Factor gap annotating into new `markup_gaps()`
Since we definitely want to markup gaps that are both data-errors and just plain old venue closures (time gaps), generalize the `gaps: pl.DataFrame` loop in a func and call it from the `ldshm` cmd for now. Some other tweaks to `store ldshm`: - add `np.median()` failover when detecting (ohlc) ts sample rate. - use new `tsp.dedupe()` signature. - differentiate between sample-period size gaps and venue closure gaps by calling `tsp.detect_time_gaps()` with diff size thresholds. - add todo for backfilling null-segs when detected.distribute_dis
Tyler Goodlet
parent
05f874001a
commit
6959429af8
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@ -24,8 +24,13 @@ from __future__ import annotations
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# AsyncExitStack,
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# )
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from pathlib import Path
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from math import copysign
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import time
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from types import ModuleType
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from typing import (
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Any,
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TYPE_CHECKING,
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)
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import polars as pl
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import numpy as np
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@ -42,15 +47,17 @@ from piker.data import (
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ShmArray,
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)
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from piker import tsp
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from . import (
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log,
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)
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from piker.data._formatters import BGM
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from . import log
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from . import (
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__tsdbs__,
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open_storage_client,
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StorageClient,
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)
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if TYPE_CHECKING:
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from piker.ui._remote_ctl import AnnotCtl
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store = typer.Typer()
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@ -203,10 +210,12 @@ def anal(
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deduped: pl.DataFrame # deduplicated dts
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(
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df,
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gaps,
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deduped,
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diff,
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) = tsp.dedupe(shm_df)
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) = tsp.dedupe(
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shm_df,
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period=period,
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)
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write_edits: bool = True
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if (
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@ -217,7 +226,6 @@ def anal(
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)
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):
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await tractor.pause()
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await client.write_ohlcv(
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fqme,
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ohlcv=deduped,
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@ -229,10 +237,117 @@ def anal(
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# is there something more minimal but nearly as
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# functional as ipython?
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await tractor.pause()
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assert not null_segs
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trio.run(main)
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async def markup_gaps(
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fqme: str,
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timeframe: float,
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actl: AnnotCtl,
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wdts: pl.DataFrame,
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gaps: pl.DataFrame,
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) -> dict[int, dict]:
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'''
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Remote annotate time-gaps in a dt-fielded ts (normally OHLC)
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with rectangles.
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'''
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aids: dict[int] = {}
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for i in range(gaps.height):
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row: pl.DataFrame = gaps[i]
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# the gap's RIGHT-most bar's OPEN value
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# at that time (sample) step.
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iend: int = row['index'][0]
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# dt: datetime = row['dt'][0]
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# dt_prev: datetime = row['dt_prev'][0]
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# dt_end_t: float = dt.timestamp()
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# TODO: can we eventually remove this
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# once we figure out why the epoch cols
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# don't match?
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# TODO: FIX HOW/WHY these aren't matching
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# and are instead off by 4hours (EST
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# vs. UTC?!?!)
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# end_t: float = row['time']
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# assert (
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# dt.timestamp()
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# ==
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# end_t
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# )
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# the gap's LEFT-most bar's CLOSE value
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# at that time (sample) step.
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prev_r: pl.DataFrame = wdts.filter(
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pl.col('index') == iend - 1
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)
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istart: int = prev_r['index'][0]
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# dt_start_t: float = dt_prev.timestamp()
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# start_t: float = prev_r['time']
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# assert (
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# dt_start_t
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# ==
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# start_t
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# )
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# TODO: implement px-col width measure
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# and ensure at least as many px-cols
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# shown per rect as configured by user.
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# gap_w: float = abs((iend - istart))
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# if gap_w < 6:
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# margin: float = 6
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# iend += margin
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# istart -= margin
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rect_gap: float = BGM*3/8
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opn: float = row['open'][0]
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ro: tuple[float, float] = (
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# dt_end_t,
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iend + rect_gap + 1,
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opn,
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)
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cls: float = prev_r['close'][0]
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lc: tuple[float, float] = (
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# dt_start_t,
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istart - rect_gap, # + 1 ,
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cls,
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)
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color: str = 'dad_blue'
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diff: float = cls - opn
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sgn: float = copysign(1, diff)
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color: str = {
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-1: 'buy_green',
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1: 'sell_red',
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}[sgn]
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rect_kwargs: dict[str, Any] = dict(
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fqme=fqme,
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timeframe=timeframe,
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start_pos=lc,
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end_pos=ro,
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color=color,
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)
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aid: int = await actl.add_rect(**rect_kwargs)
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assert aid
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aids[aid] = rect_kwargs
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# tell chart to redraw all its
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# graphics view layers Bo
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await actl.redraw(
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fqme=fqme,
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timeframe=timeframe,
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)
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return aids
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@store.command()
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def ldshm(
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fqme: str,
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@ -249,7 +364,6 @@ def ldshm(
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async def main():
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from piker.ui._remote_ctl import (
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open_annot_ctl,
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AnnotCtl,
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)
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actl: AnnotCtl
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mod: ModuleType
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@ -274,111 +388,97 @@ def ldshm(
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shm_df,
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) in tsp.iter_dfs_from_shms(fqme):
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# compute ohlc properties for naming
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times: np.ndarray = shm.array['time']
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period_s: float = float(times[-1] - times[-2])
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if period_s < 1.:
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d1: float = float(times[-1] - times[-2])
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d2: float = float(times[-2] - times[-3])
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med: float = np.median(np.diff(times))
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if (
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d1 < 1.
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and d2 < 1.
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and med < 1.
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):
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raise ValueError(
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f'Something is wrong with time period for {shm}:\n{times}'
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)
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period_s: float = float(max(d1, d2, med))
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# over-write back to shm?
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df: pl.DataFrame # with dts
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wdts: pl.DataFrame # with dts
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deduped: pl.DataFrame # deduplicated dts
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(
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df,
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gaps,
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wdts,
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deduped,
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diff,
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) = tsp.dedupe(shm_df)
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) = tsp.dedupe(
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shm_df,
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period=period_s,
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)
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null_segs: tuple = tsp.get_null_segs(
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frame=shm.array,
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period=period_s,
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)
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needs_correction: bool = (
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not gaps.is_empty()
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or null_segs
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# detect gaps from in expected (uniform OHLC) sample period
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step_gaps: pl.DataFrame = tsp.detect_time_gaps(
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wdts,
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expect_period=period_s,
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)
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# TODO: maybe only optionally enter this depending
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# on some CLI flags and/or gap detection?
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if needs_correction:
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for i in range(gaps.height):
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row: pl.DataFrame = gaps[i]
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# TODO: can we eventually remove this
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# once we figure out why the epoch cols
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# don't match?
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iend: int = row['index'][0]
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# dt: datetime = row['dt'][0]
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# dt_prev: datetime = row['dt_prev'][0]
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# TODO: by default we always want to mark these up
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# with rects showing up/down gaps Bo
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venue_gaps: pl.DataFrame = tsp.detect_time_gaps(
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wdts,
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expect_period=period_s,
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# the gap's right-most bar's OPEN value
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# at that time (sample) step.
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# dt_end_t: float = dt.timestamp()
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# TODO: actually pull the exact duration
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# expected for each venue operational period?
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gap_dt_unit='days',
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gap_thresh=1,
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)
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# TODO: FIX HOW/WHY these aren't matching
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# and are instead off by 4hours (EST
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# vs. UTC?!?!)
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# end_t: float = row['time']
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# assert (
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# dt.timestamp()
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# ==
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# end_t
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# )
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# TODO: call null-seg fixer somehow?
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if null_segs:
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await tractor.pause()
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# async with (
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# trio.open_nursery() as tn,
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# mod.open_history_client(
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# mkt,
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# ) as (get_hist, config),
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# ):
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# nulls_detected: trio.Event = await tn.start(partial(
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# tsp.maybe_fill_null_segments,
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# the gap's left-most bar's CLOSE value
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# at that time (sample) step.
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prev_r: pl.DataFrame = df.filter(
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pl.col('index') == iend - 1
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# shm=shm,
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# timeframe=timeframe,
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# get_hist=get_hist,
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# sampler_stream=sampler_stream,
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# mkt=mkt,
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# ))
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# TODO: find the disjoint set of step gaps from
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# venue (closure) set!
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# -[ ] do a set diff by checking for the unique
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# gap set only in the step_gaps?
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if (
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not venue_gaps.is_empty()
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# and not step_gaps.is_empty()
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):
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do_markup_gaps: bool = True
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if do_markup_gaps:
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aids: dict = await markup_gaps(
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fqme,
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period_s,
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actl,
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wdts,
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step_gaps,
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)
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istart: int = prev_r['index'][0]
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# dt_start_t: float = dt_prev.timestamp()
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assert aids
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# start_t: float = prev_r['time']
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# assert (
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# dt_start_t
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# ==
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# start_t
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# )
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# TODO: implement px-col width measure
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# and ensure at least as many px-cols
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# shown per rect as configured by user.
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gap_w: float = abs((iend - istart))
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if gap_w < 6:
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margin: float = 6
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iend += margin
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istart -= margin
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ro: tuple[float, float] = (
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# dt_end_t,
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iend,
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row['open'][0],
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)
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lc: tuple[float, float] = (
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# dt_start_t,
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istart,
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prev_r['close'][0],
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)
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# async with actl.open_rect(
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# ) as aid:
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aid: int = await actl.add_rect(
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fqme=fqme,
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timeframe=period_s,
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start_pos=lc,
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end_pos=ro,
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)
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assert aid
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# write to parquet file?
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if (
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write_parquet
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):
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# write to fs
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start = time.time()
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# write repaired ts to parquet-file?
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if write_parquet:
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start: float = time.time()
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path: Path = await client.write_ohlcv(
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fqme,
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ohlcv=deduped,
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@ -390,7 +490,7 @@ def ldshm(
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)
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# read back from fs
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start = time.time()
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start: float = time.time()
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read_df: pl.DataFrame = pl.read_parquet(path)
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read_delay: float = round(
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time.time() - start,
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@ -412,6 +512,8 @@ def ldshm(
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shm._array.setflags(
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write=int(1),
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)
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# last chance manual overwrites in REPL
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await tractor.pause()
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shm.push(
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new,
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prepend=True,
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@ -419,8 +521,6 @@ def ldshm(
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update_first=False, # don't update ._first
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)
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await tractor.pause()
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assert diff
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else:
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# allow interaction even when no ts problems.
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@ -428,8 +528,11 @@ def ldshm(
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assert not diff
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if df is None:
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log.error(f'No matching shm buffers for {fqme} ?')
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if shm_df is None:
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log.error(
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f'No matching shm buffers for {fqme} ?'
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)
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trio.run(main)
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Loading…
Reference in New Issue