359 lines
10 KiB
Python
359 lines
10 KiB
Python
# piker: trading gear for hackers
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# Copyright (C) Tyler Goodlet (in stewardship of pikers)
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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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from typing import AsyncIterator, Optional, Union
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import numpy as np
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from tractor.trionics._broadcast import AsyncReceiver
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from ._api import fsp
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from ..data._normalize import iterticks
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from ..data._sharedmem import ShmArray
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from ._momo import _wma
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from ..log import get_logger
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log = get_logger(__name__)
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# NOTE: is the same as our `wma` fsp, and if so which one is faster?
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# Ohhh, this is an IIR style i think? So it has an anchor point
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# effectively instead of a moving window/FIR style?
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def wap(
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signal: np.ndarray,
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weights: np.ndarray,
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) -> np.ndarray:
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'''
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Weighted average price from signal and weights.
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'''
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cum_weights = np.cumsum(weights)
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cum_weighted_input = np.cumsum(signal * weights)
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# cum_weighted_input / cum_weights
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# but, avoid divide by zero errors
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avg = np.divide(
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cum_weighted_input,
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cum_weights,
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where=cum_weights != 0
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)
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return (
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avg,
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cum_weighted_input,
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cum_weights,
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)
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@fsp
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async def tina_vwap(
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source: AsyncReceiver[dict],
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ohlcv: ShmArray, # OHLC sampled history
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# TODO: anchor logic (eg. to session start)
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anchors: Optional[np.ndarray] = None,
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) -> Union[
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AsyncIterator[np.ndarray],
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float
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]:
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'''
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Streaming volume weighted moving average.
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Calling this "tina" for now since we're using HLC3 instead of tick.
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'''
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if anchors is None:
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# TODO:
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# anchor to session start of data if possible
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pass
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a = ohlcv.array
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chl3 = (a['close'] + a['high'] + a['low']) / 3
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v = a['volume']
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h_vwap, cum_wp, cum_v = wap(chl3, v)
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# deliver historical output as "first yield"
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yield h_vwap
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w_tot = cum_wp[-1]
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v_tot = cum_v[-1]
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# vwap_tot = h_vwap[-1]
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async for quote in source:
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for tick in iterticks(
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quote,
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types=['trade'],
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):
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# c, h, l, v = ohlcv.array[-1][
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# ['closes', 'high', 'low', 'volume']
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# ]
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# this computes tick-by-tick weightings from here forward
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size = tick['size']
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price = tick['price']
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v_tot += size
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w_tot += price * size
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# yield ((((o + h + l) / 3) * v) weights_tot) / v_tot
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yield 'tina_vwap', w_tot / v_tot
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@fsp(
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outputs=(
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'dolla_vlm',
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'dark_vlm',
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'trade_count',
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'dark_trade_count',
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),
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curve_style='step',
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)
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async def dolla_vlm(
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source: AsyncReceiver[dict],
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ohlcv: ShmArray, # OHLC sampled history
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) -> AsyncIterator[
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tuple[str, Union[np.ndarray, float]],
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]:
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'''
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"Dollar Volume", aka the volume in asset-currency-units (usually
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a fiat) computed from some price function for the sample step
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*multiplied* (*) by the asset unit volume.
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Useful for comparing cross asset "money flow" in #s that are
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asset-currency-independent.
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'''
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a = ohlcv.array
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chl3 = (a['close'] + a['high'] + a['low']) / 3
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v = a['volume']
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# on first iteration yield history
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yield {
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'dolla_vlm': chl3 * v,
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'dark_vlm': None,
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}
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i = ohlcv.index
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dvlm = vlm = 0
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dark_trade_count = trade_count = 0
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async for quote in source:
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for tick in iterticks(
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quote,
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types=(
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'trade',
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'dark_trade',
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),
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deduplicate_darks=True,
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):
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# this computes tick-by-tick weightings from here forward
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size = tick['size']
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price = tick['price']
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li = ohlcv.index
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if li > i:
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i = li
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trade_count = dark_trade_count = dvlm = vlm = 0
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# TODO: for marginned instruments (futes, etfs?) we need to
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# show the margin $vlm by multiplying by whatever multiplier
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# is reported in the sym info.
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ttype = tick.get('type')
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if ttype == 'dark_trade':
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dvlm += price * size
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yield 'dark_vlm', dvlm
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dark_trade_count += 1
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yield 'dark_trade_count', dark_trade_count
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# print(f'{dark_trade_count}th dark_trade: {tick}')
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else:
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# print(f'vlm: {tick}')
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vlm += price * size
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yield 'dolla_vlm', vlm
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trade_count += 1
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yield 'trade_count', trade_count
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# TODO: plot both to compare?
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# c, h, l, v = ohlcv.last()[
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# ['close', 'high', 'low', 'volume']
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# ][0]
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# tina_lvlm = c+h+l/3 * v
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# print(f' tinal vlm: {tina_lvlm}')
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@fsp(
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# TODO: eventually I guess we should support some kinda declarative
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# graphics config syntax per output yah? That seems like a clean way
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# to let users configure things? Not sure how exactly to offer that
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# api as well as how to expose such a thing *inside* the body?
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outputs=(
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# pulled verbatim from `ib` for now
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'1m_trade_rate',
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'1m_vlm_rate',
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# our own instantaneous rate calcs which are all
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# parameterized by a samples count (bars) period
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'trade_rate',
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'dark_trade_rate',
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'dvlm_rate',
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'dark_dvlm_rate',
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),
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curve_style='line',
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)
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async def flow_rates(
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source: AsyncReceiver[dict],
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ohlcv: ShmArray, # OHLC sampled history
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# TODO (idea): a dynamic generic / boxing type that can be updated by other
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# FSPs, user input, and possibly any general event stream in
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# real-time. Hint: ideally implemented with caching until mutated
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# ;)
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period: 'Param[int]' = 6, # noqa
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# TODO: support other means by providing a map
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# to weights `partial()`-ed with `wma()`?
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mean_type: str = 'arithmetic',
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# TODO (idea): a generic for declaring boxed fsps much like ``pytest``
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# fixtures? This probably needs a lot of thought if we want to offer
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# a higher level composition syntax eventually (oh right gotta make
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# an issue for that).
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# ideas for how to allow composition / intercalling:
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# - offer a `Fsp.get_history()` to do the first yield output?
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# * err wait can we just have shm access directly?
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# - how would it work if some consumer fsp wanted to dynamically
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# change params which are input to the callee fsp? i guess we could
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# lazy copy in that case?
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# dvlm: 'Fsp[dolla_vlm]'
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) -> AsyncIterator[
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tuple[str, Union[np.ndarray, float]],
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]:
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# generally no history available prior to real-time calcs
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yield {
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# from ib
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'1m_trade_rate': None,
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'1m_vlm_rate': None,
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'trade_rate': None,
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'dark_trade_rate': None,
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'dvlm_rate': None,
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'dark_dvlm_rate': None,
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}
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# TODO: 3.10 do ``anext()``
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quote = await source.__anext__()
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# ltr = 0
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# lvr = 0
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tr = quote.get('tradeRate')
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yield '1m_trade_rate', tr or 0
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vr = quote.get('volumeRate')
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yield '1m_vlm_rate', vr or 0
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yield 'trade_rate', 0
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yield 'dark_trade_rate', 0
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yield 'dvlm_rate', 0
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yield 'dark_dvlm_rate', 0
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# NOTE: in theory we could dynamically allocate a cascade based on
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# this call but not sure if that's too "dynamic" in terms of
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# validating cascade flows from message typing perspective.
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# attach to ``dolla_vlm`` fsp running
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# on this same source flow.
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dvlm_shm = dolla_vlm.get_shm(ohlcv)
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# precompute arithmetic mean weights (all ones)
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seq = np.full((period,), 1)
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weights = seq / seq.sum()
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async for quote in source:
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if not quote:
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log.error("OH WTF NO QUOTE IN FSP")
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continue
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# dvlm_wma = _wma(
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# dvlm_shm.array['dolla_vlm'],
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# period,
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# weights=weights,
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# )
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# yield 'dvlm_rate', dvlm_wma[-1]
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if period > 1:
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trade_rate_wma = _wma(
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dvlm_shm.array['trade_count'],
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period,
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weights=weights,
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)
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trade_rate = trade_rate_wma[-1]
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# print(trade_rate)
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yield 'trade_rate', trade_rate
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else:
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# instantaneous rate per sample step
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count = dvlm_shm.array['trade_count'][-1]
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yield 'trade_rate', count
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# TODO: skip this if no dark vlm is declared
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# by symbol info (eg. in crypto$)
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# dark_dvlm_wma = _wma(
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# dvlm_shm.array['dark_vlm'],
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# period,
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# weights=weights,
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# )
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# yield 'dark_dvlm_rate', dark_dvlm_wma[-1]
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if period > 1:
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dark_trade_rate_wma = _wma(
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dvlm_shm.array['dark_trade_count'],
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period,
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weights=weights,
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)
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yield 'dark_trade_rate', dark_trade_rate_wma[-1]
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else:
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# instantaneous rate per sample step
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dark_count = dvlm_shm.array['dark_trade_count'][-1]
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yield 'dark_trade_rate', dark_count
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# XXX: ib specific schema we should
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# probably pre-pack ourselves.
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# tr = quote.get('tradeRate')
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# if tr is not None and tr != ltr:
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# # print(f'trade rate: {tr}')
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# yield '1m_trade_rate', tr
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# ltr = tr
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# vr = quote.get('volumeRate')
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# if vr is not None and vr != lvr:
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# # print(f'vlm rate: {vr}')
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# yield '1m_vlm_rate', vr
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# lvr = vr
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