I am trying to plot the Ichimoku indicator using Backtrader in Python3
It plots it well, however, I am not able to see the future Kumo cloud.
That is the extra 26 bars to the right after the last price on the X-axis
I have tried using different start/end dates, but it doesn't work.
It only plots the exacts dates, instead, what I need are 26 more bars to the right.
Can someone please advise? Here is my code so far,
import backtrader as bt
from datetime import datetime
class indicators_(bt.Strategy):
def __init__(self):
self.rsi = bt.indicators.RSI_SMA(self.data.close, period=21)
self.ichimoku = bt.indicators.ichimoku.Ichimoku(self.data)
def next(self):
pass
cerebro = bt.Cerebro()
cerebro.addstrategy(indicators_)
data = bt.feeds.YahooFinanceData(
dataname='AAPL',
fromdate = datetime(2005,1,1),
todate = datetime(2006,1,1),
buffered= True
)
cerebro.adddata(data)
cerebro.run()
cerebro.plot(style='bars',start=datetime(2005, 6, 1), end=datetime(2006, 1, 20))
Here is the link to the resulting chart with indicators added
Chart with Ichimoku indicator
I'm attempting to create a BoxPlot using Bokeh. When I get to the section where I need to identify outliers, it fails if a given category has no outliers.
If I remove the "problem" category, the BoxPlot executes properly. it's only when I attempt to create this BoxPlot with a category that has no outliers it fails.
Any instruction on how to remedy this?
The failure occurs at the commented section "Prepare outlier data for plotting [...]"
import numpy as np
import pandas as pd
import datetime
import math
from bokeh.plotting import figure, show, output_file
from bokeh.models import NumeralTickFormatter
# Create time stamps to allow for figure to display span in title
today = datetime.date.today()
delta1 = datetime.timedelta(days=7)
delta2 = datetime.timedelta(days=1)
start = str(today - delta1)
end = str(today - delta2)
#Identify location of prices
itemloc = 'Everywhere'
df = pd.read_excel(r'C:\Users\me\prices.xlsx')
# Create a list from the dataframe that identifies distinct categories for the separate box plots
cats = df['subcategory_desc'].unique().tolist()
# Find the quartiles and IQR for each category
groups = df.groupby('subcategory_desc', sort=False)
q1 = groups.quantile(q=0.25)
q2 = groups.quantile(q=0.5)
q3 = groups.quantile(q=0.75)
iqr = q3 - q1
upper = q3 + 1.5*iqr
lower = q1 - 1.5*iqr
# Find the outliers for each category
def outliers(group):
cat = group.name
return group[(group.price > upper.loc[cat][0]) | (group.price < lower.loc[cat][0])]['price']
out = groups.apply(outliers).dropna()
# Prepare outlier data for plotting, we need coordinates for every outlier.
outx = []
outy = []
for cat in cats:
# only add outliers if they exist
if not out.loc[cat].empty:
for value in out[cat]:
outx.append(cat)
outy.append(value)
I expect that the Box-and-whisker portion of categories with no outliers merely show up without the outlier dots.
Have you tried the code from official documentation, https://docs.bokeh.org/en/latest/docs/gallery/boxplot.html?
# prepare outlier data for plotting, we need coordinates for every outlier.
if not out.empty:
outx = []
outy = []
for keys in out.index:
outx.append(keys[0])
outy.append(out.loc[keys[0]].loc[keys[1]])
I have been trying to plot a graph which has a dataframe having 3 columns . One is the "Hour", Second is the "amount" in Rupees and the third consist of "machine codes". I need to analyze the amount of transaction a machine does on an hourly basis. There are total 67 unique machine codes.
Kindy check here the data sample Here
These are the Libraries i have been using
import numpy as np
from bokeh.io import output_notebook, show
from bokeh.layouts import row
from bokeh.palettes import Viridis3
from bokeh.plotting import figure
from bokeh.models import CheckboxGroup, CustomJS
output_notebook()
p = figure()
props = dict(line_width=4, line_alpha=0.7)
x = sl['Hour']
y = sl['amount']
Now I have appended a list labels[] with all the machine codes
labels = []
active1 = []
for s in sl['machinecode'].unique():
labels.append(s)
active1.append(0)
I basically want to create checkboxes for all those machine codes , a user when check any machine code , a graph gets plotted . if a user again checks another machine code , the line of that machine code gets appended into a graph so that I could compare between machines.
j =0
for i in sl['machinecode'].unique():
l = p.line(x, y, color=Viridis3[0], legend="Line:" , **props)
j=j+1
checkbox = CheckboxGroup(labels=labels,
active=active1, width=100)
checkbox.callback = CustomJS(args=dict(l=l, checkbox=checkbox),
code="""
l0.visible = 0 in checkbox.active;
l1.visible = 1 in checkbox.active;
l2.visible = 2 in checkbox.active;
""""")
layout = row(checkbox, p)
show(layout)
The above code is showing something really different kindly check here what the graph is actually showing , it is plotting for every machine with a single color , checkboxes does not command the graph actually
I am trying the new version of folium so I am using map.save instead of map.create_map. It was working with the older version but when I am using the new code, I am getting an error again and again saying SyntaxError: Invalid syntax. But I think I ma using the right code:
I am running this script:
import pandas, folium
df = pandas.read_csv(".....txt")
map = folium.Map(location= [df["LAT"].mean(), df["LON"].mean()], zoom_start = 6, tiles = "Stamen Terrain")
def color(elev):
minimum= int(min(df["ELEV"]))
step= int((max(df["ELEV"])-min(df["ELEV"]))/3 )
if elev in range(minimum,minimum+step):
col= "blue"
elif elev in range(minimum+step,(minimum+step)*2):
col= "orange"
else:
col = "red"
return col
for lat,lon,name,elev in zip(df['LAT'], df['LON'], df['NAME'], df['ELEV']):
map.add_child(folium.Marker(location=[lat, lon], popup = name, icon= folium.Icon(color= color(elev)))
map.save(outfile= "test.html")
and I am getting this error:
...:
File "<ipython-input-2-02945dfe5a14>", line 27
map.save(outfile= "test.html")
^
SyntaxError: invalid syntax
Am I doing something wrong?
Is it possible to add Tooltips to a Timeseries chart?
In the simplified code example below, I want to see a single column name ('a','b' or 'c') when the mouse hovers over the relevant line.
Instead, a "???" is displayed and ALL three lines get a tool tip (rather than just the one im hovering over)
Per the documentation (
http://docs.bokeh.org/en/latest/docs/user_guide/tools.html#hovertool), field names starting with “#” are interpreted as columns on the data source.
How can I display the 'columns' from a pandas DataFrame in the tooltip?
Or, if the high level TimeSeries interface doesn't support this, any clues for using the lower level interfaces to do the same thing? (line? multi_line?) or convert the DataFrame into a different format (ColumnDataSource?)
For bonus credit, how should the "$x" be formatted to display the date as a date?
thanks in advance
import pandas as pd
import numpy as np
from bokeh.charts import TimeSeries
from bokeh.models import HoverTool
from bokeh.plotting import show
toy_df = pd.DataFrame(data=np.random.rand(5,3), columns = ('a', 'b' ,'c'), index = pd.DatetimeIndex(start='01-01-2015',periods=5, freq='d'))
p = TimeSeries(toy_df, tools='hover')
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
("Series", "#columns"),
("Date", "$x"),
("Value", "$y"),
]
show(p)
Below is what I came up with.
Its not pretty but it works.
Im still new to Bokeh (& Python for that matter) so if anyone wants to suggest a better way to do this, please feel free.
import pandas as pd
import numpy as np
from bokeh.charts import TimeSeries
from bokeh.models import HoverTool
from bokeh.plotting import show
toy_df = pd.DataFrame(data=np.random.rand(5,3), columns = ('a', 'b' ,'c'), index = pd.DatetimeIndex(start='01-01-2015',periods=5, freq='d'))
_tools_to_show = 'box_zoom,pan,save,hover,resize,reset,tap,wheel_zoom'
p = figure(width=1200, height=900, x_axis_type="datetime", tools=_tools_to_show)
# FIRST plot ALL lines (This is a hack to get it working, why can't i pass in a dataframe to multi_line?)
# It's not pretty but it works.
# what I want to do!: p.multi_line(df)
ts_list_of_list = []
for i in range(0,len(toy_df.columns)):
ts_list_of_list.append(toy_df.index.T)
vals_list_of_list = toy_df.values.T.tolist()
# Define colors because otherwise multi_line will use blue for all lines...
cols_to_use = ['Black', 'Red', 'Lime']
p.multi_line(ts_list_of_list, vals_list_of_list, line_color=cols_to_use)
# THEN put scatter one at a time on top of each one to get tool tips (HACK! lines with tooltips not yet supported by Bokeh?)
for (name, series) in toy_df.iteritems():
# need to repmat the name to be same dimension as index
name_for_display = np.tile(name, [len(toy_df.index),1])
source = ColumnDataSource({'x': toy_df.index, 'y': series.values, 'series_name': name_for_display, 'Date': toy_df.index.format()})
# trouble formating x as datestring, so pre-formating and using an extra column. It's not pretty but it works.
p.scatter('x', 'y', source = source, fill_alpha=0, line_alpha=0.3, line_color="grey")
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Series", "#series_name"), ("Date", "#Date"), ("Value", "#y{0.00%}"),]
hover.mode = 'mouse'
show(p)
I’m not familiar with Pandas，I just use python list to show the very example of how to add tooltips to muti_lines, show series names ,and properly display date/time。Below is the result.
Thanks to #bs123's answer and #tterry's answer in Bokeh Plotting: Enable tooltips for only some glyphs
my result
# -*- coding: utf-8 -*-
from bokeh.plotting import figure, output_file, show, ColumnDataSource
from bokeh.models import HoverTool
from datetime import datetime
dateX_str = ['2016-11-14','2016-11-15','2016-11-16']
#conver the string of datetime to python datetime object
dateX = [datetime.strptime(i, "%Y-%m-%d") for i in dateX_str]
v1= [10,13,5]
v2 = [8,4,14]
v3= [14,9,6]
v = [v1,v2,v3]
names = ['v1','v2','v3']
colors = ['red','blue','yellow']
output_file('example.html',title = 'example of add tooltips to multi_timeseries')
tools_to_show = 'hover,box_zoom,pan,save,resize,reset,wheel_zoom'
p = figure(x_axis_type="datetime", tools=tools_to_show)
#to show the tooltip for multi_lines,you need use the ColumnDataSource which define the data source of glyph
#the key is to use the same column name for each data source of the glyph
#so you don't have to add tooltip for each glyph,the tooltip is added to the figure
#plot each timeseries line glyph
for i in xrange(3):
# bokeh can't show datetime object in tooltip properly,so we use string instead
source = ColumnDataSource(data={
'dateX': dateX, # python datetime object as X axis
'v': v[i],
'dateX_str': dateX_str, #string of datetime for display in tooltip
'name': [names[i] for n in xrange(3)]
})
p.line('dateX', 'v',source=source,legend=names[i],color = colors[i])
circle = p.circle('dateX', 'v',source=source, fill_color="white", size=8, legend=names[i],color = colors[i])
#to avoid some strange behavior(as shown in the picture at the end), only add the circle glyph to the renders of hover tool
#so tooltip only takes effect on circle glyph
p.tools[0].renderers.append(circle)
# show the tooltip
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("value", "#v"), ("name", "#name"), ("date", "#dateX_str")]
hover.mode = 'mouse'
show(p)
tooltips with some strange behavior,two tips displayed at the same time
Here is my solution. I inspected the glyph render data source to see what are the names on it. Then I use those names on the hoover tooltips. You can see the resulting plot here.
import numpy as np
from bokeh.charts import TimeSeries
from bokeh.models import HoverTool
from bokeh.plotting import show
toy_df = pd.DataFrame(data=np.random.rand(5,3), columns = ('a', 'b' ,'c'), index = pd.DatetimeIndex(start='01-01-2015',periods=5, freq='d'))
#Bockeh display dates as numbers so convert to string tu show correctly
toy_df.index = toy_df.index.astype(str)
p = TimeSeries(toy_df, tools='hover')
#Next 3 lines are to inspect how are names on gliph to call them with #name on hover
#glyph_renderers = p.select(dict(type=GlyphRenderer))
#bar_source = glyph_renderers[0].data_source
#print(bar_source.data) #Here we can inspect names to call on hover
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
("Series", "#series"),
("Date", "#x_values"),
("Value", "#y_values"),
]
show(p)
The original poster's code doesn't work with the latest pandas (DatetimeIndex constructor has changed), but Hovertool now supports a formatters attribute that lets you specify a format as a strftime string. Something like
fig.add_tool(HoverTool(
tooltip=[
('time', '#index{%Y-%m-%d}')
],
formatters={
'#index': 'datetime'
}
))