10 Topics
5 Days
40 Sessions
Programme Overview
Training Description
Who Should Attend
- Quantitative analysts
- Risk management professionals
- Treasury and investment professionals
- Financial technology professionals
- Banking and financial-services professionals
- Pension and retirement investment professionals
- Compliance and regulatory professionals
- Investment advisers and wealth managers
- Professionals interested in automated investing and algorithmic strategies
Session Objectives
- Understand the principles and importance of robo-advisors and algorithmic trading.
- Implement techniques for analyzing and evaluating algorithmic trading models.
- Understand the role of quantitative analysis and machine learning in algorithmic trading.
- Implement techniques for developing and backtesting trading algorithms.
- Understand the principles of robo-advisor platforms and their applications in investment management.
- Understand the legal and regulatory frameworks surrounding algorithmic trading and robo-advisors.
- Develop strategies for implementing and scaling up robo-advisor and algorithmic trading initiatives.
About the Course
Robo-Advisors and Algorithmic Trading: Automating Investment Strategies is a specialized training course designed to introduce finance and investment professionals to the technologies, methodologies, and operational considerations behind automated investment management and algorithmic trading. The course examines how algorithms, data, automation, and artificial intelligence can be used to develop, test, execute, and monitor investment strategies.Participants will explore robo-advisory models, portfolio construction, algorithmic trading strategies, quantitative analysis, risk controls, backtesting, execution, performance measurement, and regulatory considerations. The course emphasizes responsible implementation, model risk management, transparency, and the limitations of automated investment systems.
Curriculum & Topics
10 Topics | 40 Sessions
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Workshop 1.1:
• Principles and importance of robo-advisors and algorithmic trading.
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Workshop 1.2:
• Understanding the evolution of automated investment solutions.
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Workshop 1.3:
• Benefits of algorithmic trading in enhancing trading efficiency and reducing bias.
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Workshop 1.4:
• Historical context and emerging trends in automated finance.
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Workshop 2.1:
• Techniques for analyzing and evaluating algorithmic trading models.
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Workshop 2.2:
• Implementing quantitative model testing and performance assessment.
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Workshop 2.3:
• Utilizing backtesting methodologies and metrics.
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Workshop 2.4:
• Managing algorithmic model selection.
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Workshop 3.1:
• Understanding the role of quantitative analysis and machine learning.
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Workshop 3.2:
• Implementing statistical modeling and machine learning algorithms.
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Workshop 3.3:
• Utilizing time series analysis and predictive modeling.
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Workshop 3.4:
• Managing machine learning applications.
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Workshop 4.1:
• Techniques for developing and backtesting trading algorithms.
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Workshop 4.2:
• Implementing programming languages for algorithmic trading.
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Workshop 4.3:
• Utilizing trading simulation and backtesting platforms.
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Workshop 4.4:
• Managing algorithm development.
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Workshop 5.1:
• Understanding the principles of robo-advisor platforms.
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Workshop 5.2:
• Implementing portfolio construction and asset allocation strategies.
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Workshop 5.3:
• Utilizing client profiling and risk assessment tools.
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Workshop 5.4:
• Managing robo-advisor platform deployment.
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Workshop 6.1:
• Techniques for building and managing robo-advisor portfolios.
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Workshop 6.2:
• Implementing automated rebalancing and tax-loss harvesting.
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Workshop 6.3:
• Utilizing portfolio optimization and performance reporting.
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Workshop 6.4:
• Managing robo-advisor portfolios.
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Workshop 7.1:
• Understanding the role of portfolio optimization and risk management.
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Workshop 7.2:
• Implementing mean-variance optimization and risk metrics.
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Workshop 7.3:
• Utilizing risk models and stress testing.
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Workshop 7.4:
• Managing risk management strategies.
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Workshop 8.1:
• Techniques for utilizing optimization algorithms and risk metrics.
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Workshop 8.2:
• Implementing quadratic programming and Monte Carlo simulations.
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Workshop 8.3:
• Utilizing value-at-risk (VaR) and other risk measures.
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Workshop 8.4:
• Managing portfolio optimization.
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Workshop 9.1:
• Understanding legal and regulatory frameworks surrounding algorithmic trading.
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Workshop 9.2:
• Implementing compliance with trading regulations and market integrity.
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Workshop 9.3:
• Utilizing regulatory reporting and documentation.
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Workshop 9.4:
• Managing legal and regulatory risks.
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Workshop 10.1:
• Techniques for ensuring compliance with trading regulations and standards.
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Workshop 10.2:
• Implementing best execution and market surveillance.
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Workshop 10.3:
• Utilizing compliance monitoring and reporting tools.
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Workshop 10.4:
• Managing regulatory audits and examinations.
