Hongyang Yang
Co-Founder & Managing Director at Marketmind
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolHongyang Yang is a founder and FinTech entrepreneur based in New York with eight years of hands-on experience building AI-driven trading systems and financial research tools. As President of AI4Finance-Foundation, he created projects like FinRL, FinRobot and FinGPT, combining reinforcement learning, data engineering and practical trading environments to bridge research and production. His open-source contributions—ranging from FinRL trading environments and tutorials to ElegantRL examples—show a focus on reproducible RL workflows, technical indicator engineering, and packaging for broader adoption. He mentors at Columbia University, signaling a commitment to guiding applied research into usable tools. Notably, his work often sits at the intersection of back-end data processing and ML agent design, enabling reproducible finance experiments at scale.
8 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.S.), Bachelor of Science (B.S.) at The Ohio State University Fisher College of BusinessMaster's degree Data Science, Master's degree Data Science at Columbia UniversityBachelor's degree Mathematics, Bachelor's degree Mathematics at The Ohio State University
English, Chinese
Github Skills (22)
pytorch10
ppp10
python10
openai-gym10
finance10
pandas10
machine-learning10
reinforcement-learning10
data-preprocessing10
stock-trading10
dqn10
statistical-models10
data-processing10
deep-reinforcement-learning10
jupyter-notebook10
Programming languages (3)
Github contributions (5)
AI4Finance-Foundation/FinRL-Trading
Jun 2020 - Jan 2023
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 157 commits, 12 PRs in 2 years 7 months
Contributions summary:Hongyang's commits primarily focus on developing a single-stock trading environment using Python and the Stable Baselines library. The user preprocesses financial data, including calculating technical indicators like MACD, RSI, and CCI. The commits involve implementing a reinforcement learning environment for stock trading, including defining states, actions, rewards, and the terminal condition. The user also trains and evaluates machine learning models, like PPO, for stock trading strategies, and provides output from the strategy.
aifinrlquantitative-tradingdeep-reinforcement-learningstock-trading
AI4Finance-Foundation/FinRL-Tutorials
Sep 2018 - Jan 2023
FinRL® Tutorials. Please star.
Role in this project:Data Scientist
Data ScientistContributions:65 commits, 1 PR, 49 pushes in 4 years 4 months
Contributions summary:Hongyang primarily worked on calculating and analyzing financial returns using Python and data science libraries. Their commits focus on data manipulation with pandas, specifically calculating daily returns from adjusted closing prices. The user's code modifications include the implementation of financial return calculations within a Jupyter Notebook environment for time series analysis.
finrl
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