def get_stock_basic(self):
"""
从ts_pro.stock_basic获取个股的基本信息列表
return:<df> is success, None if fail
"""
file_name = PurePath('stock_basic.csv')
file_path = sub_path / file_name
self.basic = ts_pro.stock_basic(
fields=
'ts_code,symbol,name,area,industry,fullname,enname,market,exchange,curr_type,list_status,list_date,delist_date,is_hs'
)
if isinstance(self.basic, pd.DataFrame):
if len(self.basic) > 10:
self.basic.set_index('ts_code', inplace=True)
self.basic.to_csv(file_path, encoding='utf-8')
log_args = [file_path]
add_log(
40,
'[fn]:Stock_Basic.get_stock_basic() file "{0[0]}" saved',
log_args)
return self.basic
log_args = [file_path]
add_log(
20,
'[fn]:Stock_Basic.get_stock_basic() failed to save file "{0[0]}"',
log_args)
return
def que_list_date(self, ts_code):
"""查询上市时间list_date
return:<str> e.g. '19930503'
ts_code:<str> e.g. '000001.SH'
"""
from st_board import valid_date_str_fmt
try:
result = self.basic.loc[ts_code]['list_date']
except KeyError:
log_args = [ts_code]
add_log(
20,
'[fn]:Stock_Basic.que_list_date() ts_code: "{0[0]}" was not found in Stock.base. use DEFAULT_OPEN_DATE_STR instead',
log_args)
result = DEFAULT_OPEN_DATE_STR
return result
if valid_date_str_fmt(result):
return result
else:
result = DEFAULT_OPEN_DATE_STR
log_args = [ts_code, result]
add_log(
40,
'[fn]:Stock_Basic.que_list_date() ts_code: "{0[0]}" used "DEFAULT_OPEN_DATE_STR" {0[1]} instead "list_date".',
log_args)
return result
def que_list_date(self, ts_code):
"""
查询上市时间
ts_code:<str> e.g. "518880.SH"
return:<str> e.g. "19930512"
"""
from st_board import valid_date_str_fmt
ts_code = ts_code.upper()
try:
result = self.basic_e.loc[ts_code]['list_date']
except KeyError:
try:
result = self.basic_o.loc[ts_code]['list_date']
except KeyError:
log_args = [ts_code]
add_log(
20,
'[fn]:Fund_Basic.que_list_date() ts_code:{0[0]} was not found in Fund.base_e/_o',
log_args)
return
if valid_date_str_fmt(result):
return result
else:
log_args = [ts_code, result]
add_log(
20,
'[fn]:Fund_Basic.que_list_date() ts_code:{0[0]} result:{0[1]} invalid',
log_args)
return
def get_fund_basic(self, market='E'):
"""
从ts_pro.fund_basic获取个股的基本信息列表
market: <str> 'E'场内 'O'场外
return: <df> is success, None if fail
"""
if market == 'E':
file_name = PurePath('fund_e_basic.csv') # 场内基金
elif market == 'O':
file_name = PurePath('fund_o_basic.csv') # 场外基金
else:
log_args = [market]
add_log(
20,
'[fn]:Fund_Basic.get_fund_basic() unsupported market type:{0[0], aborted',
log_args)
return
file_path = sub_path / file_name
if market == 'E':
self.basic_e = ts_pro.fund_basic(market=market) # 场内基金
if isinstance(self.basic_e, pd.DataFrame):
if len(self.basic_e) > 10:
self.basic_e.set_index('ts_code', inplace=True)
self.basic_e.to_csv(file_path, encoding='utf-8')
log_args = [file_path, len(self.basic_e)]
add_log(
40,
'[fn]:Fund_Basic.get_fund_basic() file "{0[0]}" saved, items:{0[1]}',
log_args)
return self.basic_e
log_args = [file_path]
add_log(
20,
'[fn]:Fund_Basic.get_fund_basic() failed to save file "{0[0]}"',
log_args)
return
elif market == 'O':
self.basic_o = ts_pro.fund_basic(market=market) # 场外基金
if isinstance(self.basic_o, pd.DataFrame):
if len(self.basic_o) > 10:
self.basic_o.set_index('ts_code', inplace=True)
self.basic_o.to_csv(file_path, encoding='utf-8')
log_args = [file_path, len(self.basic_o)]
add_log(
40,
'[fn]:Fund.get_fund_basic() file "{0[0]}" saved, items:{0[1]}',
log_args)
return self.basic_o
log_args = [file_path]
add_log(
20,
'[fn]:Fund_Basic.get_fund_basic() failed to save file "{0[0]}"',
log_args)
return
def in_calendar(self, date_str):
"""
查看日期是否在calendar中
"""
try:
self.trade_calendar.index.get_loc(int(date_str))
return True
except Exception as e:
log_args = [type(e)]
add_log(20, '[fn]Raw_Data.in_calendar() except type:{0[0]}',
log_args)
return
def next_trade_day(self, dt_str, next_n=1):
"""
查询之后第n个交易日
dt_str: <string> in 'YYYYMMDD' e.g.'20190721'
next_n: <int>
return: <string> in 'YYYYMMDD' e.g.'20190719'
"""
SUSPENDED_DAYS_LIM = 100
if self.valid_trade_calendar() is None:
add_log(20, '[fn]Raw_Data.next_trade_day() trade_calendar invalid')
return
if next_n < 1:
log_args = [next_n]
add_log(
20,
'[fn]Raw_Data.next_trade_day() next_n:{0[0]} invalid'.format(
log_args))
return
if isinstance(dt_str, str) and (len(dt_str) == 8):
tdf = self.trade_calendar
try:
pos = tdf.index.get_loc(int(dt_str))
for _ in range(int(next_n)):
for i in range(1, SUSPENDED_DAYS_LIM): # 估计最长休市天数不超过x天
if tdf.iloc[pos + i]['is_open'] == 1:
pos = pos + i
break
else:
add_log(
20,
'[fn]Raw_Data.next_trade_day() suspended days exceed limit'
)
return
except KeyError:
log_args = [dt_str]
add_log(
10,
'[fn]Raw_Data.next_trade_day() dt_str "{0[0]}" not in stock_basic.csv. check date_str and pull Raw_Data',
log_args)
return
next_trade_date = tdf.iloc[
pos].name # tdf.iloc[pos]是Series，所以用.name
# print('[L167] next_trade_date: {}'.format())
return str(next_trade_date)
else:
log_args = [dt_str]
add_log(
20,
'[fn]Raw_Data.next_trade_day() dt_str "{0[0]}" incorrect format',
log_args)
return
def load_fund_basic(self, market='E'):
"""
从fund_e_basic.csv或fund_o_basic.csv文件读入个股的基本信息列表
return:<df> is success, None if fail
"""
if market == 'E':
file_name = PurePath('fund_e_basic.csv') # 场内基金
elif market == 'O':
file_name = PurePath('fund_o_basic.csv') # 场外基金
else:
log_args = [market]
add_log(
20,
'[fn]:Fund_Basic.load_fund_basic() unsupported market type:{0[0], aborted',
log_args)
return
file_path = sub_path / file_name
try:
if market == 'E':
self.basic_e = pd.read_csv(file_path,
dtype={
'found_date': str,
'due_date': str,
'list_date': str,
'delist_date': str,
'issue_date': str,
'purc_startdate': str,
'redm_startdate': str
},
index_col='ts_code')
return self.basic_e
elif market == 'O':
self.basic_o = pd.read_csv(file_path,
dtype={
'found_date': str,
'due_date': str,
'list_date': str,
'delist_date': str,
'issue_date': str,
'purc_startdate': str,
'redm_startdate': str
},
index_col='ts_code')
return self.basic_o
except FileNotFoundError:
log_args = [file_name]
add_log(
20,
'[fn]Fund_Basic.load_fund_basic()e. file "{0[0]}" not found',
log_args)
return
def load_all_assets_list(self):
"""
从all_assets_list.csv中读取全代码列表
"""
file_name = PurePath("all_assets_list.csv")
file_path = sub_path / sub_path_config / file_name
try:
df = pd.read_csv(file_path, index_col='ts_code')
except FileNotFoundError:
log_args = [file_path]
add_log(10,
'[fn]Raw_Data.load_all_assets_list. "{0[0]}" not found',
log_args)
df = None
self.all_assets_list = df
def query_name(self, ts_code):
"""
查询asset的name
return: <str> e.g. "上证指数"
"""
try:
name = self.all_assets_list.loc[ts_code]['name']
except Exception as e: # 具体except待细化
log_args = [ts_code, type(e)]
add_log(
20,
'[fn]Raw_Data.query_name(). ts_code:{0[0]} failed to get name, except:{0[1]}',
log_args)
return
return name
def len_trade_days(self, start_day, end_day=None):
"""
查询两个日期间所包括的交易日数，start_day算第1天，end_day也算1天
具体except处理待完善
start_day: <str> e.g. '20191231'
end_day: None = today_str() 今天的字符串
<str> e.g. '20191231'
"""
from st_board import today_str
if end_day is None:
end_day = today_str()
if self.in_calendar(start_day) is not True:
log_args = [start_day]
add_log(
20,
'[fn]Raw_Data.len_trade_days() start_day:{0[0]} not in calendar',
log_args)
return
if self.in_calendar(end_day) is not True:
log_args = [end_day]
add_log(
20,
'[fn]Raw_Data.len_trade_days() end_day:{0[0]} not in calendar',
log_args)
return
try:
int_start = int(start_day)
int_end = int(end_day)
except ValueError:
log_args = [start_day, end_day]
add_log(
20,
'[fn]Raw_Data.len_trade_days() start_day:{0[0]} or end_day:{0[1]} invalid',
log_args)
return
try:
df = self.trade_calendar[
self.trade_calendar.index.to_series().between(
int_start, int_end)]
rslt = df['is_open'].value_counts()[1]
except IndexError:
log_args = [start_day, end_day]
add_log(
30,
'[fn]Raw_Data.len_trade_days() start_day:{0[0]} or end_day:{0[1]} invalid or mis-order',
log_args)
return
except KeyError:
rslt = 0 # 之间无交易日
return rslt
def load_trade_calendar(self):
"""
load trade_calendar.csv文件，读入self.trade_calendar
"""
file_name = PurePath("trade_calendar.csv")
file_path = sub_path / file_name
try:
self.trade_calendar = pd.read_csv(file_path,
dtype={
'cal_date': str,
'pretrade_date': str
},
index_col='cal_date')
except FileNotFoundError:
log_args = [file_path]
add_log(
20,
'[fn]Raw_Data.load_trade_calendar(). file "{0[0]}" not found',
log_args)
self.trade_calendar = None
def valid_ts_code(self, ts_code):
"""验证在raw_data内ts_code是否有效,
return: <bool> True=valid
"""
if ts_code in SPC_TS_CODE:
return True
# --------------------Index---------------
try:
name = self.all_assets_list.loc[ts_code]['name']
# print("[debug L249] name:{}".format(name))
except KeyError:
log_args = [ts_code]
add_log(30,
'[fn]Raw_Data.valid_ts_code(). ts_code "{0[0]}" invalid',
log_args)
return
if isinstance(name, str):
if len(name) > 0:
return True
return
def load_stock_basic(self):
"""
从stock_basic.csv文件读入个股的基本信息列表
return:<df> is success, None if fail
"""
file_name = PurePath("stock_basic.csv")
file_path = sub_path / file_name
try:
self.basic = pd.read_csv(file_path,
dtype={
'symbol': str,
'list_date': str,
'delist_date': str
},
index_col='ts_code')
return self.basic
except FileNotFoundError:
log_args = [file_path]
add_log(
20,
'[fn]Stock_Basic.load_stock_basic()e. file "{0[0]}" not found',
log_args)
return
def load_index_basic(self):
"""
load index_basic.csv文件，读入self.index_basic_df
"""
# 上交所指数
file_name = PurePath("index_basic_sse.csv")
try:
self._sse = pd.read_csv(sub_path / file_name,
dtype={
'base_date': str,
'list_date': str
})
self.valid['index_basic_sse'] = STATUS_WORD[1]
except FileNotFoundError:
log_args = [file_name]
add_log(20,
'[fn]Index.load_index_basic(). file "{0[0]}" not found',
log_args)
self._sse = None
# 深交所指数
file_name = PurePath("index_basic_szse.csv")
try:
self._szse = pd.read_csv(sub_path / file_name,
dtype={
'base_date': str,
'list_date': str
})
self.valid['index_basic_szse'] = STATUS_WORD[1]
except FileNotFoundError:
log_args = [file_name]
add_log(20,
'[fn]Index.load_index_basic(). file "{0[0]}" not found',
log_args)
self._szse = None
# 申万指数
file_name = PurePath("index_basic_sw.csv")
try:
self._sw = pd.read_csv(sub_path / file_name,
dtype={
'base_date': str,
'list_date': str
})
self.valid['index_basic_sw'] = STATUS_WORD[1]
except FileNotFoundError:
log_args = [file_name]
add_log(20,
'[fn]Index.load_index_basic(). file "{0[0]}" not found',
log_args)
self._sw = None
self._update_index_basic_df()
return
def last_trade_day(self, dt_str=None):
"""
查询指定日当日或之前最近的交易日，返回日期字符串
dt_str: <string> in 'YYYYMMDD' e.g.'20190721'
None 代表当日
return: <string> in 'YYYYMMDD' e.g.'20190719'
"""
if self.valid_trade_calendar() is None:
add_log(20, '[fn]Raw_Data.last_trade_day() trade_calendar invalid')
return
if dt_str is None:
dt = datetime.now()
dt_str = dt.strftime("%Y%m%d")
if isinstance(dt_str, str) and (len(dt_str) == 8):
# tdf = self.trade_calendar.set_index(['cal_date'])
tdf = self.trade_calendar
try:
is_open = tdf.loc[int(dt_str)]['is_open']
if is_open == 1:
return dt_str
elif is_open == 0:
pretrade_date = tdf.loc[int(dt_str)]['pretrade_date']
return str(pretrade_date)
else:
return None
except KeyError:
log_args = [dt_str]
add_log(
10,
'[fn]Raw_Data.last_trade_day() dt_str "{0[0]}" not in stock_basic.csv. check date_str and pull Raw_Data',
log_args)
return
else:
log_args = [dt_str]
add_log(
20,
'[fn]Raw_Data.last_trade_day() dt_str "{0[0]}" incorrect format',
log_args)
return None
def report(al_file):
"""
报告：每日资产概览
al_file: <str> al_<al_file>.csv
"""
from st_board import Strategy
from st_board import today_str
# global raw_data
def head_across(row, column, text):
"""
用于辅助打印标题栏0行的一组标题
"""
nonlocal ws1
cross = 4 # 单元格跨四格
ws1.write(row, column, text, fmt_head0)
for i in range(1, cross):
ws1.write_blank(row, column + i, '', fmt_head0)
# =================报告文件=================
today_s = today_str()
trade_day_str = raw_data.last_trade_day(today_s)
file_name = PurePath('rpt_racing_' + al_file + '_' + trade_day_str +
'.xlsm')
file_path = sub_path_rpt / file_name
# =================初始化Strategy=================
stg = Strategy('report_daily_basic')
stg.add_pool(desc='p10',
al_file=al_file,
assets_daily_data='basic',
del_trsfed=None)
p10 = stg.pools[10]
# ------condition_0
pre_args1 = {'idt_type': 'ma', 'period': 2}
pre_args2 = {'idt_type': 'ma', 'period': 5}
p10.add_condition(pre_args1, pre_args2, '>')
# ------condition_1
pre_args1 = {'idt_type': 'ma', 'period': 10}
pre_args2 = {'idt_type': 'ma', 'period': 20}
p10.add_condition(pre_args1, pre_args2, '>')
# ------condition_2
pre_args1 = {'idt_type': 'ma', 'period': 60}
pre_args2 = {'idt_type': 'const', 'const_value': 0}
p10.add_condition(pre_args1, pre_args2, '>')
stg.init_pools_cnds_matrix()
stg.init_ref_assets()
end_date = trade_day_str
start_date = raw_data.previous_trade_day(end_date, 2)
stg.update_cycles(start_date=start_date, end_date=end_date)
# ----当前周期的结束日期
m1d = raw_data.previous_trade_day(end_date, 1) # 前1交易日
m2d = start_date # 前2交易日
m5d = raw_data.previous_trade_day(end_date, 5) # 前5交易日
m10d = raw_data.previous_trade_day(end_date, 10) # 前10交易日
m20d = raw_data.previous_trade_day(end_date, 20) # 前20交易日
m60d = raw_data.previous_trade_day(end_date, 60) # 前60交易日
# ----前一周期的开始结束日期
p1d_end = m1d # 前1周期结束日期
p1d_start = raw_data.previous_trade_day(p1d_end, 1) # 前1周期开始日期
p2d_end = m2d # 前1周期结束日期
p2d_start = raw_data.previous_trade_day(p2d_end, 2) # 前1周期开始日期
p5d_end = m5d # 前1周期结束日期
p5d_start = raw_data.previous_trade_day(p5d_end, 5) # 前1周期开始日期
p10d_end = raw_data.previous_trade_day(m10d, 1) # 前1周期结束日期
p10d_start = m10d # 前1周期开始日期
p20d_end = raw_data.previous_trade_day(m20d, 1) # 前1周期结束日期
p20d_start = m20d # 前1周期开始日期
p60d_end = raw_data.previous_trade_day(m60d, 1) # 前1周期结束日期
p60d_start = m60d # 前1周期开始日期
# =================数据准备=================
data = [] # [(数据1, 数据2, 数据3）, (...)]存放用于报告显示的数据
df = pd.DataFrame(columns=['ts_code', 'm1d_pct', 'close',
'p1d_pct']) # 缓存<df>
# df.set_index('ts_code', inplace=True)
for asset in p10.assets.values():
ts_code = asset.ts_code
name = asset.name
# ----最新的复权因子，当日收盘
adj = 1 # 非Stock复权因子设为1
if isinstance(asset, Stock):
adj_sr = Stock.load_adj_factor(ts_code, nrows=1)
if adj_sr is not None:
adj, = adj_sr['adj_factor'].head(1) # 最新的复权因子
try:
close, _ = asset.get_price(trade_date=end_date,
seek_direction='backwards') # 最新收盘
except Exception as e:
log_args = [asset.ts_code, e]
add_log(
20,
'[fn]report_race.report() ts_code:{0[0]}; except type:{0[1]}, no valid close recently. skip asset',
log_args)
continue
# ----1日
try:
close_m1d, _ = asset.get_price(trade_date=m1d,
seek_direction='backwards')
m1d_pct = (close - close_m1d) / close_m1d # 本周期1日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; m1d_pct TypeError, set value = -0.99',
log_args)
m1d_pct = -0.99
try:
close_p1d_end, _ = asset.get_price(trade_date=p1d_end,
seek_direction='backwards')
close_p1d_start, _ = asset.get_price(trade_date=p1d_start,
seek_direction='backwards')
p1d_pct = (close_p1d_end -
close_p1d_start) / close_p1d_start # 前周期1日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; p1d_pct TypeError, set value = -0.99',
log_args)
p1d_pct = -0.99
close_m = close / adj # 本周期最新未除权价格
# ----2日
try:
close_m2d, _ = asset.get_price(trade_date=m2d,
seek_direction='backwards')
m2d_pct = (close - close_m2d) / close_m2d # 本周期2日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; m2d_pct TypeError, set value = -0.99',
log_args)
m2d_pct = -0.99
try:
close_p2d_end, _ = asset.get_price(trade_date=p2d_end,
seek_direction='backwards')
close_p2d_start, _ = asset.get_price(trade_date=p2d_start,
seek_direction='backwards')
p2d_pct = (close_p2d_end -
close_p2d_start) / close_p2d_start # 前周期2日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; p2d_pct TypeError, set value = -0.99',
log_args)
p2d_pct = -0.99
try:
close_ma2, = asset.ma_2.df_idt.head(1)['MA'].values
close_ma2r = close_ma2 / adj # 本周期ma2未除权价格
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; close_ma2r TypeError, set value = -99',
log_args)
close_ma2r = -99
# ----5日
try:
close_m5d, _ = asset.get_price(trade_date=m5d,
seek_direction='backwards')
m5d_pct = (close - close_m5d) / close_m5d # 本周期5日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; m5d_pct TypeError, set value = -0.99',
log_args)
m5d_pct = -0.99
try:
close_p5d_end, _ = asset.get_price(trade_date=p5d_end,
seek_direction='backwards')
close_p5d_start, _ = asset.get_price(trade_date=p5d_start,
seek_direction='backwards')
p5d_pct = (close_p5d_end -
close_p5d_start) / close_p5d_start # 前周期5日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; p5d_pct TypeError, set value = -0.99',
log_args)
p5d_pct = -0.99
try:
close_ma5, = asset.ma_5.df_idt.head(1)['MA'].values
close_ma5r = close_ma5 / adj # 本周期ma5未除权价格
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; close_ma5r TypeError, set value = -99',
log_args)
close_ma5r = -99
# ----10日
try:
close_m10d, _ = asset.get_price(trade_date=m10d,
seek_direction='backwards')
m10d_pct = (close - close_m10d) / close_m10d # 本周期10日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; m10d_pct TypeError, set value = -0.99',
log_args)
m10d_pct = -0.99
try:
close_p10d_end, _ = asset.get_price(trade_date=p10d_end,
seek_direction='backwards')
close_p10d_start, _ = asset.get_price(trade_date=p10d_start,
seek_direction='backwards')
p10d_pct = (close_p10d_end -
close_p10d_start) / close_p10d_start # 前周期10日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; p10d_pct TypeError, set value = -0.99',
log_args)
p10d_pct = -0.99
try:
close_ma10, = asset.ma_10.df_idt.head(1)['MA'].values
close_ma10r = close_ma10 / adj # 本周期ma10未除权价格
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; close_ma10r TypeError, set value = -99',
log_args)
close_ma10r = -99
# ----20日
try:
close_m20d, _ = asset.get_price(trade_date=m20d,
seek_direction='backwards')
m20d_pct = (close - close_m20d) / close_m20d # 本周期20日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; m20d_pct TypeError, set value = -0.99',
log_args)
m20d_pct = -0.99
try:
close_p20d_end, _ = asset.get_price(trade_date=p20d_end,
seek_direction='backwards')
close_p20d_start, _ = asset.get_price(trade_date=p20d_start,
seek_direction='backwards')
p20d_pct = (close_p20d_end -
close_p20d_start) / close_p20d_start # 前周期20日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; p20d_pct TypeError, set value = -0.99',
log_args)
p20d_pct = -0.99
try:
close_ma20, = asset.ma_20.df_idt.head(1)['MA'].values
close_ma20r = close_ma20 / adj # 本周期ma20未除权价格
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; close_ma20r TypeError, set value = -99',
log_args)
close_ma20r = -99
# ----60日
try:
close_m60d, _ = asset.get_price(trade_date=m60d,
seek_direction='backwards')
m60d_pct = (close - close_m60d) / close_m60d # 本周期60日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; m60d_pct TypeError, set value = -0.99',
log_args)
m60d_pct = -0.99
try:
close_p60d_end, _ = asset.get_price(trade_date=p60d_end,
seek_direction='backwards')
close_p60d_start, _ = asset.get_price(trade_date=p60d_start,
seek_direction='backwards')
p60d_pct = (close_p60d_end -
close_p60d_start) / close_p60d_start # 前周期60日涨跌幅
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; p60d_pct TypeError, set value = -0.99',
log_args)
p60d_pct = -0.99
try:
close_ma60, = asset.ma_60.df_idt.head(1)['MA'].values
close_ma60r = close_ma60 / adj # 本周期ma60未除权价格
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]report_race.report() ts_code:{0[0]}; close_ma60r TypeError, set value = -99',
log_args)
close_ma60r = -99
# ----asset加入<df>中
data = {
'ts_code': ts_code,
'a_name': name,
'm1d_pct': m1d_pct,
'close': close_m,
'p1d_pct': p1d_pct,
'm2d_pct': m2d_pct,
'ma2': close_ma2r,
'p2d_pct': p2d_pct,
'm5d_pct': m5d_pct,
'ma5': close_ma5r,
'p5d_pct': p5d_pct,
'm10d_pct': m10d_pct,
'ma10': close_ma10r,
'p10d_pct': p10d_pct,
'm20d_pct': m20d_pct,
'ma20': close_ma20r,
'p20d_pct': p20d_pct,
'm60d_pct': m60d_pct,
'ma60': close_ma60r,
'p60d_pct': p60d_pct
}
df = df.append(data, ignore_index=True)
# ----本周期、前周期排名及排位变化
df.loc[:, 'm1d_rank'] = df['m1d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'p1d_rank'] = df['p1d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'chg_1d'] = df['p1d_rank'] - df['m1d_rank']
df.loc[:, 'm2d_rank'] = df['m2d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'p2d_rank'] = df['p2d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'chg_2d'] = df['p2d_rank'] - df['m2d_rank']
df.loc[:, 'm5d_rank'] = df['m5d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'p5d_rank'] = df['p5d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'chg_5d'] = df['p5d_rank'] - df['m5d_rank']
df.loc[:, 'm10d_rank'] = df['m10d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'p10d_rank'] = df['p10d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'chg_10d'] = df['p10d_rank'] - df['m10d_rank']
df.loc[:, 'm20d_rank'] = df['m20d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'p20d_rank'] = df['p20d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'chg_20d'] = df['p20d_rank'] - df['m20d_rank']
df.loc[:, 'm60d_rank'] = df['m60d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'p60d_rank'] = df['p60d_pct'].rank(ascending=False,
method='min')
df.loc[:, 'chg_60d'] = df['p60d_rank'] - df['m60d_rank']
df.set_index('ts_code', inplace=True)
# =================报告基础=================
workbook = xlw.Workbook(file_path)
ws1 = workbook.add_worksheet('日报') # worksheet #1
fmt_std = workbook.add_format(d_std)
fmt_wrap = workbook.add_format(d_wrap)
fmt_rpt_title = workbook.add_format(d_rpt_title)
fmt_head0 = workbook.add_format(d_head0)
fmt_head1 = workbook.add_format(d_head1)
fmt_center = workbook.add_format(d_center) # 居中
fmt_int = workbook.add_format(d_int) # 整数
fmt_f1d = workbook.add_format(d_f1d) # 1位小数
fmt_f2d = workbook.add_format(d_f2d) # 2位小数
fmt_f3d = workbook.add_format(d_f3d) # 3位小数
fmt_pct = workbook.add_format(d_pct) # 0%
fmt_pct1d = workbook.add_format(d_pct1d) # 1.1%
fmt_pct2d = workbook.add_format(d_pct2d) # 2.22%
fmt_pct1d_g = workbook.add_format(d_pct1d_g) # 1.1% 灰底
# =================添加VBA=================
file_bin = PurePath("rpt_racing.bin")
path_bin = sub_path_rpt / 'bin' / file_bin
workbook.add_vba_project(path_bin)
# =================报告数据=================
# ----标题栏
ws1.write_string('A1', trade_day_str + ' ' + 'Report Assets Racing',
fmt_rpt_title)
ws1.set_row(0, 19) # 第一行，标题栏行高
ws1.set_row(1, options={'hidden': True}) # 第二行，分隔行行高
# ----表头第0行
row_h0 = 2
column_h0 = 2
head_across(row_h0, column_h0, '1 Day')
column_h0 = 6
head_across(row_h0, column_h0, '2 Days')
column_h0 = 10
head_across(row_h0, column_h0, '5 Days')
column_h0 = 14
head_across(row_h0, column_h0, '10 Days')
column_h0 = 18
head_across(row_h0, column_h0, '20 Days')
column_h0 = 22
head_across(row_h0, column_h0, '60 Days')
# ----表头第1行
head2 = ('代码', '名称', 'chg%', 'C', 'rank', '+ -', 'chg%', 'ma', 'rank',
'+ -', 'chg%', 'ma', 'rank', '+ -', 'chg%', 'ma', 'rank', '+ -',
'chg%', 'ma', 'rank', '+ -', 'chg%', 'ma', 'rank', '+ -')
ws1.write_row('A4', head2, fmt_head1) # 标题栏第1行
# ----ts_code列
data = df.index.tolist()
ws1.write_column(4, 0, data, fmt_center)
# ----name列
data = df.a_name.tolist()
ws1.write_column(4, 1, data, fmt_center)
# ----chg%列
column_width = 8.38
data = df.m1d_pct.tolist()
ws1.write_column(4, 2, data, fmt_pct1d_g) # 1 Day
ws1.set_column(2, 2, width=column_width)
data = df.m2d_pct.tolist()
ws1.write_column(4, 6, data, fmt_pct1d_g) # 2 Days
ws1.set_column(6, 6, width=column_width)
data = df.m5d_pct.tolist()
ws1.write_column(4, 10, data, fmt_pct1d_g) # 5 Days
ws1.set_column(10, 10, width=column_width)
data = df.m10d_pct.tolist()
ws1.write_column(4, 14, data, fmt_pct1d_g) # 10 Days
ws1.set_column(14, 14, width=column_width)
data = df.m20d_pct.tolist()
ws1.write_column(4, 18, data, fmt_pct1d_g) # 20 Days
ws1.set_column(18, 18, width=column_width)
data = df.m60d_pct.tolist()
ws1.write_column(4, 22, data, fmt_pct1d_g) # 60 Days
ws1.set_column(22, 22, width=column_width)
# ----close及ma列
column_width = 8.38
data = df.close.tolist()
ws1.write_column(4, 3, data, fmt_f2d) # 1 Day
ws1.set_column(3, 3, width=column_width)
data = df.ma2.tolist()
ws1.write_column(4, 7, data, fmt_f2d) # 2 Days
ws1.set_column(7, 7, width=column_width)
data = df.ma5.tolist()
ws1.write_column(4, 11, data, fmt_f2d) # 5 Days
ws1.set_column(11, 11, width=column_width)
data = df.ma10.tolist()
ws1.write_column(4, 15, data, fmt_f2d) # 10 Days
ws1.set_column(15, 15, width=column_width)
data = df.ma20.tolist()
ws1.write_column(4, 19, data, fmt_f2d) # 20 Days
ws1.set_column(19, 19, width=column_width)
data = df.ma60.tolist()
ws1.write_column(4, 23, data, fmt_f2d) # 60 Days
ws1.set_column(23, 23, width=column_width)
# ----rank列
column_width = 5
data = df.m1d_rank.tolist()
ws1.write_column(4, 4, data, fmt_int) # 1 Day
ws1.set_column(4, 4, width=column_width)
data = df.m2d_rank.tolist()
ws1.write_column(4, 8, data, fmt_int) # 2 Days
ws1.set_column(8, 8, width=column_width)
data = df.m5d_rank.tolist()
ws1.write_column(4, 12, data, fmt_int) # 5 Days
ws1.set_column(12, 12, width=column_width)
data = df.m10d_rank.tolist()
ws1.write_column(4, 16, data, fmt_int) # 10 Days
ws1.set_column(16, 16, width=column_width)
data = df.m20d_rank.tolist()
ws1.write_column(4, 20, data, fmt_int) # 20 Days
ws1.set_column(20, 20, width=column_width)
data = df.m60d_rank.tolist()
ws1.write_column(4, 24, data, fmt_int) # 60 Days
ws1.set_column(24, 24, width=column_width)
# ----change列
column_width = 5
data = df.chg_1d.tolist()
ws1.write_column(4, 5, data, fmt_int) # 1 Day
ws1.set_column(5, 5, width=column_width)
data = df.chg_2d.tolist()
ws1.write_column(4, 9, data, fmt_int) # 2 Days
ws1.set_column(9, 9, width=column_width)
data = df.chg_5d.tolist()
ws1.write_column(4, 13, data, fmt_int) # 5 Days
ws1.set_column(13, 13, width=column_width)
data = df.chg_10d.tolist()
ws1.write_column(4, 17, data, fmt_int) # 10 Days
ws1.set_column(17, 17, width=column_width)
data = df.chg_20d.tolist()
ws1.write_column(4, 21, data, fmt_int) # 20 Days
ws1.set_column(21, 21, width=column_width)
data = df.chg_60d.tolist()
ws1.write_column(4, 25, data, fmt_int) # 60 Days
ws1.set_column(25, 25, width=column_width)
# =================添加按钮=================
ws1.insert_button(
"C3", {
'macro': 'sort_1D',
'caption': '1D',
'width': 40,
'height': 15,
'x_offset': 2,
'y_offset': 2
})
ws1.insert_button(
"G3", {
'macro': 'sort_2D',
'caption': '2D',
'width': 40,
'height': 15,
'x_offset': 2,
'y_offset': 2
})
ws1.insert_button(
"K3", {
'macro': 'sort_5D',
'caption': '5D',
'width': 40,
'height': 15,
'x_offset': 2,
'y_offset': 2
})
ws1.insert_button(
"O3", {
'macro': 'sort_10D',
'caption': '10D',
'width': 40,
'height': 15,
'x_offset': 2,
'y_offset': 2
})
ws1.insert_button(
"S3", {
'macro': 'sort_20D',
'caption': '20D',
'width': 40,
'height': 15,
'x_offset': 2,
'y_offset': 2
})
ws1.insert_button(
"W3", {
'macro': 'sort_60D',
'caption': '60D',
'width': 40,
'height': 15,
'x_offset': 2,
'y_offset': 2
})
# =================收尾格式=================
ws1.set_column(0, 0, 9) # 代码
ws1.set_column(1, 1, 8) # 名称
ws1.set_landscape() # 页面横向
ws1.set_paper(9) # 设置A4纸
ws1.center_horizontally() # 居中
ws1.set_margins(0.1, 0.1, 0.1, 1)
ws1.set_footer('&C&P of &N') # 设置页脚
ws1.repeat_rows(0, 3) # 重复打印行
ws1.repeat_columns(0, 1) # 重复打印列
ws1.freeze_panes(4, 2) # freeze 4行2列
ws1.fit_to_pages(1, 0) # 宽度放在1页中，长度不限
workbook.close()
# print(df)
# df.to_csv('临时report_race.csv', encoding='utf-8')
pass
def rpt_d_basic(al_file):
"""
报告：每日资产概览
al_file: <str> al_<al_file>.csv
"""
from st_board import Strategy
from st_board import today_str
# global raw_data
# =================报告文件=================
today_s = today_str()
trade_day_str = raw_data.last_trade_day(today_s)
file_name = PurePath('rpt_d_' + al_file + '_' + trade_day_str + '.xlsx')
file_path = sub_path_rpt / file_name
# =================初始化Strategy=================
stg = Strategy('report_daily_basic')
# dd = {'turnover_rate_f'} # 成交额
# stg.add_pool(desc='p10', al_file=al_file, assets_daily_data=dd, del_trsfed=None)
stg.add_pool(desc='p10',
al_file=al_file,
assets_daily_data='basic',
del_trsfed=None)
p10 = stg.pools[10]
# ------condition_0
pre_args1 = {'idt_type': 'ma', 'period': 20}
pre_args2 = {'idt_type': 'maqs', 'period': 20}
p10.add_condition(pre_args1, pre_args2, '>')
# ------condition_1
pre_args1 = {
'idt_type': 'majh',
'long_n3': 60,
'medium_n2': 20,
'short_n1': 5
}
pre_args2 = {'idt_type': 'maqs', 'period': 60}
p10.add_condition(pre_args1, pre_args2, '>')
# ------condition_2
pre_args1 = {
'idt_type': 'macd', # 日线macd
'long_n1': 26,
'short_n2': 12,
'dea_n3': 9
}
pre_args2 = {
'idt_type': 'macd', # 周线macd
'long_n1': 130,
'short_n2': 60,
'dea_n3': 45
}
p10.add_condition(pre_args1, pre_args2, '>')
# ------condition_3
pre_args1 = {
'idt_type': 'jdxz', # 绝对吸资比例
'period': 10
}
pre_args2 = {
'idt_type': 'jdxz', # 绝对吸资比例
'period': 250
}
p10.add_condition(pre_args1, pre_args2, '>')
# ------condition_4
pre_args1 = {
'idt_type': 'dktp', # 多空头排列
'short_n1': 5,
'medium_n2': 10,
'long_n3': 20
}
pre_args2 = {
'idt_type': 'dktp', # 多空头排列
'source': 'vol', # 成交量
'short_n1': 5,
'medium_n2': 10,
'long_n3': 20
}
p10.add_condition(pre_args1, pre_args2, '>')
# ------condition_5
pre_args1 = {
'idt_type': 'maqs', # 量趋势变化
'period': 10,
'source': 'vol'
}
pre_args2 = {
'idt_type': 'maqs', # 量趋势变化
'period': 20,
'source': 'vol'
}
p10.add_condition(pre_args1, pre_args2, '>')
stg.init_pools_cnds_matrix()
stg.init_ref_assets()
end_date = trade_day_str
start_date = raw_data.previous_trade_day(end_date, 2)
stg.update_cycles(start_date=start_date, end_date=end_date)
# =================数据准备=================
data = [] # [(数据1, 数据2, 数据3）, (...)]存放用于报告显示的数据
X = 2 # 聚合的程度%限值
for asset in p10.assets.values():
ts_code = asset.ts_code
name = asset.name
# ----从an_<ts_code>.xml读入comment
tree = load_xml(ts_code)
if isinstance(tree, ET.ElementTree):
root = tree.getroot()
comment1 = root.find('comment1').find('content').text
comment2 = root.find('comment2').find('content').text
else:
comment1 = ''
comment2 = ''
# ----20日归%
try:
ma20_last, = asset.ma_20.df_idt.head(1)['MA'].values
by_price = asset.by_price
ma20_gl = (by_price / ma20_last - 1) * 100 # close与ma_20的归离率
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
20,
'[fn]rpt_d_basic() ts_code:{0[0]}; ma20_gl AttributeError, set value = -99',
log_args)
ma20_gl = -99
# ----20MA变化
try:
maqs20, = asset.maqs_20.df_idt.head(1)['MAQS'].values
maqs20 = maqs20 * 1000
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
20,
'[fn]rpt_d_basic() ts_code:{0[0]}; maqs20 AttributeError, set value = -99',
log_args)
maqs20 = -999
# ----60MA变化
try:
maqs60, = asset.maqs_60.df_idt.head(1)['MAQS'].values
maqs60 = maqs60 * 1000
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]rpt_d_basic() ts_code:{0[0]}; maqs60 AttributeError, set value = -99',
log_args)
maqs60 = -999
# ----量20MA变化
try:
maqs_vol_20, = asset.maqs_vol_20.df_idt.head(1)['MAQS'].values
maqs_vol_20 = maqs_vol_20 * 1000
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]rpt_d_basic() ts_code:{0[0]}; maqs_vol_20 AttributeError, set value = -99',
log_args)
maqs_vol_20 = -99
# ----量10MA变化
try:
maqs_vol_10, = asset.maqs_vol_10.df_idt.head(1)['MAQS'].values
maqs_vol_10 = maqs_vol_10 * 1000
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]rpt_d_basic() ts_code:{0[0]}; maqs_vol_10 AttributeError, set value = -99',
log_args)
maqs_vol_10 = -99
# ----价聚合<x%天数的占比（最近20个交易日中）
df = asset.majh_60_20_5.df_idt
DAYS = 20 # 交易日窗口
try:
sr = df.head(DAYS)['MAJH'] # 如果数据少于20个，有几个取几个
n = len(sr)
if n > 0:
n_meet = len(sr[sr < X])
jh_pct = n_meet / n # 聚合天数的占比
else:
jh_pct = 0
except (AttributeError, TypeError):
log_args = [asset.ts_code]
add_log(
30,
'[fn]rpt_d_basic() ts_code:{0[0]}; jh_pct AttributeError, set value = -0.99',
log_args)
jh_pct = -0.99
# ----吸资归离, 10D 与 250D
try:
jdxz10, = asset.jdxz_10.df_idt.head(1)['JDXZ'].values
jdxz250, = asset.jdxz_250.df_idt.head(1)['JDXZ'].values
xz_rate = jdxz10 / jdxz250 - 1
except Exception as e:
log_args = [asset.ts_code, e]
add_log(
20,
'[fn]rpt_d_basic() ts_code:{0[0]}; xz_rate except type:{0[1]} to catch',
log_args)
xz_rate = -99
# ----吸资10QS` `````
try:
xz_current, xz_previous = asset.jdxz_10.df_idt.head(
2)['JDXZ'].values
xzqs = (xz_current / xz_previous - 1) * 100
except Exception as e:
log_args = [asset.ts_code, e]
add_log(
20,
'[fn]rpt_d_basic() ts_code:{0[0]}; xzqs except type:{0[1]} to catch',
log_args)
xzqs = -99
# ----价多空头排列天数
try:
p_dktp, = asset.dktp_5_10_20.df_idt.head(1)['DKTP'].values
except Exception as e:
log_args = [asset.ts_code, e]
add_log(
20,
'[fn]rpt_d_basic() ts_code:{0[0]}; p_dktp except type:{0[1]} to catch',
log_args)
p_dktp = -99
# ----量多空头排列天数
try:
v_dktp, = asset.dktp_vol_5_10_20.df_idt.head(1)['DKTP'].values
except Exception as e:
log_args = [asset.ts_code, e]
add_log(
20,
'[fn]rpt_d_basic() ts_code:{0[0]}; v_dktp except type:{0[1]} to catch',
log_args)
v_dktp = -99
# ----添加数据
data.append(
(ts_code, name, comment1, comment2, ma20_gl, maqs20, maqs60,
maqs_vol_10, maqs_vol_20, jh_pct, xz_rate, xzqs, p_dktp, v_dktp))
# =================报告基础=================
workbook = xlw.Workbook(file_path)
ws1 = workbook.add_worksheet('日报') # worksheet #1
fmt_std = workbook.add_format(d_std)
fmt_wrap = workbook.add_format(d_wrap)
fmt_rpt_title = workbook.add_format(d_rpt_title)
fmt_center = workbook.add_format(d_center) # 居中
fmt_int = workbook.add_format(d_int) # 整数
fmt_f1d = workbook.add_format(d_f1d) # 1位小数
fmt_f2d = workbook.add_format(d_f2d) # 2位小数
fmt_f3d = workbook.add_format(d_f3d) # 3位小数
fmt_pct = workbook.add_format(d_pct) # 0%
fmt_pct1d = workbook.add_format(d_pct1d) # 1.1%
fmt_pct2d = workbook.add_format(d_pct2d) # 2.22%
# 与data对应的显示格式
# ts_code 名称 备注 备注 20日归 maqs20 maqs60
formats = [
fmt_center, fmt_center, fmt_wrap, fmt_wrap, fmt_f2d, fmt_f2d, fmt_f2d
]
# 量10QS 量20QS 聚合占比 吸资归离 吸资10QS 价多空排 量多空排
formats = formats + [
fmt_f2d, fmt_f2d, fmt_pct, fmt_f2d, fmt_f2d, fmt_int, fmt_int
]
# =================报告数据=================
# ----标题栏
ws1.write_string('A1', trade_day_str + ' ' + 'Report Daily Basic',
fmt_rpt_title)
ws1.set_row(0, 19) # 第一行，标题栏行高
ws1.set_row(1, options={'hidden': True}) # 第二行，分隔行行高
# ----表头
head = ('代码', '名称', '备注', '备注', '价20归%', '价20QS‰', '价60QS‰', '量10QS‰',
'量20QS‰', '聚2%天比', '吸资归离', '吸资10QS', '价多空排', '量多空排')
ws1.write_row('A3', head, fmt_center)
ws1.write_comment('A1', '确保下载数据当日数据后再生成报表！')
ws1.write_comment('E3', '(by_price / ma20_last - 1) * 100')
ws1.write_comment('F3', 'maqs_20 * 1000')
ws1.write_comment('G3', 'maqs_60 * 1000')
ws1.write_comment('H3', 'maqs_vol_10 * 1000')
ws1.write_comment('I3', 'maqs_vol_20 * 1000')
ws1.write_comment('J3', '在20个交易日内，majh<' + str(X) + '%天数的占比 周期5, 10, 20')
ws1.write_comment('K3', '10日吸资比 / 250日吸资比 -1')
ws1.write_comment('L3', '10日吸资比变化率 * 100')
ws1.write_comment('M3', '价多空头排列天数')
ws1.write_comment('N3', '量多空头排列天数')
# ----填充数据
row = 3
assert len(head) == len(formats)
for item in data:
for column in range(len(head)):
ws1.write(row, column, item[column], formats[column])
row += 1
# =================收尾格式=================
ws1.set_column(0, 0, 10.2) # 代码
ws1.set_column(1, 1, 8.2) # 名称
ws1.set_column(2, 3, 15) # 备注
max_column = len(head) - 1
max_row = row - 1
ws1.set_landscape() # 页面横向
ws1.set_paper(9) # 设置A4纸
ws1.center_horizontally() # 居中
ws1.set_margins(0.3, 0.3, 0.3, 1)
ws1.set_footer('&C&P of &N') # 设置页脚
ws1.repeat_rows(0, 2) # 重复打印行
ws1.repeat_columns(0, 1) # 重复打印列
ws1.freeze_panes(3, 2) # freeze 3行2列
workbook.close()
pass
