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adv_fin_ml_exercises

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

anomaliesinoptions

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

build-your-own-x

🤓 Build your own (insert technology here)

deep-learning

financial-data-structures

Create structured financial data in the form of time, tick, volume, and dollar bars from unstructured tick data. From Marcos Lopez de Prado's Advances in Financial Machine Learning textbook.

finbert

BERT for Finance : UC Berkeley MIDS w266 Final Project

fx-1-minute-data

HISTDATA - Full Dataset composed of 68 FX trading pairs / Simple API to retrieve 1 Minute data Historical FX Prices (up to June 2019).

hands-on-machine-learning-for-algorithmic-trading

Hands-On Machine Learning for Algorithmic Trading, published by Packt

ib_insync

Python sync/async framework for Interactive Brokers API

mixture_models

Jupyter (IPython) notebooks for exploring mixture models

pyalgotrader

Python algotrading framework with UI. Backtesting and Live trading. Crypto and US broker connectors

python

All Algorithms implemented in Python

quantlib

The QuantLib C++ library

setup

Make your Mac Aerowesome

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.

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.