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Course: This course provides a top-down approach to algorithmic trading, including the business planning for launching algorithmic trading operations, identification of target markets and trading styles for algorithmic trading, designing trading strategies and their algorithmic components. The program also explores testing and deploying these strategies with integrated risk controls, information technology infrastructures to run algorithmic trading operations, life cycles of algorithmic trading solutions, project management for starting algorithmic trading, business strategies to keep the competitive edge in algorithmic trading.
Who should attend: Traders, trading desk directors, institutional portfolio managers, asset allocation specialists, high net worth managers, hedge fund managers, analysts, risk managers and regulators.
Key Features of the Course Include:
Technical Rigor Delivered Without Requiring Quant Expertise
This IFM course does not require specific technical or quantitative expertise, although elements of algorithm design are discussed at levels of details to explore the performance of various algorithms and their basic ingredients such as, for example, simulated annealing, stochastic optimization, fractal compression, etc. The course covers this material without going into formulas and diverging into heavy quant discussions, since historically the audience at this course is diverse and represents a cross section of the financial industry. However, the underlying principles of these ingredients are rigorously discussed, and attendees would know the resources available when building their own algorithms, integrating their algorithms with brokers/crossing networks, or if purchasing from vendors. In essence, the course is described as intermediate/expert in terms of its domain familiarity requirements, but not in terms of specific technical knowledge.
A Modular Approach to Building Algorithmic Trading Systems
The course provides a top-down and modular methodology for rapid deployment of libraries of algorithms as building blocks of diverse trading strategies. These algorithms are constructed from mathematical and heuristic logic blocks. Each of these logic blocks is made of software modules that are implemented as objects. The course discusses extensively key design issues to assure the reusability and agility of these blocks across system layers. Special emphasis is given to scaling these building blocks from low frequency to high frequency trading, and to their adaptability to rapidly changing market microstructure conditions.
In-Depth Coverage of Key IT Issues to Reach Performance Objectives
The IT issues related to algorithmic trading such as object orientation, multi-tiered architectures, multi threading, distributed deployments, service oriented architectures, collocation, server performance, scalability of trading volume and related issues in bandwidth management and computing resources, hardware acceleration for very high speed trading, building and maintaining libraries of algorithms, deploying these algorithms to execute various strategies concurrently at multiple trading venues, are covered in-depth.
Extensive Discussion of Data Management and Ticker Plant Deployment
Key data management issues such as the implementation of ticker plants, managing stream of market data from multiple sources, real time and dynamic structuring and synchronization of heterogeneous data series, data capture in relational databases versus in dynamic arrays within the algorithm itself, and the impact of such decisions on reducing data latency are covered in detail.
Strict Impartiality in Evaluating Technology Suppliers with Comprehensive Coverage of Technology Trade-Offs
Technology options such as various off-the-shelf trading software systems and data providers; basic tools to design and deploy algorithms such as C++, C#, R, S and commercial prototyping software; execution algorithms from broker-dealers, public domain sources or commercial trading software; operating system options such as Linux, Unix and Windows; integrated design environments such as Eclipse and Anjuta; algo deployment methodologies such as object orientation, procedural programming, scripting versus native code development, are discussed comprehensively and with strict impartiality. Merits and pitfalls of these choices to address different trading objectives are evaluated with straightforward cost-benefit analyses, risk assessments and yield projections.
Road Maps and Check Lists for Deploying Algorithmic Trading Systems
The course provides holistic templates for project plans and checklists for devising and implementing the algorithmic trading strategy that fits one's trading objectives, for specifying the features and performance metrics of software and hardware tools to deliver these objectives, and the road maps to integrate these tools into a scalable, agile and flexible trading system.
Success Factors for Building and Leading Algorithmic Trading Operations
The course covers in detail the management aspects of algorithmic trading, including methodologies for software development and maintenance, successful symbioses of trader-quant-IT teams, skill sets to look for building these teams, effective management of human resources for multiple algo projects, back-middle-front office integration, protection and continuous development of intellectual property, business models for scaling algorithmic trading operations. The course presents examples of successes and failures of algorithmic trading enterprises with insights about technology and management factors that led to these successes and failures.
Instructor: Mehmet Yanilmaz
Cost: New York and Chicago $1,200 Early-bird | $1,300 Standard registration
UK $1,300 Early-bird | $1,400 Standard registration
To ensure maximum classroom time during this
intensive course, complimentary continental
breakfast, lunch and refreshment breaks are provided.