Justin Simcock's repositories
robot_wealth
Work from Robot Wealth bootcamp on FX
Interactive-Python
Python files and Ipython notebooks from my progress through the interactive python site
machine-learning-for-trading
Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading
aws-cloudformation-templates
A collection of useful CloudFormation templates
Blockchain-dark-forest-selfguard-handbook
Blockchain dark forest selfguard handbook. Master these, master the security of your cryptocurrency.
cvat
Powerful and efficient Computer Vision Annotation Tool (CVAT)
dask_ml_exercise
Repo for dask_ml_exercise docker container
DeFi-Developer-Road-Map
DeFi Developer roadmap is a curated Web3.0 Developer handbook which includes a list of the best tools for DApps, development resources and lifehacks.
erc20-staking-foundry
Create, Test, deploy staking contract with foundry
ethereumbook
Mastering Ethereum, by Andreas M. Antonopoulos, Gavin Wood
fccBlockchain
Exercises and code from the Free Code Camp Blockchain course
financial-machine-learning
A curated list of practical financial machine learning (FinML) tools and applications in Python.
full-blockchain-solidity-course-py
Ultimate Solidity, Blockchain, and Smart Contract - Beginner to Expert Full Course | Python Edition
gha-python-packaging-demo
Demo of a simple way to use GitHub Actions to build your Python package, bump the version number, and publish it to GitHub releases and PyPI.org all with a single click of a button in the web interface.
jgerardsimcock
Config files for my GitHub profile.
learn-foundry
Foundry test repo
nft-collection-1
My first digital art thing
prefect
A modern data workflow platform
private-eks-cluster
CloudFormation template and associated shell script to create a VPC, an EKS cluster, and a worker node group all without internet connectivity.
pyjuque
⚡ Open Source Algorithmic Trading Bot for 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.