I am a Taiwanese. Please call me Daniel. I am a person who desires to meet new things, challenges and new ideas. I have experience in front-end development in the industry and a self-taught full-stack developer (study back-end and dev-ops).
My goal is that I can build anything as I want and becoming a software architect in this industry. After I have been worked for two years, I realized that I have to work on my own project first so that I could have more time to cover more comprehensive knowledge. In order to achieve this goal, I quit my job and study online to enhance my knowledge by studying algorithm and system design. In practice level, I strongly believe to build an enterprise level product from scratch is the best way and the only way to go.
๐ญ ๐ฆ๐ถ๐ฑ๐ฒ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐'๐บ ๐ฐ๐๐ฟ๐ฟ๐ฒ๐ป๐๐น๐ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ผ๐ป
Building a US stock screener based on the financial statement from www.sec.gov. This project means a lot for me. I can boost my career through this work in terms of technical knowledge and project management. See "Project: Full-stack: Lazy-stock-screener-demo" section for more details.
Front End
โข webpack/babel
โข React Ecosystem: React Hooks/React Router/Redux/React SSR/dynamic import/React in Typescript
โข API: Axios/Apollo-client/GraphQL
โข CSS/SCSS/Style Component/Materical UI
โข MVP pattern
Server
โข Golang Ecosystem: Fasthttp
โข Node.js Ecosystem: Express.js in Typescript/Chi+Mocha/Sequelize/Mongoose
โข Python Ecosystem: Pandas
โข System Design Pattern: DDD/Clean Architecture
โข DB/Cache: PostgreSQL/MongoDB/Redis
Dev-Ops
โข Container: Docker/Docker-Compose
โข Reverse-Proxy: Traefik
Full-stack: Lazy-stock-screener-demo
A US stock screener based on the financial statement from www.sec.gov. Design a system from use-case/user story to CI/CD. It leverages all the ideas or principles listed in the following: highly decoupled components, event-driven design, and SOLID principles. A MVP pattern is in front-end with React Hooks; Using Clean Architecture and DDD best practices with Golang in the back-end; Data pipeline is built on top of Python with various design patterns. Everything is containerized with Docker and managed by Kubernetes, while Gitlab CI/CD helped me to deploy this project to the GKE. The main project is privated and on gitlab, therefore, only part of this project are demonstrated on github origanization.
Full-stack: Price Dashboard
An internal SPA for the purpose of setting and reading prices in terms of each product in the online shop. The tech stack is MERN (react-redux, node, express, mongoDB) with Docker/Kubernetes and system structure is based on microservice architecture. The details tech-stack are Webpack/SPA/Google OAuth2/Node/Mocha/Apollo Server/MongoDB/Redis/Nginx/Docker/K8S/TravisCI/Terraform.
Data Pipeline: US Stock Financial Report Dumper
- A python version financial report dumper with Pandas/Google Sheet API/
- I also implement various design pattern like:
- TableAbstractFactory
- ScoreTableStrategy
- BuyDecisionStrategy
- InputPipeLine
- APIMediator
- OutputObserver
- Chain of Responsibility/Builder