Pinned Repositories
1990-California-Housing-Price-Kaggle
Predict the selling price of a new home
abu
阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构
algo_trading_and_quant_strategies
Supplemental Material for Algorithmic Trading and Quantitative Strategies
AMAI
Advanced Melee Artifical Intelligence Mod For Warcraft 3
ar-cutpaste
Cut and paste your surroundings using AR
awesome-AutoML
Curating a list of AutoML-related research, tools, projects and other resources
Awesome-CS-Books-and-Digests
:books: Awesome CS Books(with Digests)/Series(.pdf by git lfs) Warehouse for Geeks, ProgrammingLanguage, SoftwareEngineering, Web, AI, ServerSideApplication, Infrastructure, FE etc. :dizzy: 优秀计算机科学与技术领域相关的书籍归档，以及我的读书笔记。
Books-1
Calculating-maximum-drawdown
In quantative finance world, maimum drawdown is an important factor in strategy choosing, even more important than sharpe ratio
GreatYoungShaw's Repositories
GreatYoungShaw/Awesome-CS-Books-and-Digests
:books: Awesome CS Books(with Digests)/Series(.pdf by git lfs) Warehouse for Geeks, ProgrammingLanguage, SoftwareEngineering, Web, AI, ServerSideApplication, Infrastructure, FE etc. :dizzy: 优秀计算机科学与技术领域相关的书籍归档，以及我的读书笔记。
GreatYoungShaw/Books-1
GreatYoungShaw/1990-California-Housing-Price-Kaggle
Predict the selling price of a new home
GreatYoungShaw/algo_trading_and_quant_strategies
Supplemental Material for Algorithmic Trading and Quantitative Strategies
GreatYoungShaw/AMAI
Advanced Melee Artifical Intelligence Mod For Warcraft 3
GreatYoungShaw/ar-cutpaste
Cut and paste your surroundings using AR
GreatYoungShaw/awesome-AutoML
Curating a list of AutoML-related research, tools, projects and other resources
GreatYoungShaw/Cracking-the-Coding-Interview_solutions
Efficient solutions to "Cracking the Coding Interview" (6th Edition) problems
GreatYoungShaw/datasciencecoursera
for Data Science class on Coursera
GreatYoungShaw/deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
GreatYoungShaw/deeplearning_ai_books
deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）
GreatYoungShaw/Derivative-pricing
Deriv pricing for all
GreatYoungShaw/FFT-in-Python
This tutorial covers step by step, how to perform a Fast Fourier Transform with Python.
GreatYoungShaw/fucking-algorithm
手把手撕LeetCode题目，扒各种算法套路的裤子，not only how，but also why. English version supported!
GreatYoungShaw/gs-quant
Python toolkit for quantitative finance
GreatYoungShaw/HackerrankPractice
170+ solutions to Hackerrank.com practice problems using Python 3 and Oracle SQL
GreatYoungShaw/handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
GreatYoungShaw/hummingbot
Hummingbot: a client for crypto market making
GreatYoungShaw/Kumamon
GreatYoungShaw/Kumamon-1
Python 3.3 scripts for use with Togabou
GreatYoungShaw/Learning-SICP
MIT视频公开课《计算机程序的构造和解释》中文化项目及课程学习资料搜集。
GreatYoungShaw/Leetcode
Play Leetcode with different Programming language
GreatYoungShaw/Machine-Learning-for-Finance
Machine Learning for Finance, published by Packt
GreatYoungShaw/pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
GreatYoungShaw/qlib
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies.
GreatYoungShaw/quant-trading
Python quantitative trading strategies including MACD, Pair Trading, Heikin-Ashi, London Breakout, Awesome, Dual Thrust, Parabolic SAR, Bollinger Bands, RSI, Pattern Recognition, CTA, Monte Carlo, Options Straddle
GreatYoungShaw/riskparity.py
fast and scalable design of risk parity portfolios with TensorFlow 2.0
GreatYoungShaw/scientificProject
GreatYoungShaw/tensorforce
Tensorforce: a TensorFlow library for applied reinforcement learning
GreatYoungShaw/tensortrade
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.