def output(self):
pro = ts.pro_api(token=TS_TOKEN)
date = self._get_last_season_end(self.date)
df = pro.fina_mainbz(ts_code=self.code,
period=self.tushare_date_format(date),
type='P')
for i in range(5): # 最多尝试取6个季度的数据
if len(df) == 0:
date = self._get_last_season_end(date)
df = pro.fina_mainbz(ts_code=self.code,
period=self.tushare_date_format(date),
type='P')
text = '\0\0' + self.code_name + '（' + self.code + '）的主营业务构成为（数据截止为' + date + '）：\n'
para_1 = rl_text(text)
df = df.sort_values(by='bz_sales', axis=0, ascending=False)
# 预处理，将None转化为np.nan
df['bz_profit'] = pd.to_numeric(df['bz_profit'])
df['bz_cost'] = pd.to_numeric(df['bz_cost'])
# 计算辅助指标
df['bz_sales_ratio'] = df['bz_sales'] / np.sum(
df['bz_sales'].fillna(0.0).values) # 计算营业收入占比
df['bz_profit_ratio'] = df['bz_profit'] / df['bz_sales'] # 计算毛利率
# 文字生成部分
table_list = [['业务名称', '销售收入', '销售成本', '销售毛利', '销售毛利率', '销售收入占比']]
for i in range(len(df)):
data = df.iloc[i].values
table_list.append([
'%s' % data[2],
'%.2f万元' % (data[3] / 10000.0),
'%.2f万元' % (data[5] / 10000.0),
'%.2f万元' % (data[4] / 10000.0),
'%.2f%%' % (data[8] * 100.0),
'%.2f%%' % (data[7] * 100.0)
])
table_1 = rl_table(table_list)
# 总结内容
text = '\0\0'
if len(df) > 0:
summary_text = self._get_summary(df, self.code_name)
text = text + summary_text
else: # 无数据披露的情形
text = text + '无数据！\n'
# 结果展示
para_2 = rl_text(text)
# 饼图
texts = []
datas = []
for i in range(len(df)):
data = df.iloc[i].values
texts.append(data[2])
datas.append(data[7] * 100)
title = self.code_name + '（' + self.code + '）的主营业务百分比构成图（数据截止为' + date + '）'
image_1 = rl_pie_chart(title, datas, texts,
str(random.randint(0, MAX_RANDOM)))
return [para_1, table_1, rl_text('\0'), para_2, image_1, rl_text('\0')]
def output(self):
from single_factor import RevenueGrowthRate, EstimateNetRevenueGrowRateFY16M
factor_list = [RevenueGrowthRate, EstimateNetRevenueGrowRateFY16M]
code = [self.code]
df = get_factor_from_wind_v2(code, factor_list, self.date)
df.iloc[:, 1] = pd.to_numeric(df.iloc[:, 1])
text = '\0\0营业收入分析（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '营业收入增长率为%.2f%%；分析师一致预期营业收入增长率为%.2f%%；'
text = text % (df.iloc[:, 0].values, df.iloc[:, 1].values)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import OperateCashFlow, CashFlowCoverRatio
factor_list = [OperateCashFlow, CashFlowCoverRatio]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df.iloc[:, 1] = pd.to_numeric(df.iloc[:, 1])
text = '\0\0现金流分析（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '经营活动现金流为%.2f亿元；' + '利润现金保障倍数为%.2f倍；'
text = text % (df.iloc[:, 0].values/100000000.0, df.iloc[:, 1].values)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import InventoryTurnRatio
factor_list = [InventoryTurnRatio]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
last_year = self._get_last_year_date(self.date)
df_last_year = get_factor_from_wind_without_cache(code, factor_list, last_year)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df_last_year.iloc[:, 0] = pd.to_numeric(df_last_year.iloc[:, 0])
growth = (df.iloc[:, 0].values - df_last_year.iloc[:, 0].values) / df_last_year.iloc[:, 0].values
text = '\0\0存货周转率分析（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '存货周转率为%.2f次；上年同期为%.2f次；同比增长为%.2f%%。'
text = text % (df.iloc[:, 0].values, df_last_year.iloc[:, 0], growth * 100.0)
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import SellExpense, SellExpenseRevenue, SellExpenseGrowth
factor_list = [SellExpense, SellExpenseRevenue, SellExpenseGrowth]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df.iloc[:, 1] = pd.to_numeric(df.iloc[:, 1])
df.iloc[:, 2] = pd.to_numeric(df.iloc[:, 2])
text = '\0\0经营理念分析——销售费用（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '销售费用为为%.2f亿元；销售费用占营业收入的比重为：%.2f%%；销售费用增长率为%.2f%%；'
text = text % (df.iloc[:, 0].values/100000000.0, df.iloc[:, 1].values * 100.0, df.iloc[:, 2].values * 100)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import NetProfit, NetProfitGrowRateV2, EstimateNetProfitGrowRateFY1_6M
factor_list = [NetProfit, NetProfitGrowRateV2, EstimateNetProfitGrowRateFY1_6M]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df.iloc[:, 1] = pd.to_numeric(df.iloc[:, 1])
df.iloc[:, 2] = pd.to_numeric(df.iloc[:, 2])
text = '\0\0净利润增长分析（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '公司净利润为%.2f亿元；净利润增长率为%.2f%%；分析师一致预期净利润增长率为%.2f%%；'
text = text % (df.iloc[:, 0].values/100000000.0, df.iloc[:, 1].values, df.iloc[:, 2].values)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import AccRecTurnRatioV2
factor_list = [AccRecTurnRatioV2]
code = [self.code]
date = self._get_last_year_end(self.date)
df = get_factor_from_wind_without_cache(code, factor_list, date)
date_last = self._get_last_year_date(date)
df_last_year = get_factor_from_wind_without_cache(code, factor_list, date_last)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df_last_year.iloc[:, 0] = pd.to_numeric(df_last_year.iloc[:, 0])
growth = (df.iloc[:, 0].values - df_last_year.iloc[:, 0].values) / df_last_year.iloc[:, 0].values
text = '\0\0应收账款率分析（数据截止' + date + '）：'
para_1 = rl_text(text)
text = '应收账款周转率为%.2f次；上年同期为%.2f次；同比增长为%.2f%%。'
text = text % (df.iloc[:, 0].values, df_last_year.iloc[:, 0], growth * 100.0)
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import RDExpense, RDExpenseRevenue
factor_list = [RDExpense, RDExpenseRevenue]
code = [self.code]
date = self._get_last_year_end(self.date)
df = get_factor_from_wind_without_cache(code, factor_list, date)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df.iloc[:, 1] = pd.to_numeric(df.iloc[:, 1])
text = '\0\0经营理念分析——研发费用（数据截止' + date + '）：'
para_1 = rl_text(text)
text = '研发费用为%.2f亿元：占营业收入为%.2f%%。'
text = text % (df.iloc[:, 0].values / 100000000.0, df.iloc[:, 1].values * 100.0)
para_2 = rl_text(text)
if np.isnan(df.iloc[:, 0].values):
return []
else:
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import ROE
factor_list = [ROE]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
last_year = self._get_last_year_date(self.date)
df_last_year = get_factor_from_wind_without_cache(code, factor_list, last_year)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df_last_year.iloc[:, 0] = pd.to_numeric(df_last_year.iloc[:, 0])
growth = (df.iloc[:, 0].values - df_last_year.iloc[:, 0].values) / df_last_year.iloc[:, 0].values
text = '\0\0业绩分析（资本回报率ROE）（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '资本回报为ROE%.2f%%；上年同期为%.2f%%；同比增长率为%.2f%%；'
text = text % (df.iloc[:, 0].values, df_last_year.iloc[:, 0].values, growth * 100.0)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import AssetTurnoverRatio
factor_list = [AssetTurnoverRatio]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
last_year = self._get_last_year_date(self.date)
df_last_year = get_factor_from_wind_without_cache(code, factor_list, last_year)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df_last_year.iloc[:, 0] = pd.to_numeric(df_last_year.iloc[:, 0])
growth = (df.iloc[:, 0].values - df_last_year.iloc[:, 0].values) / df_last_year.iloc[:, 0].values
text = '\0\0管理层面分析_效率分析（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '总资产周转率本年为%.2f次；上年同期为%.2f次；同比增长率为%.2f%%；'
text = text % (df.iloc[:, 0].values, df_last_year.iloc[:, 0], growth * 100.0)
text_summary = self._get_summary(df, growth)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import NonCurrentAssetRatio
factor_list = [NonCurrentAssetRatio]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
date_last = self._get_last_year_date(self.date)
df_last_year = get_factor_from_wind_without_cache(code, factor_list, date_last)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df_last_year.iloc[:, 0] = pd.to_numeric(df_last_year.iloc[:, 0])
growth = (df.iloc[:, 0].values - df_last_year.iloc[:, 0].values) / df_last_year.iloc[:, 0].values
text = '\0\0资产结构分析（数据截止' + date + '）：'
para_1 = rl_text(text)
text = '公司非流动资产占比为%.2f%%；上年同期为%.2f%%；同比增长为%.2f%%；'
text = text % (df.iloc[:, 0].values, df_last_year.iloc[:, 0], growth * 100.0)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import DebetToAsset, TotalAsset
factor_list = [DebetToAsset, TotalAsset]
code = [self.code]
df = get_factor_from_wind_without_cache(code, factor_list, self.date)
last_year = self._get_last_year_date(self.date)
df_last_year = get_factor_from_wind_without_cache(code, factor_list, last_year)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df.iloc[:, 1] = pd.to_numeric(df.iloc[:, 1])
df_last_year.iloc[:, 0] = pd.to_numeric(df_last_year.iloc[:, 0])
growth = (df.iloc[:, 0].values - df_last_year.iloc[:, 0].values) / df_last_year.iloc[:, 0].values
text = '\0\0管理层面分析_效率分析（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '总资产规模为%.2f亿元；资产负债率为%.2f%%；上年同期为%.2f%%；同比增长为%.2f%%；'
text = text % (df.iloc[:, 1].values / 100000000.0, df.iloc[:, 0].values * 100.0, df_last_year.iloc[:, 0].values * 100.0, growth * 100.0)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
def output(self):
from single_factor import CurrentRatio
factor_list = [CurrentRatio]
code = [self.code]
df = get_factor_from_wind_v2(code, factor_list, self.date)
last_year = self._get_last_year_date(self.date)
df_last_year = get_factor_from_wind_v2(code, factor_list, last_year)
df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0])
df_last_year.iloc[:, 0] = pd.to_numeric(df_last_year.iloc[:, 0])
growth = (df.iloc[:, 0].values - df_last_year.iloc[:, 0].values
) / df_last_year.iloc[:, 0].values
text = '\0\0短期偿债能力分析（数据截止' + self.date + '）：'
para_1 = rl_text(text)
text = '流动比率为%.2f；上年同期为%.2f；同比增长率为%.2f%%；'
text = text % (df.iloc[:, 0].values, df_last_year.iloc[:, 0].values,
growth * 100.0)
text_summary = self._get_summary(df)
text = text + text_summary
para_2 = rl_text(text)
return [para_1, para_2, rl_text('\0')]
