def historical_kline(symbol: str,
start: Timestamp,
end: Timestamp,
period: str,
type: str = "before",
batch: int = 100):
start_epoch = int(start.timestamp() * 1000)
end_epoch = int(end.timestamp() * 1000)
estimated_bar_num = ((end - start).days + 1) * BAR_MULTI[period]
all_dfs = []
while True:
query = f"&symbol={symbol}&begin={start_epoch}&period={period}&type={type}&count={min(batch, estimated_bar_num)}"
url = kline_base + query
raw = fetch(url)
df_tmp = DataFrame(raw['item'], columns=raw['column'])
start_epoch = df_tmp.timestamp.max()
estimated_bar_num -= len(df_tmp)
all_dfs.append(df_tmp)
if start_epoch > end_epoch:
break
df = concat(all_dfs)
df.timestamp = to_datetime(df.timestamp)
df.set_index("timestamp", inplace=True)
return df
def test_replace_tzinfo(self):
# GH#15683
dt = datetime(2016, 3, 27, 1)
tzinfo = pytz.timezone('CET').localize(dt, is_dst=False).tzinfo
result_dt = dt.replace(tzinfo=tzinfo)
result_pd = Timestamp(dt).replace(tzinfo=tzinfo)
if PY3:
# datetime.timestamp() converts in the local timezone
with tm.set_timezone('UTC'):
assert result_dt.timestamp() == result_pd.timestamp()
assert result_dt == result_pd
assert result_dt == result_pd.to_pydatetime()
result_dt = dt.replace(tzinfo=tzinfo).replace(tzinfo=None)
result_pd = Timestamp(dt).replace(tzinfo=tzinfo).replace(tzinfo=None)
if PY3:
# datetime.timestamp() converts in the local timezone
with tm.set_timezone('UTC'):
assert result_dt.timestamp() == result_pd.timestamp()
assert result_dt == result_pd
assert result_dt == result_pd.to_pydatetime()
def test_replace_tzinfo(self):
# GH#15683
dt = datetime(2016, 3, 27, 1)
tzinfo = pytz.timezone('CET').localize(dt, is_dst=False).tzinfo
result_dt = dt.replace(tzinfo=tzinfo)
result_pd = Timestamp(dt).replace(tzinfo=tzinfo)
if PY3:
# datetime.timestamp() converts in the local timezone
with tm.set_timezone('UTC'):
assert result_dt.timestamp() == result_pd.timestamp()
assert result_dt == result_pd
assert result_dt == result_pd.to_pydatetime()
result_dt = dt.replace(tzinfo=tzinfo).replace(tzinfo=None)
result_pd = Timestamp(dt).replace(tzinfo=tzinfo).replace(tzinfo=None)
if PY3:
# datetime.timestamp() converts in the local timezone
with tm.set_timezone('UTC'):
assert result_dt.timestamp() == result_pd.timestamp()
assert result_dt == result_pd
assert result_dt == result_pd.to_pydatetime()
def _define_time_coord(
adate: pd.Timestamp,
time_bounds: Optional[Sequence[pd.Timestamp]] = None,
) -> DimCoord:
"""Define a time coordinate. The coordinate will have bounds,
if bounds are provided.
Args:
adate:
The point for the time coordinate.
time_bounds:
The values defining the bounds for the time coordinate.
Returns:
A time coordinate. This coordinate will have bounds, if bounds
are provided.
"""
return DimCoord(
np.array(adate.timestamp(), dtype=TIME_COORDS["time"].dtype),
"time",
bounds=time_bounds if time_bounds is None else [
np.array(t.timestamp(), dtype=TIME_COORDS["time"].dtype)
for t in time_bounds
],
units=TIME_COORDS["time"].units,
)
def get_kpi_day(self, station_code: str, date: pd.Timestamp) -> Dict:
url = f'{self.base_url}/getKpiStationDay'
time = int(date.timestamp()) * 1000
body = {'stationCodes': station_code, 'collectTime': time}
r = self.session.post(url=url, json=body)
self._validate_response(r)
return r.json()
def finance_report(start: Timestamp,
end: Timestamp,
market: str,
symbol: str,
report_type: str,
quarter="all") -> DataFrame:
"""
:param start: start time
:param end: end time
:param market: {'HK', 'CN'}
:param symbol: stock symbol
:param report_type: {'indicator', 'balance', 'income', 'business'}
:param quarter: {'all', 'Q1', 'Q2', ‘Q3', 'Q4'}
:return: data frame contains items of financial report
"""
count = (end.to_period(freq='Q') - start.to_period(freq='Q')).n
end_timestamp = int(end.timestamp() * 1000)
urlpath = f"{market}/{report_type}.json?symbol={symbol}&&type={quarter}" \
f"&is_detail=true&count={count}×tamp={end_timestamp}"
url = urljoin(api_ref.finance_base, urlpath)
data = utls.fetch(url)
data_list = data.pop('list')
for d in data_list:
for k in d:
if isinstance(d[k], list):
d[k] = d[k][0]
df = DataFrame(data_list).drop(columns=['ctime']).rename(
columns={
'report_date': 'date'
}).set_index('date')
df.date = df.date.astype('M8[ms]')
df.report_name = df.report_name.str.replace('年报', 'Q4').str.replace('三季报', 'Q3')\
.str.replace('中报', 'Q2').str.replace('一季报', 'Q1')
return df
def get_station_kpi_year(self, station_code: str,
date: pd.Timestamp) -> Dict:
time = int(date.timestamp()) * 1000
return self._request("getKpiStationYear", {
'stationCodes': station_code,
'collectTime': time
})
def get_dev_kpi_year(self, dev_id: str, dev_type_id: int,
date: pd.Timestamp) -> Dict:
time = int(date.timestamp()) * 1000
return self._request("getDevKpiYear", {
'devIds': dev_id,
'devTypeId': dev_type_id,
'collectTime': time
})
def generate_requests(self, timestamp: pd.Timestamp):
month = timestamp.month if timestamp.month in self.monthly_vals else 0
seconds_per_time_unit = self.__class__.SECONDS_IN_DAY // len(
self.monthly_vals[month][timestamp.weekday()])
time_unit = (int(timestamp.timestamp()) %
self.__class__.SECONDS_IN_DAY) // seconds_per_time_unit
self._populate_split_across_seconds_if_needed(timestamp, month,
seconds_per_time_unit,
time_unit)
second_in_time_unit = int(timestamp.timestamp(
)) % seconds_per_time_unit # TODO: is it correct?
self._populate_simulation_steps_in_second_if_needed(
second_in_time_unit)
return self._generate_requests_on_current_simulation_step(timestamp)
async def task():
global last_tweet_used_id, last_tweet_used_bap_id, start_time, start_ts
await bot.wait_until_ready()
for chan in config['live_scores_channels']:
c = bot.get_channel(chan)
if c:
live_scores_channel.append(c)
print('Added {}@{} for live scores'.format(c.name, 'FantasyPL'))
else:
print('Couldn\'t find channel {}'.format(chan))
for chan in config['price_changes_channels']:
c = bot.get_channel(chan)
if c:
price_changes_channel.append(c)
print('Added {}@{} for price changes'.format(c.name, 'FantasyPL'))
else:
print('Couldn\'t find channel {}'.format(chan))
for chan in config['team_news_channels']:
c = bot.get_channel(chan)
if c:
team_news_channel.append(c)
print('Added {}@{} for team news'.format(c.name, 'FantasyPL'))
else:
print('Couldn\'t find channel {}'.format(chan))
for chan in config['stats_channels']:
c = bot.get_channel(chan)
if c:
stats_channel.append(c)
print('Added {}@{} for stats'.format(c.name, 'FantasyPL'))
else:
print('Couldn\'t find channel {}'.format(chan))
start_time = Timestamp(get_ntp_time(), unit="s", tz="UTC")
start_ts = int(start_time.timestamp())
last_tweet_used_id = None
last_tweet_used_bap_id = None
async with aiohttp.ClientSession(connector=conn) as session:
await get_all_fixtures(session)
print("Ready.")
while True:
await asyncio.gather(
#get_latest_fixture_tweets(session),
get_latest_tweets(),
get_latest_tweets_bap(),
asyncio.sleep(sleep_time_seconds))
await bot.wait_until_ready()
def generate_date_feature(date: pd.Timestamp):
"""
Generate date features.
"""
result = pd.Series()
result['timestamp'] = date.timestamp()
format_string = '%Y-%m-%d'
dt_string = date.strftime(format_string)
result['holiday'] = int(
(dt_string in holiday)
or (date.weekday() in [5, 6] and dt_string not in work))
result = pd.concat(
[result, get_onehot(date.weekday(), 0, 6, name='weekday')])
return result
def test_timestamp(self):
# GH#17329
# tz-naive --> treat it as if it were UTC for purposes of timestamp()
ts = Timestamp.now()
uts = ts.replace(tzinfo=utc)
assert ts.timestamp() == uts.timestamp()
tsc = Timestamp("2014-10-11 11:00:01.12345678", tz="US/Central")
utsc = tsc.tz_convert("UTC")
# utsc is a different representation of the same time
assert tsc.timestamp() == utsc.timestamp()
# datetime.timestamp() converts in the local timezone
with tm.set_timezone("UTC"):
# should agree with datetime.timestamp method
dt = ts.to_pydatetime()
assert dt.timestamp() == ts.timestamp()
def test_timestamp(self):
# GH#17329
# tz-naive --> treat it as if it were UTC for purposes of timestamp()
ts = Timestamp.now()
uts = ts.replace(tzinfo=utc)
assert ts.timestamp() == uts.timestamp()
tsc = Timestamp('2014-10-11 11:00:01.12345678', tz='US/Central')
utsc = tsc.tz_convert('UTC')
# utsc is a different representation of the same time
assert tsc.timestamp() == utsc.timestamp()
# datetime.timestamp() converts in the local timezone
with tm.set_timezone('UTC'):
# should agree with datetime.timestamp method
dt = ts.to_pydatetime()
assert dt.timestamp() == ts.timestamp()
def test_timestamp(self, dt64, ts):
alt = Timestamp(dt64)
assert ts.timestamp() == alt.timestamp()
async def _request_bars( # noqa C901 'FTXDataClient._request_bars' is too complex (11)
self,
bar_type: BarType,
from_datetime: pd.Timestamp,
to_datetime: pd.Timestamp,
limit: int,
correlation_id: UUID4,
):
instrument = self._instrument_provider.find(bar_type.instrument_id)
if instrument is None:
self._log.error(
f"Cannot parse historical bars: "
f"no instrument found for {bar_type.instrument_id}.", )
return
if bar_type.spec.aggregation == BarAggregation.SECOND:
resolution = bar_type.spec.step
elif bar_type.spec.aggregation == BarAggregation.MINUTE:
resolution = bar_type.spec.step * 60
elif bar_type.spec.aggregation == BarAggregation.HOUR:
resolution = bar_type.spec.step * 60 * 60
elif bar_type.spec.aggregation == BarAggregation.DAY:
resolution = bar_type.spec.step * 60 * 60 * 24
else: # pragma: no cover (design-time error)
raise RuntimeError(
f"invalid aggregation type, "
f"was {BarAggregationParser.to_str_py(bar_type.spec.aggregation)}",
)
# Define validation constants
max_seconds: int = 30 * 86400
valid_windows: List[int] = [15, 60, 300, 900, 3600, 14400, 86400]
# Validate resolution
if resolution > max_seconds:
self._log.error(
f"Cannot request bars for {bar_type}: "
f"seconds window exceeds MAX_SECONDS {max_seconds}.", )
return
if resolution > 86400 and resolution % 86400 != 0:
self._log.error(
f"Cannot request bars for {bar_type}: "
f"seconds window exceeds 1 day 86,400 and not a multiple of 1 day.",
)
return
elif resolution not in valid_windows:
self._log.error(
f"Cannot request bars for {bar_type}: "
f"invalid seconds window, use one of {valid_windows}.", )
return
# Get historical bars data
data: List[Dict[str,
Any]] = await self._http_client.get_historical_prices(
market=bar_type.instrument_id.symbol.value,
resolution=resolution,
start_time=int(from_datetime.timestamp())
if from_datetime is not None else None,
end_time=int(to_datetime.timestamp())
if to_datetime is not None else None,
)
# Limit bars data
if limit:
while len(data) > limit:
data.pop(0) # Pop left
bars: List[Bar] = parse_bars_http(
instrument=instrument,
bar_type=bar_type,
data=data,
ts_event_delta=secs_to_nanos(resolution),
ts_init=self._clock.timestamp_ns(),
)
partial: Bar = bars.pop()
self._handle_bars(bar_type, bars, partial, correlation_id)
def _worker(self, exchange):
r = Rest()
storage = Storage(self.config)
for pair in self.config.backfill[exchange]:
try:
start = self.config.backfill[exchange][pair].start
while True:
end = storage.get_start_date(exchange, 'trades', pair)
if not all(e == end[0] for e in end):
raise InconsistentStorage(
"Stored data differs, cannot backfill")
end = end[0]
if end:
break
time.sleep(10)
end = Timestamp(end, unit='s')
end -= Timedelta(microseconds=1)
start = Timestamp(start)
if end <= Timestamp(start):
LOG.info(
"Data in storage is earlier than backfill start date for %s - %s",
exchange, pair)
continue
LOG.info("Backfill - Starting for %s - %s for range %s - %s",
exchange, pair, start, str(end))
# Backfill from end date to start date, 1 day at a time, in reverse order (from end -> start)
while start < end:
seg_start = end.replace(hour=0,
minute=0,
second=0,
microsecond=0,
nanosecond=0)
if start > seg_start:
seg_start = start
LOG.info("Backfill - Reading %s to %s for %s - %s",
seg_start, end, exchange, pair)
trades = []
try:
for t in r[exchange].trades(pair, str(seg_start),
str(end)):
trades.extend(t)
except Exception:
LOG.warning(
"Backfill - encountered error backfilling %s - %s, trying again...",
exchange,
pair,
exc_info=True)
time.sleep(300)
continue
if not trades:
end = seg_start - Timedelta(nanoseconds=1)
continue
storage.aggregate(trades)
storage.write(exchange, 'trades', pair, end.timestamp())
LOG.info("Backfill - Wrote %s to %s for %s - %s",
seg_start, end, exchange, pair)
end = seg_start - Timedelta(nanoseconds=1)
LOG.info("Backfill for %s - %s completed", exchange, pair)
except Exception:
LOG.error("Backfill failed for %s - %s",
exchange,
pair,
exc_info=True)