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algorithmic-trading

Repository of the code developed as part of the course Algorithm Trading by Nick McCullum.

arbitragelab

ArbitrageLab is a python library that enables traders who want to exploit mean-reverting portfolios by providing a complete set of algorithms from the best academic journals.

awesome-ml-for-trading

Awesome machine learning for trading

bybit-smm

bybit simple market maker

dbg-pds-tensorflow-demo

Making predictions on prices in the Deutsche Börse Public Dataset using neural networks

deep-daze

Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun

efficiency_futures_apmc

This repository contains replication material.

hf_micro_research

This project is to explore high-frequency model and strategy. You will expect high-frequency features mining, ml/dl models, and hf trading strategies.

jbs

Java Bitcoin Scanner

liquidity.ai

open source deep learning (LSTM's mostly) for financial instruments

newt

quant-finance-lectures

Learn quantitative finance with this comprehensive lecture series. Adapted from the Quantopian Lecture Series. Uses free sample data.

quant_tests

statarb

thesis-bitcoin-clustering

The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.