106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
import pandas as pd
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from Rating.db import find_threshold
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from common.scripts import read_json_file
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def financial_score(param1, param2):
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"""
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财务要素进行打分
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Parameters:
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param1 string 二级行业
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param2 dict 财务指标
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weights dict 财务指标权重
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refers dict 行业阈值
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refer list 指标阈值
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Returns:
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scores dict 财务要素得分
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"""
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# Parameters
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weights = read_json_file('/Rating/static/weights.json')
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refers = find_threshold(param1)[0]
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# Returns
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scores = dict()
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# main
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for key, value in param2.items():
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# 已获利息倍数为None，分数为满分
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if key == '已获利息倍数' and value is None:
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scores[key] = 5
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elif value is None:
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scores[key] = 0
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else:
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refer = list(map(float, refers[key]))
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weight = weights[key]
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standard = [weight, weight*0.8, weight*0.6, weight*0.4, weight*0.2]
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# 判断正反相关
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if refer[0] > refer[1]:
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if value >= refer[0]:
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score = weight
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elif value > refer[1]:
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score = linear_correlation_type(value, standard[0], standard[1], refer[0], refer[1])
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elif value > refer[2]:
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score = linear_correlation_type(value, standard[1], standard[2], refer[1], refer[2])
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elif value > refer[3]:
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score = linear_correlation_type(value, standard[2], standard[3], refer[2], refer[3])
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elif value > refer[4]:
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score = linear_correlation_type(value, standard[3], standard[4], refer[3], refer[4])
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else:
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score = 0
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else:
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if value <= refer[0]:
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score = weight
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elif value < refer[1]:
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score = linear_correlation_type(value, standard[0], standard[1], refer[0], refer[1])
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elif value < refer[2]:
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score = linear_correlation_type(value, standard[1], standard[2], refer[1], refer[2])
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elif value < refer[3]:
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score = linear_correlation_type(value, standard[2], standard[3], refer[2], refer[3])
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elif value < refer[4]:
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score = linear_correlation_type(value, standard[3], standard[4], refer[3], refer[4])
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else:
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score = 0
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scores[key] = round(score, 2)
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result = dict()
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fin1 = ['净资产收益率', '总资产报酬率']
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fin2 = ['总资产周转率', '应收账款周转率', '存货周转率']
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fin3 = ['资产负债率', '已获利息倍数', '速动比率']
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fin4 = ['营业增长率', '总资产增长率', '技术投入比率']
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result['盈利能力'] = dict((key, value) for key, value in scores.items() if key in fin1)
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result['资产质量'] = dict((key, value) for key, value in scores.items() if key in fin2)
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result['债务风险'] = dict((key, value) for key, value in scores.items() if key in fin3)
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result['经营增长'] = dict((key, value) for key, value in scores.items() if key in fin4)
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df_scores = pd.DataFrame([scores])
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result['盈利能力']['合计'] = round(float(df_scores[fin1].sum(axis=1).values[0]), 2)
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result['资产质量']['合计'] = round(float(df_scores[fin2].sum(axis=1).values[0]), 2)
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result['债务风险']['合计'] = round(float(df_scores[fin3].sum(axis=1).values[0]), 2)
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result['经营增长']['合计'] = round(float(df_scores[fin4].sum(axis=1).values[0]), 2)
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result['合计'] = round(sum(scores.values()), 2)
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return result
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def linear_correlation_type(value, standard1, standard2, refer1, refer2):
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"""
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线性相关类型 正相关/反相关
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Parameters
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value float 指标值
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standard1 float 标准分1
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standard2 float 标准分2
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refer1 阈值1
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refer2 阈值2
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Returns
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score float 得分
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"""
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# main
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score = standard2 + (value - refer2) / (refer1 - refer2) * (standard1 - standard2)
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return score
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