"""Time window presentation."""
import datetime
import enum
import pandas as pd
from pandas.tseries.frequencies import to_offset
class NoMatchingBucket(Exception):
"""Cannot map timestamp to any available bucket."""
[docs]class TimeBucket(enum.Enum):
"""Supported time windows for :term:`candle` and :term:`liquidity` data.
We use term "bucket", from the
`TimescaleDB slang <https://docs.timescale.com/api/latest/continuous-aggregates/refresh_continuous_aggregate/>`_
to symbol the time window of a candle data we are querying.
The raw blockchain data is assembled to 1 minute time buckets.
Then the 1 minute timebuckets are resampled to other windows.
All time windows are in UTC.
Daily time buckets have their hour, minute and second set to the zero in the outputted data.
Hourly time buckets have minute and hour set to zero, etc.
Python labels are reserved from the actual values, because Python symbol cannot start with a number.
"""
#: One minute candles
m1 = "1m"
#: Five minute candles
m5 = "5m"
#: Quarter candles
m15 = "15m"
#: Hourly candles
h1 = "1h"
#: Four hour candles
h4 = "4h"
#: Eight hour candles
h8 = "8h"
#: Daily candles
d1 = "1d"
#: Weekly candles
d7 = "7d"
#: Monthly candles
d30 = "30d"
#: We do not have "yearly" candles, but some trade statistics are calculated
#: for 360 days, thus we need a corresponding time bucket for them.
d360 = "360d"
#: Some statistics like "all time high", for example, only make sense if a "bucket"
#: spans across the entire timeline.
infinite = "infinite"
#: A placeholder value representing a "NULL value" for cases where Python's None
#: is not a favorable choice for some reason.
not_applicable = "not_applicable"
[docs] def to_hours(self) -> float:
"""The length of this bucket as hours."""
return self.to_timedelta() / datetime.timedelta(hours=1)
[docs] def to_timedelta(self) -> datetime.timedelta:
"""Get delta object for a TimeBucket definition.
You can use this to construct arbitrary timespans or iterate candle data.
"""
return _DELTAS[self]
[docs] def to_pandas_timedelta(self) -> pd.Timedelta:
"""Get pandas delta object for a TimeBucket definition.
You can use this to construct aregime-filter.ipynbrbitrary timespans or iterate candle data.
"""
return pd.Timedelta(_DELTAS[self])
[docs] def to_frequency(self) -> pd.DateOffset:
"""Get frequency input for Pandas fuctions.
You can use this to construct arbitrary timespans or iterate candle data.
"""
if self in {TimeBucket.infinite, TimeBucket.not_applicable}:
raise ValueError(f"Enum member {self} cannot be mapped to a frequency.")
delta = self.to_timedelta()
return to_offset(delta)
[docs] @staticmethod
def from_pandas_timedelta(td: pd.Timedelta) -> "TimeBucket":
"""Map Pandas timedelta to a well-known time bucket enum.
:raise NoMatchingBucket:
Could not map to any well known time bucket.
"""
assert isinstance(td, pd.Timedelta)
python_dt = td.to_pytimedelta()
for k, v in _DELTAS.items():
if python_dt == v:
return k
raise NoMatchingBucket(f"Could not map: {td}")
# datetime.timedelta equivalents of different time buckets
_DELTAS = {
TimeBucket.m1: datetime.timedelta(minutes=1),
TimeBucket.m5: datetime.timedelta(minutes=5),
TimeBucket.m15: datetime.timedelta(minutes=15),
TimeBucket.h1: datetime.timedelta(hours=1),
TimeBucket.h4: datetime.timedelta(hours=4),
TimeBucket.h8: datetime.timedelta(hours=8),
TimeBucket.d1: datetime.timedelta(days=1),
TimeBucket.d7: datetime.timedelta(days=7),
TimeBucket.d30: datetime.timedelta(days=30),
TimeBucket.d360: datetime.timedelta(days=360),
TimeBucket.infinite: datetime.timedelta.max,
TimeBucket.not_applicable: datetime.timedelta(0), # some sort of a NULL value
}