Machine Learning and Reinforcement Learning in Finance New York University Tandon School of Engineering

The code used for the free quants@dev Webinar series on Reinforcement Learning in Finance

Implementation of "AlphaQCM: Alpha Discovery in Finance with Distributional Reinforcement Learning"

Practical Deep Reinforcement Learning Approach for Stock Trading. NeurIPS 2018 AI in Finance.

Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets.

Materials for blogs and conferences

Multi-agent Reinforcement Learning for Liquidation Strategy Analysis. ICML 2019 AI in Finance.

Note about cousera online course: ml and rl in finance

AI-driven Personal Goal Assistant: Reinforcement learning-powered software mimics user behavior, interacts with computer inputs, and autonomously achieves goals in finance, social networking, and productivity. Open-source, Python-based RL agent.

https://www.coursera.org/learn/advanced-methods-reinforcement-learning-finance?

Financial Reinforcement Learning Projects using Python

Reinforcement Learning in Finance

Machine Learning and Reinforcement Learning in Finance New York University Tandon School of Engineering

Stock investment can be one of the ways to manage one’s asset. Technical analysis is sometimes used in financial markets to assist traders make buying and selling decisions [1]. Many technical analysis trading rules are deterministic trading policies. [2] uses genetic algorithm to find technical trading rule. [3] studies evolutionary algorithms in optimization of technical rules for automated stock trading. [4] proposed a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. [5] studies the selection of the optimal trading model for stock investment in different industries. [6] describes the optimization of trading strategies. The optimization of trading rule using genetic algorithm or evolutionary algorithms belongs to policy-based method, which is a branch of Machine Learning. Policy-based methods try to directly optimize for the optimal policy which is an important branch for domains with continuous action spaces [7]. There are studies focus on how to find a trading strategy via Reinforcement Learning (RL) [8] or using Deep-Q learning for automatic trading algorithm [9]. But in this study we will focus on the policy-based method using Generic Algorithm that directly search for the optimal parameters of a deterministic policy. Yahoo Finance’s stock history data [10] will be used in this study. The reason to choose Yahoo Finance data is because it is free and available for public to assess. The performance of algorithm will be evaluated using different stocks. The purpose of the study is to see the difference between using an agent with optimized policy to manage one’s asset with buy-and-hold strategy, or manage one’s asset with an agent with unoptimized policy.

Machine Learning and Reinforcement Learning in Finance Specialization (MOOC) Assignments

Code (Jupyter Notebooks) for Coursera - Machine Learning and Reinforcement Learning in Finance Specialization

ML x Health consists of Bayesian ML, Representation Learning, Graph Neural Networks, Computer Vision, Knowledge Graphs, Knowledge-Aware ML, Symbolic Reasoning, Neuro-Symbolic AI, EHR, Imaging, Genomics, and Multi-Omics. ML x Finance consists of Gaussian Processes, Time Series, Multi-Lingual NLP, Reinforcement Learning, Building Market Simulators, Trading, Asset Management, Financial Inclusion, ESG, Emerging Risks, and Economic Prosperity. Achievement - Completing all tasks before the deadline with 100% output and achieving the Certificate of Completion. Health and Finance Tracks were crucial in completing the MSc Data Science Final Year Dissertation.

Application of reinforcement learning to the development of execution algorithms in the field of finance. These algorithms break CARE orders into CHILD orders and exectute them in the market.

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An Archive containing solutions and notes for Machine Learning and Reinforcement Learning in Finance Specialization by NYU Specialization - Coursera

Reinforcement Learning in Quantitative Finance

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This is my implementation in Scala of the code from book "Foundations of Reinforcement Learning with Applications in Finance" by Rao and Jelvis.

Experience the convergence of reinforcement learning and finance in this project, which implements a Q-learning agent for option pricing under the Black–Scholes model. Leveraging Monte Carlo simulation, B-spline basis functions, and a variance-based reward, the agent learns optimal hedging strategies to generate accurate, risk-neutral option price.

Experience the fusion of finance and artificial intelligence with this cutting-edge project that implements a deep reinforcement learning agent for automated stock trading. Leveraging advanced neural network architectures and a meticulously crafted reward function, the agent learns to make profitable trading decisions in a simulated environment

Machine Learning and Reinforcement Learning in Finance, New York University-Tandon School of Engineering.

Machine learning application in Finance using recurrent neural networks and reinforcement learning.

2025 Spring Semester Reinforcement Learning Foundations and Its Applications in Finance Final Assignment, in collaboration with Huang Jinming