Level up your career: Understanding advanced trading strategies, Impact of Machine Learning and methods for research into new alpha sources. This course takes a hands-on approach to building trading pipelines, from data to features to modelling to allocation to execution to performance measurement, guiding the student through common practice as well as areas of innovation.
Level up your career: Understanding advanced trading strategies, Impact of Machine Learning and methods for research into new alpha sources. This course takes a hands-on approach to building trading pipelines, from data to features to modelling to allocation to execution to performance measurement, guiding the student through common practice as well as areas of innovation.
500+ already enrolled
Only For India
Global Course Fee:
₹4,10,000 (£3,950)
Exclusive Discounted Wright Offer:
₹200,000 ₹4,10,000 (£3,950)
50% OFF
Time Commitment:
8 - 10 Hours Weekly | 12 Lecture Weeks
Start Date: 3rd March, 2025
Evaluation:
Final Project + Certificate
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Algorithmic trading is a broad term for trading which uses mathematical models and algorithms. As markets have become more automated over time, banks and retail brokerages increasingly make markets for their clients using algorithms (e-trade). Some quant hedge funds have relied heavily on algorithms from the start, with a notable few enjoying huge success, while others have employed algorithms as one of many elements in their approach. In recent times, high-frequency market-makers and proprietary trading shops have come to dominate the exchange traded (and some OTC) markets, with increased volume and speed leading to higher net returns. Algorithms can be used for all aspects of the business, from trade decisions to portfolio allocations to placing market and limit orders with the time horizons involved being anywhere from a nanosecond to many days.
The rationale is simple – efficient, scalable models with consistent and testable performance. The aim is to use a scientific approach which can generate extremely fast responses to market events.
While some exchange traded markets (liquid futures, equities, ETFs) and some highly liquid OTC markets (FX, US Treasuries) have been dominated by algorithmic trading for some time, more recent developments include increasing algo presence in less liquid markets such as non-liquid energy futures (energy futures outside of WTI, Brent, and standard energy complex), OTC rates markets (USD and EUR swaps), highly illiquid corporate bonds and even in areas once fully dominated by voice-trading such as EUR government bonds. Some of this evolution is the quest for new markets in which to apply what has been successfully developed elsewhere but much of it is just the natural diffusion of talent into areas which are both more risky and possibly more lucrative. Irrespective of whether it be market-making in banks and speciality prop shops or finding and executing on alphas, balancing portfolios and optimising execution in asset managers, today’s traders, quants and managers must know about systematic trading.
The goal of this class is to provide students with a strong foundation in algorithmic trading as well as the tools and techniques used in the industry. The class will cover everything from basic programming concepts to advanced trading strategies and methods for research into new alpha sources. Students will have the opportunity to apply what they learn in hands-on projects throughout the course.
The goal of this class is to provide students with a strong foundation in algorithmic trading as well as the tools and techniques used in the industry. The class will cover everything from basic programming concepts to advanced trading strategies and methods for research into new alpha sources. Students will have the opportunity to apply what they learn in hands-on projects throughout the course.
This course is an up-to-date version of the course, Algorithmic Trading Strategies. Nick taught a version of this course at University College London, to Computational Finance and Risk Management PhD and MSc students from 2015 – 2023, and online through QuantsHub and other platforms. With over 500 students having successfully completed earlier versions of this course, and the curriculum continually being re-jigged, it seemed an appropriate time for a larger update to broaden the perspective and make the course more applied, with the goal of having students be able to implement methods, models and frameworks themselves. Changes to previous courses include:
ATC concludes with a practical final project that gives you the opportunity to implement the knowledge and skills you have acquired during the course of the programme.
Dr. Nick Firoozye is a mathematician with over 20 years of experience in the finance industry, in both buy and sell-side firms, in research, structuring and systematic trading. He started his finance career in Lehman Brothers doing MBS/ABS modelling, heading teams in Portfolio Strategy and EM Quant Research, later taking a variety of senior roles at Goldman Sachs, and Deutsche Bank, and at asset managers and hedge-funds, Sanford Bernstein, Citadel, and Exodus Point, in areas ranging from Quantitative Strategy, Relative Value Strategy and Trading, to Asset Allocation. He is currently Managing Director and Head of FI Systematic Trading at a small securities trading shop in NY. He is an Honorary Professor in Computer Science at University College London, focusing on Online Learning, Reinforcement Learning, Robust Machine Learning and of course Statistics in Finance. Two of Nick’s PhD students have completed their degrees, and he has six doing areas in quant finance from systematic trading to recommender systems. He co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty in finance, in light of the many recent financial crises. Nick began teaching Algorithmic Trading Strategies as a PhD reading course in 2015 and since then Nick adapted the material for a few online courses and created an MSc course by the same name which has run for the past 4 years. Nick has had over 500 students successfully taking his online and UCL courses to date. Nick got his PhD at Courant Institute, NYU, and taught for a number of years at U of MN, Heriot-Watt, University of Bonn, NYU, and then finally at University of Illinois where he was an Asst Prof, before leaving academia for Wall Street.
Dr Brian Healy is a mathematician with over 20 years experience in financial markets as a quant, trader, researcher and strategist. He began his career as an exotic options quant & trader with extensive experience in all asset classes, particularly fixed income and foreign exchange, at leading investment banks including Citigroup, Barclays Capital and Deutsche Bank. In addition to his core responsibilities of pricing and managing risk he also designed a large number of original strategies and indices for clients and for internal risk taking. Since leaving banking he has run a very successful consultancy business which specialises in building models using the latest mathematical, statistical and machine learning techniques. Clients include asset managers, market-making firms, private capital firms as well as tech companies. Brian is an expert in all aspects of markets, particularly quantitative strategies, options and other derivatives and predictive modelling. In addition to his work with industry he is also an industry professor of machine learning and data analytics at UCL, a lecturer in finance at UCD, a researcher and lecturer in mathematical and computational finance at Stanford University, is an author of many peer reviewed papers in mathematical finance and frequent speaker at conferences and seminars.
The comprehensive coverage of various topics provided me with a solid understanding of the intricacies of algorithmic trading. Now I have a clear direction on where to begin and how to approach each strategy effectively. I now feel equipped with the knowledge and confidence to navigate the complexities of algorithmic trading with ease. I highly recommend this training to anyone looking to dive into the world of algorithmic trading.
I had been looking for an online course on Algorithmic Trading by a reputable organization for years but none had the in-depth syllabus I was looking for. When last year ATC was announced by WBS, I did not hesitate to sign up. Professors Nick and Brian have extensive experience on the subject and combine theoretical classes and practical sessions with valuable insights of the current industry trends. The course is well structured and paced, covering all aspects needed to build a profitable trading strategy. A well-curated list of bibliography and paper references is provided during the course (although one could not probably be able to digest all material in a lifetime). The live sessions give students a unique chance to discuss the material and ideas with professors weekly. I strongly recommend this course if you want a thorough introduction to the field of algorithmic trading.
ATC covers a vast panel of topics structured around building trading strategies and leveraging modern infrastructures. From time series theory to risk management, the authors of the course tried to give a wide overview on the field, and provided the students with valuable references/ books/ resources. I have enjoyed this course and would recommend it.
The comprehensive coverage of various topics provided me with a solid understanding of the intricacies of algorithmic trading. Now I have a clear direction on where to begin and how to approach each strategy effectively. I now feel equipped with the knowledge and confidence to navigate the complexities of algorithmic trading with ease. I highly recommend this training to anyone looking to dive into the world of algorithmic trading.
I had been looking for an online course on Algorithmic Trading by a reputable organization for years but none had the in-depth syllabus I was looking for. When last year ATC was announced by WBS, I did not hesitate to sign up. Professors Nick and Brian have extensive experience on the subject and combine theoretical classes and practical sessions with valuable insights of the current industry trends. The course is well structured and paced, covering all aspects needed to build a profitable trading strategy. A well-curated list of bibliography and paper references is provided during the course (although one could not probably be able to digest all material in a lifetime). The live sessions give students a unique chance to discuss the material and ideas with professors weekly. I strongly recommend this course if you want a thorough introduction to the field of algorithmic trading.
ATC covers a vast panel of topics structured around building trading strategies and leveraging modern infrastructures. From time series theory to risk management, the authors of the course tried to give a wide overview on the field, and provided the students with valuable references/ books/ resources. I have enjoyed this course and would recommend it.
Algorithmic trading involves using mathematical models and algorithms to execute trades. It leverages automated systems to make market decisions, manage portfolios, and execute orders across various time horizons—from nanoseconds to days. This type of trading is prevalent in both high-frequency trading environments and traditional trading setups.
Algorithmic trading offers several advantages, including faster response to market events, enhanced efficiency, and the ability to test and scale trading strategies systematically. It's crucial for markets that demand quick decision-making and where large volumes of trades are executed automatically.
This course is designed for students, traders, quants, and asset managers who aspire to understand and utilize systematic trading methods. It's particularly beneficial for those interested in applying scientific approaches to trading in exchange-traded and OTC markets.
The course covers a range of topics from basic programming concepts to advanced trading strategies. It includes:
The course begins with fundamental programming concepts, progressing to more complex topics such as trading strategy development and alpha research. It includes hands-on projects allowing students to apply their learning practically.
The examined part of the course takes place over 12 weeks, with the final project taking place at the end of the course.
All the lectures are filmed and recordings are available for you on the ATC Student Portal for the duration of the course
As it’s a short course there are no staggered payments.
The live streaming will be available on Cisco Webex, you will be given weekly login access details.
Students are expected to have a working knowledge of programming as the course delves into algorithmic trading's technical aspects. Familiarity with basic financial concepts and models is also beneficial.
The primary goal is to equip students with a strong foundation in both the theoretical and practical aspects of algorithmic trading. By the end of the course, students should be capable of developing, implementing, and optimizing their own trading models and strategies.
By providing both theoretical knowledge and practical skills, the course prepares students for roles in algorithmic trading at banks, proprietary trading firms, and investment management companies. It focuses on real-world applications and innovations in trading, making graduates attractive candidates for a range of positions in finance and technology.
Yes, the course is designed to bridge the gap between academic learning and real-world trading needs. Students will engage in projects that simulate actual trading situations and implement strategies that can be applied in live trading environments.
The current pricing of ₹ 2,00,000 is at a 50%+ discount compared to global fees of ₹ 4,10,000 (£ 3,950). As this is a negotiated & heavily discounted price specifically for the Wright community, there are no further discounts or early bird offers.
However, we do offer volume discounts, so if 2 or more people from your institution wish to take the course please contact us and we will be happy to discuss the pricing.
In addition to this, if you are interested in taking multiple courses from the following list, then we can offer you specific discounts:
The current pricing of ₹ 2,00,000 is at a 50%+ discount compared to global fees of ₹ 4,10,000 (£ 3,950). As this is a negotiated & heavily discounted price specifically for the Wright community, there are no further discounts or early bird offers. However, we do offer volume discounts, so if 2 or more people from your institution wish to take the course please contact us and we will be happy to discuss the pricing.
In addition to this, if you are interested in taking multiple courses from the following list, then we can offer you specific discounts:
You will receive a mail with further instructions and a payment link within 24 hours.
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