282 lines
6.8 KiB
Python
282 lines
6.8 KiB
Python
# piker: trading gear for hackers
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# Copyright (C) 2018-present 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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"""
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Super fast ``QPainterPath`` generation related operator routines.
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"""
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import numpy as np
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from numpy.lib import recfunctions as rfn
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from numba import (
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# types,
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njit,
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float64,
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int64,
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# optional,
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)
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# TODO: for ``numba`` typing..
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# from ._source import numba_ohlc_dtype
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from ._m4 import ds_m4
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def xy_downsample(
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x,
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y,
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uppx,
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x_spacer: float = 0.5,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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float,
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float,
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]:
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'''
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Downsample 1D (flat ``numpy.ndarray``) arrays using M4 given an input
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``uppx`` (units-per-pixel) and add space between discreet datums.
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'''
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# downsample whenever more then 1 pixels per datum can be shown.
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# always refresh data bounds until we get diffing
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# working properly, see above..
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m4_out = ds_m4(
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x,
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y,
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uppx,
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)
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if m4_out is not None:
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bins, x, y, ymn, ymx = m4_out
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# flatten output to 1d arrays suitable for path-graphics generation.
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x = np.broadcast_to(x[:, None], y.shape)
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x = (x + np.array(
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[-x_spacer, 0, 0, x_spacer]
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)).flatten()
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y = y.flatten()
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return x, y, ymn, ymx
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# XXX: we accept a None output for the case where the input range
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# to ``ds_m4()`` is bad (-ve) and we want to catch and debug
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# that (seemingly super rare) circumstance..
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return None
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@njit(
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# NOTE: need to construct this manually for readonly
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# arrays, see https://github.com/numba/numba/issues/4511
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# (
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# types.Array(
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# numba_ohlc_dtype,
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# 1,
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# 'C',
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# readonly=True,
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# ),
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# int64,
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# types.unicode_type,
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# optional(float64),
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# ),
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nogil=True
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)
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def path_arrays_from_ohlc(
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data: np.ndarray,
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start: int64,
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bar_w: float64,
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bar_gap: float64 = 0.16,
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use_time_index: bool = True,
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# XXX: ``numba`` issue: https://github.com/numba/numba/issues/8622
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# index_field: str,
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) -> tuple[
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np.ndarray,
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np.ndarray,
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np.ndarray,
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]:
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'''
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Generate an array of lines objects from input ohlc data.
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'''
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size = int(data.shape[0] * 6)
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# XXX: see this for why the dtype might have to be defined outside
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# the routine.
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# https://github.com/numba/numba/issues/4098#issuecomment-493914533
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x = np.zeros(
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shape=size,
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dtype=float64,
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)
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y, c = x.copy(), x.copy()
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half_w: float = bar_w/2
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# TODO: report bug for assert @
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# ../piker/env/lib/python3.8/site-packages/numba/core/typing/builtins.py:991
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for i, q in enumerate(data[start:], start):
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open = q['open']
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high = q['high']
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low = q['low']
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close = q['close']
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if use_time_index:
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index = float64(q['time'])
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else:
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index = float64(q['index'])
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# XXX: ``numba`` issue: https://github.com/numba/numba/issues/8622
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# index = float64(q[index_field])
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# AND this (probably)
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# open, high, low, close, index = q[
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# ['open', 'high', 'low', 'close', 'index']]
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istart = i * 6
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istop = istart + 6
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# x,y detail the 6 points which connect all vertexes of a ohlc bar
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mid: float = index + half_w
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x[istart:istop] = (
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index + bar_gap,
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mid,
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mid,
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mid,
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mid,
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index + bar_w - bar_gap,
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)
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y[istart:istop] = (
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open,
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open,
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low,
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high,
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close,
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close,
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)
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# specifies that the first edge is never connected to the
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# prior bars last edge thus providing a small "gap"/"space"
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# between bars determined by ``bar_gap``.
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c[istart:istop] = (1, 1, 1, 1, 1, 0)
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return x, y, c
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def hl2mxmn(
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ohlc: np.ndarray,
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index_field: str = 'index',
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) -> np.ndarray:
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'''
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Convert a OHLC struct-array containing 'high'/'low' columns
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to a "joined" max/min 1-d array.
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'''
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index = ohlc[index_field]
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hls = ohlc[[
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'low',
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'high',
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]]
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mxmn = np.empty(2*hls.size, dtype=np.float64)
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x = np.empty(2*hls.size, dtype=np.float64)
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trace_hl(hls, mxmn, x, index[0])
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x = x + index[0]
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return mxmn, x
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@njit(
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# TODO: the type annots..
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# float64[:](float64[:],),
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)
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def trace_hl(
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hl: 'np.ndarray',
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out: np.ndarray,
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x: np.ndarray,
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start: int,
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# the "offset" values in the x-domain which
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# place the 2 output points around each ``int``
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# master index.
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margin: float = 0.43,
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) -> None:
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'''
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"Trace" the outline of the high-low values of an ohlc sequence
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as a line such that the maximum deviation (aka disperaion) between
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bars if preserved.
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This routine is expected to modify input arrays in-place.
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'''
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last_l = hl['low'][0]
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last_h = hl['high'][0]
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for i in range(hl.size):
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row = hl[i]
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lo, hi = row['low'], row['high']
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up_diff = hi - last_l
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down_diff = last_h - lo
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if up_diff > down_diff:
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out[2*i + 1] = hi
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out[2*i] = last_l
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else:
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out[2*i + 1] = lo
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out[2*i] = last_h
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last_l = lo
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last_h = hi
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x[2*i] = int(i) - margin
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x[2*i + 1] = int(i) + margin
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return out
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def ohlc_flatten(
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ohlc: np.ndarray,
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use_mxmn: bool = True,
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index_field: str = 'index',
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) -> tuple[np.ndarray, np.ndarray]:
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'''
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Convert an OHLCV struct-array into a flat ready-for-line-plotting
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1-d array that is 4 times the size with x-domain values distributed
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evenly (by 0.5 steps) over each index.
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'''
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index = ohlc[index_field]
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if use_mxmn:
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# traces a line optimally over highs to lows
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# using numba. NOTE: pretty sure this is faster
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# and looks about the same as the below output.
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flat, x = hl2mxmn(ohlc)
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else:
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flat = rfn.structured_to_unstructured(
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ohlc[['open', 'high', 'low', 'close']]
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).flatten()
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x = np.linspace(
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start=index[0] - 0.5,
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stop=index[-1] + 0.5,
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num=len(flat),
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)
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return x, flat
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