bullish-bear Goto Github PK

advanced-deep-trading

Mostly experiments based on "Advances in financial machine learning" book

alpaca_correlation_viz

alpaca_tech_screener

backtrader

Python Backtesting library for trading strategies

btreport

Extension for creating Performance Reports with Backtrader

deep-belief-network

A Python implementation of Deep Belief Networks built upon NumPy and TensorFlow with scikit-learn compatibility

deep-belief-network-pytorch

This repository has implementation and tutorial for Deep Belief Network

finlib

A streamlined library for getting historical financial price data, fundamental data, and financial ratios.

leanoptimization

Genetic optimization using LEAN

lstm_stock_price_prediction

Udacity Machine Learning Engineer Nanodegree Capstone Project-Machine Learning Approach to Stock Price Prediction

market_profile

How to make simple market profile charts in seaborn.

mastering-python-for-finance-second-edition

Sources codes for: Mastering Python for Finance, Second Edition

mlfinlab

Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning

ninjatrader-spreadtrader

Add on for Spread Display and DOM with exit strategies for Spreads for NinjaTrader.com

omegaui

Backtrader Front End

restricted-boltzmann-machines

Restricted Boltzmann Machines in Python.

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

vix_calculation