Trading Games as Data Engines: Rethinking & Innovating Financial Data Products

Join us for an insightful presentation on the development of a sophisticated financial data product that bridges the gap between gaming and financial data annotation. Patrick John (PJ) Fitzpatrick will introduce his innovative dual-platform solution: a fully private iOS/Android app and complementary Python package designed to generate large volumes of high-quality annotated time series data.

This data is uniquely created through user interactions within a trading game featuring historical financial time series across multiple asset classes (FX, Interest Rates, Equities, Commodities, and Cryptocurrencies). The system transforms user decisions into valuable annotations that can be leveraged for analysis, modeling, and machine learning applications.

During this comprehensive session, PJ will dive deep into:

• The specific design choices implemented to maximize data quality from user interactions
• Technical challenges and solutions for synchronizing a complex Python data model (30+ classes) with the Dart programming language used in mobile development
• Creative techniques for transforming collected data into animated visualizations using matplotlib for app promotion
• How this approach can be extended beyond financial markets to other time series annotation challenges

The presentation will include live demonstrations, code examples, and insights gained from combining financial domain expertise with software engineering principles. Whether you're interested in financial analytics, time series data processing, cross-platform development, or innovative data collection methodologies, this talk offers valuable practical knowledge.

## Speaker:

Patrick John (PJ) Fitzpatrick, Financial Technology Expert
LinkedIn: https://ie.linkedin.com/in/pjfitzpatrick

Bio: Patrick John (PJ) Fitzpatrick is an IT/Finance professional with over 30 years of experience spanning trading, risk management, and technology development across multiple asset classes including fixed income, foreign exchange, equities, commodities, and cryptocurrencies. His extensive career includes roles as a trader, risk analyst, quantitative analyst, and system developer for major financial institutions.

A passionate Python developer since 2006, PJ also works with Dart and JavaScript to create cross-platform solutions. His unique perspective combines deep financial domain knowledge with technical expertise, allowing him to build innovative tools that bridge theoretical finance and practical technology implementation.

## Key Takeaways:

• Understand a novel approach to collecting annotated time series data that combines gamification with financial expertise
• Learn practical strategies for maintaining complex data models across different programming languages and platforms
• Discover techniques for visualizing time series data in engaging ways that can drive user engagement
• Gain insights into app design principles that optimize for both user experience and data quality
• See how trading mechanics can be leveraged to create labeled datasets for machine learning applications
• Learn how this methodology can be applied to non-financial time series data collection challenges
• Get exclusive access to test the latest version of the app featuring diverse financial instruments (FX, Interest Rates, Equity, Commodity, and Crypto time series)