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SR&ED Claim Information for Chuen Eric Lam (Terry Li)

Source: Notion | Last edited: 2023-11-25 | ID: a295de22-69f...

Name: Terry Li

Position: Director of Operations

Duration of Employment for Fiscal Year: January 1, 2023 - December 31, 2023

SR&ED-able Time: 30%

Primary Project in SR&ED Claim Information

Section titled “Primary Project in SR&ED Claim Information”
  1. Project Title: Advancement in Investment Time Horizon (ITH) Analysis
  • Brief Description: Development of a novel method to quantify and measure risk in a time-varying framework, focusing on short-term Investment Time Horizon (ITH).
  1. Technological Advancements and Challenges:
  • Innovative Technologies/Methods: Developed new approaches surpassing traditional performance metrics like the Sharpe Ratio, focusing on short-term ITH (≤ 28 days).
  • Technological Challenges/Uncertainties: Overcoming challenges in quantifying risks in short-term investments, surpassing limitations of traditional investment performance metrics.
  1. Experimental Development and Analysis:
  • Experimental Processes/Methods: Implementation of a script for ITH calculation, assessing risk versus return.
  • Analysis and Findings: Refined understanding of investment performance, especially in short-term scenarios, enabling better risk management and strategy formulation.
  1. Results and Impact:
  • Results Achieved: Refined framework for evaluating investment strategies, focusing on short-term horizons.
  • Impact on Field/Industry: Advanced investment analysis methods, offering a comprehensive tool for volatile market assessment.
  1. Documentation and Supporting Evidence:
  • Types of Documentation: Project reports, ITH calculation script code, analysis results, backtesting data.
  • Supporting Documents:
  1. Time Allocation:
  • Estimated Time on SR&ED Activities: 80%

Secondary Project in SR&ED Claim Information

Section titled “Secondary Project in SR&ED Claim Information”
  1. Project Title: Exploration and Integration of Advanced Trading Systems
  • Brief Description: Investigating QuantConnect and NautilusTrader as advanced platforms for back-testing macro ideas to enhance discretionary trading decision-making processes.
  1. Technological Advancements and Challenges:
  • Innovative Technologies/Methods: Evaluation of new trading systems to complement existing machine learning-based algorithms.
  • Technological Challenges/Uncertainties: Compatibility issues with existing systems, notably with proprietary technologies like PINE language, and adapting to closed-source environments such as TradingView.com.
  1. Experimental Development and Analysis:
  • Experimental Processes/Methods: Ongoing investigation, testing, and development of sample cases using QuantConnect and NautilusTrader frameworks.
  • Analysis and Findings: Ongoing assessment of the ease of use and long-term applicability of these platforms.
  1. Results and Impact:
  • Results Achieved: [Details pending as the investigation is ongoing].
  • Impact on Field/Industry: Potential enhancement of the decision-making process in trading through improved algorithmic strategies.
  1. Documentation and Supporting Evidence:
  • Types of Documentation: Research notes, testing results, algorithm development documentation (pending completion).
  1. Time Allocation:
  • Estimated Time on SR&ED Activities: 10%

Other Project in SR&ED Claim Information

Section titled “Other Project in SR&ED Claim Information”
  1. Project Title: Methodological Evaluation of OHLCV Data Quality from Various Providers
  • The project aimed to develop a methodology for evaluating the quality of 5-minute OHLCV data provided by sources like Bloomberg, TradeStation, BarChart.com, TradingView.com, and DTN’s IQFeed.net.
  • Focus on identifying and compensating for missing data and discrepancies among different providers to determine the most accurate representation of market conditions.
  1. Technological Advancements and Challenges:
  • Developed techniques and algorithms to methodologically assess data quality and integrity.
  • Faced challenges with varying data quality and missing information across different sources, necessitating a robust evaluation framework.
  1. Experimental Development and Analysis:
  • Conducted comparative analysis of data from multiple providers.
  • Developed assumptions and criteria to distinguish good from bad data.
  1. Results and Impact:
  • Achieved a reliable method for evaluating and selecting the most representative data sources.
  • Enhanced the accuracy and reliability of financial data used in Eon Labs Ltd.’s trading algorithms and strategies.
  1. Documentation and Supporting Evidence:
  1. Time Allocation:
  • Estimated Time on SR&ED Activities: 10%

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