zhaosongyi Goto Github PK
Name: q12920
Type: User
Name: q12920
Type: User
An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.
The main problem facing both amusement park customers and owners such as Disney World is customer satisfaction and efficiency, which are both negatively effected by high wait times. As a result, these parks have spent significant time and money to implement methods which reduce wait times to both increase customer satisfaction and efficiency. We explored the implementation of an reservation-dependent priority queuing system to devise how to best reduce average customer wait times for Expedition Everest, a popular ride at Disney World. Using third-party data, we first built constant-rate and time-dependent-rate queuing systems to model current behavior, followed by implementing an Express Queue into the system. We found a decrease in average wait time of 18.31% through simulating 30 days of typical customer behavior with the improvement strategy. Finally, we performed sensitivity analysis to optimize the parameters of our improvements, finding an ultimate optimal decrease in wait times of 44.42%.
Tutorial on building an angular application.
A curated list of awesome Machine Learning frameworks, libraries and software.
Reinforcement learning resources curated
An internship project: Implement Barra model to take risk or style factor attribution based on multi-factor model.
Barra-Multiple-factor-risk-model
sources for book *Bayesian Workflow Using Stan", (working title)
R & Stan code associated with my "Bayes Days" workshop
Full Bayesian Inference for Hidden Markov Models
Bayesian Analysis with Python - Second Edition, published by Packt
Code repository for Building RESTful Python Web Services, published by Packt
Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code
Code repository for Deep Learning with Keras published by Packt
Algorithmic trading with deep learning experiments
Deep Learning models for network traffic classification
Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke
Code examples from the book.
Example code and files from "Prometheus: Up and Running"
Solution Code to Full Stack Foundations (ud088)
All of the source code for the Single Page Web Applications with AngularJS course.
A high-performance GEA framework for Python. Welcome to star and fork.
Bayesian Hierarchical Hidden Markov Models applied to financial time series, a research replication project for Google Summer of Code 2017.
Hands on Markov Models with Python, published by Packt
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
Keras implementations of Generative Adversarial Networks.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.