What are you trying to do?
i am trying to plot the open trade and exit trade on the candle stick chart. like long entry and short entry.
so what i want to do is i have another dataframe which has list of trades. like ‘Type’, ‘entry_price’, ‘entry_time’, ‘trade_size’, ‘exit_price’,‘exit_time’, ‘pct’.
i want to plot this long entries and short entries on the candlestick chart how can i do this.
if anyone got any suggestions really appreciate it thanks. i am using bokeh==1.4.0
You should first calculate where the buy and sell points are located (basically your strategy), to define where the points will be displayed on your chart, you could choose the ‘close’ field for example. Let’s suppose you have an indicator called EMA , when Close is smaller than EMA , it will be the entry point and when it is the other way around it will be the exit point:
df[‘entryPoints’] = np.where(df[‘ema’] >df[‘close’] , df[‘close’], np.NaN).
df[‘exitPoints’]= np.where(df[‘ema’] < df[‘close’] , df[‘close’], np.NaN)
Then you just have to add the points in the graph, let’s put the inputs with a green triangle and the outputs in a red inverted triangle.
source = ColumnDataSource(df)
Add a triangle glyph to the plot :
plot.triangle(x=‘date’, y=‘entryPoints’, size=10, color=‘green’, source=source)
plot.inverted_triangle(x=‘date’, y=‘exitPoints’, size=10, color=‘red’, source=source)
yes i have entry_price , entry_time , exit_price and exist time.
so my xaxis is time and yaxis is price.
import pandas as pd
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.models import CustomJS, ColumnDataSource, HoverTool, NumeralTickFormatter , Span , Circle
from bokeh.transform import factor_cmap
import numpy as np
def candlestick_plot(df,df2, name):
# Select the datetime format for the x axis depending on the timeframe
xaxis_dt_format = '%d %b %Y'
if df['Date'][0].hour > 0:
xaxis_dt_format = '%d %b %Y, %H:%M:%S'
fig = figure(sizing_mode='stretch_both',
tools="xpan,xwheel_zoom,reset,save",
active_drag='xpan',
active_scroll='xwheel_zoom',
x_axis_type='linear',
# x_range=Range1d(df.index[0], df.index[-1], bounds="auto"),
title=name
)
fig.yaxis[0].formatter = NumeralTickFormatter(format="$5.3f")
inc = df.Close > df.Open
dec = ~inc
# Colour scheme for increasing and descending candles
INCREASING_COLOR = '#17BECF'
DECREASING_COLOR = '#7F7F7F'
# cmap = factor_cmap('returns_positive', COLORS, ['0', '1'])
width = 0.5
inc_source = ColumnDataSource(data=dict(
x1=df.index[inc],
top1=df.Open[inc],
bottom1=df.Close[inc],
high1=df.High[inc],
low1=df.Low[inc],
Date1=df.Date[inc]
))
dec_source = ColumnDataSource(data=dict(
x2=df.index[dec],
top2=df.Open[dec],
bottom2=df.Close[dec],
high2=df.High[dec],
low2=df.Low[dec],
Date2=df.Date[dec]
))
# Plot candles
# High and low
fig.segment(x0='x1', y0='high1', x1='x1', y1='low1', source=inc_source, color=INCREASING_COLOR)
fig.segment(x0='x2', y0='high2', x1='x2', y1='low2', source=dec_source, color=DECREASING_COLOR)
# Open and close
r1 = fig.vbar(x='x1', width=width, top='top1', bottom='bottom1', source=inc_source,
fill_color=INCREASING_COLOR, line_color="black")
r2 = fig.vbar(x='x2', width=width, top='top2', bottom='bottom2', source=dec_source,
fill_color=DECREASING_COLOR, line_color="black")
# Add on extra lines (e.g. moving averages) here
fig.line(df.index, df['sma10'] ,color='orange')
# Add on a vertical line to indicate a trading signal here
# vline = Span(location=df2.index[0], dimension='height',
# line_color="green", line_width=2)
# fig.scatter(x="entry_time",
# y="entry_price",
# size=15,
# source=df2,
# # fill_color="red",
# fill_alpha=0.5,
# line_color="red",
# )
# fig.renderers.extend([vline])
# Add date labels to x axis
fig.xaxis.major_label_overrides = {
i: date.strftime(xaxis_dt_format) for i, date in enumerate(pd.to_datetime(df["Date"]))
}
# Set up the hover tooltip to display some useful data
fig.add_tools(HoverTool(
renderers=[r1],
tooltips=[
("Open", "$@top1"),
("High", "$@high1"),
("Low", "$@low1"),
("Close", "$@bottom1"),
("Date", "@Date1{" + xaxis_dt_format + "}"),
],
formatters={
'Date1': 'datetime',
}))
fig.add_tools(HoverTool(
renderers=[r2],
tooltips=[
("Open", "$@top2"),
("High", "$@high2"),
("Low", "$@low2"),
("Close", "$@bottom2"),
("Date", "@Date2{" + xaxis_dt_format + "}")
],
formatters={
'Date2': 'datetime'
}))
# JavaScript callback function to automatically zoom the Y axis to
# view the data properly
source = ColumnDataSource({'Index': df.index, 'High': df.High, 'Low': df.Low})
callback = CustomJS(args={'y_range': fig.y_range, 'source': source}, code='''
clearTimeout(window._autoscale_timeout);
var Index = source.data.Index,
Low = source.data.Low,
High = source.data.High,
start = cb_obj.start,
end = cb_obj.end,
min = Infinity,
max = -Infinity;
for (var i=0; i < Index.length; ++i) {
if (start <= Index[i] && Index[i] <= end) {
max = Math.max(High[i], max);
min = Math.min(Low[i], min);
}
}
var pad = (max - min) * .06;
window._autoscale_timeout = setTimeout(function() {
y_range.start = min - pad;
y_range.end = max + pad;
});
''')
# Finalise the figure
fig.x_range.callback = callback
show(fig)
my trades df looks like this
so how can i plot this df ? on candlestick ?
# Create a ColumnDataSource for the entry times and prices
entry_source = ColumnDataSource(data=dict(x=df2['entry_time'], y=df2['entry_price']))
# Add the vertical lines for entry times using the segment glyph
entry_lines = fig.segment(x0='x', y0=0, x1='x', y1='y', color='green', line_width=2, source=entry_source)
# Create a ColumnDataSource for the exit times and prices
exit_source = ColumnDataSource(data=dict(x=df2['exit_time'], y=df2['exit_price']))
# Add the vertical lines for exit times using the segment glyph
exit_lines = fig.segment(x0='x', y0=0, x1='x', y1='y', color='red', line_width=2, source=exit_source)
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