Andrea is available for hire

Andrea Nalon

Verified Expert in Engineering

Data Scientist and Python Developer

Location

Venice, Metropolitan City of Venice, Italy

Toptal Member Since

March 23, 2016

Andrea is a senior data scientist with extensive experience as a Python programmer and quantitative analyst. He holds a master's degree in engineering and several certifications in quantitative analysis and machine learning. He excels in data analysis, written communication, flexibility, and thinking out of the box. Among Andrea's most notable accomplishments is finding valuable consumer behavior insights for PepsiCo to help guide its marketing campaign across different sub-brands.

Back-end Data Analysis Algorithms Data Engineering Scientific Data Analysis Algorithmic Trading Machine Learning SQL Microsoft Access Microsoft Excel MySQL Pandas Python NumPy Time Series Linear Regression Spotify Interactive Brokers API

Portfolio

Private Investor

Python 3, FastAPI, Pandas, NumPy, AWS Fargate, Amazon DynamoDB...

InvestVerte

Algorithmic Trading, Machine Learning, Interactive Brokers API, FastAPI...

PepsiCo

Machine Learning, Algorithms, Big Data, Databricks, Snowflake, Data Analysis...

Experience

Data Science - 11 years Data Analysis - 10 years Python - 6 years Algorithmic Trading Analysis - 4 years Machine Learning - 4 years Algorithmic Trading - 4 years Trading Systems - 2 years Interactive Brokers API - 1 year

Availability

Part-time

Preferred Environment

Visual Studio Code (VS Code), Jupyter Notebook, Pandas, Scikit-learn, Python, NumPy, CSV, XLSX File Processing, SQL, Machine Learning

The most amazing...

...thing I've created is a trading strategy for a proprietary trading firm by analyzing trading patterns with statistical and machine-learning techniques.

Work Experience

Day Trading Strategy Expert I Expert Advisor

2023 - 2024

Private Investor

Developed and implemented an automated ES-mini futures trading system through the Interactive Brokers API to execute transactions on his account.
Designed a proprietary algorithm for daily trading with long-term positive returns and built a backtest engine to simulate order execution before live deployment.
Simulated an annual return of nearly 160% and achieved almost 40% in return in three months of live trading.
Generated and dispatched daily Excel reports detailing live and backtested trade lists to compare the performance of actual versus backtested operations.

Technologies: Python 3, FastAPI, Pandas, NumPy, AWS Fargate, Amazon DynamoDB, Interactive Brokers API, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Container Registry (ECR), Amazon Virtual Private Cloud (VPC), Data Science, Scientific Data Analysis, Statistics, Trading Systems, Docker, Algorithmic Trading

Senior Data Scientist

2022 - 2023

InvestVerte

Researched and constructed a backtesting engine tailored for evaluating trading strategies, with a specific focus on the S&P500 trading future ES.
Used machine learning scikit-learn package to find trading patterns and train effective models, tested with several backtests over many years of historical data.
Implemented the model using Python and FastAPI, enabling it to operate in real time by connecting with Interactive Brokers to place trading orders. The trading bot could be accessed and queried via the Uvicorn and FastAPI web interface.

Technologies: Algorithmic Trading, Machine Learning, Interactive Brokers API, FastAPI, Multithreading, Scikit-learn, Uvicorn, APIs, Python 3, Supervised Machine Learning, Algorithms, Trading, Stock Trading, Neural Networks, Bots, Git, Microsoft Visual Studio, Linux, Anaconda, Time Series Analysis, Time Series, Jupyter Notebook, GitHub, Financial Data, Programming, REST APIs, JSON, Automation, Trading Systems, Scientific Data Analysis

Senior Data Scientist

2021 - 2022

PepsiCo

Created tools in Databricks and Jupyter Notebook to conduct an in-depth analysis of consumer purchases, identifying correlations and connections among various items, categories, and brands. This helped the marketing sector finalize product campaigns.
Executed customized scripts to create audience profiles, enabling the marketing team to target product campaigns effectively.
Increased efficiency in managing large relational datasets with billions of rows using Spark and PySpark, effectively distributing computational tasks across server clusters.

Technologies: Machine Learning, Algorithms, Big Data, Databricks, Snowflake, Data Analysis, Data Mining, Market Basket Analysis, Python 3, Statistics, SQL, Jupyter Notebook, Spark, PySpark, Git, Microsoft Visual Studio, Linux, Anaconda, Time Series Analysis, Time Series, GitHub, Programming, Scientific Data Analysis, Scripting

Senior Data Scientist

2021 - 2021

LGO Capital Holdings

Applied machine learning techniques to predict cryptocurrency movements for trading.
Analyzed Bitcoin and Ethereum cryptocurrencies using quotations and additional features from Glassnode.
Delivered a model to the client leveraging various machine learning tools for classifying the anticipated price movement of the underlying asset, assigning a "1" for a recommended buy the next day and a "-1" for a suggested sell.
Applied feature selection and scaling throughout the analysis to improve prediction accuracy.
Employed feature engineering techniques and utilized multiple parallel machine learning models, resulting in an overall system accuracy exceeding 80%.
Conducted an exhausting test by applying cross-validation methods for backtesting the model from over six years of historical prices.

Technologies: Python 3, Time Series Analysis, Supervised Machine Learning, Bitcoin, Scikit-learn, Trading, Algorithms, Stock Trading, Neural Networks, Git, Microsoft Visual Studio, SQLite, Linux, Anaconda, Algorithmic Trading, CSV File Processing, Time Series, Jupyter Notebook, GitHub, Financial Data, CSV, Programming, REST APIs, Scientific Data Analysis, MacOS, Scripting

Data Scientist

2017 - 2021

Osprey Underwriters

Created a back-end system and implemented various algorithms to compute the insurance premiums of different products.
Handled the data cleaning and entire database architecture design across several DB schemas, both manually and via the programming of many Python scripts and Jupyter notebooks.
Integrated and cooperated on automated tasks running between different servers as required by the customer.
Customized an already installed and running Moodle server used for video courses–enabling the authentication of users on an external MySQL database with various levels of control.
Implemented several MySQL stored procedures—using JSON strings as a list of parameters to be transferred to the database—for improved and more accessible integration between various web apps' back and front end.
Managed the migration of a MySQL production database from one provider (Compose) to another (DigitalOcean) by upgrading its major version from 5 to 8.
Created a testing MySQL database on Amazon AWS infrastructure, using their RDS service.
Managed a small team of two developers, coordinating their front-end development work to integrate it with the code and database I created in the back end. I also owned their technical interviews during the hiring process.

Technologies: JSON, Moodle, SQL, SQLAlchemy, Pandas, Python, MySQL, DigitalOcean, Amazon Web Services (AWS), Linux, Linux CentOS 7, Back-end, Python 3, Algorithms, Git, Anaconda, CSV File Processing, APIs, GitHub, CSV, Programming, REST APIs, Excel 365, Automation, ETL, Web Scraping, Scripting

Financial Model Builder

2016 - 2017

Strategic Project Partners

Migrated all features and elaboration tasks of a complex budget model created with several large Excel workbooks to two Python scripts to accelerate all calculations from over 10 hours to two minutes.
Established a thorough quality check for input data and implemented constraints to guarantee dependable and resilient execution of the created scripts.
Created Linux and Windows shell batch programs to run complex data computations automatically.

Technologies: Visual Basic for Applications (VBA), Microsoft Excel, NumPy, Pandas, Python, Algorithms, Data Science, Data Engineering, Back-end, CSV, CSV File Processing, Excel VBA, Python 3, XLSX File Processing, Windows, Git, Anaconda, Time Series Analysis, Time Series, Jupyter Notebook, GitHub, Programming, Automation, Scientific Data Analysis, Scripting

Quantitative Analyst/Trader

2015 - 2015

Glory Trading Systems GmbH

Developed algorithmic trading strategies by analyzing time-series historical data (OHLC and tick data).
Implemented statistical analysis, linear regression, and machine learning.
Developed code in R and Python by including markdown documents (knitr and Jupyter notebooks).

Technologies: Statistics, Algorithmic Trading, Machine Learning, Pandas, NumPy, Python, R, Trading, Algorithmic Trading Analysis, Algorithms, Data Science, RStudio, Data Engineering, Back-end, Python 3, Stock Trading, Neural Networks, Time Series Analysis, Time Series, Jupyter Notebook, Financial Data, CSV File Processing, CSV, Programming, Scientific Data Analysis

Data Analyst

2011 - 2015

Avepa

Implemented an automatic report generator to produce statistics reports for the European Commission with detailed payment data.
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 payment information.
Created R scripts to sync a Pentaho repository with an Oracle database to align metadata and descriptions of every Pentaho report.
Developed a repository with specifications of Oracle views through interviews with different stakeholders.

Technologies: Microsoft Access, Microsoft Excel, Python, R, PL/SQL, Oracle, Algorithms, Pandas, SQL, Oracle PL/SQL, Windows, Data Engineering, Back-end, Python 3, SQLAlchemy, Visual Basic, Git, Microsoft Visual Studio, SQLite, Java, Linux, Anaconda, CSV File Processing, APIs, Jupyter Notebook, GitHub, CSV, XLSX File Processing, Programming, JavaScript, Excel 365, JSON, XML, Automation, Web Services, Scientific Data Analysis

Data Analyst

2010 - 2010

City of Treviso

Created a system for the employment center to statistically analyze labor market data and dynamically create reports with KPI data required by the client.
Wrote reports with VBA programming to dynamically gather data from the back-end MySQL database.
Created stored procedures and views in the MySQL back-end database to calculate and filter out unnecessary data.

Technologies: Key Performance Indicators (KPIs), Statistics, Visual Basic for Applications (VBA), Microsoft Excel, MySQL, Algorithms, Microsoft Access, SQL, Data Science, Windows, Data Engineering, Back-end, Excel VBA, Programming, Scientific Data Analysis

Business Analyst

2007 - 2010

GN ReSound

Created a data warehouse reporting solution to monitor finance, sales, and production departments. The system was connected to the back-end ERP to gather data and create several Excel reports and a Microsoft Access database to interact with.
Wrote Excel reports with a dynamical update feature where data was downloaded from a back-end database and 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 Microsoft Access database for more complex analysis and let the client choose from different filters and sales aggregation; the client could also print reports of his queries.
Developed software that contained a real-time calendar with upcoming orders and a display of KPI indicators to monitor the production process of hearing instruments.

Technologies: Microsoft Excel, Visual Basic for Applications (VBA), Microsoft SQL Server, Algorithms, Microsoft Access, SQL, Visual Basic, Data Engineering, Windows, Programming, Automation

Experience

A Simple Quantitative Approach of the Three-bar Reversal Pattern

https://nalon99.github.io/publications/three_bars

Conducted a study on the reliability of the three-bar reversal pattern for entering trades with a high-win rate, mainly focusing on the S&P 500 index. I used R to run simulations and R Markdown to publish the research findings.

Development of Algorithmic Trading Strategies for a Prop Trading Firm

Developed an adaptive trading strategy for Glory Trading Systems GmbH, a proprietary trading firm. This involved a comprehensive study of various futures, including CL, GC, HG, NG, ES, YM, NQ, and ZB, across different market trends: bullish, bearish, and sideways. Additionally, I conducted a thorough analysis of seasonal patterns across all mentioned assets and discovered an effective method to capitalize on this seasonality, ultimately leading to better returns for the firm's shareholders.

Machine Learning Applied to Human Activity Recognition

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.

Music Composition | Mixing and Producing Audio Tracks

https://www.youtube.com/@mrkey-music

Shared my original compositions on platforms like Spotify and Apple Music since 2020. Also, I created my own YouTube channel dedicated to helping fellow music enthusiasts learn the art of recording and producing songs from scratch. As a musician and sound engineer who records and processes audio, I am also a member of one of the largest music talent marketplaces.

Publication

The Rise Of Automated Trading: Machines Trading the S&P 500

https://www.toptal.com/machine-learning/s-p-500-automated-trading

Skills

Languages

SQL, Python, Visual Basic for Applications (VBA), R, Visual Basic, Python 3, XML, Java, Snowflake, Excel VBA, Assembler 68000, Python 2, Markdown, JavaScript

Libraries/APIs

NumPy, Pandas, Scikit-learn, SQLAlchemy, Matplotlib, TensorFlow, Keras, Interactive Brokers API, PySpark, REST APIs

Tools

Microsoft Access, Microsoft Excel, Eclipse IDE, Git, GitHub, Moodle, Microsoft Visual Studio, Subversion (SVN), AWS Fargate, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Container Registry (ECR), Amazon Virtual Private Cloud (VPC)

Platforms

Jupyter Notebook, Oracle, Windows, RStudio, Amazon Web Services (AWS), MacOS, Anaconda, Linux, Linux CentOS 7, DigitalOcean, Databricks, Visual Studio Code (VS Code), Docker

Storage

MySQL, PL/SQL, JSON, Oracle PL/SQL, Microsoft SQL Server, SQLite, Amazon DynamoDB

Other

Algorithmic Trading, Data Analysis, Machine Learning, Mathematics, Algorithms, Data Engineering, Back-end, Scientific Data Analysis, Scripting, Algorithmic Trading Analysis, Time Series, Time Series Analysis, Supervised Machine Learning, CSV, CSV File Processing, Excel 365, Web Services, Key Performance Indicators (KPIs), Statistics, Trading, Big Data, Data Mining, Market Basket Analysis, Forex Trading, Bitcoin, Regression, Classification, Statistical Analysis, Financial Data, FastAPI, Multithreading, Uvicorn, APIs, Stock Trading, Neural Networks, Bots, XLSX File Processing, Programming, Electronics, CPU Boards, Signal Analysis, Physics, Computer, Networks, Random Forests, Decision Trees, Logistic Regression, Support Vector Machines (SVM), knitr, Clustering, CI/CD Pipelines, Quantitative Analysis, Data Cleaning, Data Inference, Linear Regression, Linear Algebra, Web Scraping, Audio, Audio Processing, Apple Music, Spotify

Paradigms

Data Science, Automation, Anomaly Detection, ETL

Industry Expertise

Trading Systems

Frameworks

Spark

Education

1989 - 1998

Master's Degree in Computer Engineering

University of Padova - Padova, Italy

University of Washington | via Coursera

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