Add marketstore client as storage-backend module
To kick off our (tsdb) storage backends this adds our first implementing a new `Storage(Protocol)` client interface. Going foward, the top level `.storage` pkg-module will now expose backend agnostic APIs and helpers whilst specific backend implementations will adhere to that middle-ware layer. Deats: - add `.storage.marketstore.Storage` as the first client implementation, moving all needed (import) dependencies out from `.service.marketstore` as well as `.ohlc_key_map` and `get_client()`. - move root `conf.toml` loading from `.data.history` into `.storage.__init__.open_storage_client()` which now takes in a `name: str` and does all the work of loading the correct backend module, its config, and determining if a service-instance can be contacted and a client loaded; in the case where this fails we raise a new `StorageConnectionError`. - add a new `.storage.get_storagemod()` just like we have for brokers. - make `open_storage_client()` also return the backend module such that the history-data layer can make backend specific calls as needed (eg. ohlc_key_map). - fall back to a basic non-tsdb backfill when `open_storage_client()` raises the new connection error.basic_buy_bot
Tyler Goodlet
parent
29211b200d
commit
7ab97fb21d
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@ -121,7 +121,7 @@ def storesh(
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Start an IPython shell ready to query the local marketstore db.
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'''
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from piker.data.marketstore import open_tsdb_client
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from piker.storage import open_tsdb_client
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from piker.service import open_piker_runtime
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async def main():
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@ -171,7 +171,7 @@ def storage(
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Start an IPython shell ready to query the local marketstore db.
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'''
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from piker.service.marketstore import open_tsdb_client
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from piker.storage import open_tsdb_client
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from piker.service import open_piker_runtime
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async def main():
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@ -28,7 +28,6 @@ import time
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from types import ModuleType
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from typing import (
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Callable,
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Optional,
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TYPE_CHECKING,
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)
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@ -38,16 +37,12 @@ import tractor
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import pendulum
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import numpy as np
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from .. import config
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from ..accounting import (
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MktPair,
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)
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from ._util import (
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log,
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)
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from ..service import (
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check_for_service,
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)
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from ._sharedmem import (
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maybe_open_shm_array,
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ShmArray,
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@ -91,8 +86,8 @@ async def start_backfill(
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sampler_stream: tractor.MsgStream,
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feed_is_live: trio.Event,
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last_tsdb_dt: Optional[datetime] = None,
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storage: Optional[Storage] = None,
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last_tsdb_dt: datetime | None = None,
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storage: Storage | None = None,
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write_tsdb: bool = True,
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tsdb_is_up: bool = False,
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@ -391,7 +386,7 @@ async def basic_backfill(
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async def tsdb_backfill(
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mod: ModuleType,
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marketstore: ModuleType,
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storemod: ModuleType,
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bus: _FeedsBus,
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storage: Storage,
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mkt: MktPair,
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@ -542,7 +537,7 @@ async def tsdb_backfill(
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prepend=True,
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# update_first=False,
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# start=prepend_start,
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field_map=marketstore.ohlc_key_map,
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field_map=storemod.ohlc_key_map,
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)
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tsdb_last_frame_start = tsdb_history['Epoch'][0]
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@ -580,7 +575,7 @@ async def tsdb_backfill(
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shm.push(
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to_push,
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prepend=True,
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field_map=marketstore.ohlc_key_map,
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field_map=storemod.ohlc_key_map,
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)
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log.info(f'Loaded {to_push.shape} datums from storage')
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@ -626,12 +621,11 @@ async def manage_history(
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) -> None:
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'''
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Load and manage historical data including the loading of any
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available series from `marketstore` as well as conducting real-time
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update of both that existing db and the allocated shared memory
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buffer.
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available series from any connected tsdb as well as conduct
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real-time update of both that existing db and the allocated shared
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memory buffer.
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'''
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# TODO: is there a way to make each shm file key
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# actor-tree-discovery-addr unique so we avoid collisions
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# when doing tests which also allocate shms for certain instruments
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@ -711,52 +705,17 @@ async def manage_history(
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None,
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)
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assert open_history_client
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tsdb_is_up: bool = False
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try_remote_tsdb: bool = False
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conf, path = config.load('conf', touch_if_dne=True)
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net = conf.get('network')
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if net:
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tsdbconf = net.get('tsdb')
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# lookup backend tsdb module by name and load any user service
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# settings for connecting to the tsdb service.
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tsdb_backend: str = tsdbconf.pop('backend')
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tsdb_host: str = tsdbconf['host']
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# TODO: import and load storagemod by name
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# mod = get_storagemod(tsdb_backend)
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from ..service import marketstore
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if tsdb_host == 'localhost':
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log.info('Scanning for existing `{tsbd_backend}`')
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tsdb_is_up: bool = await check_for_service(f'{tsdb_backend}d')
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else:
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try_remote_tsdb: bool = True
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if (
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tsdb_is_up
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or try_remote_tsdb
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and (
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opened
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and open_history_client
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)
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):
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log.info('Found existing `marketstored`')
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async with (
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marketstore.open_storage_client(
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**tsdbconf
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) as storage,
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):
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from .. import storage
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try:
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async with storage.open_storage_client() as (storemod, client):
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log.info(f'Found existing `{storemod.name}`')
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# TODO: drop returning the output that we pass in?
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await bus.nursery.start(
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tsdb_backfill,
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mod,
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marketstore,
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storemod,
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bus,
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storage,
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client,
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mkt,
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{
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1: rt_shm,
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@ -784,11 +743,11 @@ async def manage_history(
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# what data is loaded for viewing.
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await trio.sleep_forever()
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# load less history if no tsdb can be found
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elif (
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not tsdb_is_up
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and opened
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):
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except storage.StorageConnectionError:
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log.exception(
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"Can't connect to tsdb backend!?\n"
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'Starting basic backfille to shm..'
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)
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await basic_backfill(
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bus,
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mod,
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@ -327,16 +327,6 @@ _ohlcv_dt = [
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]
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ohlc_key_map = bidict({
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'Epoch': 'time',
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'Open': 'open',
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'High': 'high',
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'Low': 'low',
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'Close': 'close',
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'Volume': 'volume',
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})
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def mk_tbk(keys: tuple[str, str, str]) -> str:
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'''
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Generate a marketstore table key from a tuple.
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@ -388,24 +378,6 @@ def quote_to_marketstore_structarray(
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return np.array([tuple(array_input)], dtype=_quote_dt)
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@acm
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async def get_client(
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host: str | None,
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port: int | None,
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) -> MarketstoreClient:
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'''
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Load a ``anyio_marketstore`` grpc client connected
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to an existing ``marketstore`` server.
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'''
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async with open_marketstore_client(
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host or 'localhost',
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port or _config['grpc_listen_port'],
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) as client:
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yield client
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class MarketStoreError(Exception):
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"Generic marketstore client error"
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@ -444,6 +416,7 @@ async def ingest_quote_stream(
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Ingest a broker quote stream into a ``marketstore`` tsdb.
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'''
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from piker.storage.marketstore import get_client
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async with (
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maybe_open_feed(brokername, symbols, loglevel=loglevel) as feed,
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get_client() as ms_client,
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@ -25,68 +25,67 @@
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- test harness utilities for data-processing verification.
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'''
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from __future__ import annotations
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from abc import abstractmethod
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from contextlib import asynccontextmanager as acm
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from functools import partial
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from importlib import import_module
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from datetime import datetime
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# from pprint import pformat
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from types import ModuleType
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from typing import (
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Union,
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# Callable,
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# Awaitable,
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# Any,
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# AsyncIterator,
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Protocol,
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# Generic,
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# TypeVar,
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)
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import tractor
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import numpy as np
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from anyio_marketstore import (
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Params,
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from .. import config
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from ..service import (
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check_for_service,
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)
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import pendulum
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import purerpc
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from . import config
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from ..service.marketstore import (
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MarketstoreClient,
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tf_in_1s,
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mk_tbk,
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_ohlcv_dt,
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MarketStoreError,
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from ..log import (
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get_logger,
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get_console_log,
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)
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subsys: str = 'piker.storage'
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log = get_logger(subsys)
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get_console_log = partial(
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get_console_log,
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name=subsys,
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)
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from ..data.feed import maybe_open_feed
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from ..log import get_logger
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from .._profile import Profiler
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log = get_logger(__name__)
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class Storage:
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class Storage(
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Protocol,
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):
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'''
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High level storage api for both real-time and historical ingest.
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Api description that all storage backends must implement
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in order to suffice the historical data mgmt layer.
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'''
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def __init__(
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self,
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client: MarketstoreClient,
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) -> None:
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# TODO: eventually this should be an api/interface type that
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# ensures we can support multiple tsdb backends.
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self.client = client
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# series' cache from tsdb reads
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self._arrays: dict[str, np.ndarray] = {}
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@abstractmethod
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async def list_keys(self) -> list[str]:
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return await self.client.list_symbols()
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async def search_keys(self, pattern: str) -> list[str]:
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'''
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Search for time series key in the storage backend.
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'''
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...
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async def write_ticks(self, ticks: list) -> None:
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@abstractmethod
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def search_keys(self) -> list[str]:
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...
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# @abstractmethod
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# async def write_ticks(
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# self,
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# ticks: list,
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# ) -> ReceiveType:
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# ...
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# ``trio.abc.AsyncResource`` methods
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@abstractmethod
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async def load(
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self,
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fqme: str,
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@ -97,30 +96,19 @@ class Storage:
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datetime | None, # first dt
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datetime | None, # last dt
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]:
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...
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first_tsdb_dt, last_tsdb_dt = None, None
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hist = await self.read_ohlcv(
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fqme,
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# on first load we don't need to pull the max
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# history per request size worth.
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limit=3000,
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timeframe=timeframe,
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)
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log.info(f'Loaded tsdb history {hist}')
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@abstractmethod
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async def delete_ts(
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self,
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key: str,
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timeframe: int | str | None = None,
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fmt: str = 'OHLCV',
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if len(hist):
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times = hist['Epoch']
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first, last = times[0], times[-1]
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first_tsdb_dt, last_tsdb_dt = map(
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pendulum.from_timestamp, [first, last]
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)
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return (
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hist, # array-data
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first_tsdb_dt, # start of query-frame
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last_tsdb_dt, # most recent
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)
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) -> bool:
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...
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@abstractmethod
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async def read_ohlcv(
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self,
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fqme: str,
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@ -129,94 +117,7 @@ class Storage:
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limit: int = int(800e3),
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) -> np.ndarray:
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client = self.client
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syms = await client.list_symbols()
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if fqme not in syms:
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return {}
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# use the provided timeframe or 1s by default
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tfstr = tf_in_1s.get(timeframe, tf_in_1s[1])
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params = Params(
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symbols=fqme,
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timeframe=tfstr,
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attrgroup='OHLCV',
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end=end,
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# limit_from_start=True,
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# TODO: figure the max limit here given the
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# ``purepc`` msg size limit of purerpc: 33554432
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limit=limit,
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)
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for i in range(3):
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try:
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result = await client.query(params)
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break
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except purerpc.grpclib.exceptions.UnknownError as err:
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if 'snappy' in err.args:
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await tractor.breakpoint()
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# indicate there is no history for this timeframe
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log.exception(
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f'Unknown mkts QUERY error: {params}\n'
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f'{err.args}'
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)
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else:
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return {}
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# TODO: it turns out column access on recarrays is actually slower:
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# https://jakevdp.github.io/PythonDataScienceHandbook/02.09-structured-data-numpy.html#RecordArrays:-Structured-Arrays-with-a-Twist
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# it might make sense to make these structured arrays?
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data_set = result.by_symbols()[fqme]
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array = data_set.array
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# XXX: ensure sample rate is as expected
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time = data_set.array['Epoch']
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if len(time) > 1:
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time_step = time[-1] - time[-2]
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ts = tf_in_1s.inverse[data_set.timeframe]
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if time_step != ts:
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log.warning(
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f'MKTS BUG: wrong timeframe loaded: {time_step}'
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'YOUR DATABASE LIKELY CONTAINS BAD DATA FROM AN OLD BUG'
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f'WIPING HISTORY FOR {ts}s'
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)
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await self.delete_ts(fqme, timeframe)
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# try reading again..
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return await self.read_ohlcv(
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fqme,
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timeframe,
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end,
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limit,
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)
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return array
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async def delete_ts(
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self,
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key: str,
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timeframe: Union[int, str | None] = None,
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fmt: str = 'OHLCV',
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) -> bool:
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client = self.client
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syms = await client.list_symbols()
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if key not in syms:
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await tractor.breakpoint()
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raise KeyError(f'`{key}` table key not found in\n{syms}?')
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tbk = mk_tbk((
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key,
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tf_in_1s.get(timeframe, tf_in_1s[60]),
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fmt,
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))
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return await client.destroy(tbk=tbk)
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...
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async def write_ohlcv(
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self,
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@ -227,106 +128,74 @@ class Storage:
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limit: int = int(800e3),
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) -> None:
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# build mkts schema compat array for writing
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mkts_dt = np.dtype(_ohlcv_dt)
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mkts_array = np.zeros(
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len(ohlcv),
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dtype=mkts_dt,
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)
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# copy from shm array (yes it's this easy):
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# https://numpy.org/doc/stable/user/basics.rec.html#assignment-from-other-structured-arrays
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mkts_array[:] = ohlcv[[
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'time',
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'open',
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'high',
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'low',
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'close',
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'volume',
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]]
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...
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m, r = divmod(len(mkts_array), limit)
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tfkey = tf_in_1s[timeframe]
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for i in range(m, 1):
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to_push = mkts_array[i-1:i*limit]
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class StorageConnectionError(ConnectionError):
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'''
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Can't connect to the desired tsdb subsys/service.
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# write to db
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resp = await self.client.write(
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to_push,
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tbk=f'{fqme}/{tfkey}/OHLCV',
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'''
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# NOTE: will will append duplicates
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# for the same timestamp-index.
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# TODO: pre-deduplicate?
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isvariablelength=append_and_duplicate,
|
||||
)
|
||||
def get_storagemod(name: str) -> ModuleType:
|
||||
mod: ModuleType = import_module(
|
||||
'.' + name,
|
||||
'piker.storage',
|
||||
)
|
||||
|
||||
log.info(
|
||||
f'Wrote {mkts_array.size} datums to tsdb\n'
|
||||
)
|
||||
|
||||
for resp in resp.responses:
|
||||
err = resp.error
|
||||
if err:
|
||||
raise MarketStoreError(err)
|
||||
|
||||
if r:
|
||||
to_push = mkts_array[m*limit:]
|
||||
|
||||
# write to db
|
||||
resp = await self.client.write(
|
||||
to_push,
|
||||
tbk=f'{fqme}/{tfkey}/OHLCV',
|
||||
|
||||
# NOTE: will will append duplicates
|
||||
# for the same timestamp-index.
|
||||
# TODO: pre deduplicate?
|
||||
isvariablelength=append_and_duplicate,
|
||||
)
|
||||
|
||||
log.info(
|
||||
f'Wrote {mkts_array.size} datums to tsdb\n'
|
||||
)
|
||||
|
||||
for resp in resp.responses:
|
||||
err = resp.error
|
||||
if err:
|
||||
raise MarketStoreError(err)
|
||||
|
||||
# XXX: currently the only way to do this is through the CLI:
|
||||
|
||||
# sudo ./marketstore connect --dir ~/.config/piker/data
|
||||
# >> \show mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
|
||||
# and this seems to block and use up mem..
|
||||
# >> \trim mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
|
||||
|
||||
# relevant source code for this is here:
|
||||
# https://github.com/alpacahq/marketstore/blob/master/cmd/connect/session/trim.go#L14
|
||||
# def delete_range(self, start_dt, end_dt) -> None:
|
||||
# ...
|
||||
# we only allow monkeying because it's for internal keying
|
||||
mod.name = mod.__name__.split('.')[-1]
|
||||
return mod
|
||||
|
||||
|
||||
@acm
|
||||
async def open_storage_client(
|
||||
host: str,
|
||||
grpc_port: int,
|
||||
name: str | None = None,
|
||||
|
||||
) -> tuple[Storage, dict[str, np.ndarray]]:
|
||||
) -> tuple[ModuleType, Storage]:
|
||||
'''
|
||||
Load a series by key and deliver in ``numpy`` struct array format.
|
||||
Load the ``Storage`` client for named backend.
|
||||
|
||||
'''
|
||||
from piker.service.marketstore import get_client
|
||||
# load root config for tsdb
|
||||
conf, path = config.load('conf', touch_if_dne=True)
|
||||
net = conf.get('network')
|
||||
if net:
|
||||
tsdbconf = net.get('tsdb')
|
||||
|
||||
async with (
|
||||
# eventually a storage backend endpoint
|
||||
get_client(
|
||||
host=host,
|
||||
port=grpc_port,
|
||||
) as client,
|
||||
# lookup backend tsdb module by name and load any user service
|
||||
# settings for connecting to the tsdb service.
|
||||
name: str = tsdbconf.pop('backend')
|
||||
tsdb_host: str = tsdbconf['host']
|
||||
|
||||
if name is None:
|
||||
raise RuntimeError('No tsdb backend has been set!?')
|
||||
|
||||
# import and load storagemod by name
|
||||
mod: ModuleType = get_storagemod(name)
|
||||
get_client = mod.get_client
|
||||
|
||||
log.info('Scanning for existing `{tsbd_backend}`')
|
||||
tsdb_is_up: bool = await check_for_service(f'{name}d')
|
||||
if (
|
||||
tsdb_host == 'localhost'
|
||||
or tsdb_is_up
|
||||
):
|
||||
# slap on our wrapper api
|
||||
yield Storage(client)
|
||||
log.info(f'Connecting to local {name}@{tsdbconf}')
|
||||
else:
|
||||
log.info(f'Attempting to connect to remote {name}@{tsdbconf}')
|
||||
|
||||
try:
|
||||
async with (
|
||||
get_client(**tsdbconf) as client,
|
||||
):
|
||||
# slap on our wrapper api
|
||||
yield mod, client
|
||||
|
||||
except Exception as err:
|
||||
raise StorageConnectionError(
|
||||
f'No connection to {name}'
|
||||
) from err
|
||||
|
||||
|
||||
# NOTE: pretty sure right now this is only being
|
||||
|
|
@ -362,22 +231,15 @@ async def open_tsdb_client(
|
|||
# * the original data feed arch blurb:
|
||||
# - https://github.com/pikers/piker/issues/98
|
||||
#
|
||||
from .._profile import Profiler
|
||||
profiler = Profiler(
|
||||
disabled=True, # not pg_profile_enabled(),
|
||||
delayed=False,
|
||||
)
|
||||
from ..data.feed import maybe_open_feed
|
||||
|
||||
# load any user service settings for connecting to
|
||||
rootconf, path = config.load(
|
||||
'conf',
|
||||
touch_if_dne=True,
|
||||
)
|
||||
tsdbconf = rootconf['network'].get('tsdb')
|
||||
# backend = tsdbconf.pop('backend')
|
||||
async with (
|
||||
open_storage_client(
|
||||
**tsdbconf,
|
||||
) as storage,
|
||||
open_storage_client() as (_, storage),
|
||||
|
||||
maybe_open_feed(
|
||||
[fqme],
|
||||
|
|
|
|||
|
|
@ -0,0 +1,342 @@
|
|||
# piker: trading gear for hackers
|
||||
# Copyright (C) Tyler Goodlet (in stewardship for 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/>.
|
||||
|
||||
'''
|
||||
marketstore tsdb backend:
|
||||
https://github.com/alpacahq/marketstore
|
||||
|
||||
|
||||
We wrote an async gGRPC client:
|
||||
https://github.com/pikers/anyio-marketstore
|
||||
|
||||
which is normally preferred minus the discovered issues
|
||||
in https://github.com/pikers/piker/issues/443
|
||||
|
||||
Which is the main reason for us moving away from this
|
||||
platform..
|
||||
|
||||
'''
|
||||
from __future__ import annotations
|
||||
from contextlib import asynccontextmanager as acm
|
||||
from datetime import datetime
|
||||
# from pprint import pformat
|
||||
from typing import (
|
||||
Union,
|
||||
)
|
||||
|
||||
from bidict import bidict
|
||||
import tractor
|
||||
import numpy as np
|
||||
from anyio_marketstore import (
|
||||
Params,
|
||||
)
|
||||
import pendulum
|
||||
import purerpc
|
||||
|
||||
from ..service.marketstore import (
|
||||
MarketstoreClient,
|
||||
tf_in_1s,
|
||||
mk_tbk,
|
||||
_ohlcv_dt,
|
||||
MarketStoreError,
|
||||
)
|
||||
from anyio_marketstore import ( # noqa
|
||||
open_marketstore_client,
|
||||
MarketstoreClient,
|
||||
Params,
|
||||
)
|
||||
from ..log import get_logger
|
||||
# from .._profile import Profiler
|
||||
|
||||
|
||||
log = get_logger(__name__)
|
||||
|
||||
|
||||
class Storage:
|
||||
'''
|
||||
High level storage api for both real-time and historical ingest.
|
||||
|
||||
'''
|
||||
def __init__(
|
||||
self,
|
||||
client: MarketstoreClient,
|
||||
|
||||
) -> None:
|
||||
# TODO: eventually this should be an api/interface type that
|
||||
# ensures we can support multiple tsdb backends.
|
||||
self.client = client
|
||||
|
||||
# series' cache from tsdb reads
|
||||
self._arrays: dict[str, np.ndarray] = {}
|
||||
|
||||
async def list_keys(self) -> list[str]:
|
||||
return await self.client.list_symbols()
|
||||
|
||||
async def search_keys(self, pattern: str) -> list[str]:
|
||||
'''
|
||||
Search for time series key in the storage backend.
|
||||
|
||||
'''
|
||||
...
|
||||
|
||||
async def write_ticks(self, ticks: list) -> None:
|
||||
...
|
||||
|
||||
async def load(
|
||||
self,
|
||||
fqme: str,
|
||||
timeframe: int,
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray, # timeframe sampled array-series
|
||||
datetime | None, # first dt
|
||||
datetime | None, # last dt
|
||||
]:
|
||||
|
||||
first_tsdb_dt, last_tsdb_dt = None, None
|
||||
hist = await self.read_ohlcv(
|
||||
fqme,
|
||||
# on first load we don't need to pull the max
|
||||
# history per request size worth.
|
||||
limit=3000,
|
||||
timeframe=timeframe,
|
||||
)
|
||||
log.info(f'Loaded tsdb history {hist}')
|
||||
|
||||
if len(hist):
|
||||
times = hist['Epoch']
|
||||
first, last = times[0], times[-1]
|
||||
first_tsdb_dt, last_tsdb_dt = map(
|
||||
pendulum.from_timestamp, [first, last]
|
||||
)
|
||||
|
||||
return (
|
||||
hist, # array-data
|
||||
first_tsdb_dt, # start of query-frame
|
||||
last_tsdb_dt, # most recent
|
||||
)
|
||||
|
||||
async def read_ohlcv(
|
||||
self,
|
||||
fqme: str,
|
||||
timeframe: int | str,
|
||||
end: int | None = None,
|
||||
limit: int = int(800e3),
|
||||
|
||||
) -> np.ndarray:
|
||||
|
||||
client = self.client
|
||||
syms = await client.list_symbols()
|
||||
|
||||
if fqme not in syms:
|
||||
return {}
|
||||
|
||||
# use the provided timeframe or 1s by default
|
||||
tfstr = tf_in_1s.get(timeframe, tf_in_1s[1])
|
||||
|
||||
params = Params(
|
||||
symbols=fqme,
|
||||
timeframe=tfstr,
|
||||
attrgroup='OHLCV',
|
||||
end=end,
|
||||
# limit_from_start=True,
|
||||
|
||||
# TODO: figure the max limit here given the
|
||||
# ``purepc`` msg size limit of purerpc: 33554432
|
||||
limit=limit,
|
||||
)
|
||||
|
||||
for i in range(3):
|
||||
try:
|
||||
result = await client.query(params)
|
||||
break
|
||||
except purerpc.grpclib.exceptions.UnknownError as err:
|
||||
if 'snappy' in err.args:
|
||||
await tractor.breakpoint()
|
||||
|
||||
# indicate there is no history for this timeframe
|
||||
log.exception(
|
||||
f'Unknown mkts QUERY error: {params}\n'
|
||||
f'{err.args}'
|
||||
)
|
||||
else:
|
||||
return {}
|
||||
|
||||
# TODO: it turns out column access on recarrays is actually slower:
|
||||
# https://jakevdp.github.io/PythonDataScienceHandbook/02.09-structured-data-numpy.html#RecordArrays:-Structured-Arrays-with-a-Twist
|
||||
# it might make sense to make these structured arrays?
|
||||
data_set = result.by_symbols()[fqme]
|
||||
array = data_set.array
|
||||
|
||||
# XXX: ensure sample rate is as expected
|
||||
time = data_set.array['Epoch']
|
||||
if len(time) > 1:
|
||||
time_step = time[-1] - time[-2]
|
||||
ts = tf_in_1s.inverse[data_set.timeframe]
|
||||
|
||||
if time_step != ts:
|
||||
log.warning(
|
||||
f'MKTS BUG: wrong timeframe loaded: {time_step}'
|
||||
'YOUR DATABASE LIKELY CONTAINS BAD DATA FROM AN OLD BUG'
|
||||
f'WIPING HISTORY FOR {ts}s'
|
||||
)
|
||||
await self.delete_ts(fqme, timeframe)
|
||||
|
||||
# try reading again..
|
||||
return await self.read_ohlcv(
|
||||
fqme,
|
||||
timeframe,
|
||||
end,
|
||||
limit,
|
||||
)
|
||||
|
||||
return array
|
||||
|
||||
async def delete_ts(
|
||||
self,
|
||||
key: str,
|
||||
timeframe: Union[int, str | None] = None,
|
||||
fmt: str = 'OHLCV',
|
||||
|
||||
) -> bool:
|
||||
|
||||
client = self.client
|
||||
syms = await client.list_symbols()
|
||||
if key not in syms:
|
||||
await tractor.breakpoint()
|
||||
raise KeyError(f'`{key}` table key not found in\n{syms}?')
|
||||
|
||||
tbk = mk_tbk((
|
||||
key,
|
||||
tf_in_1s.get(timeframe, tf_in_1s[60]),
|
||||
fmt,
|
||||
))
|
||||
return await client.destroy(tbk=tbk)
|
||||
|
||||
async def write_ohlcv(
|
||||
self,
|
||||
fqme: str,
|
||||
ohlcv: np.ndarray,
|
||||
timeframe: int,
|
||||
append_and_duplicate: bool = True,
|
||||
limit: int = int(800e3),
|
||||
|
||||
) -> None:
|
||||
# build mkts schema compat array for writing
|
||||
mkts_dt = np.dtype(_ohlcv_dt)
|
||||
mkts_array = np.zeros(
|
||||
len(ohlcv),
|
||||
dtype=mkts_dt,
|
||||
)
|
||||
# copy from shm array (yes it's this easy):
|
||||
# https://numpy.org/doc/stable/user/basics.rec.html#assignment-from-other-structured-arrays
|
||||
mkts_array[:] = ohlcv[[
|
||||
'time',
|
||||
'open',
|
||||
'high',
|
||||
'low',
|
||||
'close',
|
||||
'volume',
|
||||
]]
|
||||
|
||||
m, r = divmod(len(mkts_array), limit)
|
||||
|
||||
tfkey = tf_in_1s[timeframe]
|
||||
for i in range(m, 1):
|
||||
to_push = mkts_array[i-1:i*limit]
|
||||
|
||||
# write to db
|
||||
resp = await self.client.write(
|
||||
to_push,
|
||||
tbk=f'{fqme}/{tfkey}/OHLCV',
|
||||
|
||||
# NOTE: will will append duplicates
|
||||
# for the same timestamp-index.
|
||||
# TODO: pre-deduplicate?
|
||||
isvariablelength=append_and_duplicate,
|
||||
)
|
||||
|
||||
log.info(
|
||||
f'Wrote {mkts_array.size} datums to tsdb\n'
|
||||
)
|
||||
|
||||
for resp in resp.responses:
|
||||
err = resp.error
|
||||
if err:
|
||||
raise MarketStoreError(err)
|
||||
|
||||
if r:
|
||||
to_push = mkts_array[m*limit:]
|
||||
|
||||
# write to db
|
||||
resp = await self.client.write(
|
||||
to_push,
|
||||
tbk=f'{fqme}/{tfkey}/OHLCV',
|
||||
|
||||
# NOTE: will will append duplicates
|
||||
# for the same timestamp-index.
|
||||
# TODO: pre deduplicate?
|
||||
isvariablelength=append_and_duplicate,
|
||||
)
|
||||
|
||||
log.info(
|
||||
f'Wrote {mkts_array.size} datums to tsdb\n'
|
||||
)
|
||||
|
||||
for resp in resp.responses:
|
||||
err = resp.error
|
||||
if err:
|
||||
raise MarketStoreError(err)
|
||||
|
||||
# XXX: currently the only way to do this is through the CLI:
|
||||
|
||||
# sudo ./marketstore connect --dir ~/.config/piker/data
|
||||
# >> \show mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
|
||||
# and this seems to block and use up mem..
|
||||
# >> \trim mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
|
||||
|
||||
# relevant source code for this is here:
|
||||
# https://github.com/alpacahq/marketstore/blob/master/cmd/connect/session/trim.go#L14
|
||||
# def delete_range(self, start_dt, end_dt) -> None:
|
||||
# ...
|
||||
|
||||
|
||||
ohlc_key_map = bidict({
|
||||
'Epoch': 'time',
|
||||
'Open': 'open',
|
||||
'High': 'high',
|
||||
'Low': 'low',
|
||||
'Close': 'close',
|
||||
'Volume': 'volume',
|
||||
})
|
||||
|
||||
|
||||
@acm
|
||||
async def get_client(
|
||||
grpc_port: int, # required
|
||||
host: str | None,
|
||||
|
||||
) -> MarketstoreClient:
|
||||
'''
|
||||
Load a ``anyio_marketstore`` grpc client connected
|
||||
to an existing ``marketstore`` server.
|
||||
|
||||
'''
|
||||
async with open_marketstore_client(
|
||||
host or 'localhost',
|
||||
grpc_port,
|
||||
) as client:
|
||||
yield Storage(client)
|
||||
Loading…
Reference in New Issue