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filterpy

Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

gitignore

A collection of useful .gitignore templates

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

kelly_code

Code and examples for the project on risk-constrained Kelly gambling

keras-self-attention

Attention mechanism for processing sequential data that considers the context for each timestamp.

lean

Lean Algorithmic Trading Engine by QuantConnect (C#, Python, F#)

lightweight-charts-jupyter-bridge

An example of how Lightweight Charts can be integrated into Jupyter Notebook

microprice

mlfinlab

MlFinlab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

numpy_ext

An extension library for NumPy that implements common array operations not present in NumPy

pairs-trading-with-ml

presentations

Slide show presentations regarding data driven investing.

recombinator

Recombinator is a Python package for statistical resampling in Python. It provides various algorithms for the iid bootstrap, the block bootstrap, as well as optimal block-length selection.

research

Notebooks based on financial machine learning.

santimentmobile

Mobile MVP for Santiment project http://santiment.net

spacedrive

Spacedrive is an open source cross-platform file explorer, powered by a virtual distributed filesystem written in Rust.

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.

synthetic_time_series

tartakovsky

trading-pnl

FIFO Trading Model for PnL Calculation

ttc-parsing

xplorer

Xplorer, a customizable, modern file manager