Copy non-base dtype fields on bar increment
parent
80f191c57d
commit
da2325239c
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@ -1,8 +1,6 @@
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"""
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Chart axes graphics and behavior.
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"""
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import time
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from functools import partial
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from typing import List
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@ -12,7 +10,6 @@ import pyqtgraph as pg
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from PyQt5 import QtCore, QtGui
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from PyQt5.QtCore import QPointF
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from .quantdom.utils import fromtimestamp
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from ._style import _font, hcolor
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@ -78,7 +75,8 @@ class DynamicDateAxis(pg.AxisItem):
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bars = self.linked_charts.chart._array
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times = bars['time']
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bars_len = len(bars)
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delay = times[-1] - times[times != times[-1]][-1]
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# delay = times[-1] - times[times != times[-1]][-1]
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delay = times[-1] - times[-2]
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epochs = times[list(
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map(int, filter(lambda i: i < bars_len, indexes))
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@ -87,7 +85,6 @@ class DynamicDateAxis(pg.AxisItem):
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dts = pd.to_datetime(epochs, unit='s') - 4*pd.offsets.Hour()
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return dts.strftime(self.tick_tpl[delay])
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def tickStrings(self, values: List[float], scale, spacing):
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return self._indexes_to_timestrs(values)
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@ -22,7 +22,7 @@ from .. import brokers
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from .. import data
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from ..log import get_logger
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from ._exec import run_qtractor
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from ._source import ohlc_dtype
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from ._source import base_ohlc_dtype
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from ._interaction import ChartView
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from .. import fsp
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@ -507,13 +507,13 @@ class ChartPlotWidget(pg.PlotWidget):
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# TODO: should probably just have some kinda attr mark
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# that determines this behavior based on array type
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try:
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ylow = bars['low'].min()
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yhigh = bars['high'].max()
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ylow = np.nanmin(bars['low'])
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yhigh = np.nanmax(bars['high'])
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# std = np.std(bars['close'])
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except IndexError:
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# must be non-ohlc array?
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ylow = bars.min()
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yhigh = bars.max()
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ylow = np.nanmin(bars)
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yhigh = np.nanmax(bars)
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# std = np.std(bars)
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# view margins: stay within 10% of the "true range"
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@ -589,14 +589,13 @@ async def add_new_bars(delay_s, linked_charts):
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def incr_ohlc_array(array: np.ndarray):
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(index, t, close) = array[-1][['index', 'time', 'close']]
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new_array = np.append(
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array,
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np.array(
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[(index + 1, t + delay_s, close, close,
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close, close, 0)],
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dtype=array.dtype
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),
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)
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# this copies non-std fields (eg. vwap) from the last datum
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_next = np.array(array[-1], dtype=array.dtype)
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_next[
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['index', 'time', 'volume', 'open', 'high', 'low', 'close']
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] = (index + 1, t + delay_s, 0, close, close, close, close)
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new_array = np.append(array, _next,)
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return new_array
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# add new increment/bar
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@ -668,6 +667,9 @@ async def _async_main(
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# figure out the exact symbol
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bars = await client.bars(symbol=sym)
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# allow broker to declare historical data fields
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ohlc_dtype = getattr(brokermod, 'ohlc_dtype', base_ohlc_dtype)
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# remember, msgpack-numpy's ``from_buffer` returns read-only array
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bars = np.array(bars[list(ohlc_dtype.names)])
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@ -8,7 +8,7 @@ import numpy as np
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import pandas as pd
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ohlc_dtype = np.dtype(
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base_ohlc_dtype = np.dtype(
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[
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('index', int),
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('time', float),
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@ -38,7 +38,7 @@ def ohlc_zeros(length: int) -> np.ndarray:
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For "why a structarray" see here: https://stackoverflow.com/a/52443038
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Bottom line, they're faster then ``np.recarray``.
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"""
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return np.zeros(length, dtype=ohlc_dtype)
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return np.zeros(length, dtype=base_ohlc_dtype)
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@dataclass
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@ -88,7 +88,7 @@ def from_df(
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df = df.rename(columns=columns)
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for name in df.columns:
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if name not in ohlc_dtype.names[1:]:
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if name not in base_ohlc_dtype.names[1:]:
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del df[name]
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# TODO: it turns out column access on recarrays is actually slower:
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