Robert Corwin, Financial Markets Developer in Austin, TX, United States
Robert Corwin

Financial Markets Developer in Austin, TX, United States

Member since June 25, 2020
Robert has 20 years of experience working with and analyzing large data sets, quantitative modeling, and data science. He has been a founder and/or an early employee at his last three companies. Robert's expertise as a portfolio manager and quantitative researcher includes skills in SQL, NoSQL, C++, C#, Python, AWS, and web technologies (HTML, JavaScript, and Angular).
Robert is now available for hire

Portfolio

Experience

Location

Austin, TX, United States

Availability

Part-time

Preferred Environment

Microsoft SQL Server, PyCharm, Windows

The most amazing...

...things I've developed were the trading strategies underpinning the hedge fund I ran and a retail gasoline price optimizer.

Employment

  • Principal Data Scientist

    2019 - 2020
    Zdaly
    • Created software products that deliver advanced insights into their businesses, predicting sales based upon factors such as weather, seasonality, and demographics.
    • Applied machine learning techniques for predictive purposes; techniques include regression/correlation, logit models, random forest models, clustering algorithms, lead/lag analysis, some NLP, multithreading, and multiprocessing.
    • Optimized client retail prices based upon price elasticities and competitor behavior. Recursively propagated potential price changes throughout the entire client/competitor network and determined optimal price.
    Technologies: Machine Learning, Python, Elasticsearch, SQL
  • Co-Chief Investment Officer

    2016 - 2019
    EVA Capital Management
    • Served as the co-CIO of a quantitative asset management firm. With a business partner, laid the groundwork for company operations, hiring, and trading.
    • Pitched business plan and trading strategy to potential clients and business partners and raised seed capital.
    • Led the researcher and portfolio manager responsible for daily R&D and trading on a systematic EVA (economic value added)-based stock selection strategy. Managed one analyst. Implemented in both hedge fund and index/ETF formats.
    • Maintained terabytes of data in a SQL database which served a C++ trading algorithm to optimize the portfolio; a C# desktop app for data manipulation; and Python, MATLAB, and R scripts for statistical analyses.
    • Utilized finance frameworks such as Fama-Macbeth regression, mean-variance optimization, multifactor risk modeling, as well as ML such as NLP and clustering.
    • Coded the company web site using Node.js, Angular, and Highcharts.
    Technologies: Python, MATLAB, AngularJS, C++, SQL
  • Head of Quantitative Research

    2007 - 2015
    EVA Dimensions
    • Hire as the fifth employee at a financial company startup based around the Economic Value Added (EVA) valuation framework. Instrumental in growing revenues to over $7 million and employees to over 20.
    • Created and marketed equity research reports, data visualizations, and interactive web tools to buy- and sell-side clients. Mined proprietary databases and tied statistical observations to non-technical, actionable investment recommendations.
    • Researched finding areas of under/overvaluation, commenting on trends in market or factor behavior, and custom client projects.
    • Led the research on many quantitative financial models, including an EVA-based global stock selection system and simulated ETFs designed to capture premia (beta) on various factors.
    • Aggregated a DCF analysis, a custom-built portfolio analysis, risk, and attribution system; cost of capital models; and thematic models tying factor returns and exposures to the business and company life-cycles.
    Technologies: Java, MATLAB, C#, C++, SQL
  • Senior Analyst

    2003 - 2006
    The Rohatyn Group
    • Contributed as an analyst at a multi-billion dollar emerging markets hedge fund.
    • Provided support for an earnings-estimates based trading model. Created a 100GB store of data; researched underlying data quality and effectiveness of various trading signals/indicators; backtested. Implemented in the master fund at $50 million.
    • Created a cointegration-based pairs trading model and presented results to the trading desk weekly.
    Technologies: C#.NET, C#, SQL
  • Analyst (Intern)

    2002 - 2003
    Citigroup
    • Created a multi-factor model to forecast US corporate bond spreads utilizing equity market information.
    • Presented results to the entire fixed-income research division for discussion.
    • Received internship credit for my master's in financial engineering program at Berkeley.
  • Associate

    1999 - 2001
    Nomura Securities International
    • Worked for the quantitative unit in an internal fund-of-funds.
    • Created a custom performance attribution system (Brinson-based).
    • Automated the pricing of options in the portfolio.

Experience

  • Predictive Maintenance for Printers

    A system to ingest, clean, and process large data sets from industrial printers and predict when there will be a failure that would require service technicians to be deployed to reduce such calls.

  • Long and Short Equity Hedge Fund

    Lead researcher and portfolio manager responsible for daily R&D and trading on a systematic EVA (economic value added)-based stock selection strategy. Managed one analyst. Implemented in both hedge fund and index and ETF formats. Created the Wilshire EVA 5000 Indexes.

    Maintained TBs of data in a SQL database, which served a C++ trading algorithm to optimize the portfolio, a C# desktop app for data manipulation, Python, MATLAB, and R scripts for statistical analyses. Techniques utilized finance frameworks such as Fama-Macbeth regression, mean-variance optimization, multifactor risk modeling, and ML such as NLP and clustering.

  • Retail Gasoline Price Optimizer

    Fuel Network Optimizer is a next-generation, AI-enabled software product for the oil and gas industry. It takes massive amounts of individual retail and wholesale fuel transactions as inputs, cleans them, and quickly computes a suite of business intelligence metrics (like volumes, profits, prices, etc.). It then combines these internal metrics with external factors such as weather, demographics, population density, traffic, proximity to major attractions, seasonality, and holiday effects, as well as features about each specific client retail site (like physical characteristics and customer loyalty), into machine learning models. The models predict sales, profits, etc. on a forward-looking basis, and also decompose history to increase understanding of what has transpired. Elaborate modeling of competitor behavior also exists, with a sophisticated price elasticity model that anticipates competitor reaction and recursively propagates price changes throughout the entire network of sites.

Skills

  • Languages

    Python, SQL, C#, R, C++, Java, C#.NET
  • Tools

    PyCharm, MATLAB
  • Paradigms

    Data Science, ETL
  • Platforms

    Amazon Web Services (AWS), Windows, Azure
  • Storage

    SQL Server 2014, PostgreSQL, MySQL, Elasticsearch, Microsoft SQL Server, SQL Server Integration Services (SSIS)
  • Other

    Machine Learning, Financial Markets, Risk Models, Data Warehouse Design, AWS, Natural Language Processing (NLP), Deep Learning, Oil & Gas, Artificial Intelligence (AI), Options Pricing
  • Frameworks

    AngularJS

Education

  • Master's degree in Financial Engineering
    2002 - 2003
    University of California at Berkeley/Haas School of Business - Berkeley, CA
  • Bachelor's degree in Chemical Engineering
    1994 - 1998
    Cornell University - Ithaca, NY

Certifications

  • Chartered Financial Analyst
    JULY 2005 - PRESENT
    CFA Institute

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