dash_online.py
__author__ = 'gkour'
from bokeh.plotting import figure, output_file, show, save
from bokeh.models import ColumnDataSource, Slider, Select
import numpy as np
def _create_prices(t):
last_average = 100
returns = np.asarray(np.random.lognormal(mean.value, stddev.value, 1))
average = last_average * np.cumprod(returns)
high = average * np.exp(abs(np.random.gamma(1, 0.03, size=1)))
low = average / np.exp(abs(np.random.gamma(1, 0.03, size=1)))
delta = high - low
open = low + delta * np.random.uniform(0.05, 0.95, size=1)
close = low + delta * np.random.uniform(0.05, 0.95, size=1)
return open[0], high[0], low[0], close[0], average[0]
def _moving_avg(prices, days=10):
if len(prices) < days: return [100]
return np.convolve(prices[-days:], np.ones(days, dtype=float), mode="valid") / days
def _ema(prices, days=10):
if len(prices) < days or days < 2: return [prices[-1]]
a = 2.0 / (days + 1)
kernel = np.ones(days, dtype=float)
kernel[1:] = 1 - a
kernel = a * np.cumprod(kernel)
# The 0.8647 normalizes out that we stop the EMA after a finite number of terms
return np.convolve(prices[-days:], kernel, mode="valid") / (0.8647)
MA12, MA26, EMA12, EMA26 = '12-tick Moving Avg', '26-tick Moving Avg', '12-tick EMA', '26-tick EMA'
mean = Slider(title="mean", value=0, start=-0.01, end=0.01, step=0.001)
stddev = Slider(title="stddev", value=0.04, start=0.01, end=0.1, step=0.01)
mavg = Select(value=MA12, options=[MA12, MA26, EMA12, EMA26])
class Dashborad:
def __init__(self, file_path="lines.html"):
self._source = ColumnDataSource(dict(
time=[], average=[], low=[], high=[], open=[], close=[],
ma=[], macd=[], macd9=[], macdh=[], color=[]))
output_file(file_path)
p = figure(plot_height=500, tools="xpan,xwheel_zoom,xbox_zoom,reset", x_axis_type=None, y_axis_location="right")
p.x_range.follow = "end"
p.x_range.follow_interval = 100
p.x_range.range_padding = 0
p.line(x='time', y='average', alpha=0.2, line_width=3, color='navy', source=self._source)
p.line(x='time', y='ma', alpha=0.8, line_width=2, color='orange', source=self._source)
p.segment(x0='time', y0='low', x1='time', y1='high', line_width=2, color='black', source=self._source)
p.segment(x0='time', y0='open', x1='time', y1='close', line_width=8, color='color', source=self._source)
show(p)
def update(self, t):
# df = pd.DataFrame.from_csv(path=self._input_csv, header=0, index_col=0)
#
# x = [1, 2, 3, 4, 5]
# y = np.random.randint(1, 10, 5)
#
# self._p.line(x, y, legend="Temp.", line_width=2)
# save(self._p)
open, high, low, close, average = _create_prices(t)
color = "green" if open < close else "red"
new_data = dict(
time=[t],
open=[open],
high=[high],
low=[low],
close=[close],
average=[average],
color=[color],
)
close = self._source.data['close'] + [close]
ma12 = _moving_avg(close[-12:], 12)[0]
ma26 = _moving_avg(close[-26:], 26)[0]
ema12 = _ema(close[-12:], 12)[0]
ema26 = _ema(close[-26:], 26)[0]
if mavg.value == MA12:
new_data['ma'] = [ma12]
elif mavg.value == MA26:
new_data['ma'] = [ma26]
elif mavg.value == EMA12:
new_data['ma'] = [ema12]
elif mavg.value == EMA26:
new_data['ma'] = [ema26]
macd = ema12 - ema26
new_data['macd'] = [macd]
macd_series = self._source.data['macd'] + [macd]
macd9 = _ema(macd_series[-26:], 9)[0]
new_data['macd9'] = [macd9]
new_data['macdh'] = [macd - macd9]
self._source.stream(new_data, 300)
dash = Dashborad()
for t in range(1000):
dash.update(t)
