Wesseldr

vagrant-QSTK

A complete installed Ubuntu desktop VirtualMachine with python2 and QuantSoftwareToolkit

airflow

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

airflow-local-setup

Local DevSetup Airflow Kubernetes

anomaliesinoptions

In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.

CSharp-NT8-OrderFlowKit

Hi I'm Gabriel Zenobi, this is a toolkit that I developed for investment funds, banks and traders of all kinds.

deep-trader

Deep Reinforcement Learning applied to trading

deep-trading-agent

Deep Reinforcement Learning based Trading Agent for Bitcoin

deep_portfolio

docker-ib-tws

Interactive Brokers' Trader Workstation (TWS) running in Docker

fmpsdk

SDK for Financial Modeling Prep's (FMP) API

fruty_trading-automation

Gold-Futures-Algorithmic-Trading-System-in-R

homeassistant-afvalwijzer

Provides sensors for some Dutch waste collectors

ib-trading

Cloud-based algorithmic trading with Interactive Brokers

ibapir

Interactive Brokers api for R

IBrokers

R API to Interactive Brokers Trader Workstation

IBrokers2

Functions for executing trading strategies via the API of Interactive Brokers

livezilla-server

LiveZilla includes a live chat software with multi-website support, visitor monitoring and a help desk system that allows you to not only integrate emails that you receive from customers but also messages from Twitter and Facebook in your ticket system.

m1_max_testing

Tensorflow testing on m1 Mac

mage-ai

🧙 Build, run, and manage data pipelines for integrating and transforming data.

MPI-course

Supporting files for the Manageable Puppet Infrastructure course on udemy

ninjatrader-data-provider-example

Releases of the BitMEX <-> NinjaTrader Adapter.

seriesnet

Time series prediction using dilated causal convolutional neural nets (temporal CNN)

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.

synology-prometheus

Setup script to install the prometheus stack on a synology NAS

TechnicalAnalysisAutomation

tpo_project

Visualisation for auction market theory with live charts

vagrant-ark_agent

Distributed Agent Based On Celery Used To Collect End Of Day Stock Data

ZigZag

Python library for identifying the peaks and valleys of a time series.