WesdR

Releases of the BitMEX <-> NinjaTrader Adapter.

Tiered Self-Correcting Multi-Agent System for Invoice Extraction. Capstone Project for Google AI Agents Intensive (Nov 2025).

Free Order Flow indicators for NinjaTrader 8

Homelab Goodies

Provides sensors for some Dutch waste collectors

SDK for Financial Modeling Prep's (FMP) API

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

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

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

A complete installed Ubuntu desktop VirtualMachine with python2 and QuantSoftwareToolkit

No description provided.

Visualisation for auction market theory with live charts

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

Functions for executing trading strategies via the API of Interactive Brokers

R API to Interactive Brokers Trader Workstation

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.

Local DevSetup Airflow Kubernetes

Interactive Brokers api for R

Cloud-based algorithmic trading with Interactive Brokers

Tensorflow testing on m1 Mac

No description provided.

No description provided.

Interactive Brokers' Trader Workstation (TWS) running in Docker

Setup script to install the prometheus stack on a synology NAS

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

Deep Reinforcement Learning applied to trading

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

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.

Deep Reinforcement Learning based Trading Agent for Bitcoin

No description provided.