227 lines
6.3 KiB
Python
227 lines
6.3 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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"""
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Momentum bby.
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"""
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from typing import AsyncIterator, Optional
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import numpy as np
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from numba import jit, float64, optional, int64
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from ..data._normalize import iterticks
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# TODO: things to figure the fuck out:
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# - how to handle non-plottable values
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# - composition of fsps / implicit chaining
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@jit(
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float64[:](
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float64[:],
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optional(float64),
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optional(float64)
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),
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nopython=True,
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nogil=True
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)
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def ema(
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y: 'np.ndarray[float64]',
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alpha: optional(float64) = None,
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ylast: optional(float64) = None,
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) -> 'np.ndarray[float64]':
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r"""Exponential weighted moving average owka 'Exponential smoothing'.
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- https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
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- https://en.wikipedia.org/wiki/Exponential_smoothing
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Fun facts:
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A geometric progression is the discrete version of an
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exponential function, that is where the name for this
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smoothing method originated according to statistics lore. In
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signal processing parlance, an EMA is a first order IIR filter.
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.. math::
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.tex
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{S_{t}={\begin{cases}Y_{1},&t=1
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\\\alpha Y_{t}+(1-\alpha )\cdot S_{t-1},&t>1\end{cases}}}
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.nerd
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(2) s = {
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s[0] = y[0]; t = 0
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s[t] = a*y[t] + (1-a)*s[t-1], t > 0.
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}
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More discussion here:
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https://stackoverflow.com/questions/42869495/numpy-version-of-exponential-weighted-moving-average-equivalent-to-pandas-ewm
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"""
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n = y.shape[0]
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if alpha is None:
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# https://en.wikipedia.org/wiki/Moving_average#Relationship_between_SMA_and_EMA
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# use the "center of mass" convention making an ema compare
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# directly to the com of a SMA or WMA:
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alpha = 2 / float(n + 1)
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s = np.empty(n, dtype=float64)
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if n == 1:
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s[0] = y[0] * alpha + ylast * (1 - alpha)
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else:
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if ylast is None:
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s[0] = y[0]
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else:
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s[0] = ylast
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for i in range(1, n):
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s[i] = y[i] * alpha + s[i-1] * (1 - alpha)
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return s
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# @jit(
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# float64[:](
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# float64[:],
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# int64,
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# float64,
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# float64,
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# ),
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# nopython=True,
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# nogil=True
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# )
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def rsi(
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signal: 'np.ndarray[float64]',
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period: int64 = 14,
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up_ema_last: float64 = None,
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down_ema_last: float64 = None,
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) -> 'np.ndarray[float64]':
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alpha = 1/period
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df = np.diff(signal)
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up = np.where(df > 0, df, 0)
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up_ema = ema(up, alpha, up_ema_last)
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down = np.where(df < 0, -df, 0)
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down_ema = ema(down, alpha, down_ema_last)
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# avoid dbz errors
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rs = np.divide(
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up_ema,
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down_ema,
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out=np.zeros_like(up_ema),
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where=down_ema != 0
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)
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# map rs through sigmoid (with range [0, 100])
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rsi = 100 - 100 / (1 + rs)
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# rsi = 100 * (up_ema / (up_ema + down_ema))
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# also return the last ema state for next iteration
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return rsi, up_ema[-1], down_ema[-1]
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def wma(
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signal: np.ndarray,
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length: int,
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weights: Optional[np.ndarray] = None,
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) -> np.ndarray:
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if weights is None:
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# default is a standard arithmetic mean
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seq = np.full((length,), 1)
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weights = seq / seq.sum()
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assert length == len(weights)
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return np.convolve(signal, weights, 'valid')
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# @piker.fsp.signal(
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# timeframes=['1s', '5s', '15s', '1m', '5m', '1H'],
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# )
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async def _rsi(
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source: 'QuoteStream[Dict[str, Any]]', # noqa
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ohlcv: "ShmArray[T<'close'>]",
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period: int = 14,
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) -> AsyncIterator[np.ndarray]:
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"""Multi-timeframe streaming RSI.
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https://en.wikipedia.org/wiki/Relative_strength_index
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"""
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sig = ohlcv.array['close']
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# wilder says to seed the RSI EMAs with the SMA for the "period"
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seed = wma(ohlcv.last(period)['close'], period)[0]
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# TODO: the emas here should be seeded with a period SMA as per
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# wilder's original formula..
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rsi_h, last_up_ema_close, last_down_ema_close = rsi(sig, period, seed, seed)
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up_ema_last = last_up_ema_close
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down_ema_last = last_down_ema_close
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# deliver history
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yield rsi_h
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index = ohlcv.index
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async for quote in source:
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# tick based updates
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for tick in iterticks(quote):
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# though incorrect below is interesting
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# sig = ohlcv.last(period)['close']
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# get only the last 2 "datums" which will be diffed to
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# calculate the real-time RSI output datum
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sig = ohlcv.last(2)['close']
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# the ema needs to be computed from the "last bar"
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# TODO: how to make this cleaner
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if ohlcv.index > index:
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last_up_ema_close = up_ema_last
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last_down_ema_close = down_ema_last
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index = ohlcv.index
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rsi_out, up_ema_last, down_ema_last = rsi(
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sig,
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period=period,
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up_ema_last=last_up_ema_close,
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down_ema_last=last_down_ema_close,
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)
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yield rsi_out[-1:]
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async def _wma(
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source, #: AsyncStream[np.ndarray],
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length: int,
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ohlcv: np.ndarray, # price time-frame "aware"
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) -> AsyncIterator[np.ndarray]: # maybe something like like FspStream?
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"""Streaming weighted moving average.
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``weights`` is a sequence of already scaled values. As an example
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for the WMA often found in "techincal analysis":
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``weights = np.arange(1, N) * N*(N-1)/2``.
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"""
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# deliver historical output as "first yield"
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yield wma(ohlcv.array['close'], length)
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# begin real-time section
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async for quote in source:
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for tick in iterticks(quote, type='trade'):
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yield wma(ohlcv.last(length))
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