def summary_release_2_docx(title, img_meta_dic_list, stg_run_id=None, enable_clean_cache=True):
"""
生成 预测成功率趋势报告
:param title:
:param img_meta_dic_list:
:param stg_run_id:
:param enable_clean_cache:
:return:
"""
logger.debug('生成报告开始')
# 生成 docx 文件
document = docx.Document()
# 设置默认字体
document.styles['Normal'].font.name = '微软雅黑'
document.styles['Normal']._element.rPr.rFonts.set(docx.oxml.ns.qn('w:eastAsia'), '微软雅黑')
# 创建自定义段落样式(第一个参数为样式名, 第二个参数为样式类型, 1为段落样式, 2为字符样式, 3为表格样式)
UserStyle1 = document.styles.add_style('UserStyle1', 1)
# 设置字体尺寸
UserStyle1.font.size = docx.shared.Pt(40)
# 设置字体颜色
UserStyle1.font.color.rgb = docx.shared.RGBColor(0xff, 0xde, 0x00)
# 居中文本
UserStyle1.paragraph_format.alignment = docx.enum.text.WD_ALIGN_PARAGRAPH.CENTER
# 设置中文字体
UserStyle1.font.name = '微软雅黑'
UserStyle1._element.rPr.rFonts.set(docx.oxml.ns.qn('w:eastAsia'), '微软雅黑')
# 文件内容
document.add_heading(title, 0).alignment = docx.enum.text.WD_ALIGN_PARAGRAPH.CENTER
document.add_paragraph('')
document.add_paragraph('')
heading_size = 1
for num, info_dic in enumerate(img_meta_dic_list, start=1):
trade_date_last_train = info_dic['trade_date_last_train']
trade_date_end = info_dic['trade_date_end']
document.add_heading(
f"{num}、{date_2_str(trade_date_last_train)} - {date_2_str(trade_date_end)}", heading_size)
split_point_list = info_dic['split_point_list']
if split_point_list is None:
p = document.add_paragraph(f"{num}.1） 日期区间段1个:\n")
p.add_run(f'\t1) {date_2_str(trade_date_last_train)} ~ {date_2_str(trade_date_end)}\n')
else:
p = document.add_paragraph(f"{num}.1） 日期区间段{len(split_point_list) - 1}个:\n")
for num2, (point1, point2) in enumerate(
iter_2_range(split_point_list, has_left_outer=False, has_right_outer=False), start=1):
p.add_run(f'\t{num2}) {date_2_str(point1)} ~ {date_2_str(point2)}\n')
document.add_paragraph(f"{num}.2） 模型路径：\n\t{info_dic['module_file_path']}")
document.add_paragraph(f"{num}.3） 取样状态(random_state)：\n\t{info_dic['predict_test_random_state']}")
document.add_paragraph(f"{num}.4） 展示数据长度：\n\t{info_dic['in_range_count']}")
document.add_paragraph(f"{num}.5） 预测准确率趋势图：")
document.add_picture(info_dic['img_file_path'])
file_name = f"{title}.docx"
file_path = os.path.join(get_report_folder_path(stg_run_id), file_name)
document.save(file_path)
if enable_clean_cache:
clean_cache()
logger.debug('生成报告结束。%s', file_path)
return file_path
def get_df_iter(self,
date_start,
date_end,
step,
df_len_limit=3000,
deep=0):
"""
获取日期范围内的数据，当数据记录大于上限条数时，将日期范围进行二分法拆分，迭代进行查询
:param date_start:
:param date_end:
:param step:
:param df_len_limit:
:param deep:
:return:
"""
for num, (date_from, date_to) in enumerate(iter_2_range(
range_date(date_start, date_end, step),
has_left_outer=False,
has_right_outer=False),
start=1):
q = query(self.statement).filter(
self.statement.pub_date > date_2_str(date_from),
self.statement.pub_date <= date_2_str(date_to))
df = finance.run_query(q)
df_len = df.shape[0]
if df_len >= df_len_limit:
if step >= 2:
self.logger.warning(
'%s%s%d) [%s ~ %s] 包含 %d 条数据，可能已经超越 %d 条提取上限，开始进一步分割日期',
self.table_name, ' ' * deep, num, date_from, date_to,
df_len, df_len_limit)
yield from self.get_df_iter(date_from,
date_to,
step // 2,
deep=deep + 1)
else:
self.logger.warning(
'%s%s%d) [%s ~ %s] 包含 %d 条数据，可能已经超越 %d 条提取上限且无法再次分割日期范围，手动需要补充提取剩余数据',
self.table_name, ' ' * deep, num, date_from, date_to,
df_len, df_len_limit)
yield df, date_from, date_to
else:
self.logger.debug('%s%s%d) [%s ~ %s] 包含 %d 条数据',
self.table_name, ' ' * deep, num, date_from,
date_to, df_len)
yield df, date_from, date_to
def merge_ifind_stock_daily(ths_code_set: set = None, date_from=None):
"""将ds his 以及财务数据合并为 daily 数据"""
table_name = 'ifind_stock_daily'
logging.info("合成 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
if date_from is None and has_table:
sql_str = "select adddate(max(`time`),1) from {table_name}".format(
table_name=table_name)
with with_db_session(engine_md) as session:
date_from = date_2_str(session.execute(sql_str).scalar())
# 獲取各個表格數據
ifind_his_df = get_ifind_daily_df('ifind_stock_daily_his', date_from)
ifind_ds_df = get_ifind_daily_df('ifind_stock_daily_ds', date_from)
ifind_report_date_df = get_ifind_report_date_df('ifind_stock_report_date',
None)
ifind_fin_df = get_ifind_daily_df('ifind_stock_fin', None)
ifind_fin_df_g = ifind_fin_df.groupby('ths_code')
ths_code_set_4_daily = set(ifind_fin_df_g.size().index)
# 合并 ds his 数据
ifind_his_ds_df = pd.merge(ifind_his_df,
ifind_ds_df,
how='outer',
on=['ths_code', 'time']) # 拼接後續有nan,無數據
ifind_his_ds_df_g = ifind_his_ds_df.groupby('ths_code')
logger.debug("提取数据完成")
# 计算 财报披露时间
report_date_dic_dic = {}
for report_date_g in [
ifind_report_date_df.groupby(
['ths_code', 'ths_regular_report_actual_dd_stock'])
]:
for num, ((ths_code, report_date), data_df) in enumerate(report_date_g,
start=1):
if ths_code_set is not None and ths_code not in ths_code_set:
continue
if is_nan_or_none(report_date):
continue
report_date_dic = report_date_dic_dic.setdefault(ths_code, {})
if ths_code not in ths_code_set_4_daily:
logger.error('fin 表中不存在 %s 的財務數據', ths_code)
continue
ifind_fin_df_temp = ifind_fin_df_g.get_group(ths_code)
if report_date not in report_date_dic_dic:
ifind_fin_df_temp = ifind_fin_df_temp[
ifind_fin_df_temp['time'] <= report_date]
if ifind_fin_df_temp.shape[0] > 0:
report_date_dic[
report_date] = ifind_fin_df_temp.sort_values(
'time').iloc[0]
# # 设置 dtype
dtype = {'report_date': Date}
for dic in [
DTYPE_STOCK_DAILY_DS, DTYPE_STOCK_REPORT_DATE,
DTYPE_STOCK_DAILY_FIN, DTYPE_STOCK_DAILY_HIS
]:
for key, val in dic.items():
dtype[key] = val
logger.debug("计算财报日期完成")
# 整理 data_df 数据
tot_data_count, data_count, data_df_list, for_count = 0, 0, [], len(
report_date_dic_dic)
try:
for num, (ths_code,
report_date_dic) in enumerate(report_date_dic_dic.items(),
start=1): # key:ths_code
# TODO: 檢查判斷 ths_code 是否存在在ifind_fin_df_g 裏面,,size暫時使用 以後在驚醒改進
if ths_code not in ifind_his_ds_df_g.size():
logger.error('fin 表中不存在 %s 的財務數據', ths_code)
continue
# open low 等 is NAN 2438
ifind_his_ds_df_cur_ths_code = ifind_his_ds_df_g.get_group(
ths_code) # shape[1] 30
logger.debug('%d/%d) 处理 %s %d 条数据', num, for_count, ths_code,
ifind_his_ds_df_cur_ths_code.shape[0])
report_date_list = list(report_date_dic.keys())
report_date_list.sort()
for report_date_from, report_date_to in iter_2_range(
report_date_list):
logger.debug('%d/%d) 处理 %s [%s - %s]', num, for_count,
ths_code, date_2_str(report_date_from),
date_2_str(report_date_to))
# 计算有效的日期范围
if report_date_from is None:
is_fit = ifind_his_ds_df_cur_ths_code[
'time'] < report_date_to
elif report_date_to is None:
is_fit = ifind_his_ds_df_cur_ths_code[
'time'] >= report_date_from
else:
is_fit = (ifind_his_ds_df_cur_ths_code['time'] <
report_date_to) & (
ifind_his_ds_df_cur_ths_code['time'] >=
report_date_from)
# 获取日期范围内的数据
ifind_his_ds_df_segment = ifind_his_ds_df_cur_ths_code[
is_fit].copy()
segment_count = ifind_his_ds_df_segment.shape[0]
if segment_count == 0:
continue
fin_s = report_date_dic[
report_date_from] if report_date_from is not None else None
for key in DTYPE_STOCK_DAILY_FIN.keys():
if key in ('ths_code', 'time'):
continue
ifind_his_ds_df_segment[key] = fin_s[
key] if fin_s is not None and key in fin_s else None
ifind_his_ds_df_segment['report_date'] = report_date_from
# 添加数据到列表
data_df_list.append(ifind_his_ds_df_segment)
data_count += segment_count
if DEBUG and len(data_df_list) > 1:
break
# 保存数据库
if data_count > 10000:
# 保存到数据库
data_df = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(
data_df, table_name, engine_md, dtype)
tot_data_count += data_count
data_count, data_df_list = 0, []
finally:
# 保存到数据库
if len(data_df_list) > 0:
data_df = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(
data_df, table_name, engine_md, dtype)
tot_data_count += data_count
logger.info('%s 新增或更新记录 %d 条', table_name, tot_data_count)
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
return pd.DataFrame(data_list)
def get_ifind_daily_df(table_name, date_from) -> pd.DataFrame:
if date_from is None:
sql_str = "select * from {table_name}".format(table_name=table_name)
data_df = pd.read_sql(sql_str, engine_md) # , index_col='ths_code'
else:
sql_str = "select * from {table_name} where time >= %s".format(
table_name=table_name)
data_df = pd.read_sql(sql_str, engine_md,
params=[date_from]) # , index_col='ths_code'
return data_df
def get_wind_daily_df(table_name, date_from) -> pd.DataFrame:
if date_from is None:
sql_str = "select * from {table_name}".format(table_name=table_name)
data_df = pd.read_sql(sql_str, engine_md) # , index_col='ths_code'
else:
sql_str = "select * from {table_name} where time >= %s".format(
table_name=table_name)
data_df = pd.read_sql(sql_str, engine_md,
params=[date_from]) # , index_col='ths_code'
return data_df
if __name__ == "__main__":
for x in iter_2_range([1, 2, 3]):
print(x)
def plot_industry_classified_mid(col_name='ev2_to_ebitda'):
# sql_str = """select sector_code, sector_name,base.trade_date, sum(ev2_to_ebitda) tot_val
# from (
# SELECT * FROM fof_ams_dev.wind_sectorconstituent where sector_name like 'cs%%'
# ) base
# LEFT JOIN
# (
# select trade_date, wind_code, ev2_to_ebitda from wind_stock_daily where ev2_to_ebitda is not null
# ) val
# on base.trade_date = val.trade_date
# and base.wind_code = val.wind_code
# group by sector_code, base.trade_date
# having tot_val is not null"""
# TODO: 待行业数据下载齐全后可生成相应的分布图
sector_sql_str = """SELECT sector_name, trade_date, wind_code FROM fof_ams_dev.wind_sectorconstituent
where sector_name like 'cs%'"""
with with_db_session(engine_md) as session:
table = session.execute(sector_sql_str)
sector_trade_date_wind_code_list_dic = defaultdict(dict)
num = 0
for num, (sector_name, trade_date,
wind_code) in enumerate(table.fetchall(), start=1):
if sector_name not in sector_trade_date_wind_code_list_dic:
sector_trade_date_wind_code_list_dic[sector_name] = {
'trade_date_set': set(),
'trade_date_wind_code_list_dic': defaultdict(list)
}
sector_trade_date_wind_code_list_dic[sector_name][
'trade_date_set'].add(trade_date)
sector_trade_date_wind_code_list_dic[sector_name][
'trade_date_wind_code_list_dic'][trade_date].append(wind_code)
sector_count = len(sector_trade_date_wind_code_list_dic)
logger.debug('获取行业数据 %d 条 %d 个行业', num, sector_count)
stock_sql_str = f"""select wind_code, trade_date, `{col_name}` from wind_stock_daily
where `{col_name}` is not null"""
data_df = pd.read_sql(stock_sql_str, engine_md)
logger.debug('获取行情数据 %d 条', data_df.shape[0])
pivot_df = data_df.pivot(index='trade_date',
columns='wind_code',
values=col_name).sort_index()
logger.debug('转换数据 %s', pivot_df.shape)
sector_trade_date_val_list_dic, sector_trade_date_val_dic = {}, {}
logger.debug('计算 %d 个行业中位数', sector_count)
for num, (sector_name, data_dic) in enumerate(
sector_trade_date_wind_code_list_dic.items(), start=1):
trade_date_list = list(data_dic['trade_date_set'])
trade_date_list.sort()
trade_date_list_len = len(trade_date_list)
logger.debug('%d/%d) %s %d 个交易日', num, sector_count, sector_name,
trade_date_list_len)
trade_date_wind_code_list_dic = data_dic[
'trade_date_wind_code_list_dic']
# for trade_date, wind_code_list in trade_date_wind_code_list_dic.items():
for num2, (trade_date_from, trade_date_to) in enumerate(iter_2_range(
trade_date_list, has_left_outer=False),
start=1):
wind_code_list = trade_date_wind_code_list_dic[trade_date_from]
# logger.debug('%d/%d) [%d/%d] %s [%s %s)', num, sector_count, num2, trade_date_list_len,
# sector_name, trade_date_from, trade_date_to, )
# 计算中位数
try:
tmp_df = pivot_df.loc[trade_date_from:trade_date_to,
wind_code_list]
if tmp_df.shape[0] == 0:
continue
except KeyError:
continue
val_s = tmp_df.median(axis=1)
if trade_date_to is not None:
# 去除最后一天
val_s = val_s.iloc[:-1]
# 保存到dict
if sector_name not in sector_trade_date_val_list_dic:
sector_trade_date_val_list_dic[sector_name] = [val_s]
else:
sector_trade_date_val_list_dic[sector_name].append(val_s)
# 合并计算结果成为 一个 Series
if sector_name in sector_trade_date_val_list_dic and len(
sector_trade_date_val_list_dic[sector_name]) > 0:
logger.debug('%s %d 个交易日合并数据', sector_name, len(trade_date_list))
sector_trade_date_val_dic[sector_name] = pd.concat(
sector_trade_date_val_list_dic[sector_name])
# 数据合并
# 将所有 sector 的 数据合并成为 DataFrame
logger.debug('合并 %d 个行业数据', sector_count)
data_df = pd.DataFrame(sector_trade_date_val_dic)
data_df.to_excel('median.xls', legend=False)
data_df.plot()
plt.show()