MasterFaculty of Data Science and Analytics

Algorithmic Trading and Quantitative Finance (M.Sc.)

Navigating the Markets with Intelligence: Algorithmic Trading and Quantitative Finance Your Guide to Mastering Algorithmic Trading at Nexier University Welcome to the cutting edge of financial markets. I am Prof. Dr. Haruki Hattori. As a specialist in mastering the design and deployment of high-frequency trading algorithms, I lead the master's students in the Algorithmic Trading and Quantitative Finance (M.Sc.) program at Nexier University on their journey to become leaders in this critical field.

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the Design and Deployment of High-Frequency Trading Algorithms; Deeply Understanding Market Microstructure, Quantitative Risk Management, and AI-Driven Trading Strategies.

Practical focus

Advanced Quantitative Modeling, High-Frequency Data Analysis, Risk Management, C++/Python Programming for Finance, Machine Learning for Financial Markets.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Quantitative Trader for a hedge fund or investment bank
Algorithmic Trading Strategist for a proprietary trading firm
Financial Data Scientist for a technology company
Risk Manager for a financial institution

Career opportunities

Quantitative Trader for a hedge fund or investment bank
Algorithmic Trading Strategist for a proprietary trading firm
Financial Data Scientist for a technology company
Risk Manager for a financial institution

Jobs and projects

Advanced analytical and problem-solving skills for financial challenges
Strategic thinking and design for algorithmic trading solutions
Effective communication and presentation of complex financial concepts

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering the practical application of advanced quantitative modeling and high-frequency data analysis.
- Gaining expertise in risk management and C++/Python programming for finance.
- Developing a deep understanding of machine learning for financial markets.
- Cultivating a commitment to building a more intelligent and data-driven financial world.
Skills you build
- Mastering the design and deployment of high-frequency trading algorithms.
- Gaining expertise in market microstructure, quantitative risk management, and AI-driven trading strategies.
- Developing strategic thinking for leveraging AI for financial market prediction.
- Cultivating an interdisciplinary approach, integrating computer science, statistics, and finance.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Algorithmic Trading and Quantitative Finance (M.Sc.)

01Advanced Quantitative Modeling
1. FoundationsFoundations of Advanced Quantitative Modeling
  The learner can master the practical application of advanced quantitative modeling and high-frequency data analysis, as applied to Advanced Quantitative Modeling.
  The learner can gain expertise in risk management and C++/Python programming for finance, as applied to Advanced Quantitative Modeling.
2. MethodsMethods in Advanced Quantitative Modeling
  The learner can develop a deep understanding of machine learning for financial markets, as applied to Advanced Quantitative Modeling.
  The learner can cultivating a commitment to building a more intelligent and data-driven financial world, as applied to Advanced Quantitative Modeling.
3. ApplicationApplication of Advanced Quantitative Modeling
  The learner can master the design and deployment of high-frequency trading algorithms, as applied to Advanced Quantitative Modeling.
  The learner can gain expertise in market microstructure, quantitative risk management, and AI-driven trading strategies, as applied to Advanced Quantitative Modeling.
02High-Frequency Data Analysis
1. FoundationsFoundations of High-Frequency Data Analysis
  The learner can develop strategic thinking for leveraging AI for financial market prediction, as applied to High-Frequency Data Analysis.
  The learner can cultivating an interdisciplinary approach, integrating computer science, statistics, and finance, as applied to High-Frequency Data Analysis.
2. MethodsMethods in High-Frequency Data Analysis
  The learner can apply a method from High-Frequency Data Analysis to a documented case.
  The learner can select an appropriate method from High-Frequency Data Analysis for a stated problem.
3. ApplicationApplication of High-Frequency Data Analysis
  The learner can evaluate a practice of High-Frequency Data Analysis against a stated criterion.
  The learner can transfer High-Frequency Data Analysis to a new documented context.
03Risk Management in Algorithmic Trading
1. FoundationsFoundations of Risk Management in Algorithmic Trading
  The learner can explain the core terms of Risk Management in Algorithmic Trading.
  The learner can distinguish related ideas inside Risk Management in Algorithmic Trading.
2. MethodsMethods in Risk Management in Algorithmic Trading
  The learner can apply a method from Risk Management in Algorithmic Trading to a documented case.
  The learner can select an appropriate method from Risk Management in Algorithmic Trading for a stated problem.
3. ApplicationApplication of Risk Management in Algorithmic Trading
  The learner can evaluate a practice of Risk Management in Algorithmic Trading against a stated criterion.
  The learner can transfer Risk Management in Algorithmic Trading to a new documented context.
04C++/Python Programming for Finance
1. FoundationsFoundations of C++/Python Programming for Finance
  The learner can explain the core terms of C++/Python Programming for Finance.
  The learner can distinguish related ideas inside C++/Python Programming for Finance.
2. MethodsMethods in C++/Python Programming for Finance
  The learner can apply a method from C++/Python Programming for Finance to a documented case.
  The learner can select an appropriate method from C++/Python Programming for Finance for a stated problem.
3. ApplicationApplication of C++/Python Programming for Finance
  The learner can evaluate a practice of C++/Python Programming for Finance against a stated criterion.
  The learner can transfer C++/Python Programming for Finance to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My academic focus is on the strategic application of AI to revolutionize financial markets. I delve into the complexities of high-frequency trading algorithms, the intricacies of market microstructure, and the transformative power of quantitative risk management and AI-driven trading strategies. My work seamlessly integrates computer science, statistics, and finance to create a holistic understanding of how AI can drive efficiency, profitability, and stability in financial services. I am widely recognized for my contributions, with fictional publications like "High-Frequency Trading Strategies with Reinforcement Learning" and "Market Microstructure Analysis for Algorithmic Liquidity Provision" are listed on these platforms. I hold prestigious memberships as a "Head of Quantitative Strategies" at Goldman Sachs (or a fictional equivalent) and a "Keynote Speaker" at the Quant World Conference. My thought leadership is evident through my advanced research on algorithmic market making, portfolio optimization, and the ethical implications of AI in financial markets, frequently featured in publications like Quantitative Finance or Journal of Financial Markets.

Applied mentorship

His expertise lies in the practical application of quantitative finance principles to solve complex market problems. He specializes in advanced quantitative modeling, high-frequency data analysis, and risk management. He is passionate about C++/Python programming for finance and machine learning for financial markets, and he is committed to helping his students to design and implement trading solutions that are not only efficient but also effective and ethical. My work is dedicated to helping my students to understand not just the theory, but also the practice of algorithmic trading. My publications, such as the coding manual on "Algorithmic Trading Strategies for Beginners: A Python Implementation Guide" and the research paper on "Backtesting Methodologies for High-Frequency Trading Algorithms," are a testament to my commitment to research that is both intellectually rigorous and practically relevant.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

Book: "The Algorithmic Trader: Algorithmic Trading and Quantitative Finance." This book provides advanced insights into mastering the design and deployment of high-frequency trading algorithms. It covers market microstructure, quantitative risk management, and AI-driven trading strategies.

Peer-Reviewed Journal Article: "High-Frequency Trading Strategies with Reinforcement Learning." (Journal of Algorithmic Finance, Fictional) This article presents groundbreaking research on the development and deployment of high-frequency trading algorithms using reinforcement learning. It details novel AI models that can learn optimal trading strategies from real-time market data, adapting to changing market conditions and maximizing profitability.

Article: "AI for Algorithmic Market Making: Optimizing Bid-Ask Spreads and Liquidity Provision." This article details the application of AI and reinforcement learning algorithms for algorithmic market making, where AI agents automatically quote bid and ask prices for financial assets to provide liquidity and profit from the spread. It explores how AI can dynamically adjust pricing strategies.

Blog Post (Current Academic Topic): "The Rise of Explainable AI in Algorithmic Trading: Enhancing Trust and Compliance in Black-Box Models." This blog post academically explores the critical need for Explainable AI (XAI) in algorithmic trading, where opaque 'black-box' models make rapid, high-stakes financial decisions. It discusses how XAI techniques can provide human-interpretable explanations for trading strategies.

Blog Post (Sensational/Controversial Topic): "The Algorithmic Market Manipulator: Can AI Cause a Financial Crisis? The Unforeseen Dangers of Autonomous Trading." This article provocatively discusses the highly controversial and alarming potential for advanced AI-powered algorithmic trading to inadvertently or deliberately cause financial instability or even a market crash. It explores scenarios where AI-driven trading could create unpredictable and cascading feedback loops.

R / 02

Mentor practice lens

My publications are focused on the practical challenges of navigating financial markets with algorithms:

"Algorithmic Trading Strategies for Beginners: A Python Implementation Guide" (Coding Manual): A practical guide to implementing algorithmic trading strategies in Python.

"Backtesting Methodologies for High-Frequency Trading Algorithms" (Technical Report): A detailed analysis of the different backtesting methodologies that can be used for high-frequency trading algorithms.

"Risk Management in Algorithmic Trading: VaR and CVaR Applications" (Research Paper): An analysis of the different risk management techniques that can be used in algorithmic trading.

Adaptive capability

Professor superpower

He possesses the "Market Microstructure Analyzer," a superpower that allows me to foresee and engineer the success of algorithmic trading. When a student proposes a new high-frequency trading algorithm, the GAF-powered analyzer can instantly analyze real-time order book data, predict price movements, and identify optimal trading strategies. This provides my students with an unparalleled ability to design strategies that are not just innovative, but also effective, ethical, and truly transformative.

Adaptive capability

Mentor superpower

I provide my students with the "Algorithmic Risk Visualizer." This GAF-powered tool is a virtual laboratory for the quantitative analyst. When a student is designing an algorithmic trading strategy, the Visualizer allows them to see how it will perform in the real world. It can simulate various market stress scenarios and visually display the algorithm's potential losses, volatility exposure, and systemic risk. This allows my students to move beyond the limitations of traditional, manual risk analysis and to design solutions that are not just efficient, but also effective and ethical.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Haruki Hattori

Mastering the Design and Deployment of High-Frequency Trading Algorithms; Deeply Understanding Market Microstructure, Quantitative Risk Management, and AI-Driven Trading Strategies.

Meet your professor Open the classroom

AI Super Mentor

Dr. Jacob Wallace

Advanced Quantitative Modeling, High-Frequency Data Analysis, Risk Management, C++/Python Programming for Finance, Machine Learning for Financial Markets.

Meet your mentor Open the classroom

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Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

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Algorithmic Trading and Quantitative Finance (M.Sc.)

Academic focus

Practical focus

Quantitative Trader for a hedge fund or investment bank

Algorithmic Trading Strategist for a proprietary trading firm

Financial Data Scientist for a technology company

Risk Manager for a financial institution

Quantitative Trader for a hedge fund or investment bank

Algorithmic Trading Strategist for a proprietary trading firm

Financial Data Scientist for a technology company

Risk Manager for a financial institution

Advanced analytical and problem-solving skills for financial challenges

Strategic thinking and design for algorithmic trading solutions

Effective communication and presentation of complex financial concepts

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Haruki Hattori

Dr. Jacob Wallace

Find the program that expands your universe.