Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies.

question-generation-paper-list

A summary of must-read papers for Neural Question Generation (NQG)

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

repaint

Official PyTorch Code and Models of "RePaint: Inpainting using Denoising Diffusion Probabilistic Models", CVPR 2022

rl-adventure

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

scamander

Counterfactual Explanations and How to Find Them

secure_ml

reveal the vulnerabilities of machine learning models

shap

A game theoretic approach to explain the output of any machine learning model.

stackelberg-adv

Code for SALT: Stackelberg Adversarial Regularization, EMNLP 2021.

stackelberg-gan

Codes for Stackelberg GAN

steal-ml

Model extraction attacks on Machine-Learning-as-a-Service platforms.

stock-market-forecasting

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

stock-move-prediction-with-adversarial-training-replicate

This github repo contains my replicate experiments of paper 'Enhancing Stock Movement Prediction with Adversarial Training'.

stocknet-dataset

A comprehensive dataset for stock movement prediction from tweets and historical stock prices.

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.

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