Serjio Borisov's repositories
hmmlearn
Hidden Markov Models in Python, with scikit-learn like API
deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Tree_based_algorithms_in_R_and_Python
Книга "Древовидные алгоритмы в R и Python"
rulefit
Python implementation of the rulefit algorithm
feature-selector
Development and implementation of the feature selector
tsfresh
Automatic extraction of relevant features from time series:
mcp
Tools for Measuring Classification Performance for R, Python and Spark
hmeasure-python
Measuring Classification Performance: the hmeasure package for Python.
TradzQAI
Trading environnement for RL agents, backtesting and training.
pysystemtrade
Systematic Trading in python
15-minute-apps
15 minute (small) desktop apps built with PyQt
deep-trading-agent
Deep Reinforcement Learning based Trading Agent for Bitcoin
backtrader
Python Backtesting library for trading strategies
pyqtgraph
Fast data visualization and GUI tools for scientific / engineering applications
PyTrendFollow
PyTrendFollow - systematic futures trading using trend following
osbrain
osBrain - A general-purpose multi-agent system module written in Python
pyalgotrade
Python Algorithmic Trading Library
btgym
OpenAI Gym environment for Backtrader trading platform
SmartComPy
Module provide access to SmartCOM API of versions 3 and 4 from ITI Capital (IT Invest).
pywin32
Python for Windows (pywin32) Extensions
feature-engineering-book
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
MLSTM-FCN
Multivariate LSTM Fully Convolutional Networks for Time Series Classification
prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
MAS-Template-OSBrain
Template for MAS developed with OSBrain
qstrader
QuantStart.com - Advanced Trading Infrastructure
mifs
Parallelized Mutual Information based Feature Selection module.
mlcourse_open
OpenDataScience Machine Learning course. Launches on Feb, 5 both in English and Russian