Chelsea Wang (KoalaChelsea)

KoalaChelsea

Geek Repo

Location:Falls Church, VA

Github PK Tool:Github PK Tool

Chelsea Wang's starred repositories

gym-anytrading

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)

Language:PythonLicense:MITStargazers:2075Issues:0Issues:0

machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition.

Language:Jupyter NotebookStargazers:12263Issues:0Issues:0

CASIE

CyberAttack Sensing and Information Extraction

Language:PythonStargazers:64Issues:0Issues:0

q-trading-pytorch

DQN stock trading pytorch implementation

Stargazers:17Issues:0Issues:0

gym-mtsim

A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)

Language:PythonLicense:MITStargazers:412Issues:0Issues:0

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

Language:Jupyter NotebookLicense:MITStargazers:20251Issues:0Issues:0

climate-cooperation-competition

AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N. ai4climatecoop.org

Language:PythonLicense:NOASSERTIONStargazers:34Issues:0Issues:0

PandemicSimulator

Pandemic Simulator

Language:PythonLicense:Apache-2.0Stargazers:42Issues:0Issues:0

deep-RL-trading

playing idealized trading games with deep reinforcement learning

Language:PythonLicense:MITStargazers:349Issues:0Issues:0

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

Language:Jupyter NotebookLicense:MITStargazers:5232Issues:0Issues:0

Manifold-Learning

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

Language:Jupyter NotebookStargazers:209Issues:0Issues:0

Yelp-Restaurant-Data-Analysis

The purpose of our study is to utilize data-driven methods in analyzing the crucial factors contributing towards the success of restaurants and thus provide data-science solutions to restaurant owners to help them to improve their business.

Language:PythonStargazers:4Issues:0Issues:0

2D-Drawing-Application

The application will contain the following elements: a) an Undo button to undo the last shape drawn. b) a Clear button to clear all shapes from the drawing. c) a combo box for selecting the shape to draw, a line, oval, or rectangle. d) a checkbox which specifies if the shape should be filled or unfilled. e) a checkbox to specify whether to paint using a gradient. f) two JButtons that each show a JColorChooser dialog to allow the user to choose the first and second color in the gradient. g) a text field for entering the Stroke width. h) a text field for entering the Stroke dash length. I) a checkbox for specifying whether to draw a dashed or solid line. j) a JPanel on which the shapes are drawn. k) a status bar JLabel at the bottom of the frame that displays the current location of the mouse on the draw panel.

Language:JavaStargazers:4Issues:0Issues:0

Database-Management-System-with-C-Language

In this project I wrote a database management system with C language, which can operate the commands of SQL and guarantee proper output at the same time.

Language:CStargazers:7Issues:0Issues:0

Flight-Scheduler-using-Derby-database

This application have a very nice GUI interface and will be a Derby database driven application.

Language:JavaStargazers:1Issues:0Issues:0

Data-Exploration

This tool provides data summaries and plots for sample data sets and for data that can be uploaded by by users.

Language:RStargazers:1Issues:0Issues:0

Factors-affecting-inference-for-proportions

This app represents an interactive supplementary module embedded in statistics lessons with two features. The first feature visualizes how the variations in confidence levels and sample size affect the outcome confidence interval in a single mean. The second feature tests the difference between two means by adjusting confidence levels and sample size and generating calculation results with explanations. The app requires students to engage in the interaction with the scenarios provided in context.

Language:RStargazers:1Issues:0Issues:0

Factors-affecting-inference-for-means

Factors-affecting-inference-for-means

Language:RStargazers:1Issues:0Issues:0