zhaosongyi Goto Github PK

alphatrading

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.

amusement-park-queuing-optimization

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%.

angular-phonecat

Tutorial on building an angular application.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-rl

Reinforcement learning resources curated

barra-model

An internship project: Implement Barra model to take risk or style factor attribution based on multi-factor model.

barra-multiple-factor-risk-model

Barra-Multiple-factor-risk-model

bayes-workflow-book

sources for book *Bayesian Workflow Using Stan", (working title)

bayes_days

R & Stan code associated with my "Bayes Days" workshop

bayeshmm

Full Bayesian Inference for Hidden Markov Models

bayesian-analysis-with-python-second-edition

Bayesian Analysis with Python - Second Edition, published by Packt

book-resources

building-restful-python-web-services

Code repository for Building RESTful Python Web Services, published by Packt

dbda-python

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

deep-learning-with-keras

Code repository for Deep Learning with Keras published by Packt

deep-trading

Algorithmic trading with deep learning experiments

deeptraffic

Deep Learning models for network traffic classification

doing_bayesian_data_analysis

Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

examples

Code examples from the book.

examples-1

Example code and files from "Prometheus: Up and Running"

floydhub.fast.ai

full-stack-foundations

Solution Code to Full Stack Foundations (ud088)

fullstack-course5

All of the source code for the Single Page Web Applications with AngularJS course.

fullstack-nanodegree-vm

geatpy

A high-performance GEA framework for Python. Welcome to star and fork.

gsoc17-hhmm

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

Hands on Markov Models with Python, published by Packt

handson-ml2

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.

handson-unsupervised-learning

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

keras-gan

Keras implementations of Generative Adversarial Networks.