zhliaoli

zhliaoli's Stars

• huipingcao/nmsu_yhao_ijcai2020

  This is the GitHub repository for our publication "A new attention mechanism to classify multivariate time series", by Yifan Hao and Huiping Cao, which has been accepted to be published in IJCAI 2020.

  Language:Python288

• rian-dolphin/stock-embeddings

  Code relating to the paper - Stock Embeddings: Learning Distributed Representations for Financial Assets

  Language:Jupyter Notebook6218

• jiewwantan/XGBoost_stock_prediction

  XGBoost is known to be fast and achieve good prediction results as compared to the regular gradient boosting libraries. This project attempts to predict stock price direction by using the stock's daily data and indicators derived from its daily data as predictors. As such this is a classification problem.

  Language:Jupyter Notebook2411

• stxupengyu/LSTM-regression-and-classification

  使用LSTM对股票价格进行回归预测，对股价涨跌进行分类预测。We use LSTM to forecast the stock price and classify the rise and fall of the stock price.

  Language:Jupyter Notebook173

• ZhangMian-CentraleSupelec/Transformer-for-Stock-Price-Prediction

  Language:Jupyter Notebook92

• Dedalo314/Stock-prediction-using-transformers

  Code to train stock prediction models based on a combination of convolutional neural networks and transformers.

  Language:Python61

• KangOxford/Volume-Forecasting

  Time Series Prediction of Volume in LOB

  Language:Python529

• hfawaz/dl-4-tsc

  Deep Learning for Time Series Classification

  Language:Python1.5k545

• xtekky/gpt4free

  The official gpt4free repository | various collection of powerful language models

  Language:Python60k13.2k

• dhingratul/Stock-Price-Prediction

  Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016

  Language:Python13952

• borisbanushev/predictions

  Language:Jupyter Notebook6391

• LeonardoBerti00/Deep-Learning-Models-for-financial-time-serie-forecasting-with-LOB-Data

  Pytorch implementation of deep learning models for financial time series forecasting using LOB

  Language:Jupyter Notebook94

• BirdiD/Stock-trends-prediction-with-macroeconomic-indicators

  Stock markets are an essential component of the economy. Their prediction naturally arouses afascination in the academic and financial world. Indeed, financial time series, due to their widerange application fields, have seen numerous studies being published for their prediction. Some ofthese studies aim to establish whether there is a strong and predictive link between macroeconomicindicators and stock market trends and thus predict market returns. Stock market prediction howeverremains a challenging task due to uncertain noise. To what extent can macroeconomic indicatorsbe strong predictors of stock price ? Can they be used for stock trends modeling ? To answer thesequestions, we will focus on several time series forecasting models. We will on the one hand usestatistical time series models, more specifically the most commonly used time series approachesfor stock prediction : the Autoregressive Integrated Moving Average (ARIMA), the GeneralizedAutoregressive Conditional Heteroscedasticity (GARCH) and the Vector Autoregressive (VAR)approach. On the other hand, we will be using two deep learning models : the Long-Short TermMemory (LSTM) and the Gated Recurrent Unit (GRU) for our prediction task. In the final section ofthis paper, we look directly at companies to detect trends

  Language:Jupyter Notebook173

• huseinzol05/Stock-Prediction-Models

  Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

  Language:Jupyter Notebook7.9k2.8k

• 6mao6/1

  这里是直播平台永久回家页，有最新app下载地址。

  22025

• xavier-zy/Awesome-pytorch-list-CNVersion

  Awesome-pytorch-list 翻译工作进行中......

  Language:Jupyter Notebook1.7k395

• Sjj1024/Sjj1024

  快速找到1024小神

  Language:Python221

• bannedbook/fanqiang

  翻墙-科学上网

  Language:Kotlin38.1k7.2k

• sgoal/deeplearning.ai

  deeplearning.ai , By Andrew Ng, All slide and notebook + data (without solution) and video link

  Language:HTML149

• ricequant/rqalpha

  A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities

  Language:Python5.3k1.6k

• happynoom/DeepTrade

  A LSTM model using Risk Estimation loss function for stock trades in market

  Language:Python312139

• ucaiado/QLearning_Trading

  Learning to trade under the reinforcement learning framework

  Language:Jupyter Notebook495181

• lijingpeng/dl-docker

  An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

  Language:Python1411

• ofrik/deep-learning-stocks

  Language:Jupyter Notebook83

• yidao620c/python3-cookbook

  《Python Cookbook》 3rd Edition Translation

  Language:Jupyter Notebook11.6k3k

• guillaume-chevalier/LSTM-Human-Activity-Recognition

  Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

  Language:Jupyter Notebook3.3k935

• zck119/3dgan-release

  3D Generative Adversarial Network

  Language:Python820206

• deependersingla/deep_trader

  This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

  Language:Python1.5k504

• Rachnog/Deep-Trading

  Algorithmic trading with deep learning experiments

  Language:OpenEdge ABL1.4k697

• hayatoy/ml-forex-prediction

  Predicting Forex Future Price with Machine Learning

  Language:Jupyter Notebook277114