def _gen_next_update(date_string=""):
if date_string:
dt = parse_date(date_string)
now = datetime.now()
if dt.weekday() == now.weekday():
now = now + rdelta(weeks=1)
else:
now = now + rdelta(weekday=dt.weekday())
return datetime.combine(now.date(), dt.time())
def calculate_momentum(self, recalc: bool = False):
for ticker in set(self.tickers_for_mom):
df = tl.load_csv(ticker)
for mom in self.momentums.keys():
if recalc or 'Momentum_' + str(mom) not in df.keys():
print(
f"Calculate momentum for {ticker} with {mom} month-period"
)
mom_list = []
for day in range(len(df.Date)):
if day != len(df) - 1 and df.Date[
day].month != df.Date[day + 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_cls = df[
(df.Date.dt.year == find_month.year)
& (df.Date.dt.month == find_month.month)].Close
if len(month_cls) != 0:
mom_list.append(df.Close[day] /
month_cls.iloc[-1])
else:
mom_list.append(None)
elif day != 0 and df.Date[day].month != df.Date[
day - 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_opn = df[
(df.Date.dt.year == find_month.year)
& (df.Date.dt.month == find_month.month)].Open
if len(month_opn) != 0:
mom_list.append(df.Open[day] /
month_opn.iloc[0])
else:
mom_list.append(None)
else:
mom_list.append(None)
else:
continue
df['Momentum_' + str(mom)] = mom_list
tl.save_csv(self.FOLDER_WITH_DATA, ticker, df)
def calculate_momentum(self, asset: str, recalculate: bool = False):
if asset == 'stocks':
ticker, mom = self.signal_stocks, self.momentum_stocks
elif asset == 'bonds':
ticker, mom = self.signal_bonds, self.momentum_bonds
else:
raise ValueError(
"Incorrect value for 'asset' in 'calculate_momentum' func")
df = tl.load_csv(ticker)
if recalculate or 'Momentum_' + str(mom) not in df.keys():
print(f"Calculate momentum for {ticker} with {mom} month-period")
mom_list = []
for day in range(len(df.Date)):
if day != len(df) - 1 and df.Date[day].month != df.Date[
day + 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_cls = df[(df.Date.dt.year == find_month.year) & (
df.Date.dt.month == find_month.month)].Close
if len(month_cls) != 0:
mom_list.append(df.Close[day] / month_cls.iloc[-1])
else:
mom_list.append(None)
elif day != 0 and df.Date[day].month != df.Date[day - 1].month:
find_month = df.Date[day] - rdelta(months=mom)
month_opn = df[(df.Date.dt.year == find_month.year) &
(df.Date.dt.month == find_month.month)].Open
if len(month_opn) != 0:
mom_list.append(df.Open[day] / month_opn.iloc[0])
else:
mom_list.append(None)
else:
mom_list.append(None)
else:
return
df['Momentum_' + str(mom)] = mom_list
tl.save_csv(self.FOLDER_WITH_DATA, ticker, df)
def data_sufficiency_check(self, start_index: int):
for index in range(start_index, len(self.trading_days)):
if self.vol_calc_period == 'month' and \
(self.trading_days[index] - rdelta(months=self.vol_calc_range) >= self.trading_days[0]):
break
elif self.vol_calc_period == 'day' and len(
self.trading_days[:index + 1]) >= self.vol_calc_range:
break
return index
def purge_old_records(sess, months):
# We're doing bulk deletes in unsynchronized mode, so first flush all
# pending changes and expire all in-memory ORM instances, but don't
# commit yet. Expiring prevents inconsistencies because the session
# doesn't know which records are deleted. It doesn't actually matter in
# this program because the changes are to a different table than the
# deletions, but it's a good principle for safety.
sess.flush()
sess.expire_all()
cutoff = datetime.date.today() - rdelta(months=months, day=1)
sess.query(Access).filter(Access.ts < cutoff).delete(False)
sess.query(Referer).filter(Referer.ts < cutoff).delete(False)
def purge_old_records(sess, months):
# We're doing bulk deletes in unsynchronized mode, so first flush all
# pending changes and expire all in-memory ORM instances, but don't
# commit yet. Expiring prevents inconsistencies because the session
# doesn't know which records are deleted. It doesn't actually matter in
# this program because the changes are to a different table than the
# deletions, but it's a good principle for safety.
sess.flush()
sess.expire_all()
cutoff = datetime.date.today() - rdelta(months=months, day=1)
sess.query(Access).filter(Access.ts < cutoff).delete(False)
sess.query(Referer).filter(Referer.ts < cutoff).delete(False)
def calculate_current_vol(self, day_number: int):
if self.rebalance_at == 'open':
day_number -= 1
data = self.all_tickers_data
strata = self.strategy_data
# Find index for start_date
if self.vol_calc_period == 'month':
start_date = self.trading_days[day_number] - rdelta(
months=self.vol_calc_range)
print(start_date)
start_date = self.trading_days[
(self.trading_days.dt.year == start_date.year)
& (self.trading_days.dt.month == start_date.month)].iloc[-1]
start_index = list(self.trading_days).index(start_date)
else:
start_index = day_number - self.vol_calc_range
# Calculate capital, if port is not placed yet or we calc standard volatility type
if self.capital_not_placed or self.vola_type == 'standard':
virtual_port = np.zeros(
len(self.trading_days[start_index:day_number + 1]))
for ticker in self.portfolios['risk_on'].keys():
ticker_data = data[ticker]
prices = ticker_data[start_index:day_number + 1][self.price]
virtual_port += np.array(
prices) * self.portfolios['risk_on'][ticker]
# Calculate modify volatility type
else:
total_cap = 0
for ticker in self.portfolios['risk_on'].keys():
total_cap += strata['On_Shares_' + ticker][
day_number - 1] * strata['On_Price_' + ticker][day_number]
for ticker in self.portfolios['risk_off'].keys():
total_cap += strata['Off_Shares_' + ticker][
day_number - 1] * strata['Off_Price_' + ticker][day_number]
strata['Capital'][day_number] = total_cap + strata['Cash'][
day_number - 1]
capitals = strata['Capital'][start_index:day_number + 1]
virtual_port = np.array(capitals)
virtual_port_cng = np.diff(virtual_port) / virtual_port[:-1] * 100
vol_coff = self.vol_target / (np.std(virtual_port_cng, ddof=1) *
np.sqrt(252))
self.cur_leverage = min(vol_coff,
1) if self.use_margin is False else vol_coff
def get_news(self):
print('ニュース取得開始')
now = dt.now()
table = self.base.classes.gis_utils_app_newsadmin
news_admins = self.session.query(table)
for news_admin in news_admins:
print(news_admin.serch_keyword)
news = self.newsapi.get_everything(
q=news_admin.serch_keyword.replace(' ', ' AND '),
from_param=(now - rdelta(months=1)).strftime('%Y-%m-%d'),
to=now.strftime('%Y-%m-%d'))
print(news)
if (news['totalResults'] == 0):
print('取得したニュースはありません。')
continue
self.limit = int(news['totalResults']) if self.limit > int(
news['totalResults']) else self.limit
models = []
for i in range(self.limit):
print('count : %d' % i)
article = news['articles'][i]
table = self.base.classes.gis_utils_app_news
model = table(
source_id=article['source']['id'] or "",
source_name=article['source']['name'] or "",
author=article['author'] or "",
title=article['title'] or "",
# google翻訳の上限に注意
title_jp=self.translator.translate(article['title'],
dest='ja').text or "",
# title_jp = 'google 翻訳機能停止中',
description=article['description'] or "",
# google翻訳の上限に注意
description_jp=self.translator.translate(
article['description'], dest='ja').text or "",
# description_jp = 'google 翻訳機能停止中',
url=article['url'] or "",
url_to_image=article['urlToImage'] or "",
published_at=dt.fromisoformat(
article['publishedAt'].replace('Z', '+00:00')) if
article['publishedAt'] != None else article['publishedAt'],
content=article['content'] or "",
topic_id=news_admin.topic_id)
models.append(model)
self.count = self.count + 1
self.session.add_all(models)
print('ニュース取得終了')
def mk_periods(self):
"""Generate list of periods the Project contains.
Each element of the list is a list of four elements:
[start, end, dta, q, status]
start: datetime() of start of period
end: datetime() of end of period
dta: duration of period, in days (yes, redundant)
q: quota, in GB
status: either 'ok', 'ko' or 'buffer'
"""
periods = []
old_end, old_q = None, None
for p in Period.objects.filter(proj=self.id).order_by("start"):
start_date = p.start
if old_end and start_date.date() == old_end.date():
start_date += rdelta(days=1)
# Check for a possible expired period in
# between end of previous period and start
# of current one:
if old_end and (p.start - old_end).days > 0:
dta = (p.start - old_end).total_seconds() / 86400.
q = old_q
periods.append([old_end, p.start, dta, q, 'ko'])
dta = (p.end - p.start).total_seconds() / 86400.
status2str = {
"expired": "ko",
"frozen": "buffer",
"active": "ok",
}
periods.append(
[start_date, p.end, dta, p.quota, status2str[p.status]])
old_end = p.end
old_q = p.quota
# If project finished, that's it. Else:
if not self.finished:
now = timezone.now()
if old_end < now:
dta = (now - old_end).total_seconds() / 86400.
if self.in_buffer:
periods.append([old_end, now, dta, old_q, 'buffer'])
else:
periods.append([old_end, now, dta, old_q, 'ko'])
return periods
def create(self, vals):
# Get 2 Digits Org
formatCode = ""
Employees = self.env["hr.employee"]
Orgs = self.env['res.org']
Emp = Employees.search([['id', '=', vals.get('admin_id', False)]],
limit=1)
Org = Orgs.search([['id', '=', Emp.org_id.id]], limit=1)
if not Org:
msg = _('You cannot create PO contract without Org')
raise osv.except_osv(_(u'Error!'), msg)
three_mon_rel = rdelta(months=3)
if vals.get('action_date', False):
ActionDate = datetime.datetime.strptime(
vals.get('action_date'), '%Y-%m-%d') + three_mon_rel
RevNo = vals.get('poc_rev', 0)
# New Contract (CENTRAL-2016-322-R1)
if RevNo == 0:
self.env.cr.execute("SELECT Count(id) AS c"
" FROM purchase_contract"
" WHERE poc_org = %s"
" AND year = %s"
" AND poc_rev = 0",
(str(Org.code),
str(ActionDate.year)))
datas = self.env.cr.fetchone()
CountPO = datas and datas[0] or 0
running = str(CountPO + 1)
formatCode = str(Org.code)
formatCode += '-' + str(ActionDate.year)
formatCode += '-' + str(running)
# Reversion (CO-51-2016-322-R1)
else:
running = vals.get('running', 0)
formatCode = str(Org.code)
formatCode += '-' + str(ActionDate.year)
formatCode += '-' + str(running)
vals.update({'poc_rev': RevNo})
vals.update({'poc_org': str(Org.code)})
vals.update({'poc_code': formatCode})
vals.update({'year': str(ActionDate.year)})
vals.update({'running': running})
vals.update({'state': GENERATE})
po_contract = super(PurchaseContract, self).create(vals)
return po_contract
def create(self, vals):
# Get 2 Digits Org
formatCode = ""
Employees = self.env["hr.employee"]
Orgs = self.env['res.org']
Emp = Employees.search(
[['id', '=', vals.get('admin_id', False)]], limit=1)
Org = Orgs.search([['id', '=', Emp.org_id.id]], limit=1)
if not Org:
msg = _('You cannot create PO contract without Org')
raise osv.except_osv(_(u'Error!'), msg)
three_mon_rel = rdelta(months=3)
if vals.get('action_date', False):
ActionDate = datetime.datetime.strptime(vals.get('action_date'),
'%Y-%m-%d') + three_mon_rel
RevNo = vals.get('poc_rev', 0)
# New Contract (CENTRAL-2016-322-R1)
if RevNo == 0:
self.env.cr.execute(
"SELECT Count(id) AS c"
" FROM purchase_contract"
" WHERE poc_org = %s"
" AND year = %s"
" AND poc_rev = 0", (str(Org.code), str(ActionDate.year)))
datas = self.env.cr.fetchone()
CountPO = datas and datas[0] or 0
running = str(CountPO + 1)
formatCode = str(Org.code)
formatCode += '-' + str(ActionDate.year)
formatCode += '-' + str(running)
# Reversion (CO-51-2016-322-R1)
else:
running = vals.get('running', 0)
formatCode = str(Org.code)
formatCode += '-' + str(ActionDate.year)
formatCode += '-' + str(running)
vals.update({'poc_rev': RevNo})
vals.update({'poc_org': str(Org.code)})
vals.update({'poc_code': formatCode})
vals.update({'year': str(ActionDate.year)})
vals.update({'running': running})
vals.update({'state': GENERATE})
po_contract = super(PurchaseContract, self).create(vals)
return po_contract
def _correlation_gear(fav_prods: List[str], plant_id: str, order_date: str):
"""
Find goods that are usually taken with fav_prods.
It is supposed that fav_prods were gone through the similarity_gear.
"""
order_date = datetime.strptime(order_date, '%Y-%m-%d')
bottom_date = order_date - rdelta(months=1)
prev_month_transaction_df = transaction_df[
(transaction_df.chq_date_e >= bottom_date)
& (transaction_df.chq_date_e <= order_date)]
cl_mat_counts_df = pd.pivot_table(prev_month_transaction_df,
index='client_id',
columns='material',
values='chq_date_e',
aggfunc='count')
# HACK: We can work only with goods that are represented well
# (more than 30 clients bought them)
cl_mat_counts_df = cl_mat_counts_df.loc[:, (
cl_mat_counts_df.count(axis=0) >= 30)]
# HACK: Avoid difference in transaction_df and materials.
# Better to make them consistent and use random.
# fav_prods_sample = np.random.choice(fav_prods, size=2, replace=False)
fav_prods_sample = fav_prods[:2]
add_prods = []
for p in fav_prods_sample:
prod_to_complement = cl_mat_counts_df[p]
similar_prods = cl_mat_counts_df.corrwith(prod_to_complement)
similar_prods = similar_prods.dropna()
similar_prods = pd.DataFrame(similar_prods)
similar_prods = similar_prods.sort_values(0, ascending=False)
similar_prods = similar_prods[~similar_prods.index.
isin(fav_prods_sample)]
for sim_p, corr in similar_prods.iterrows():
if all(_check_availability([sim_p], plant_id, order_date)):
add_prods.append(sim_p)
break
return fav_prods + add_prods
def _compute_duration_start2end(self):
for rec in self:
start2end = '0 Day'
if rec.start_date and rec.end_date:
if rec.start_date <= rec.end_date:
start_date = datetime.strptime(rec.start_date, '%Y-%m-%d')
end_date = datetime.strptime(rec.end_date, '%Y-%m-%d')
start = start_date.date()
end = end_date.date()
if end < start:
rec.duration_start2end = start2end
return
enddate = end + timedelta(days=1)
rd = rdelta(enddate, start)
start2end = _('%s Year %s Month %s Day') % \
(str(rd.years), str(rd.months), str(rd.days))
else:
start2end = _("** Wrong Date **")
rec.duration_start2end = start2end
def update_month(site, date, sess, feed_callback):
log.debug("Updating site '{}' year {} month {}".format(
site, date.year, date.month))
adding = False
# Make a site & month filter for Access queries. (``smf``)
start = date.replace(day=1)
end = date + rdelta(months=1, day=1)
smf = sa.and_(Access.site == site, Access.ts >= start, Access.ts < end)
# Fetch the Monthly record. (``mth``)
q_mth = sess.query(Monthly)
q_mth = q_mth.filter_by(site=site, year=date.year, month=date.month)
mth = q_mth.first()
# Create the Monthly record if it doesn't exist.
if not mth:
mth = Monthly(site=site, year=date.year, month=date.month)
mth.feeds = 0
mth.feedlinks = 0
adding = True
# Don't add it to the session yet because not all fields are
# initialized.
# Set page view count.
static_prefixes = [u"/{}".format(x[:6]) for x in const.STATIC_SECTIONS]
non_static_filter = ~sa.func.substr(Access.url, 1, 7).in_(static_prefixes)
q = sess.query(sa.func.count()).select_from(Access).filter(smf)
q = q.filter(non_static_filter)
q = q.filter(~Access.url.in_(INEWS_FEED_URLS))
mth.page_views = q.scalar()
# Set unique remote IP count.
q = sess.query(sa.func.count(Access.remote_addr.distinct())).filter(smf)
mth.ips = q.scalar()
# Set unique session ID count.
q = sess.query(sa.func.count(Access.session.distinct())).filter(smf)
mth.sessions = q.scalar()
# Set feed count and feed link count.
if feed_callback:
feeds, feedlinks = feed_callback(site, smf, sess)
mth.feeds = feeds
mth.feedlinks = feedlinks
mth.updated = sa.func.current_timestamp()
if adding:
sess.add(mth)
def update_month(site, date, sess, feed_callback):
log.debug("Updating site '{}' year {} month {}".format(
site, date.year, date.month))
adding = False
# Make a site & month filter for Access queries. (``smf``)
start = date.replace(day=1)
end = date + rdelta(months=1, day=1)
smf = sa.and_(Access.site == site, Access.ts >= start, Access.ts < end)
# Fetch the Monthly record. (``mth``)
q_mth = sess.query(Monthly)
q_mth = q_mth.filter_by(site=site, year=date.year, month=date.month)
mth = q_mth.first()
# Create the Monthly record if it doesn't exist.
if not mth:
mth = Monthly(site=site, year=date.year, month=date.month)
mth.feeds = 0
mth.feedlinks = 0
adding = True
# Don't add it to the session yet because not all fields are
# initialized.
# Set page view count.
static_prefixes = [u"/{}".format(x[:6]) for x in const.STATIC_SECTIONS]
non_static_filter = ~ sa.func.substr(Access.url, 1, 7).in_(static_prefixes)
q = sess.query(sa.func.count()).select_from(Access).filter(smf)
q = q.filter(non_static_filter)
q = q.filter(~ Access.url.in_(INEWS_FEED_URLS))
mth.page_views = q.scalar()
# Set unique remote IP count.
q = sess.query(sa.func.count(Access.remote_addr.distinct())).filter(smf)
mth.ips = q.scalar()
# Set unique session ID count.
q = sess.query(sa.func.count(Access.session.distinct())).filter(smf)
mth.sessions = q.scalar()
# Set feed count and feed link count.
if feed_callback:
feeds, feedlinks = feed_callback(site, smf, sess)
mth.feeds = feeds
mth.feedlinks = feedlinks
mth.updated = sa.func.current_timestamp()
if adding:
sess.add(mth)
def retirement_main(g: argparse.Namespace) -> tuple:
"""
hiredate -- used for calculating years of service.
birthday -- to determine age
min_age -- gotta be old enough
req_years -- gotta work here a while
magic_number -- there always is one.
returns -- a tuple of possibly relevant info, or None if there is
no ability to retire.
"""
now = datetime.date.today()
# Only the most cursory checking.
if not g.birthday < g.hiredate < now:
print("\n".join([
"you must have been born before you were hired,",
"and you must have been hired before today."
]))
sys.exit(os.EX_DATAERR)
current_age = now - g.birthday
current_service = now - g.hiredate
old_enough_on = g.birthday + rdelta(years=g.min_age)
old_enough_now = old_enough_on < now
long_enough_on = g.hiredate + rdelta(years=g.req_years)
long_enough_now = long_enough_on < now
if long_enough_now and old_enough_now:
return now, 0, old_enough_on, long_enough_on, current_age.days, current_service.days
no_earlier_than = max(long_enough_on, old_enough_on)
magic_date = now + rdelta(
rdelta(years=g.magic_number) - rdelta(current_age) -
rdelta(current_service)) / 2
quit_on = max(magic_date, no_earlier_than)
if old_enough_on < g.hiredate:
old_enough_on = 'when you started.'
elif old_enough_on < now:
old_enough_on = 'now'
if long_enough_on < now:
long_enough_on = 'now'
return quit_on, (
quit_on - now
).days, old_enough_on, long_enough_on, current_age.days, current_service.days
def main():
parser = get_parser()
args = parser.parse_args()
init_logging(args.debug, args.sql)
engine = sa.create_engine(args.dburl)
conn = engine.connect()
sess = orm.Session(bind=conn)
this_month = datetime.date.today().replace(day=1)
last_month = this_month - rdelta(months=1)
update_month(u"cameo", this_month, sess, None)
update_month(u"cameo", last_month, sess, None)
update_month(u"inews", this_month, sess, get_inews_feeds)
update_month(u"inews", last_month, sess, get_inews_feeds)
update_month(u"rlink", this_month, sess, None)
update_month(u"rlink", last_month, sess, None)
update_month(u"goods", this_month, sess, None)
update_month(u"goods", last_month, sess, None)
update_month(u"goods-erd", this_month, sess, None)
update_month(u"goods-erd", last_month, sess, None)
if args.delete_months_ago:
purge_old_records(sess, args.delete_months_ago)
sess.commit()
def main():
parser = get_parser()
args = parser.parse_args()
init_logging(args.debug, args.sql)
engine = sa.create_engine(args.dburl)
conn = engine.connect()
sess = orm.Session(bind=conn)
this_month = datetime.date.today().replace(day=1)
last_month = this_month - rdelta(months=1)
update_month(u"cameo", this_month, sess, None)
update_month(u"cameo", last_month, sess, None)
update_month(u"inews", this_month, sess, get_inews_feeds)
update_month(u"inews", last_month, sess, get_inews_feeds)
update_month(u"rlink", this_month, sess, None)
update_month(u"rlink", last_month, sess, None)
update_month(u"goods", this_month, sess, None)
update_month(u"goods", last_month, sess, None)
update_month(u"goods-erd", this_month, sess, None)
update_month(u"goods-erd", last_month, sess, None)
if args.delete_months_ago:
purge_old_records(sess, args.delete_months_ago)
sess.commit()
def date_interval_endpoints(starttime, endtime, day_of_new_interval):
"""
Return a list of half-month endpoints.
Keyword arguments:
- starttime: datetime or date
- endtime: datetime or date
- day_of_new_interval: int
Returns:
- dates: list(datetime)
"""
from datetime import datetime
from dateutil.relativedelta import relativedelta as rdelta
from dateutil.rrule import rrule, MONTHLY
from pandas import to_datetime
starttime = datetime(*starttime.timetuple()[:3],0,0)
endtime = datetime(*endtime.timetuple()[:3],0,0)
d=day_of_new_interval
dates = list(rrule(MONTHLY, dtstart=starttime, until=endtime, bymonthday=[1,d-1,d,-1]))
# add starttime/endtime if not included in dates
if not dates[0].day == 1 and not dates[0].day == d:
dates = [starttime] + dates
if (not dates[-1].day == to_datetime(dates[-1]).daysinmonth and
not dates[-1].day == d-1):
dates = dates + [endtime]
# set time of right endpoints to 23:59:59
for i in range(1,len(dates),2):
dates[i] = dates[i]+rdelta(hour=23, minute=59, second=59)
return dates
def _compute_duration_start2end(self):
start2end = '0 Day'
if self.start_date and self.end_date:
if self.start_date <= self.end_date:
start_date = datetime.datetime.strptime(
self.start_date, "%Y-%m-%d")
end_date = datetime.datetime.strptime(self.end_date,
"%Y-%m-%d")
start = start_date.date()
end = end_date.date()
if end < start:
self.duration_start2end = start2end
return
enddate = end + datetime.timedelta(days=1)
rd = rdelta(enddate, start)
start2end = str(rd.years)
start2end += _(' Year ')
start2end += str(rd.months)
start2end += _(' Month ')
start2end += str(rd.days)
start2end += _(' Day ')
else:
start2end = _("** Wrong Date **")
self.duration_start2end = start2end
def _compute_duration_start2end(self):
start2end = '0 Day'
if self.start_date and self.end_date:
if self.start_date <= self.end_date:
start_date = datetime.datetime.strptime(self.start_date,
"%Y-%m-%d")
end_date = datetime.datetime.strptime(self.end_date,
"%Y-%m-%d")
start = start_date.date()
end = end_date.date()
if end < start:
self.duration_start2end = start2end
return
enddate = end + datetime.timedelta(days=1)
rd = rdelta(enddate, start)
start2end = str(rd.years)
start2end += _(' Year ')
start2end += str(rd.months)
start2end += _(' Month ')
start2end += str(rd.days)
start2end += _(' Day ')
else:
start2end = _("** Wrong Date **")
self.duration_start2end = start2end
async def on_message(self, m):
if m.channel.id in []: #グローバルチャットの外部との相互連携作成時用
return
if m.content.startswith("s-"):
return
if m.content.startswith("//"):
return
if m.author.id == self.bot.user.id:
return
if m.is_system():
return
if "cu:on_msg" in self.bot.features.get(m.author.id, []):
return
if isinstance(m.channel, discord.DMChannel):
return
if m.webhook_id:
return
self.bot.cursor.execute("select * from users where id=?",
(m.author.id, ))
upf = self.bot.cursor.fetchone()
gchat_cinfo = self.bot.cursor.execute(
"select * from gchat_cinfo where id == ?",
(m.channel.id, )).fetchone()
if gchat_cinfo:
if upf["gban"] == 1:
if not gchat_cinfo["connected_to"] in self.without_react:
dc = await ut.opendm(m.author)
await dc.send(
self.bot._(m.author, "global-banned",
m.author.mention))
await self.repomsg(m, "思惟奈ちゃんグローバルチャットの使用禁止")
await m.add_reaction("❌")
await asyncio.sleep(5)
await m.remove_reaction("❌", self.bot.user)
if (datetime.datetime.now() - rdelta(hours=9) - rdelta(days=7) >=
m.author.created_at) or upf["gmod"] or upf["gstar"]:
try:
content_checker(self.bot, m)
except MaliciousInput as err:
await self.repomsg(m, err.reason, err.should_ban)
return
try:
if not gchat_cinfo["connected_to"] in self.without_react:
await m.add_reaction(
self.bot.get_emoji(653161518346534912))
except:
pass
self.bot.cursor.execute("select * from guilds where id=?",
(m.guild.id, ))
gpf = self.bot.cursor.fetchone()
status_embed = discord.Embed(title="",
description="",
color=upf["gcolor"])
status_embed.set_author(
name=f"{ut.ondevicon(m.author)},({str(m.author.id)})")
if gpf["verified"]:
status_embed.set_footer(
text=f"✅:{m.guild.name}(id:{m.guild.id})",
icon_url=m.guild.icon_url_as(static_format="png"))
else:
status_embed.set_footer(
text=f"{m.guild.name}(id:{m.guild.id})",
icon_url=m.guild.icon_url_as(static_format="png"))
if m.type == discord.MessageType.default and m.reference:
ref = m.reference
if ref.cached_message:
msg = ref.cached_message
else:
try:
msg = await self.bot.get_channel(
ref.channel_id).fetch_message(ref.message_id)
except:
msg = None
if msg:
status_embed.add_field(
name=f"{msg.author.display_name}のメッセージへの返信",
value=f"{msg.clean_content}")
else:
status_embed.add_field(
name="メッセージへの返信",
value="(このメッセージは削除されている等で取得できません。)")
if gchat_cinfo["connected_to"] in self.without_react:
embeds = []
else:
embeds = [status_embed]
if m.stickers:
sticker = m.stickers[0]
sembed = discord.Embed(title=f"スタンプ:{sticker.name}", )
if sticker.format == discord.StickerType.png:
sembed.set_image(url=sticker.image_url)
elif sticker.format == discord.StickerType.apng:
sembed.set_image(
url=
f"https://dsticker.herokuapp.com/convert.gif?url={sticker.image_url}"
)
elif sticker.format == discord.StickerType.lottie:
# メモ: https://cdn.discordapp.com/stickers/{id}/{hash}.json?size=1024
sembed.description = "画像取得非対応のスタンプです。"
embeds.append(sembed)
embeds = embeds + m.embeds[0:10 - len(embeds)]
attachments = m.attachments
spicon = ""
if m.author.id in self.bot.team_sina: # チーム☆思惟奈ちゃん
spicon = spicon + "🌠"
if m.author.bot:
spicon = spicon + "⚙"
if upf["sinapartner"]:
spicon = spicon + "💠" # 認証済みアカウント
if m.author.id in config.partner_ids:
spicon = spicon + "🔗"
if upf["gmod"]:
spicon = spicon + "🔧"
if upf["gstar"]:
spicon = spicon + "🌟"
if spicon == "":
spicon = "👤"
name = f"[{spicon}]{upf['gnick']}"
sendto = self.bot.cursor.execute(
"select * from gchat_cinfo where connected_to = ?",
(gchat_cinfo["connected_to"], )).fetchall()
rtn = await self.gchat_send(
sendto, m.channel, m.clean_content, name,
m.author.avatar_url_as(static_format="png"), embeds,
attachments)
self.bot.cursor.execute(
"INSERT INTO gchat_pinfo(id,content,allids,author_id,guild_id,timestamp) VALUES(?,?,?,?,?,?)",
(m.id, [m.clean_content], rtn, m.author.id, m.guild.id, [
str(
m.created_at.strftime(
'%Y{0}%m{1}%d{2} %H{3}%M{4}%S{5}').format(
*'年月日時分秒'))
]))
try:
if not gchat_cinfo["connected_to"] in self.without_react:
await m.remove_reaction(
self.bot.get_emoji(653161518346534912),
self.bot.user)
await m.add_reaction(
self.bot.get_emoji(653161518195539975))
await asyncio.sleep(5)
await m.remove_reaction(
self.bot.get_emoji(653161518195539975),
self.bot.user)
except:
pass
else:
await self.repomsg(m, "作成後7日に満たないアカウント")
async def msg_info(self, ctx, target:Union[commands.MessageConverter,None]):
if target:
fetch_from = "引数"
msg = target
else:
if ctx.message.reference and ctx.message.type == discord.MessageType.default:
if ctx.message.reference.cached_message:
fetch_from = "返信"
msg = ctx.message.reference.cached_message
else:
try:
fetch_from = "返信"
msg = await self.bot.get_channel(ctx.message.reference.channel_id).fetch_message(ctx.message.reference.message_id)
except:
fetch_from = "コマンド実行メッセージ"
msg = ctx.message
else:
fetch_from = "コマンド実行メッセージ"
msg = ctx.message
#msgに入ったメッセージで詳細情報Embedを作成して送信。
e = discord.Embed(title=f"メッセージ情報({fetch_from}より取得)", description=msg.system_content, color=self.bot.ec)
e.set_author(name=f"{msg.author.display_name}({msg.author.id}){'[bot]' if msg.author.bot else ''}のメッセージ",icon_url=msg.author.avatar_url_as(static_format="png"))
post_time = (msg.created_at + rdelta(hours=9)
).strftime("%Y{0}%m{1}%d{2} %H{3}%M{4}%S{5}").format(*"年月日時分秒")
if msg.edited_at:
edit_time = (msg.edited_at + rdelta(hours=9)
).strftime("%Y{0}%m{1}%d{2} %H{3}%M{4}%S{5}").format(*"年月日時分秒")
else:
edit_time = "なし"
e.set_footer(text=f"メッセージ送信時間:{post_time}/最終編集時間:{edit_time}")
e.add_field(name="含まれる埋め込みの数",value=f"{len(msg.embeds)}個")
e.add_field(name="含まれる添付ファイルの数",value=f"{len(msg.attachments)}個")
e.add_field(name="システムメッセージかどうか",value=msg.is_system())
if msg.guild.rules_channel and msg.channel.id == msg.guild.rules_channel.id:
chtype = f"{msg.channel.name}({msg.channel.id}):ルールチャンネル"
elif msg.channel.is_news():
chtype = f"{msg.channel.name}({msg.channel.id}):アナウンスチャンネル"
else:
chtype = f"{msg.channel.name}({msg.channel.id}):テキストチャンネル"
e.add_field(name="メッセージの送信先チャンネル",value=chtype)
if msg.reference:
e.add_field(name="あるメッセージへの返信等",value=f"返信元確認用:`{msg.reference.channel_id}-{msg.reference.message_id}`")
e.add_field(name="メンションの内訳",value=f"全員あてメンション:{msg.mention_everyone}\nユーザーメンション:{len(msg.mentions)}個\n役職メンション:{len(msg.role_mentions)}個\nチャンネルメンション:{len(msg.channel_mentions)}個")
e.add_field(name="メッセージID",value=str(msg.id))
if msg.webhook_id:
e.add_field(name="Webhook投稿",value=f"ID:{msg.webhook_id}")
e.add_field(name="ピン留めされているかどうか",value=str(msg.pinned))
if len(msg.reactions) != 0:
e.add_field(name="リアクション",value=",".join([f"{r.emoji}:{r.count}" for r in msg.reactions]))
e.add_field(name="メッセージのフラグ",value=[i[0] for i in iter(msg.flags) if i[1]])
e.add_field(name="このメッセージに飛ぶ",value=msg.jump_url)
try:
await ctx.reply(embed=e,mention_author=False)
except:
await ctx.send(embed=e)
def deletion_date(self):
"""Return date in which project will be deleted from IHBuffer."""
return self.get_end() + rdelta(months=+18)
def ihbuffer_date(self):
"""Date in which project will go to IHBuffer."""
return self.get_end() + rdelta(months=+6)
def backtest_basket(
series: list,
weights: list,
costs: list = None,
rebal_freq: RebalFreq = RebalFreq.DAILY,
):
num_assets = len(series)
costs = costs or [0] * num_assets
if not all(isinstance(x, pd.Series) for x in series):
raise MqTypeError("expected a list of series")
if len(weights) != num_assets or len(weights) != len(costs):
raise MqValueError(
"series, weights, and cost lists must have the same length")
# For all inputs which are Pandas series, get the intersection of their calendars
cal = pd.DatetimeIndex(
reduce(
np.intersect1d,
(curve.index for curve in series + weights + costs
if isinstance(curve, pd.Series)),
))
# Reindex inputs and convert to pandas dataframes
series = pd.concat([curve.reindex(cal) for curve in series], axis=1)
weights = pd.concat([pd.Series(w, index=cal) for w in weights], axis=1)
costs = pd.concat([pd.Series(c, index=cal) for c in costs], axis=1)
if rebal_freq == RebalFreq.DAILY:
rebal_dates = cal
else:
# Get hypothetical monthly rebalances
num_rebals = (cal[-1].year -
cal[0].year) * 12 + cal[-1].month - cal[0].month
rebal_dates = [
cal[0] + i * rdelta(months=1) for i in range(num_rebals + 1)
]
# Convert these to actual calendar days
rebal_dates = [d for d in rebal_dates if d < max(cal)]
rebal_dates = [min(cal[cal >= date]) for date in rebal_dates]
# Create Units dataframe
units = pd.DataFrame(index=cal, columns=series.columns)
actual_weights = pd.DataFrame(index=cal, columns=series.columns)
output = pd.Series(dtype='float64', index=cal)
# Initialize backtest
output.values[0] = 100
units.values[0, ] = (output.values[0] * weights.values[0, ] /
series.values[0, ])
actual_weights.values[0, ] = weights.values[0, ]
# Run backtest
prev_rebal = 0
for i, date in enumerate(cal[1:], 1):
# Update performance
output.values[i] = output.values[i - 1] + np.dot(
units.values[i - 1, ], series.values[i, ] - series.values[i - 1, ])
actual_weights.values[i, ] = (
weights.values[prev_rebal, ] *
(series.values[i, ] / series.values[prev_rebal, ]) *
(output.values[prev_rebal] / output.values[i]))
# Rebalance on rebal_dates
if date in rebal_dates:
# Compute costs
output.values[i] -= (np.dot(
costs.values[i, ],
np.abs(weights.values[i, ] - actual_weights.values[i, ])) *
output.values[i])
# Rebalance
units.values[i, ] = (output.values[i] * weights.values[i, ] /
series.values[i, ])
prev_rebal = i
actual_weights.values[i, ] = weights.values[i, ]
else:
units.values[i, ] = units.values[i - 1, ]
return output, actual_weights
def basket_series(
series: list,
weights: list,
costs: list = None,
rebal_freq: RebalFreq = RebalFreq.DAILY,
return_type: ReturnType = ReturnType.EXCESS_RETURN,
) -> pd.Series:
"""
Calculates a basket return series.
:param series: list of time series of instrument prices
:param weights: list of weights
:param costs: list of execution costs in decimal; defaults to costs of 0
:param rebal_freq: rebalancing frequency - Daily or Monthly
:param return_type: return type of underlying instruments - only excess return is supported
:return: time series of the resulting basket
**Usage**
Calculates a basket return series.
**Examples**
Generate price series and combine them in a basket (weights 70%/30%) which rebalances monthly and assumes
execution costs 5bps and 10bps each time the constituents are traded.
>>> prices1 = generate_series(100)
>>> prices2 = generate_series(100)
>>> mybasket = basket_series([prices1, prices2], [0.7, 0.3], [0.0005, 0.001], monthly)
**See also**
:func:`prices`
"""
num_assets = len(series)
costs = costs or [0] * num_assets
if not all(isinstance(x, pd.Series) for x in series):
raise MqTypeError("expected a list of series")
if len(weights) != num_assets or len(weights) != len(costs):
raise MqValueError(
"series, weights, and cost lists must have the same length")
# For all inputs which are Pandas series, get the intersection of their calendars
cal = pd.DatetimeIndex(
reduce(
np.intersect1d,
(curve.index for curve in series + weights + costs
if isinstance(curve, pd.Series)),
))
# Reindex inputs and convert to pandas dataframes
series = pd.concat([curve.reindex(cal) for curve in series], axis=1)
weights = pd.concat([pd.Series(w, index=cal) for w in weights], axis=1)
costs = pd.concat([pd.Series(c, index=cal) for c in costs], axis=1)
if rebal_freq == RebalFreq.DAILY:
rebal_dates = cal
else:
# Get hypothetical monthly rebalances
num_rebals = (cal[-1].year -
cal[0].year) * 12 + cal[-1].month - cal[0].month
rebal_dates = [
cal[0] + i * rdelta(months=1) for i in range(num_rebals + 1)
]
# Convert these to actual calendar days
rebal_dates = [d for d in rebal_dates if d < max(cal)]
rebal_dates = [min(cal[cal >= date]) for date in rebal_dates]
# Create Units dataframe
units = pd.DataFrame(index=cal, columns=series.columns)
output = pd.Series(index=cal)
# Initialize backtest
output.values[0] = 100
units.values[0, ] = (output.values[0] * weights.values[0, ] /
series.values[0, ])
# Run backtest
prev_rebal = 0
for i, date in enumerate(cal[1:], 1):
# Update performance
output.values[i] = output.values[i - 1] + np.dot(
units.values[i - 1, ], series.values[i, ] - series.values[i - 1, ])
# Rebalance on rebal_dates
if date in rebal_dates:
# Compute costs
actual_weights = (
weights.values[prev_rebal, ] *
(series.values[i, ] / series.values[prev_rebal, ]) *
(output.values[prev_rebal] / output.values[i]))
output.values[i] -= (
np.dot(costs.values[i, ],
np.abs(weights.values[i, ] - actual_weights)) *
output.values[i])
# Rebalance
units.values[i, ] = (output.values[i] * weights.values[i, ] /
series.values[i, ])
prev_rebal = i
else:
units.values[i, ] = units.values[i - 1, ]
return output