Investment Data Sessions
Interactive Real-Time Classes for Alternative Data Professionals
STAY AHEAD OF THE INDUSTRY
Techniques used by Industry-leaders
Intensive Seminars
Step-by-Step Tutorials
By experts from NYU, Oxford, Point72
Used by Funds
Technology at the edge of Alt. Data utilization
Real Data
Transactions +
Web Crawling + Images
​Practical Data
Cleansing
​Powerful NLP
One-Liners
​Application to Investing
​Predict Performance KPIs
Learning
Using real-world alternative data, this course will cover the quantitative aspects of ticker tagging, panel stabilization of non-uniform panels, sample bias reduction, KPI extraction and revenue models.
At the end of this course, you will have gained the quantitative and technical skills to transform raw alternative data into fully fledged investment products for both quantitative and fundamental insitiutal investors.
Outcome
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Proficiency in importing, transforming, and pre-processing large alternative datasets to be used within an institutional investment framework
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Ability to clean, aggregate and utilize unstructured, raw alt. datasets
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Deep understanding of the advantages and pitfalls of the alternative data ecosystem.
Courses
ENROLLING NOW
Images and computer vision
FORMAT
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2 Hours
CURRICULUM
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Introduction to satellite imagery and aerial photography
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Computer vision
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Why do we need to automate analysis of images?
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Feature engineering in image recognition
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OpenCV and cvlib
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Deep learning and CNNs for image classification
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Optical character recognition (OCR)
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Case study: Earnings and car counts for European retailers
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Tutorial exercises
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OCR extracting text from images
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Object detection examples
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Creating a car count time series from images
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Transactions to revenue predictions
Hands-on computer lab using code snippets to develop revenue models from raw consumer transaction data
FORMAT
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2 Days / 6 Hours
CURRICULUM
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Removing bias from data to increase revenue production accuracy
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Maximizing panel constituent counts with non-linear imputation
PREREQUISITES
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Availability
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Beginner+ Python and SQL
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Excel
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Alt. Data Familiarity
​Text and NLP
FORMAT
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2 Hours
CURRICULUM
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Background on NLP
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Word embeddings
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Topic modeling
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Tools to extract text in Python (eg. regular expressions, BeautifulSoup etc.)
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NLP tools with a focus on Python (NLTK, spaCy, Transformers etc.)
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Challenges in NLP
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Legal risks associated with webscraping
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Case study: text analysis of FOMC communications
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Tutorial exercises:
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Webscraping news articles
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entity detection, word clouds etc.
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topic modelling with pyLDAvis
Seminar Computing Platform
The custom-made computing platform demonstrated in these classes comprises a toolkit of algorithms and code tailored to solve common challenges of the alternative data industry including:
Platform Components
DATA REPROCESSING
ticker mapping
Revenue modeling
backtesting system
Seminar Participants are eligible to receive a full transfer of the initialized platform and codebase into an AWS account of their choice
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Instructors
FEATURED INDUSTRY EXPERTS
Gene Ekster
Gene Ekster is a professor at NYU where he teaches a master's course in alternative data. He is the founder of AltDG, an alternative data software company. Previously he helped establish the alternative data practices at Point72 Asset Management, Balyasny Asset Management, Lone Pine, 1010 Data, and Majestic Research. Gene is a board member of IDSO (a compliance organization), Eagle Alpha, Ottoquant and Super Signal Capital. He holds a degree in Artificial Intelligence from U.C. Berkeley, an MBA from Cornell University and is a CFA charter holder.
Saeed Amen is the founder of Cuemacro and co-founder of Turnleaf Analytics. Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the coauthor of The Book of Alternative Data (Wiley). Cuemacro consults for clients in the area of systematic trading. Turnleaf Analytics generates long term forecasts for EM inflation. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. Clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a visiting lecturer at Queen Mary University of London and a co-founder of the Thalesians.
Saeed Amen
Alexander Denev
Alexander Denev is Head of AI, Financial Services – Risk Advisory at Deloitte LLP. Prior to that he led Quantitative Research & Advanced Analytics at IHS Markit. Previously, he held roles at the Royal Bank of Scotland, Societe Generale, and European Investment Bank. Denev is a visiting lecturer at the University of Oxford where he graduated with a degree in Mathematical Finance. He is author of numerous papers and books on novel methods of financial modeling with applications ranging from stress testing to asset allocation.
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