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.