Using multiple sliders to create dynamic chart in bqplt/jupyter

Question

I am trying to plot a dynamic portfolio performance that changes as the weights of the portfolio change

Assume a portfolio has 2 components with a 50% weighting each. I want to show a chart of the portfolio with sliders representing the weights of the components. I then want to be able to slide the values of the weights around and have the portfolio chart dynamically update.

I have done this for a portfolio that consists of one weight but cant figure out how to amend for more than 1 weight - maybe I need a different approach.

Example below substitutes a random df with 1 column in place of my portfolio df - process should be the same.

In terms of this example if the df had 2 columns - how can I get it working with 2 sliders controlling each weight ?

---

from bqplot import DateScale, LinearScale, Axis, Figure, Lines

from ipywidgets import FloatSlider, VBox

import pandas as pd

import numpy as np

slider = FloatSlider(value=1, min = 0, max = 1, step = .01, description = 'Weight A')

df = pd.DataFrame(np.random.randint(0,100,size=(100, 1)), columns=list('A'))

x_sc = LinearScale()

y_sc = LinearScale()

ax_x = Axis(label='Date', scale=x_sc, grid_lines='solid')

ax_y = Axis(label='Price', scale=y_sc, orientation='vertical', grid_lines='solid')

line = Lines(y=df['A'],x=df.index , scales={'x': x_sc, 'y': y_sc}, colors = ['#FF0000'])

line2 = Lines(y=df['A'],x=df.index , scales={'x': x_sc, 'y': y_sc})

fig = Figure(axes=[ax_x, ax_y], marks=[line, line2], title='Price Chart')

def new_chart(value):

new_y = df[['A']]*slider.value

line.y = new_y

slider.observe(new_chart,'value')

VBox([fig,slider])

Answer 1

Not sure if I have understood you. Do you mean this?

from bqplot import DateScale, LinearScale, Axis, Figure, Lines

from ipywidgets import FloatSlider, VBox

import pandas as pd

import numpy as np

slider = FloatSlider(value=1, min = 0, max = 1, step = .01, description = 'Weight A')
sliderB = FloatSlider(value=1, min = 0, max = 1, step = .01, description = 'Weight B')

df = pd.DataFrame(np.random.randint(0,100,size=(100, 1)), columns=list('A'))
df['B'] = np.random.randint(0,100,size=(100, 1))

x_sc = LinearScale()

y_sc = LinearScale()

ax_x = Axis(label='Date', scale=x_sc, grid_lines='solid')

ax_y = Axis(label='Price', scale=y_sc, orientation='vertical', grid_lines='solid')

line = Lines(y=df['A']+df['B'],x=df.index , scales={'x': x_sc, 'y': y_sc}, colors = ['#FF0000'])

line2 = Lines(y=df['A']+df['B'],x=df.index , scales={'x': x_sc, 'y': y_sc})

fig = Figure(axes=[ax_x, ax_y], marks=[line, line2, ], title='Price Chart')

def new_chart(change):
    line.y = df['A']*slider.value + df['B']*sliderB.value

slider.observe(new_chart,'value')

sliderB.observe(new_chart,'value')

VBox([fig,slider,sliderB])