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Python Versions and Package Support on Blueshift®

Python versions

We use a single engine for both backtesting and live trading. This engine support only Python 3.6+ and is at present Python 3.7 in the lastest release.

Supported Python packages

We have a white-list of packages, listed below, which you can use. Please reach out to us if you need any other (public and well-known) packages to be added. We will review the use case and security aspects and update the white-list accordingly. If you need any personal package, consider using the workspace feature to implement the same on the site. We support only pure Python code on the site (no c-extensions etc.).

Deprecated package support.

Package johansen is no longer supported. Use the coint_johansen from statsmodels.tsa.vector_ar.vecm instead.

package use case
bisect An useful array sorting package.
cmath Provides access to mathematical functions for complex numbers.
cvxopt Package for convex optimization.
cvxpy A "nice and disciplined" interface to cvxopt.
datetime For manipulating dates and times in both simple and complex ways.
functools Higher-order functions and operations on callable objects.
hmmlearn For unsupervised learning and inference of Hidden Markov Models.
hurst for analysing random walks and evaluating the Hurst exponent.
arch ARCH and other tools for financial econometrics.
keras A deep learning API running on top of TensorFlow.
math Provides access to the mathematical functions defined by the C standard.
numpy Package for scientific computing with Python.
pandas High-performance, easy-to-use data structures and data analysis tools.
pykalman Implements Kalman filter and Kalman smoother in Python.
pytz Allows accurate and cross platform timezone calculations.
random Random number generators for various distributions.
scipy Efficient numerical routines for scientific computing.
sklearn For machine learning in Python.
statsmodels For statistics in Python.
talib For technical analysis in Python.
xgboost XGBoost library interface for Python.