Add `.data._timeseries` utility mod
Org all the new (time) gap detection routines here and also move in the `slice_from_time()` epoch -> index converter routine from `._pathops` B)basic_buy_bot
Tyler Goodlet
parent
54f8a615fc
commit
f25248c871
|
|
@ -1,5 +1,5 @@
|
|||
# piker: trading gear for hackers
|
||||
# Copyright (C) 2018-present Tyler Goodlet (in stewardship of piker0)
|
||||
# Copyright (C) 2018-present Tyler Goodlet (in stewardship of pikers)
|
||||
|
||||
# This program is free software: you can redistribute it and/or modify
|
||||
# it under the terms of the GNU Affero General Public License as published by
|
||||
|
|
@ -289,158 +289,3 @@ def ohlc_flatten(
|
|||
num=len(flat),
|
||||
)
|
||||
return x, flat
|
||||
|
||||
|
||||
def slice_from_time(
|
||||
arr: np.ndarray,
|
||||
start_t: float,
|
||||
stop_t: float,
|
||||
step: float, # sampler period step-diff
|
||||
|
||||
) -> slice:
|
||||
'''
|
||||
Calculate array indices mapped from a time range and return them in
|
||||
a slice.
|
||||
|
||||
Given an input array with an epoch `'time'` series entry, calculate
|
||||
the indices which span the time range and return in a slice. Presume
|
||||
each `'time'` step increment is uniform and when the time stamp
|
||||
series contains gaps (the uniform presumption is untrue) use
|
||||
``np.searchsorted()`` binary search to look up the appropriate
|
||||
index.
|
||||
|
||||
'''
|
||||
profiler = Profiler(
|
||||
msg='slice_from_time()',
|
||||
disabled=not pg_profile_enabled(),
|
||||
ms_threshold=ms_slower_then,
|
||||
)
|
||||
|
||||
times = arr['time']
|
||||
t_first = floor(times[0])
|
||||
t_last = ceil(times[-1])
|
||||
|
||||
# the greatest index we can return which slices to the
|
||||
# end of the input array.
|
||||
read_i_max = arr.shape[0]
|
||||
|
||||
# compute (presumed) uniform-time-step index offsets
|
||||
i_start_t = floor(start_t)
|
||||
read_i_start = floor(((i_start_t - t_first) // step)) - 1
|
||||
|
||||
i_stop_t = ceil(stop_t)
|
||||
|
||||
# XXX: edge case -> always set stop index to last in array whenever
|
||||
# the input stop time is detected to be greater then the equiv time
|
||||
# stamp at that last entry.
|
||||
if i_stop_t >= t_last:
|
||||
read_i_stop = read_i_max
|
||||
else:
|
||||
read_i_stop = ceil((i_stop_t - t_first) // step) + 1
|
||||
|
||||
# always clip outputs to array support
|
||||
# for read start:
|
||||
# - never allow a start < the 0 index
|
||||
# - never allow an end index > the read array len
|
||||
read_i_start = min(
|
||||
max(0, read_i_start),
|
||||
read_i_max - 1,
|
||||
)
|
||||
read_i_stop = max(
|
||||
0,
|
||||
min(read_i_stop, read_i_max),
|
||||
)
|
||||
|
||||
# check for larger-then-latest calculated index for given start
|
||||
# time, in which case we do a binary search for the correct index.
|
||||
# NOTE: this is usually the result of a time series with time gaps
|
||||
# where it is expected that each index step maps to a uniform step
|
||||
# in the time stamp series.
|
||||
t_iv_start = times[read_i_start]
|
||||
if (
|
||||
t_iv_start > i_start_t
|
||||
):
|
||||
# do a binary search for the best index mapping to ``start_t``
|
||||
# given we measured an overshoot using the uniform-time-step
|
||||
# calculation from above.
|
||||
|
||||
# TODO: once we start caching these per source-array,
|
||||
# we can just overwrite ``read_i_start`` directly.
|
||||
new_read_i_start = np.searchsorted(
|
||||
times,
|
||||
i_start_t,
|
||||
side='left',
|
||||
)
|
||||
|
||||
# TODO: minimize binary search work as much as possible:
|
||||
# - cache these remap values which compensate for gaps in the
|
||||
# uniform time step basis where we calc a later start
|
||||
# index for the given input ``start_t``.
|
||||
# - can we shorten the input search sequence by heuristic?
|
||||
# up_to_arith_start = index[:read_i_start]
|
||||
|
||||
if (
|
||||
new_read_i_start <= read_i_start
|
||||
):
|
||||
# t_diff = t_iv_start - start_t
|
||||
# print(
|
||||
# f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
|
||||
# f'start_t:{start_t} -> 0index start_t:{t_iv_start}\n'
|
||||
# f'diff: {t_diff}\n'
|
||||
# f'REMAPPED START i: {read_i_start} -> {new_read_i_start}\n'
|
||||
# )
|
||||
read_i_start = new_read_i_start
|
||||
|
||||
t_iv_stop = times[read_i_stop - 1]
|
||||
if (
|
||||
t_iv_stop > i_stop_t
|
||||
):
|
||||
# t_diff = stop_t - t_iv_stop
|
||||
# print(
|
||||
# f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
|
||||
# f'calced iv stop:{t_iv_stop} -> stop_t:{stop_t}\n'
|
||||
# f'diff: {t_diff}\n'
|
||||
# # f'SHOULD REMAP STOP: {read_i_start} -> {new_read_i_start}\n'
|
||||
# )
|
||||
new_read_i_stop = np.searchsorted(
|
||||
times[read_i_start:],
|
||||
# times,
|
||||
i_stop_t,
|
||||
side='right',
|
||||
)
|
||||
|
||||
if (
|
||||
new_read_i_stop <= read_i_stop
|
||||
):
|
||||
read_i_stop = read_i_start + new_read_i_stop + 1
|
||||
|
||||
# sanity checks for range size
|
||||
# samples = (i_stop_t - i_start_t) // step
|
||||
# index_diff = read_i_stop - read_i_start + 1
|
||||
# if index_diff > (samples + 3):
|
||||
# breakpoint()
|
||||
|
||||
# read-relative indexes: gives a slice where `shm.array[read_slc]`
|
||||
# will be the data spanning the input time range `start_t` ->
|
||||
# `stop_t`
|
||||
read_slc = slice(
|
||||
int(read_i_start),
|
||||
int(read_i_stop),
|
||||
)
|
||||
|
||||
profiler(
|
||||
'slicing complete'
|
||||
# f'{start_t} -> {abs_slc.start} | {read_slc.start}\n'
|
||||
# f'{stop_t} -> {abs_slc.stop} | {read_slc.stop}\n'
|
||||
)
|
||||
|
||||
# NOTE: if caller needs absolute buffer indices they can
|
||||
# slice the buffer abs index like so:
|
||||
# index = arr['index']
|
||||
# abs_indx = index[read_slc]
|
||||
# abs_slc = slice(
|
||||
# int(abs_indx[0]),
|
||||
# int(abs_indx[-1]),
|
||||
# )
|
||||
|
||||
return read_slc
|
||||
|
|
|
|||
|
|
@ -0,0 +1,309 @@
|
|||
# piker: trading gear for hackers
|
||||
# Copyright (C) 2018-present Tyler Goodlet (in stewardship of pikers)
|
||||
|
||||
# This program is free software: you can redistribute it and/or modify
|
||||
# it under the terms of the GNU Affero General Public License as published by
|
||||
# the Free Software Foundation, either version 3 of the License, or
|
||||
# (at your option) any later version.
|
||||
|
||||
# This program is distributed in the hope that it will be useful,
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
# GNU Affero General Public License for more details.
|
||||
|
||||
# You should have received a copy of the GNU Affero General Public License
|
||||
# along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
'''
|
||||
Financial time series processing utilities usually
|
||||
pertaining to OHLCV style sampled data.
|
||||
|
||||
Routines are generally implemented in either ``numpy`` or ``polars`` B)
|
||||
|
||||
'''
|
||||
from __future__ import annotations
|
||||
from typing import Literal
|
||||
from math import (
|
||||
ceil,
|
||||
floor,
|
||||
)
|
||||
|
||||
import numpy as np
|
||||
import polars as pl
|
||||
|
||||
from ._sharedmem import ShmArray
|
||||
from .._profile import (
|
||||
Profiler,
|
||||
pg_profile_enabled,
|
||||
ms_slower_then,
|
||||
)
|
||||
|
||||
|
||||
def slice_from_time(
|
||||
arr: np.ndarray,
|
||||
start_t: float,
|
||||
stop_t: float,
|
||||
step: float, # sampler period step-diff
|
||||
|
||||
) -> slice:
|
||||
'''
|
||||
Calculate array indices mapped from a time range and return them in
|
||||
a slice.
|
||||
|
||||
Given an input array with an epoch `'time'` series entry, calculate
|
||||
the indices which span the time range and return in a slice. Presume
|
||||
each `'time'` step increment is uniform and when the time stamp
|
||||
series contains gaps (the uniform presumption is untrue) use
|
||||
``np.searchsorted()`` binary search to look up the appropriate
|
||||
index.
|
||||
|
||||
'''
|
||||
profiler = Profiler(
|
||||
msg='slice_from_time()',
|
||||
disabled=not pg_profile_enabled(),
|
||||
ms_threshold=ms_slower_then,
|
||||
)
|
||||
|
||||
times = arr['time']
|
||||
t_first = floor(times[0])
|
||||
t_last = ceil(times[-1])
|
||||
|
||||
# the greatest index we can return which slices to the
|
||||
# end of the input array.
|
||||
read_i_max = arr.shape[0]
|
||||
|
||||
# compute (presumed) uniform-time-step index offsets
|
||||
i_start_t = floor(start_t)
|
||||
read_i_start = floor(((i_start_t - t_first) // step)) - 1
|
||||
|
||||
i_stop_t = ceil(stop_t)
|
||||
|
||||
# XXX: edge case -> always set stop index to last in array whenever
|
||||
# the input stop time is detected to be greater then the equiv time
|
||||
# stamp at that last entry.
|
||||
if i_stop_t >= t_last:
|
||||
read_i_stop = read_i_max
|
||||
else:
|
||||
read_i_stop = ceil((i_stop_t - t_first) // step) + 1
|
||||
|
||||
# always clip outputs to array support
|
||||
# for read start:
|
||||
# - never allow a start < the 0 index
|
||||
# - never allow an end index > the read array len
|
||||
read_i_start = min(
|
||||
max(0, read_i_start),
|
||||
read_i_max - 1,
|
||||
)
|
||||
read_i_stop = max(
|
||||
0,
|
||||
min(read_i_stop, read_i_max),
|
||||
)
|
||||
|
||||
# check for larger-then-latest calculated index for given start
|
||||
# time, in which case we do a binary search for the correct index.
|
||||
# NOTE: this is usually the result of a time series with time gaps
|
||||
# where it is expected that each index step maps to a uniform step
|
||||
# in the time stamp series.
|
||||
t_iv_start = times[read_i_start]
|
||||
if (
|
||||
t_iv_start > i_start_t
|
||||
):
|
||||
# do a binary search for the best index mapping to ``start_t``
|
||||
# given we measured an overshoot using the uniform-time-step
|
||||
# calculation from above.
|
||||
|
||||
# TODO: once we start caching these per source-array,
|
||||
# we can just overwrite ``read_i_start`` directly.
|
||||
new_read_i_start = np.searchsorted(
|
||||
times,
|
||||
i_start_t,
|
||||
side='left',
|
||||
)
|
||||
|
||||
# TODO: minimize binary search work as much as possible:
|
||||
# - cache these remap values which compensate for gaps in the
|
||||
# uniform time step basis where we calc a later start
|
||||
# index for the given input ``start_t``.
|
||||
# - can we shorten the input search sequence by heuristic?
|
||||
# up_to_arith_start = index[:read_i_start]
|
||||
|
||||
if (
|
||||
new_read_i_start <= read_i_start
|
||||
):
|
||||
# t_diff = t_iv_start - start_t
|
||||
# print(
|
||||
# f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
|
||||
# f'start_t:{start_t} -> 0index start_t:{t_iv_start}\n'
|
||||
# f'diff: {t_diff}\n'
|
||||
# f'REMAPPED START i: {read_i_start} -> {new_read_i_start}\n'
|
||||
# )
|
||||
read_i_start = new_read_i_start
|
||||
|
||||
t_iv_stop = times[read_i_stop - 1]
|
||||
if (
|
||||
t_iv_stop > i_stop_t
|
||||
):
|
||||
# t_diff = stop_t - t_iv_stop
|
||||
# print(
|
||||
# f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
|
||||
# f'calced iv stop:{t_iv_stop} -> stop_t:{stop_t}\n'
|
||||
# f'diff: {t_diff}\n'
|
||||
# # f'SHOULD REMAP STOP: {read_i_start} -> {new_read_i_start}\n'
|
||||
# )
|
||||
new_read_i_stop = np.searchsorted(
|
||||
times[read_i_start:],
|
||||
# times,
|
||||
i_stop_t,
|
||||
side='right',
|
||||
)
|
||||
|
||||
if (
|
||||
new_read_i_stop <= read_i_stop
|
||||
):
|
||||
read_i_stop = read_i_start + new_read_i_stop + 1
|
||||
|
||||
# sanity checks for range size
|
||||
# samples = (i_stop_t - i_start_t) // step
|
||||
# index_diff = read_i_stop - read_i_start + 1
|
||||
# if index_diff > (samples + 3):
|
||||
# breakpoint()
|
||||
|
||||
# read-relative indexes: gives a slice where `shm.array[read_slc]`
|
||||
# will be the data spanning the input time range `start_t` ->
|
||||
# `stop_t`
|
||||
read_slc = slice(
|
||||
int(read_i_start),
|
||||
int(read_i_stop),
|
||||
)
|
||||
|
||||
profiler(
|
||||
'slicing complete'
|
||||
# f'{start_t} -> {abs_slc.start} | {read_slc.start}\n'
|
||||
# f'{stop_t} -> {abs_slc.stop} | {read_slc.stop}\n'
|
||||
)
|
||||
|
||||
# NOTE: if caller needs absolute buffer indices they can
|
||||
# slice the buffer abs index like so:
|
||||
# index = arr['index']
|
||||
# abs_indx = index[read_slc]
|
||||
# abs_slc = slice(
|
||||
# int(abs_indx[0]),
|
||||
# int(abs_indx[-1]),
|
||||
# )
|
||||
|
||||
return read_slc
|
||||
|
||||
|
||||
def detect_null_time_gap(shm: ShmArray) -> tuple[float, float] | None:
|
||||
# detect if there are any zero-epoch stamped rows
|
||||
zero_pred: np.ndarray = shm.array['time'] == 0
|
||||
zero_t: np.ndarray = shm.array[zero_pred]
|
||||
if zero_t.size:
|
||||
istart, iend = zero_t['index'][[0, -1]]
|
||||
start, end = shm._array['time'][
|
||||
[istart - 2, iend + 2]
|
||||
]
|
||||
return istart - 2, start, end, iend + 2
|
||||
|
||||
return None
|
||||
|
||||
|
||||
t_unit: Literal[
|
||||
'days',
|
||||
'hours',
|
||||
'minutes',
|
||||
'seconds',
|
||||
'miliseconds',
|
||||
'microseconds',
|
||||
'nanoseconds',
|
||||
]
|
||||
|
||||
|
||||
def with_dts(
|
||||
df: pl.DataFrame,
|
||||
time_col: str = 'time',
|
||||
) -> pl.DataFrame:
|
||||
'''
|
||||
Insert datetime (casted) columns to a (presumably) OHLC sampled
|
||||
time series with an epoch-time column keyed by ``time_col``.
|
||||
|
||||
'''
|
||||
return df.with_columns([
|
||||
pl.col(time_col).shift(1).suffix('_prev'),
|
||||
pl.col(time_col).diff().alias('s_diff'),
|
||||
pl.from_epoch(pl.col(time_col)).alias('dt'),
|
||||
]).with_columns([
|
||||
pl.from_epoch(pl.col(f'{time_col}_prev')).alias('dt_prev'),
|
||||
pl.col('dt').diff().alias('dt_diff'),
|
||||
]) #.with_columns(
|
||||
# pl.col('dt').diff().dt.days().alias('days_dt_diff'),
|
||||
# )
|
||||
|
||||
|
||||
def detect_time_gaps(
|
||||
df: pl.DataFrame,
|
||||
|
||||
time_col: str = 'time',
|
||||
# epoch sampling step diff
|
||||
expect_period: float = 60,
|
||||
|
||||
# datetime diff unit and gap value
|
||||
# crypto mkts
|
||||
# gap_dt_unit: t_unit = 'minutes',
|
||||
# gap_thresh: int = 1,
|
||||
|
||||
# legacy stock mkts
|
||||
gap_dt_unit: t_unit = 'days',
|
||||
gap_thresh: int = 2,
|
||||
|
||||
) -> pl.DataFrame:
|
||||
'''
|
||||
Filter to OHLC datums which contain sample step gaps.
|
||||
|
||||
For eg. legacy markets which have venue close gaps and/or
|
||||
actual missing data segments.
|
||||
|
||||
'''
|
||||
dt_gap_col: str = f'{gap_dt_unit}_diff'
|
||||
return with_dts(
|
||||
df
|
||||
).filter(
|
||||
pl.col('s_diff').abs() > expect_period
|
||||
).with_columns(
|
||||
getattr(
|
||||
pl.col('dt_diff').dt,
|
||||
gap_dt_unit, # NOTE: must be valid ``Expr.dt.<name>``
|
||||
)().alias(dt_gap_col)
|
||||
).filter(
|
||||
pl.col(dt_gap_col).abs() > gap_thresh
|
||||
)
|
||||
|
||||
|
||||
def detect_price_gaps(
|
||||
df: pl.DataFrame,
|
||||
gt_multiplier: float = 2.,
|
||||
price_fields: list[str] = ['high', 'low'],
|
||||
|
||||
) -> pl.DataFrame:
|
||||
'''
|
||||
Detect gaps in clearing price over an OHLC series.
|
||||
|
||||
2 types of gaps generally exist; up gaps and down gaps:
|
||||
|
||||
- UP gap: when any next sample's lo price is strictly greater
|
||||
then the current sample's hi price.
|
||||
|
||||
- DOWN gap: when any next sample's hi price is strictly
|
||||
less then the current samples lo price.
|
||||
|
||||
'''
|
||||
# return df.filter(
|
||||
# pl.col('high') - ) > expect_period,
|
||||
# ).select([
|
||||
# pl.dt.datetime(pl.col(time_col).shift(1)).suffix('_previous'),
|
||||
# pl.all(),
|
||||
# ]).select([
|
||||
# pl.all(),
|
||||
# (pl.col(time_col) - pl.col(f'{time_col}_previous')).alias('diff'),
|
||||
# ])
|
||||
...
|
||||
|
|
@ -54,9 +54,6 @@ from contextlib import asynccontextmanager as acm
|
|||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
import time
|
||||
from typing import (
|
||||
Literal,
|
||||
)
|
||||
|
||||
# from bidict import bidict
|
||||
# import tractor
|
||||
|
|
@ -374,104 +371,3 @@ async def get_client(
|
|||
client = NativeStorageClient(datadir)
|
||||
client.index_files()
|
||||
yield client
|
||||
|
||||
|
||||
def with_dts(
|
||||
df: pl.DataFrame,
|
||||
time_col: str = 'time',
|
||||
) -> pl.DataFrame:
|
||||
'''
|
||||
Insert datetime (casted) columns to a (presumably) OHLC sampled
|
||||
time series with an epoch-time column keyed by ``time_col``.
|
||||
|
||||
'''
|
||||
return df.with_columns([
|
||||
pl.col(time_col).shift(1).suffix('_prev'),
|
||||
pl.col(time_col).diff().alias('s_diff'),
|
||||
pl.from_epoch(pl.col(time_col)).alias('dt'),
|
||||
]).with_columns([
|
||||
pl.from_epoch(pl.col(f'{time_col}_prev')).alias('dt_prev'),
|
||||
pl.col('dt').diff().alias('dt_diff'),
|
||||
]) #.with_columns(
|
||||
# pl.col('dt').diff().dt.days().alias('days_dt_diff'),
|
||||
# )
|
||||
|
||||
|
||||
t_unit: Literal[
|
||||
'days',
|
||||
'hours',
|
||||
'minutes',
|
||||
'seconds',
|
||||
'miliseconds',
|
||||
'microseconds',
|
||||
'nanoseconds',
|
||||
]
|
||||
|
||||
|
||||
def detect_time_gaps(
|
||||
df: pl.DataFrame,
|
||||
|
||||
time_col: str = 'time',
|
||||
# epoch sampling step diff
|
||||
expect_period: float = 60,
|
||||
|
||||
# datetime diff unit and gap value
|
||||
# crypto mkts
|
||||
# gap_dt_unit: t_unit = 'minutes',
|
||||
# gap_thresh: int = 1,
|
||||
|
||||
# legacy stock mkts
|
||||
gap_dt_unit: t_unit = 'days',
|
||||
gap_thresh: int = 2,
|
||||
|
||||
) -> pl.DataFrame:
|
||||
'''
|
||||
Filter to OHLC datums which contain sample step gaps.
|
||||
|
||||
For eg. legacy markets which have venue close gaps and/or
|
||||
actual missing data segments.
|
||||
|
||||
'''
|
||||
dt_gap_col: str = f'{gap_dt_unit}_diff'
|
||||
return with_dts(
|
||||
df
|
||||
).filter(
|
||||
pl.col('s_diff').abs() > expect_period
|
||||
).with_columns(
|
||||
getattr(
|
||||
pl.col('dt_diff').dt,
|
||||
gap_dt_unit, # NOTE: must be valid ``Expr.dt.<name>``
|
||||
)().alias(dt_gap_col)
|
||||
).filter(
|
||||
pl.col(dt_gap_col).abs() > gap_thresh
|
||||
)
|
||||
|
||||
|
||||
def detect_price_gaps(
|
||||
df: pl.DataFrame,
|
||||
gt_multiplier: float = 2.,
|
||||
price_fields: list[str] = ['high', 'low'],
|
||||
|
||||
) -> pl.DataFrame:
|
||||
'''
|
||||
Detect gaps in clearing price over an OHLC series.
|
||||
|
||||
2 types of gaps generally exist; up gaps and down gaps:
|
||||
|
||||
- UP gap: when any next sample's lo price is strictly greater
|
||||
then the current sample's hi price.
|
||||
|
||||
- DOWN gap: when any next sample's hi price is strictly
|
||||
less then the current samples lo price.
|
||||
|
||||
'''
|
||||
# return df.filter(
|
||||
# pl.col('high') - ) > expect_period,
|
||||
# ).select([
|
||||
# pl.dt.datetime(pl.col(time_col).shift(1)).suffix('_previous'),
|
||||
# pl.all(),
|
||||
# ]).select([
|
||||
# pl.all(),
|
||||
# (pl.col(time_col) - pl.col(f'{time_col}_previous')).alias('diff'),
|
||||
# ])
|
||||
...
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ from ..data._formatters import (
|
|||
OHLCBarsAsCurveFmtr, # OHLC converted to line
|
||||
StepCurveFmtr, # "step" curve (like for vlm)
|
||||
)
|
||||
from ..data._pathops import (
|
||||
from ..data._timeseries import (
|
||||
slice_from_time,
|
||||
)
|
||||
from ._ohlc import (
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ import pendulum
|
|||
import pyqtgraph as pg
|
||||
|
||||
from ..data.types import Struct
|
||||
from ..data._pathops import slice_from_time
|
||||
from ..data._timeseries import slice_from_time
|
||||
from ..log import get_logger
|
||||
from .._profile import Profiler
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue