Publication
The Rise Of Automated Trading: Machines Trading the S&P 500
https://www.toptal.com/developers/machine-learning/s-p-500-automated-trading
Andrea is available for hire
Hire AndreaAndrea Nalon
Verified Expert in Engineering
Data Scientist and Python Developer
Venice, Metropolitan City of Venice, Italy
Toptal member since March 23, 2016
Bio
Andrea is a senior AI engineer with 20+ years' experience bridging legacy data with production GenAI and RAG solutions. Combining a master's degree in engineering with quant finance and LLM expertise, he converts complex data—from PepsiCo insights to hedge fund algorithms—into AI systems. With real-world, problem-solving experience, he provides the strategic oversight required to move organizations from AI experimentation to disciplined, high-ROI implementation.
Portfolio
Options Trader - Investor
Python, Trading, Finance APIs, Schwab, Charles Schwab...
Entrypoint Capital, LLC
Python, Finance, Amazon Web Services (AWS), Equities, Equity Trading...
Visual Candy Systems Limited
Algorithmic Trading, Algorithmic Trading Analysis, Python, Pandas, NumPy...
Experience
- Python - 12 years
- Data Science - 12 years
- Machine Learning - 10 years
- Algorithmic Trading Analysis - 6 years
- Interactive Brokers API - 2 years
- Trading Systems - 2 years
- AI Tools - 2 years
- Open-source LLMs - 1 year
Preferred Environment
Visual Studio Code (VS Code), Jupyter Notebook, Pandas, Python, NumPy, CSV, SQL, Machine Learning, Large Language Models (LLMs), OpenAI, Cloud
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
Trading Bot Developer
2025 - 2026
Options Trader - Investor
- Engineered an automated options trading bot in Python for a client, implementing different SPX strategies with dynamic contract sizing driven by a configurable max-risk cap. The bot automatically places orders via the Charles Schwab API.
- Deployed a fault-tolerant bot on AWS ECS Fargate with EventBridge timezone-aware scheduling, DynamoDB order audit trail, and S3 state persistence.
- Built real-time Telegram trade notifications, a 2-second polling exit-monitoring loop, and an end-of-day reconciliation engine that cross-checks Schwab transactions against DynamoDB to ensure complete P&L accuracy.
Technologies: Python, Trading, Finance APIs, Schwab, Charles Schwab, Amazon Web Services (AWS), AWS Fargate, AWS Lambda, Amazon DynamoDB, Telegram Bot API, Telegram Messenger API, Amazon CloudWatch, Serverless, AI-assisted Development, AI Tools, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC), Derivatives, Investments, Options, Docker, REST APIs, Quantitative Modeling, Bots, APIs
Data Scientist
2024 - 2025
Entrypoint Capital, LLC
- Built and deployed a production daily P&L calculation tool and trade-reconciliation checks (per-symbol and portfolio-level), integrating trades, holdings, corporate actions, dividends, and settlement-aware financing.
- Developed scripts for risk calculation using Barra factor modeling that project current factor exposures through historical factor returns to evaluate factor P&L, VaR, and alpha time series.
- Generated automated sectors and profits attribution reports used by portfolio managers and risk teams.
Technologies: Python, Finance, Amazon Web Services (AWS), Equities, Equity Trading, Profit & Loss (P&L), Trade Finance, Stock Trading, Stock Market, Stock Exchange, Tickers, Trading, Trading Systems, Hedge Funds, Fund Management, Accounting, Risk Models, Risk Management, Algorithmic Trading, Equity Investment, Cloud, AI-assisted Development, AI Tools, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC), Investments, Quantitative Modeling
Quantum Research Analyst, Trading Strategy
2024 - 2025
Visual Candy Systems Limited
- Assessed various trading strategies in the Quant Finance space.
- Analyzed and tested each strategy using multiple Jupyter notebooks to include the code base, graphs, and tables for easier understanding and reliability. Different assets, such as ES, TTF, EUA, and BTC, were used.
- Applied a walking forward analysis to assess the robustness and adaptability of a strategy over time by testing it on unseen data.
- Used the backtesting.py framework to simulate trading orders for each strategy.
Technologies: Algorithmic Trading, Algorithmic Trading Analysis, Python, Pandas, NumPy, Statistical Analysis, Hidden Markov Model, Time Series Analysis, Quantitative Finance, Data Science, Quantitative Analysis, Finance APIs, Derivatives, Investments, Quantitative Modeling
Financial Data Mining Specialist
2024 - 2024
Zonal Photon Conversion Inc.
- Handled a very specific request concerning the provision of financial data of the S&P 500 index constituent stocks.
- Suggested relying on Nasdaq Data Link as a data provider and implemented some Python scripts to get all the necessary data over a period of about 26 years.
- Downloaded daily OHLC data for about 1,118 different stocks, as well as market capitalization and price earning ratio.
- Helped the client access a large amount of data by creating several Excel sheets with all the stocks listed as columns and the time series prices on rows.
Technologies: Data Mining, Python, Finance, Trading, Big Data, Data, Equity Investment, Quantitative Finance, Data Science, Quantitative Analysis, API Integration, Finance APIs, REST APIs, APIs
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 (ECS), Amazon Elastic Container Registry (ECR), Amazon Virtual Private Cloud (VPC), Data Science, Scientific Data Analysis, Statistics, Trading Systems, Docker, Algorithmic Trading, Automated Trading Software, Python, Trading Bots, TradingView, Backtesting Trading Strategies, Equity Investment, Quantitative Finance, Quantitative Analysis, Cloud, Amazon S3 (AWS S3), Amazon RDS, Finance APIs, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC), Derivatives, Investments, REST APIs, Unit Testing, Quantitative Modeling, Bots, APIs
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, Automated Trading Software, Python, Trading Bots, Quantitative Finance, Quantitative Analysis, Artificial Intelligence (AI), Cloud, Amazon S3 (AWS S3), Amazon RDS, Amazon Elastic Container Registry (ECR), Finance APIs, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC), Derivatives, Investments, Docker, Quantitative Modeling
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, Python, Quantitative Analysis, Amazon S3 (AWS S3), Amazon EC2, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC), Technical Writing
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, Automated Trading Software, Python, Trading Bots, Backtesting Trading Strategies, Quantitative Finance, Quantitative Analysis, Finance APIs, Crypto, Investments, Crypto, Quantitative Modeling, APIs
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, API Integration, Data Scraping, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC), System Architecture
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, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC)
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, Automated Trading Software, Backtesting Trading Strategies, Quantitative Finance, Quantitative Analysis, Amazon RDS, Finance APIs, Software Development Lifecycle (SDLC), Architecture, System Development Life Cycle (SDLC), Derivatives, Investments, Quantitative Modeling
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, API Integration, Technical Writing, CI/CD Pipelines, System Architecture, Unit Testing
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, System Architecture
Experience
AI Engineering Bootcamp
https://github.com/AI-engineering-projects/ShowcaseAI engineering portfolio projects (3-month intensive AI engineering bootcamp).
I built production-grade large language model (LLM) applications using Python, OpenAI (GPT-4/GPT-4o), LangChain, FAISS, Sentence Transformers, and Langfuse:
• Simple Chatbot: Secure support agent with structured JSON outputs, input moderation, cost/latency tracking
• FAQ RAG: HR FAQ system with local embeddings, FAISS vector search, recursive chunking, and grounded GPT-4 responses
• Multi-agent RAG: Intent-based orchestrator routing to domain agents (Finance/HR/Tech), safeguards, memory, and evaluation
• Legal Contract Amendment Analyzer: Multimodal GPT-4o tool to parse/align contract images, detect changes, summarize amendments, and output validated Pydantic JSON
• MCP Chatbot: FastAPI-based support bot using Model Context Protocol tools for product search, orders, and verification (GPT-4o-mini)
Showcased expertise in LLM integration, RAG pipelines, agent orchestration, multimodal processing, prompt engineering, security, and observability.
I built production-grade large language model (LLM) applications using Python, OpenAI (GPT-4/GPT-4o), LangChain, FAISS, Sentence Transformers, and Langfuse:
• Simple Chatbot: Secure support agent with structured JSON outputs, input moderation, cost/latency tracking
• FAQ RAG: HR FAQ system with local embeddings, FAISS vector search, recursive chunking, and grounded GPT-4 responses
• Multi-agent RAG: Intent-based orchestrator routing to domain agents (Finance/HR/Tech), safeguards, memory, and evaluation
• Legal Contract Amendment Analyzer: Multimodal GPT-4o tool to parse/align contract images, detect changes, summarize amendments, and output validated Pydantic JSON
• MCP Chatbot: FastAPI-based support bot using Model Context Protocol tools for product search, orders, and verification (GPT-4o-mini)
Showcased expertise in LLM integration, RAG pipelines, agent orchestration, multimodal processing, prompt engineering, security, and observability.
Machine Learning Applied to Human Activity Recognition
http://nalon99.github.io/Machine_Learning/The goal of this research is to explore a dataset of recorded values from life log systems for monitoring energy expenditure and supporting weight-loss programs, and from digital assistants for weight-lifting exercises.
A Simple Quantitative Approach of the Three-bar Reversal Pattern
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.
Music Composition | Mixing and Producing Audio Tracks
https://www.youtube.com/@mrkey-musicShared 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.
Education
1989 - 1998
Master's Degree in Computer Engineering
University of Padova - Padova, Italy
Certifications
DECEMBER 2025 - PRESENT
AI Engineering
An Online Freelance Agency
SEPTEMBER 2025 - SEPTEMBER 2027
GitHub Copilot
Microsoft
AUGUST 2022 - PRESENT
Unsupervised Learning
Stanford Online University | via Coursera
JULY 2022 - PRESENT
Advanced Learning Algorithms
Stanford Online University | via Coursera
JUNE 2022 - PRESENT
Supervised Machine Learning: Regression and Classification
Stanford Online University | via Coursera
JULY 2021 - PRESENT
Cloud Computing Foundations
Duke University | via Coursera
JANUARY 2016 - PRESENT
Machine Learning: Classification
University of Washington | via Coursera
JANUARY 2015 - PRESENT
An Introduction to Interactive Programming in Python
Rice University | via Coursera
JANUARY 2015 - PRESENT
Programming for Everybody: Python
University of Michigan | via Coursera
JANUARY 2015 - PRESENT
Practical Machine Learning
Johns Hopkins University | via Coursera
JANUARY 2015 - PRESENT
Machine Learning: Foundations
University of Washington | via Coursera
JANUARY 2015 - PRESENT
Machine Learning: Regression, Research Methodology, and Quantitative Methods
University of Washington | via Coursera
JANUARY 2014 - PRESENT
High Performance Scientific Computing
University of Washington | via Coursera
JANUARY 2014 - PRESENT
Introduction to Computational Finance and Financial Econometrics
University of Washington | via Coursera
JANUARY 2014 - PRESENT
Financial Markets
Yale University | via Coursera
JANUARY 2014 - PRESENT
R Programming
Johns Hopkins University | via Coursera
JANUARY 2014 - PRESENT
The Data Scientist's Toolbox
Johns Hopkins University | via Coursera
JANUARY 2014 - PRESENT
Getting and Cleaning Data
Johns Hopkins University | via Coursera
JANUARY 2014 - PRESENT
Statistical Inference
Johns Hopkins University | via Coursera
JANUARY 2014 - PRESENT
Regression Models
Johns Hopkins University | via Coursera
JANUARY 2013 - PRESENT
Computational Investing
Georgia Institute of Technology | via Coursera
JANUARY 2013 - PRESENT
Mathematical Methods for Quantitative Finance
University of Washington | via Coursera
Skills
Libraries/APIs
NumPy, Pandas, REST APIs, Scikit-learn, Interactive Brokers API, SQLAlchemy, Matplotlib, TensorFlow, Keras, PySpark, Telegram Bot API, Telegram Messenger API
Tools
Microsoft Access, Microsoft Excel, Eclipse IDE, Git, GitHub, Moodle, Microsoft Visual Studio, Subversion (SVN), Uvicorn, AWS Fargate, Amazon Elastic Container Service (ECS), Amazon Elastic Container Registry (ECR), Amazon Virtual Private Cloud (VPC), Hidden Markov Model, Amazon CloudWatch
Languages
SQL, Python, Visual Basic for Applications (VBA), R, Visual Basic, Python 3, XML, Java, Snowflake, Excel VBA, Assembler 68000, Python 2, Markdown, JavaScript
Platforms
Jupyter Notebook, Amazon EC2, Oracle, Windows, RStudio, Amazon Web Services (AWS), Docker, MacOS, Anaconda, Linux, Linux CentOS 7, DigitalOcean, Databricks, Visual Studio Code (VS Code), Langfuse, AWS Lambda
Storage
MySQL, Amazon S3 (AWS S3), PL/SQL, JSON, Oracle PL/SQL, Microsoft SQL Server, SQLite, Amazon DynamoDB
Industry Expertise
System Development Life Cycle (SDLC), Trading Systems, Accounting
Paradigms
Automation, Unit Testing, Anomaly Detection, ETL
Frameworks
Spark
Other
Algorithmic Trading, Data Analysis, Machine Learning, Data Science, Mathematics, Algorithms, Data Engineering, Back-end, FastAPI, APIs, Stock Trading, Bots, Quantitative Analysis, Scientific Data Analysis, Scripting, Backtesting Trading Strategies, Quantitative Finance, Artificial Intelligence (AI), Cloud, Amazon RDS, Finance APIs, AI-assisted Development, Software Development Lifecycle (SDLC), AI Enablement, Architecture, Derivatives, System Architecture, Quantitative Modeling, Algorithmic Trading Analysis, Time Series, Time Series Analysis, Supervised Machine Learning, CSV, CSV File Processing, Excel 365, Web Services, Automated Trading Software, Trading Bots, Equity Investment, API Integration, AI Tools, Crypto, Investments, Options, Technical Writing, Agentic AI, Prompt Engineering, Cursor AI, Key Performance Indicators (KPIs), Statistics, Trading, Big Data, Data Mining, Market Basket Analysis, Forex Trading, Bitcoin, Regression, Classification, Statistical Analysis, Financial Data, Multithreading, Neural Networks, 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, Data Cleaning, Data Inference, Linear Regression, Linear Algebra, Web Scraping, Audio, Audio Processing, Apple Music, Spotify, Finance, Data, TradingView, Data Scraping, GitHub Copilot Chat, Large Language Models (LLMs), Equities, Equity Trading, Profit & Loss (P&L), Trade Finance, Stock Market, Stock Exchange, Tickers, Hedge Funds, Fund Management, Risk Models, AI Agents, Risk Management, AI Engineering, LangChain, OpenAI, Multistage LLM Chains, OpenRouter, Open-source LLMs, Schwab, Charles Schwab, Serverless, Crypto, Generative Pre-trained Transformers (GPT)
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring