439 lines
15 KiB
Python
439 lines
15 KiB
Python
def profitability(param):
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"""
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资质荣誉 区分行业
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Parameters:
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param: 数据库数据
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"""
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industry = param['综合评价填报']['行业选择'][0]
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if industry == '制造业':
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return profitability_manufacturing(param)
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elif industry == '信息技术业':
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return profitability_infomation(param)
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def profitability_manufacturing(param):
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"""
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盈利能力-制造业
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清洗数据库数据，根据需要返回
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Parameters:
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param: 数据库数据
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Returns:
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盈利能力数据
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"""
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# Params
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data_01 = param['综合评价填报']
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data_02 = param['评价结果']
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data_03 = param['指标明细']
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# Returns
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result = list()
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part_01 = dict()
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def para_02():
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part_02 = dict()
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# 年度
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part_02['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
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# 净资产收益率
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part_02['return_on_net_assets_analysis'] = str(data_03[1]['净资产收益率'])
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# 总资产报酬率
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part_02['return_on_total_assets_analysis'] = str(data_03[1]['总资产报酬率'])
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def income_scale():
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# 收入规模
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income = float(data_01['财务填报']['利润表'][0]['营业收入'])
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if income > 400000000:
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part_02['income_scale'] = '较大'
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elif income > 20000000:
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part_02['income_scale'] = '处于行业中等水平'
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elif income > 3000000:
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part_02['income_scale'] = '较小'
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else:
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part_02['income_scale'] = '小'
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def technology_level():
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# 产品技术含量
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rona = data_02['财务评分']['盈利能力']['净资产收益率']
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if rona >= 8:
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part_02['technology_level'] = '高'
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elif rona >= 8 * 0.8:
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part_02['technology_level'] = '较高'
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elif rona >= 8 * 0.6:
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part_02['technology_level'] = '处于行业平均水平'
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elif rona >= 8 * 0.4:
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part_02['technology_level'] = '较低'
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else:
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part_02['technology_level'] = '低'
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def profitability_analysis():
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# 盈利能力
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p_score = data_02['财务评分']['盈利能力']['净资产收益率'] + data_02['财务评分']['盈利能力']['总资产报酬率']
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if p_score >= 16:
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part_02['profitability_analysis'] = '好'
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elif p_score >= 16 * 0.8:
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part_02['profitability_analysis'] = '较好'
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elif p_score >= 16 * 0.8:
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part_02['profitability_analysis'] = '有待提高'
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elif p_score >= 16 * 0.8:
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part_02['profitability_analysis'] = '较弱'
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else:
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part_02['profitability_analysis'] = '弱'
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income_scale()
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technology_level()
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profitability_analysis()
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return part_02
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result.append(part_01)
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result.append(para_02())
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return result
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def profitability_infomation(param):
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"""
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盈利能力-信息技术业
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清洗数据库数据，根据需要返回
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Parameters:
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param: 数据库数据
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Returns:
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盈利能力数据
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"""
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# Params
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data_01 = param['综合评价填报']
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data_03 = param['指标明细']
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# Returns
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result = list()
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part_01 = dict()
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part_02 = dict()
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def para_03():
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part_03 = dict()
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# 年度
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part_03['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
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# 净资产收益率
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part_03['return_on_net_assets_analysis'] = str(data_03[1]['净资产收益率'])
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try:
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# 是否高于平均水平
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if data_03[1]['净资产收益率'] > 4.7:
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part_03['high_low_01'] = '高于'
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else:
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part_03['high_low_01'] = '低于'
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except TypeError:
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part_03['high_low_01'] = '低于'
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# 总资产报酬率
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part_03['return_on_total_assets_analysis'] = str(data_03[1]['总资产报酬率'])
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try:
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# 是否高于平均水平
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if data_03[1]['总资产报酬率'] > 2.7:
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part_03['high_low_02'] = '高于'
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else:
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part_03['high_low_02'] = '低于'
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except TypeError:
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part_03['high_low_02'] = '低于'
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return part_03
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result.append(part_01)
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result.append(part_02)
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result.append(para_03())
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return result
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def asset_quality(param):
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"""
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资产质量 区分行业
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Parameters:
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param: 数据库数据
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"""
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industry = param['综合评价填报']['行业选择'][0]
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if industry == '制造业':
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return asset_quality_manufacturing(param)
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elif industry == '信息技术业':
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return asset_quality_infomation(param)
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def asset_quality_manufacturing(param):
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"""
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资产质量-制造业
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清洗数据库数据，根据需要返回
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Parameters:
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param: 数据库数据
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Returns:
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资产质量数据
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"""
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# Params
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data_01 = param['综合评价填报']
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data_02 = param['评价结果']
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data_03 = param['指标明细']
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# Returns
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result = list()
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part_01 = dict()
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part_02 = dict()
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def para_03():
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part_03 = dict()
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# 年度
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part_03['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
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# 应收账款周转率
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part_03['account_turnover_analysis'] = str(data_03[0]['应收账款周转率'])
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# 存货周转率
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part_03['inventory_turnover_analysis'] = str(data_03[0]['存货周转率'])
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# 总资产周转率
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part_03['total_assets_turnover_analysis'] = str(data_03[0]['总资产周转率'])
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def industry_asset():
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a = float(data_02['财务评分']['资产质量']['总资产周转率'])
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s = float(data_02['财务评分']['资产质量']['应收账款周转率'])
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c = float(data_02['财务评分']['资产质量']['存货周转率'])
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a_s = a + s + c
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if a_s >= 18:
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part_03['industry_level'] = '高'
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part_03['asset_quality'] = '好'
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elif a_s >= 18 * 0.8:
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part_03['industry_level'] = '较高'
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part_03['asset_quality'] = '较好'
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elif a_s >= 18 * 0.6:
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part_03['industry_level'] = '中等'
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part_03['asset_quality'] = '有待改善'
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elif a_s >= 18 * 0.4:
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part_03['industry_level'] = '偏低'
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part_03['asset_quality'] = '较差'
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else:
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part_03['industry_level'] = '低'
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part_03['asset_quality'] = '差'
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industry_asset()
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return part_03
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result.append(part_01)
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result.append(part_02)
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result.append(para_03())
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return result
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def asset_quality_infomation(param):
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"""
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资产质量-信息技术业
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清洗数据库数据，根据需要返回
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Parameters:
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param: 数据库数据
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Returns:
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资产质量数据
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"""
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# Params
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data_01 = param['综合评价填报']
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data_02 = param['评价结果']
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data_03 = param['指标明细']
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# Returns
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result = list()
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def para_01():
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part_01 = dict()
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# 年度
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part_01['date'] = data_01['财务填报']['利润表'][1]['报告期'][:4]
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# 应收账款周转率
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part_01['account_turnover_analysis'] = str(data_03[1]['应收账款周转率'])
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try:
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# 是否高于平均水平
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if data_03[1]['应收账款周转率'] > 0.4:
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part_01['high_low_01'] = '高'
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else:
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part_01['high_low_01'] = '低'
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except TypeError:
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part_01['high_low_01'] = '低'
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# 总资产周转率
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part_01['total_assets_turnover_analysis'] = str(data_03[1]['总资产周转率'])
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try:
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# 是否高于平均水平
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if data_03[1]['总资产周转率'] > 3.9:
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part_01['high_low_02'] = '高于'
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else:
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part_01['high_low_02'] = '低于'
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except TypeError:
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part_01['high_low_02'] = '低于'
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return part_01
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result.append(para_01())
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return result
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def debt_risk(param):
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"""
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债务风险
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清洗数据库数据，根据需要返回
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Parameters:
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param: 数据库数据
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Returns:
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债务风险数据
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"""
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# Params
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data_02 = param['评价结果']
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data_03 = param['指标明细']
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# Returns
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result = list()
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part_01 = dict()
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def para_02():
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part_02 = dict()
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def debt_risk_detail():
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a = float(data_02['财务评分']['债务风险']['资产负债率'])
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d = float(data_02['财务评分']['债务风险']['已获利息倍数'])
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s = float(data_02['财务评分']['债务风险']['速动比率'])
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d_s = a + d + s
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if d_s >= 18:
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part_02['debt_risk'] = '大'
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elif d_s >= 18 * 0.8:
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part_02['debt_risk'] = '较大'
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elif d_s >= 18 * 0.6:
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part_02['debt_risk'] = '适中'
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elif d_s >= 18 * 0.4:
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part_02['debt_risk'] = '较小'
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else:
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part_02['debt_risk'] = '小'
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def asset_liability_ratio():
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# 资产负债率区间
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if len(data_03) >= 3:
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rate = [data_03[0]['资产负债率'], data_03[1]['资产负债率'], data_03[2]['资产负债率']]
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rate.sort()
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part_02['min_asset_liability_ratio'] = str(rate[0])
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part_02['max_asset_liability_ratio'] = str(rate[2])
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else:
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rate_01 = data_03[0]['资产负债率']
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rate_02 = data_03[1]['资产负债率']
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if rate_01 > rate_02:
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part_02['max_asset_liability_ratio'] = str(rate_01)
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part_02['min_asset_liability_ratio'] = str(rate_02)
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else:
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part_02['max_asset_liability_ratio'] = str(rate_02)
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part_02['min_asset_liability_ratio'] = str(rate_01)
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def fin_structure_analysis():
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# 财务结构
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alr = data_03[0]['资产负债率']
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if alr < 0.4:
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part_02['fin_structure_analysis'] = '稳健'
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elif alr < 0.55:
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part_02['fin_structure_analysis'] = '较稳健'
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elif alr < 0.7:
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part_02['fin_structure_analysis'] = '稳健性有待进一步加强'
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else:
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part_02['fin_structure_analysis'] = '欠稳健'
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def operating_cash_flow_to_debt():
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# 经营性现金流对利息的保障程度
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ic_s = data_02['财务评分']['债务风险']['已获利息倍数']
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if ic_s >= 5:
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part_02['operating_cash_flow_to_debt'] = '强'
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elif ic_s >= 5 * 0.8:
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part_02['operating_cash_flow_to_debt'] = '较强'
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elif ic_s >= 5 * 0.6:
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part_02['operating_cash_flow_to_debt'] = '尚可'
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elif ic_s >= 5 * 0.4:
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part_02['operating_cash_flow_to_debt'] = '较弱'
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else:
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part_02['operating_cash_flow_to_debt'] = '弱'
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def assets_liquidity():
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# 资产流动性
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qr_s = data_02['财务评分']['债务风险']['速动比率']
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if qr_s >= 5:
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part_02['assets_liquidity'] = '好'
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elif qr_s >= 5 * 0.8:
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part_02['assets_liquidity'] = '较好'
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elif qr_s >= 5 * 0.6:
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part_02['assets_liquidity'] = '一般'
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elif qr_s >= 5 * 0.4:
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part_02['assets_liquidity'] = '较差'
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else:
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part_02['assets_liquidity'] = '差'
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# 已获利息倍数
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if data_03[0]['已获利息倍数'] is None:
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part_02['interest_cover'] = ''
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else:
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part_02['interest_cover'] = '，已获利息倍数为{}倍'.format(str(data_03[0]['已获利息倍数']))
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# 速动比率
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part_02['quick_ratio'] = str(data_03[0]['速动比率'])
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debt_risk_detail()
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asset_liability_ratio()
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asset_liability_ratio()
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fin_structure_analysis()
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operating_cash_flow_to_debt()
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assets_liquidity()
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return part_02
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result.append(part_01)
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result.append(para_02())
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return result
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def growth_ability(param):
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"""
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成长能力
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清洗数据库数据，根据需要返回
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Parameters:
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param: 数据库数据
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Returns:
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成长能力数据
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"""
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# Params
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data_02 = param['评价结果']
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data_03 = param['指标明细']
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# Returns
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result = list()
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part_01 = dict()
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def para_02():
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part_02 = dict()
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def degree_of_imprtance():
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# 重视程度
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ti_s = data_02['财务评分']['经营增长']['技术投入比率']
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if ti_s >= 5 * 0.7:
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part_02['degree_of_imprtance'] = '重视技术研发或先进工艺的开发'
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elif ti_s >= 5 * 0.4:
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part_02['degree_of_imprtance'] = '对技术研发或先进工艺的开发的重视程度一般'
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else:
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part_02['degree_of_imprtance'] = '不重视技术研发或先进工艺的开发'
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# 技术投入比
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part_02['technology_input_ratio'] = str(data_03[1]['技术投入比率'])
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def contribution_growth_ability():
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g = float(data_02['财务评分']['经营增长']['营业增长率'])
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a = float(data_02['财务评分']['经营增长']['总资产增长率'])
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s = float(data_02['财务评分']['经营增长']['技术投入比率'])
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ga_s = g + a + s
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if ga_s >= 18:
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part_02['contribution'] = '大'
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part_02['growth_ability'] = '好'
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elif ga_s >= 18 * 0.8:
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part_02['contribution'] = '较大'
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part_02['growth_ability'] = '较好'
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elif ga_s >= 18 * 0.6:
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part_02['contribution'] = '尚可'
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part_02['growth_ability'] = '一般'
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elif ga_s >= 18 * 0.4:
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part_02['contribution'] = '较小'
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part_02['growth_ability'] = '较差'
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else:
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part_02['contribution'] = '小'
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part_02['growth_ability'] = '差'
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# 营业收入增长率
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part_02['growth_rate_of_operating_revenue'] = str(data_03[1]['营业增长率'])
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# 总资产增长率
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part_02['growth_rate_of_total_assets'] = str(data_03[1]['总资产增长率'])
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degree_of_imprtance()
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contribution_growth_ability()
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return part_02
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result.append(part_01)
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result.append(para_02())
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return result
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