94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
# piker: trading gear for hackers
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# Copyright (C) 2018-present Tyler Goodlet (in stewardship of 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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from typing import AsyncIterator, Optional
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import numpy as np
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from ..data._normalize import iterticks
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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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"""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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async def _tina_vwap(
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source, #: AsyncStream[np.ndarray],
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ohlcv: np.ndarray, # price time-frame "aware"
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anchors: Optional[np.ndarray] = None,
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) -> AsyncIterator[np.ndarray]: # maybe something like like FspStream?
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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(quote, types=['trade']):
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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 w_tot / v_tot
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