Kevin Chen's repositories
rapid-java-persistence-and-microservices
Source Code for 'Rapid Java Persistence and Microservices' by Raj Malhotra
Advanced-Deep-Trading
Mostly experiments based on "Advances in financial machine learning" book
baseballr
A package written for R focused on baseball analysis. Currently in development.
Binary-Option-Pricing
Currency Binary Option Pricing with 3 methods and implied smile
cloud-native-book-demos
Cloud Native Samples. Cloud Native 案例大全/《Cloud Native 分布式架构原理与实践》示例源码
cqrs-workshop
Sample project for CQRS workshop leaded by me.
DataCamp-Projects
Datacamp practice projects
ddd-workshop
Materials for our DDD training
Deep-Reinforcement-Learning-Book
書籍「つくりながら学ぶ！深層強化学習」のサポートリポジトリです
ebooks
books of it tech
IDDD_Samples
These are the sample Bounded Contexts from the book "Implementing Domain-Driven Design" by Vaughn Vernon: http://vaughnvernon.co/?page_id=168
Introduction-to-Time-Series-forecasting-Python
Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.
JavaGuide
【Java学习+面试指南】 一份涵盖大部分Java程序员所需要掌握的核心知识。
liyu_pdf
learning
practical-microservices-architectural-patterns
Source Code for 'Practical Microservices Architectural Patterns' by Binildas Christudas
Prediction-in-Stock-Market-Based-on-SVM
ML Scikit-learn
programmer_math
程序员的数学三本书
py4fi2nd
Jupyter Notebooks and codes for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.
pybaseball
Pull current and historical baseball statistics using Python (Statcast, Baseball Reference, FanGraphs)
spring-boot-vuejs-websockets
✔️ Simple spring-boot vue.js app with websockets and docker support
spring-ddd-bank
A sample project following Domain Driven Design with Spring Data JPA
Stock-Forecast
Stock Market Prediction using Support Vector Machine
stock-rnn
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
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.
StockSentimentTrading
Algorithmic Trading using Sentiment Analysis on News Articles
Swift-30-Projects
30 mini Swift Apps for self-study
WebSocketDemo
在Spring Boot中使用WebSocket，示例包括简单模式、STOMP模式消息、处理对方不在线情况、分布式WebSocket等。