603 lines
17 KiB
Python
603 lines
17 KiB
Python
# piker: trading gear for hackers
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# Copyright (C) Tyler Goodlet (in stewardship for piker0)
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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'''
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``marketstore`` integration.
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- client management routines
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- ticK data ingest routines
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- websocket client for subscribing to write triggers
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- todo: tick sequence stream-cloning for testing
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'''
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from contextlib import asynccontextmanager as acm
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from pprint import pformat
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from typing import (
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Any,
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Optional,
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Union,
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# Callable,
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# TYPE_CHECKING,
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)
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import time
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from math import isnan
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from bidict import bidict
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import msgpack
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import numpy as np
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import pandas as pd
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import tractor
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from trio_websocket import open_websocket_url
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from anyio_marketstore import (
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open_marketstore_client,
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MarketstoreClient,
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Params,
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)
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import purerpc
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from .feed import maybe_open_feed
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from ._source import (
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mk_fqsn,
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# Symbol,
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)
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from ..log import get_logger, get_console_log
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# if TYPE_CHECKING:
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# from ._sharedmem import ShmArray
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log = get_logger(__name__)
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_tick_tbk_ids: tuple[str, str] = ('1Sec', 'TICK')
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_tick_tbk: str = '{}/' + '/'.join(_tick_tbk_ids)
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_tick_dt = [
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# these two are required for as a "primary key"
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('Epoch', 'i8'),
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('Nanoseconds', 'i4'),
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('IsTrade', 'i1'),
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('IsBid', 'i1'),
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('Price', 'f4'),
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('Size', 'f4')
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]
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_quote_dt = [
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# these two are required for as a "primary key"
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('Epoch', 'i8'),
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('Nanoseconds', 'i4'),
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('Tick', 'i4'), # (-1, 0, 1) = (on bid, same, on ask)
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# ('fill_time', 'f4'),
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('Last', 'f4'),
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('Bid', 'f4'),
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('Bsize', 'i8'),
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('Asize', 'i8'),
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('Ask', 'f4'),
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('Size', 'i8'),
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('Volume', 'i8'),
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# ('brokerd_ts', 'i64'),
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# ('VWAP', 'f4')
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]
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_quote_tmp = {}.fromkeys(dict(_quote_dt).keys(), np.nan)
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_tick_map = {
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'Up': 1,
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'Equal': 0,
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'Down': -1,
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None: np.nan,
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}
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_ohlcv_dt = [
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# these two are required for as a "primary key"
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('Epoch', 'i8'),
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# ('Nanoseconds', 'i4'),
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# ohlcv sampling
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('Open', 'f4'),
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('High', 'f4'),
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('Low', 'i8'),
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('Close', 'i8'),
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('Volume', 'f4'),
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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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Converts,
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``('SPY', '1Sec', 'TICK')`` -> ``"SPY/1Sec/TICK"```
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'''
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return '/'.join(keys)
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def quote_to_marketstore_structarray(
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quote: dict[str, Any],
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last_fill: Optional[float]
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) -> np.array:
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'''
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Return marketstore writeable structarray from quote ``dict``.
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'''
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if last_fill:
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# new fill bby
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now = timestamp(last_fill)
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else:
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# this should get inserted upstream by the broker-client to
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# subtract from IPC latency
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now = time.time_ns()
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secs, ns = now / 10**9, now % 10**9
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# pack into list[tuple[str, Any]]
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array_input = []
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# insert 'Epoch' entry first and then 'Nanoseconds'.
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array_input.append(int(secs))
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array_input.append(int(ns))
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# append remaining fields
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for name, dt in _quote_dt[2:]:
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if 'f' in dt:
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none = np.nan
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else:
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# for ``np.int`` we use 0 as a null value
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none = 0
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# casefold? see https://github.com/alpacahq/marketstore/issues/324
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val = quote.get(name.casefold(), none)
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array_input.append(val)
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return np.array([tuple(array_input)], dtype=_quote_dt)
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def timestamp(date, **kwargs) -> int:
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'''
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Return marketstore compatible 'Epoch' integer in nanoseconds
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from a date formatted str.
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'''
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return int(pd.Timestamp(date, **kwargs).value)
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@acm
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async def get_client(
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host: str = 'localhost',
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port: int = 5995
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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,
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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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# def err_on_resp(response: dict) -> None:
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# """Raise any errors found in responses from client request.
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# """
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# responses = response['responses']
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# if responses is not None:
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# for r in responses:
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# err = r['error']
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# if err:
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# raise MarketStoreError(err)
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tf_in_1s = bidict({
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1: '1Sec',
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60: '1Min',
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60*5: '5Min',
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60*15: '15Min',
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60*30: '30Min',
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60*60: '1H',
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60*60*24: '1D',
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})
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class Storage:
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'''
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High level storage api for both real-time and historical ingest.
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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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async def write_ticks(self, ticks: list) -> None:
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...
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async def write_ohlcv(self, ohlcv: np.ndarray) -> None:
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...
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async def read_ohlcv(
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self,
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fqsn: str,
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timeframe: Optional[Union[int, str]] = None,
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) -> tuple[
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MarketstoreClient,
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Union[dict, np.ndarray]
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]:
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client = self.client
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syms = await client.list_symbols()
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if fqsn not in syms:
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return {}
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if timeframe is None:
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log.info(f'starting {fqsn} tsdb granularity scan..')
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# loop through and try to find highest granularity
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for tfstr in tf_in_1s.values():
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try:
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log.info(f'querying for {tfstr}@{fqsn}')
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result = await client.query(Params(fqsn, tfstr, 'OHLCV',))
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break
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except purerpc.grpclib.exceptions.UnknownError:
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# XXX: this is already logged by the container and
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# thus shows up through `marketstored` logs relay.
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# log.warning(f'{tfstr}@{fqsn} not found')
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continue
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else:
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return {}
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else:
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tfstr = tf_in_1s[timeframe]
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result = await client.query(Params(fqsn, tfstr, 'OHLCV',))
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# Fill out a `numpy` array-results map
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arrays = {}
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for fqsn, data_set in result.by_symbols().items():
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arrays.setdefault(fqsn, {})[
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tf_in_1s.inverse[data_set.timeframe]
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] = data_set.array
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return (
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client,
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arrays[fqsn][timeframe] if timeframe else arrays,
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)
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@acm
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async def open_storage_client(
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fqsn: str,
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period: Optional[Union[int, str]] = None, # in seconds
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) -> tuple[Storage, dict[str, np.ndarray]]:
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'''
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Load a series by key and deliver in ``numpy`` struct array format.
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'''
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async with get_client() as client:
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storage_client = Storage(client)
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arrays = await storage_client.read_ohlcv(
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fqsn,
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period,
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)
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yield storage_client, arrays
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async def backfill_history_diff(
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# symbol: Symbol
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) -> list[str]:
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# TODO: real-time dedicated task for ensuring
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# history consistency between the tsdb, shm and real-time feed..
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# update sequence design notes:
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# - load existing highest frequency data from mkts
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# * how do we want to offer this to the UI?
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# - lazy loading?
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# - try to load it all and expect graphics caching/diffing
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# to hide extra bits that aren't in view?
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# - compute the diff between latest data from broker and shm
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# * use sql api in mkts to determine where the backend should
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# start querying for data?
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# * append any diff with new shm length
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# * determine missing (gapped) history by scanning
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# * how far back do we look?
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# - begin rt update ingest and aggregation
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# * could start by always writing ticks to mkts instead of
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# worrying about a shm queue for now.
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# * we have a short list of shm queues worth groking:
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# - https://github.com/pikers/piker/issues/107
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# * the original data feed arch blurb:
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# - https://github.com/pikers/piker/issues/98
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#
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broker = 'ib'
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symbol = 'mnq.globex'
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# broker = 'binance'
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# symbol = 'btcusdt'
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fqsn = mk_fqsn(broker, symbol)
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async with (
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get_client() as client,
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maybe_open_feed(
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broker,
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[symbol],
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loglevel='info',
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# backpressure=False,
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start_stream=False,
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) as (feed, stream),
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):
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syms = await client.list_symbols()
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log.info(f'Existing symbol set:\n{pformat(syms)}')
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# diff db history with shm and only write the missing portions
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ohlcv = feed.shm.array
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key = (fqsn, '1Sec', 'OHLCV')
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tbk = mk_tbk(key)
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# diff vs. existing array and append new history
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# TODO:
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# TODO: should be no error?
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# assert not resp.responses
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start = time.time()
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qr = await client.query(
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# Params(fqsn, '1Sec`', 'OHLCV',)
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Params(*key),
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)
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# # Dig out `numpy` results map
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arrays: dict[tuple[str, int], np.ndarray] = {}
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for name, data_set in qr.by_symbols().items():
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in_secs = tf_in_1s.inverse[data_set.timeframe]
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arrays[(name, in_secs)] = data_set.array
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s1 = arrays[(fqsn, 1)]
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to_append = ohlcv[ohlcv['time'] > s1['Epoch'][-1]]
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end_diff = time.time()
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diff_ms = round((end_diff - start) * 1e3, ndigits=2)
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log.info(
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f'Appending {to_append.size} datums to tsdb from shm\n'
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f'Total diff time: {diff_ms} ms'
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)
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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(to_append),
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dtype=mkts_dt,
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)
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# copy from shm array
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mkts_array[:] = to_append[[
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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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# write to db
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resp = await client.write(
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mkts_array,
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tbk=tbk,
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# NOTE: will will append duplicates
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# for the same timestamp-index.
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isvariablelength=True,
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)
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end_write = time.time()
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diff_ms = round((end_write - end_diff) * 1e3, ndigits=2)
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log.info(
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f'Wrote {to_append.size} datums to tsdb\n'
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f'Total write time: {diff_ms} ms'
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)
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for resp in resp.responses:
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err = resp.error
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if err:
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raise MarketStoreError(err)
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# TODO: backfiller loop
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# await tractor.breakpoint()
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async def ingest_quote_stream(
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symbols: list[str],
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brokername: str,
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tries: int = 1,
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loglevel: str = None,
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) -> None:
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'''
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Ingest a broker quote stream into a ``marketstore`` tsdb.
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'''
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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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):
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async for quotes in feed.stream:
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log.info(quotes)
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for symbol, quote in quotes.items():
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for tick in quote.get('ticks', ()):
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ticktype = tick.get('type', 'n/a')
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# techtonic tick write
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array = quote_to_marketstore_structarray({
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'IsTrade': 1 if ticktype == 'trade' else 0,
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'IsBid': 1 if ticktype in ('bid', 'bsize') else 0,
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'Price': tick.get('price'),
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'Size': tick.get('size')
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}, last_fill=quote.get('broker_ts', None))
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await ms_client.write(array, _tick_tbk)
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# LEGACY WRITE LOOP (using old tick dt)
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# quote_cache = {
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# 'size': 0,
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# 'tick': 0
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# }
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# async for quotes in qstream:
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# log.info(quotes)
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# for symbol, quote in quotes.items():
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# # remap tick strs to ints
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# quote['tick'] = _tick_map[quote.get('tick', 'Equal')]
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# # check for volume update (i.e. did trades happen
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# # since last quote)
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# new_vol = quote.get('volume', None)
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# if new_vol is None:
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# log.debug(f"No fills for {symbol}")
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# if new_vol == quote_cache.get('volume'):
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# # should never happen due to field diffing
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# # on sender side
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# log.error(
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# f"{symbol}: got same volume as last quote?")
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# quote_cache.update(quote)
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# a = quote_to_marketstore_structarray(
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# quote,
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# # TODO: check this closer to the broker query api
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# last_fill=quote.get('fill_time', '')
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# )
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# await ms_client.write(symbol, a)
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async def stream_quotes(
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symbols: list[str],
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host: str = 'localhost',
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port: int = 5993,
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diff_cached: bool = True,
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loglevel: str = None,
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) -> None:
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'''
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Open a symbol stream from a running instance of marketstore and
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log to console.
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'''
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# XXX: required to propagate ``tractor`` loglevel to piker logging
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get_console_log(loglevel or tractor.current_actor().loglevel)
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tbks: dict[str, str] = {sym: f"{sym}/*/*" for sym in symbols}
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async with open_websocket_url(f'ws://{host}:{port}/ws') as ws:
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# send subs topics to server
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resp = await ws.send_message(
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msgpack.dumps({'streams': list(tbks.values())})
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)
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log.info(resp)
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async def recv() -> dict[str, Any]:
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return msgpack.loads((await ws.get_message()), encoding='utf-8')
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streams = (await recv())['streams']
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log.info(f"Subscribed to {streams}")
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_cache = {}
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while True:
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msg = await recv()
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# unpack symbol and quote data
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# key is in format ``<SYMBOL>/<TIMEFRAME>/<ID>``
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symbol = msg['key'].split('/')[0]
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data = msg['data']
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# calc time stamp(s)
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s, ns = data.pop('Epoch'), data.pop('Nanoseconds')
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ts = s * 10**9 + ns
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data['broker_fill_time_ns'] = ts
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quote = {}
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for k, v in data.items():
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if isnan(v):
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continue
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quote[k.lower()] = v
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quote['symbol'] = symbol
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quotes = {}
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if diff_cached:
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last = _cache.setdefault(symbol, {})
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new = set(quote.items()) - set(last.items())
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if new:
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log.info(f"New quote {quote['symbol']}:\n{new}")
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# only ship diff updates and other required fields
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payload = {k: quote[k] for k, v in new}
|
|
payload['symbol'] = symbol
|
|
|
|
# if there was volume likely the last size of
|
|
# shares traded is useful info and it's possible
|
|
# that the set difference from above will disregard
|
|
# a "size" value since the same # of shares were traded
|
|
size = quote.get('size')
|
|
volume = quote.get('volume')
|
|
if size and volume:
|
|
new_volume_since_last = max(
|
|
volume - last.get('volume', 0), 0)
|
|
log.warning(
|
|
f"NEW VOLUME {symbol}:{new_volume_since_last}")
|
|
payload['size'] = size
|
|
payload['last'] = quote.get('last')
|
|
|
|
# XXX: we append to a list for the options case where the
|
|
# subscription topic (key) is the same for all
|
|
# expiries even though this is uncessary for the
|
|
# stock case (different topic [i.e. symbol] for each
|
|
# quote).
|
|
quotes.setdefault(symbol, []).append(payload)
|
|
|
|
# update cache
|
|
_cache[symbol].update(quote)
|
|
else:
|
|
quotes = {
|
|
symbol: [{key.lower(): val for key, val in quote.items()}]}
|
|
|
|
if quotes:
|
|
yield quotes
|