elif param.name in ['经营异常', '欠税公告', '税收违法']:
|
||
impact = '异常'
|
||
elif param.name in ['立案信息', '行政处罚', '环保处罚']:
|
||
impact = '中等'
|
||
else:
|
||
impact = ''
|
||
return impact
|
||
|
||
df = pd.DataFrame({'合规风险': risk_data['合规风险']})
|
||
df = df.drop(df[df['合规风险'] == 0].index)
|
||
df['影响程度'] = df.T.apply(lambda x: degree_of_impact(x))
|
||
df_list = df_iterrows(df)
|
||
self.compliance_risk = list()
|
||
if df_list:
|
||
for item in df_list:
|
||
cr = self.ComplianceRisk()
|
||
cr.genre = item[0]
|
||
cr.quantity = item[1]
|
||
cr.affect = item[2]
|
||
self.compliance_risk.append(cr.dict_to_save())
|
||
|
||
def operating_risk():
|
||
"""经营风险"""
|
||
|
||
def degree_of_impact(param):
|
||
if param.name in ['开庭公告', '法院公告', '诉讼', '送达公告'] and param.values[0] >= 50:
|
||
impact = '异常'
|
||
elif param.name in ['开庭公告', '法院公告', '诉讼', '送达公告'] and param.values[0] >= 25:
|
||
impact = '中等'
|
||
elif param.name in ['开庭公告', '法院公告', '诉讼', '送达公告'] and param.values[0] >= 10:
|
||
impact = '警示'
|
||
elif param.name in ['被执行人'] and param.values[0] >= 30:
|
||
impact = '异常'
|
||
elif param.name in ['被执行人'] and param.values[0] >= 15:
|
||
impact = '中等'
|
||
elif param.name in ['被执行人'] and param.values[0] >= 6:
|
||
impact = '警示'
|
||
elif param.name in ['股权出质'] and param.values[0] >= 10:
|
||
impact = '异常'
|
||
elif param.name in ['股权出质'] and param.values[0] >= 5:
|
||
impact = '中等'
|
||
elif param.name in ['股权出质'] and param.values[0] >= 3:
|
||
impact = '警示'
|
||
else:
|
||
impact = '轻微'
|
||
return impact
|
||
|
||
df = pd.DataFrame({'经营风险': risk_data['经营风险']})
|
||
df = df.drop(df[df['经营风险'] == 0].index)
|
||
df['影响程度'] = df.T.apply(lambda x: degree_of_impact(x))
|
||
df_list = df_iterrows(df)
|
||
self.operational_risk = list()
|
||
if df_list:
|
||
for item in df_list:
|
||
or_ = self.OperationalRisk()
|
||
or_.genre = item[0]
|
||
or_.quantity = item[1]
|
||
or_.affect = item[2]
|
||
self.compliance_risk.append(or_.dict_to_save())
|
||
|
||
def associate_risk():
|
||
"""周边风险"""
|
||
data = associate_risk_detail(rating_result['企业名称'])
|
||
self.peripheral_risk = list()
|
||
if data:
|
||
df_list = df_iterrows(pd.DataFrame(data).sort_values('total', ascending=False).set_index('title'))
|
||
for item in df_list:
|
||
pr = self.PeripheralRisk()
|
||
pr.genre = item[0]
|
||
pr.quantity = item[1]
|
||
pr.detail = item[2]
|
||
self.peripheral_risk.append(pr.dict_to_save())
|
||
|
||
def change_log():
|
||
"""变更记录"""
|
||
data = change_log_detail(rating_result['企业名称'])
|
||
|
||
def degree_of_impact(param):
|
||
if param.name in ['法定代表人变更', '主要人员变更'] and param.values[0] >= 20:
|
||
impact = '异常'
|
||
elif param.name in ['法定代表人变更', '主要人员变更'] and param.values[0] >= 10:
|
||
impact = '中等'
|
||
elif param.name in ['法定代表人变更', '主要人员变更'] and param.values[0] >= 4:
|
||
impact = '警示'
|
||
else:
|
||
impact = '轻微'
|
||
return impact
|
||
|
||
df = pd.DataFrame(data).sort_values('total', ascending=False).set_index('title')
|
||
df['影响程度'] = df.T.apply(lambda x: degree_of_impact(x))
|
||
df_list = df_iterrows(df[['total', '影响程度']])
|
||
self.change_log = list()
|
||
for item in df_list:
|
||
cl = self.ChangeLog()
|
||
cl.genre = item[0]
|
||
cl.quantity = item[1]
|
||
cl.affect = item[2]
|
||
self.change_log.append(cl.dict_to_save())
|
||
|
||
def _main_():
|
||
head_info()
|
||
risk_relative_score()
|
||
risk_level()
|
||
risk_num()
|
||
is_break_trust()
|
||
eligibility_risk()
|
||
operating_risk()
|
||
associate_risk()
|
||
change_log()
|
||
result = self.dict_to_save()
|
||
self.db.upsert_single_data(
|
||
'企业数据',
|
||
'经营风险分析',
|
||
{'评价ID': self.rid},
|
||
result
|
||
)
|
||
|
||
_main_()
|
||
|
||
|
||
class EsgAnalysisImpl(EsgRatingAnalysis):
|
||
"""ESG评价分析类"""
|
||
db = MongoHelper("tfse_v0.21")
|
||
|
||
def egs_analysis_etl(self):
|
||
rating_record = self.db.find_single_data(
|
||
'ESG评价数据',
|
||
'评价结果',
|
||
{"评价ID": self.rid},
|
||
["企业ID", "评价等级", "ESG得分", "环境得分", "社会得分", "公司治理得分"]
|
||
)
|
||
rating_input = self.db.find_single_data(
|
||
'ESG评价数据',
|
||
'填报数据',
|
||
{"评价ID": self.rid},
|
||
["所属行业"]
|
||
)
|
||
|
||
self.cid = rating_record['企业ID']
|
||
self.update_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||
|
||
def esg_rating():
|
||
"""esg评级"""
|
||
rating = self.EsgRating()
|
||
rating.rating = rating_record['评价等级']
|
||
rating.score = rating_record['ESG得分']
|
||
rating.industry = rating_input['所属行业'][0]
|
||
self.esg_rating = rating
|
||
|
||
def dimension_rating():
|
||
"""维度得分情况"""
|
||
self.dimension_score = list()
|
||
|
||
def e():
|
||
"""环境"""
|
||
dimension = self.DimensionScore()
|
||
dimension.dimension = '环境'
|
||
dimension.weights = '35%'
|
||
dimension.score = rating_record['环境得分']['合计']
|
||
dimension.score_change = '-%'
|
||
dimension.industry_average = '-'
|
||
self.dimension_score.append(dimension.dict_to_save())
|
||
|
||
def s():
|
||
"""社会"""
|
||
dimension = self.DimensionScore()
|
||
dimension.dimension = '社会'
|
||
dimension.weights = '35%'
|
||
dimension.score = rating_record['社会得分']['合计']
|
||
dimension.score_change = '-%'
|
||
dimension.industry_average = '-'
|
||
self.dimension_score.append(dimension.dict_to_save())
|
||
|
||
def g():
|
||
"""治理"""
|
||
dimension = self.DimensionScore()
|
||
dimension.dimension = '治理'
|
||
dimension.weights = '30%'
|
||
dimension.score = rating_record['公司治理得分']['合计']
|
||
dimension.score_change = '-%'
|
||
dimension.industry_average = '-'
|
||
self.dimension_score.append(dimension.dict_to_save())
|
||
|
||
def _main_():
|
||
e()
|
||
s()
|
||
g()
|
||
|
||
_main_()
|
||
|
||
def __main__():
|
||
esg_rating()
|
||
dimension_rating()
|
||
result = self.dict_to_save()
|
||
self.db.upsert_single_data(
|
||
"企业数据",
|
||
"ESG评价分析",
|
||
{"评价ID": self.rid},
|
||
result
|
||
)
|
||
|
||
__main__() |