Asynchronous, event-driven algorithmic trading in Python and C++
Applied Deep Learning
Must-read papers on improving efficiency for pre-trained language models.
:mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.
COVID-19 Mobility Data Aggregator. Scraper of Google, Apple, Waze and TomTom COVID-19 Mobility Reports🚶🚘🚉
This project is trying to fetch real time balance & orderbook of ETH and visualise using dash
C++ Code Repository.
Datasets, papers and books on AI & Finance.
In this repository, an event-driven backtester is implemented based on QuantStart articles. The backtester is programmed in Python featuring numerous improvements, in terms of coding structure, data handling, and simple trading strategies.
Financial Sentiment Analysis with BERT
Python toolkit for quantitative finance
This book was written for you: an aspiring data scientist with a quantitative background, facing down the gauntlet of the interview process in an increasingly competitive field. For most of you, the interview process is the most significant hurdle between you and a dream job.
一本关于排序算法的 GitBook 在线书籍 《十大经典排序算法》，多语言实现。
Keras documentation, hosted live at keras.io
Code for Machine Learning for Algorithmic Trading, 2nd edition.
Implementing a Generative Adversarial Network on the Stock Market
Productionise & schedule your Jupyter Notebooks as easily as you wrote them.
EPFL Course - Optimization for Machine Learning - CS-439
Master repository for the pandas-ml modules
Materials of the Nordic Probabilistic AI School 2021.
Notebooks for "Probabilistic Machine Learning" book
Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance.
Implicit economic sentiment classification experiments
This code accompanies the the paper Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection (https://arxiv.org/pdf/2105.13727.pdf).
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.