Investment Management with Python and Machine Learning

Gain an intuitive understanding for the underlying theory behind Modern Portfolio Construction Techniques.

• Write custom Python code to estimate risk and return parameters.
• Utilize powerful Python optimization libraries to build scientifically and systematically diversified portfolios.
• Build custom utilities in Python to test and compare portfolio strategies.

Analyze style and factor exposures of portfolios and implement robust estimates for the covariance matrix.

• Implement Black-Litterman portfolio construction analysis.
• Implement a variety of robust portfolio construction models.

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets.

• Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes.
• Utilize powerful Python libraries to implement machine learning algorithms in case studies.
• Learn about factor models and regime switching models and their use in investment management.

Learn what alternative data is and how it is used in financial market applications.

• Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.
• Perform data analysis of real-world alternative datasets using Python.
• Gain an understanding and hands-on experience in data analytics, visualization, and quantitative modeling applied to alternative data in finance.