This post is going to be fairly straight-forward. If you have followed my previous post, we should have a virtual environment with Catalyst ready.

Install Node.js

You can easily install Node.js from the NodeSource repository. Since adding extra repo into Debian is not really recommended by security freaks like me, I would suggest an alternavtive method through npm. Open the terminal

curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.34.0/install.sh | bash

At the end of the installation, close the current terminal and open a new one. Check if npm is properly installed.

npm --version

Great! Let us install the latest LTS node.js

nvm install --lts

If you live on the edge, try this instead.

nvm install node

Install ipympl via pip

Activate our venv

source venv/bin/activate

Install ipympl

(venv) pip install ipympl (venv) jupyter labextension install @jupyter-widgets/jupyterlab-manager (venv) jupyter labextension install jupyter-matplotlib

Check if it works

Start JupyterLab from our venv

(venv) jupypter lab

Create a new file and add the following:

% matplotlib widget from catalyst.api import symbol , record from catalyst import run_algorithm import matplotlib.pyplot as plt import numpy as np import pandas as pd import stationarity_test def initialize ( context ): context . asset = symbol ( 'btc_usdt' ) def handle_data ( context , data ): current_price = data . history ( context . asset , 'price' , 1 , '1d' ) record ( price = current_price ) if __name__ == '__main__' : perf = run_algorithm ( capital_base = 1000 , data_frequency = 'daily' , initialize = initialize , handle_data = handle_data , exchange_name = 'poloniex' , quote_currency = 'usdt' , start = pd . to_datetime ( '2017-11-1' , utc = True ), end = pd . to_datetime ( '2018-11-1' , utc = True )) # Plots fig = plt . figure () ax1 = plt . subplot2grid (( 1 , 1 ), ( 0 , 0 ), rowspan = 3 , colspan = 1 ) ax1 . plot ( perf . index , perf . price , label = 'BTC/USDT' ) fig . legend () plt . show ()

If everyting works, you should see the interactive plot of bitcoin price from 2017/11/01 to 2018/11/01.

Interactive plots in JupyterLab

That is all for this post. I hope you find something useful in it, and let me know if you have any questions/comments. Feel like stopping by and say hi? Let us talk more about crypto and quantitative trading over there. Here is the discord invite link. Ciao!