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Andrea Nalon

Andrea Nalon

Mestre, Italy
Member since January 19, 2016
Andrea is a data scientist with a great deal of experience in programming with R, Python, VBA, Excel, SQL, and about 4 years as quantitative analyst/trader. In addition to a master's degree in engineering, he has several certifications in quantitative analysis, machine learning as well as computational finance. His strong points are data analysis in order to find out predictive patterns with mathematical and statistical analysis.
Andrea is now available for hire
Portfolio
Experience
  • Visual Basic, 10 years
  • Data Science, 10 years
  • Python, 5 years
  • Pandas, 5 years
  • Visual Basic for Applications (VBA), 4 years
  • NumPy, 4 years
  • R, 3 years
  • Machine Learning, 2 years
Mestre, Italy
Availability
Part-time
Preferred Environment
Windows, OS X, Linux, RStudio, Anaconda, Git, SVN
The most amazing...
...thing I've created is a successful trading strategy that fits with machine learning techniques across different futures contracts CME (ES, YM, CL, GC, etc.).
Employment
  • Data Analyst
    2011 - PRESENT
    Avepa
    • Implemented an automatic report generator in order to produce statistics reports for the European Commission with detailed data of payments.
    • Created software to replicate the legacy software used to calculate payments and check the integrity of an internal calculus algorithm.
    • Wrote several views and queries to an Oracle back-end database to retrieve payments information.
    • Created R scripts to sync a Pentaho repository with a Oracle database in order to align metadata and descriptions of every Pentaho report.
    • Developed a repository with specifications of Oracle views through interviews to different stakeholders.
    Technologies: Oracle, PL/SQL, R, Python, Excel, MS Access
  • Financial Model Builder
    2016 - 2016
    SPP (via Toptal)
    • Migrated all the features and elaboration tasks of a complex financial model built with several huge Excel workbooks into 2 Python scripts written to speed up all computation—from more than 10 hours down to a few minutes. Both scripts also implement quality check of input data and have constraints to assure reliable and robust computation.
    Technologies: Python 2.7, Pandas, Numpy, Excel, VBA
  • Quantitative Analyst/Trader
    2015 - 2015
    Glory Trading Systems GmbH
    • Developed algorithmic trading strategies.
    • Performed time series analysis (OHLC and tick data).
    • Implemented statistical analysis, linear regression, and machine learning.
    • Implemented code in R and Python.
    Technologies: R, Python, Numpy, Pandas, Machine Learning, Algorithmic Trading, Statistics
  • Data Analyst
    2010 - 2010
    City of Treviso
    • Created a system for the employment center in order to statistically analyze labor market data and dynamically create reports with KPI data required by the client.
    • Wrote reports with VBA programming in order to dynamically gather data from the back-end MySQL database.
    • Created stored procedures and views in the MySQL back-end database in order to calculate and filter out any unnecessary data.
    Technologies: MySQL, Excel, VBA, Statistics, KPI
  • Business Analyst
    2007 - 2010
    GN ReSound
    • Created a data warehouse reporting solution in order to monitor finance, sales, and production departments.The system was connected to the back-end ERP (Navision) in order to gather data, and dynamically create several Excel reports, and a MS Access database to interact with.
    • Created Excel reports that had a dynamical update feature where data was downloaded from a back-end database and then the cells inside the sheet were filled in and elaborated as required by the client.
    • Backed up some data from SQL Server into a local MS Access database for more complex analysis and also to let the client choose from different filters and aggregation of sales; he could also print reports of his queries.
    • Developed software that contained a real-time calendar with upcoming orders and display of some KPI indicators to monitor the production process of hearing instruments.
    Technologies: MS Access, SQL Server, VBA, Excel
Experience
  • A Simple Quantitative Approach of the Three-Bar Reversal Pattern (Other amazing things)
    http://seekingalpha.com/instablog/33479285-andrea-nalon/4531286-quantitative-approach-reveal-goodness-three-bars-reversal-pattern

    As many discretionary traders know, the Three-Bar Reversal pattern is known as a good pattern to enter a trade with a high-win rate. I've tried to study that pattern with the S&P 500 index in order to see if it is reliable, and I've studied a modified version that's more reliable.

  • Machine Learning Applied to Human Activity Recognition (Development)
    http://nalon99.github.io/Machine_Learning/

    The goal of this research is to explore a data set of recorded values from life log systems for monitoring energy expenditure and for supporting weight-loss programs, and digital assistants for weight-lifting exercises.

  • Coursera Certificates (Other amazing things)

    Received a certificate in the following Coursera courses:
    1) University of Washington
    -Machine Learning: Classification (2016)
    -Machine Learning: Regression (2016)
    -Machine Learning Foundations: A Case Study Approach (2015-2016)

    2) University of Michigan
    -Programming for Everybody: Python (2015)

    3) Johns Hopkins University
    -Practical Machine Learning (2015)

    4) Rice University
    -An Introduction to Interactive Programming in Python (2014)

    5) Yale University
    -Financial Markets (2014)

    6) John Hopkins University
    -Regression Models (2014)
    -Statistical Inference (2014)
    -Getting and Cleaning Data (2014)
    -The Data Scientist's toolbox (2014)
    -R Programming (2014)

    7) Georgia Institute of Technology
    -Computational Investing (2013)

  • The Rise Of Automated Trading: Machines Trading the S&P 500 (Publication)
    More than 60 percent of trading activities with different assets rely on automated trading and machine learning instead of human traders. Today, specialized programs based on particular algorithms and learned patterns automatically buy and sell assets in various markets, with a goal to achieve a positive return in the long run. In this article, Toptal Freelance Data Scientist Andrea Nalon explains how to predict, using machine learning and Python, which trade should be made next on the S&P 500 to get a positive gain.
Skills
  • Languages
    Python, R, Visual Basic for Applications (VBA), Visual Basic, SQL, Java
  • Frameworks
    GraphLab, Machine Learning
  • Libraries/APIs
    NumPy, Pandas, Matplotlib
  • Tools
    Microsoft Excel, Microsoft Access, Eclipse IDE, Git, Subversion (SVN), Visual Studio
  • Other
    Data Analysis, Math, Statistics
  • Paradigms
    Data Science
  • Platforms
    RStudio, Mac OS, Windows, Oracle, Linux
  • Storage
    Oracle PL/SQL, MySQL, SQLite
Education
  • Master's degree in Computer Engineering
    1989 - 1998
    University of Padova - Padova, Italy
Certifications
  • Machine Learning: Classification
    JANUARY 2016 - PRESENT
    University of Washington via Coursera
  • An Introduction to Interactive Programming in Python
    JANUARY 2015 - PRESENT
    Rice University via Coursera
  • Programming for Everybody: Python
    JANUARY 2015 - PRESENT
    University of Michigan via Coursera
  • Practical Machine Learning
    JANUARY 2015 - PRESENT
    Johns Hopkins University via Coursera
  • Machine Learning: Foundations
    JANUARY 2015 - PRESENT
    University of Washington via Coursera
  • Machine Learning: Regression, Research Methodology and Quantitative Methods
    JANUARY 2015 - PRESENT
    University of Washington via Coursera
  • High Performance Scientific Computing
    JANUARY 2014 - PRESENT
    University of Washington via Coursera
  • Introduction to Computational Finance and Financial Econometrics
    JANUARY 2014 - PRESENT
    University of Washington via Coursera
  • Financial Markets
    JANUARY 2014 - PRESENT
    Yale University via Coursera
  • R Programming
    JANUARY 2014 - PRESENT
    Johns Hopkins University via Coursera
  • The Data Scientist's Toolbox
    JANUARY 2014 - PRESENT
    Johns Hopkins University via Coursera
  • Getting and Cleaning Data
    JANUARY 2014 - PRESENT
    Johns Hopkins University via Coursera
  • Statistical Inference
    JANUARY 2014 - PRESENT
    Johns Hopkins University via Coursera
  • Regression Models
    JANUARY 2014 - PRESENT
    Johns Hopkins University via Coursera
  • Computational Investing
    JANUARY 2013 - PRESENT
    Georgia Institute of Technology via Coursera
  • Mathematical Methods for Quantitative Finance
    JANUARY 2013 - PRESENT
    University of Washington via Coursera
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