Victor's Project Details
Source: Notion | Last edited: 2025-03-25 | ID: 1c12d2dc-3ef...
SR&ED Claim – Victor’s Work on Financial Time Series Forecasting and Algorithmic Trading Enhancements
Section titled “SR&ED Claim – Victor’s Work on Financial Time Series Forecasting and Algorithmic Trading Enhancements”Technological Uncertainty & Advancement
Section titled “Technological Uncertainty & Advancement”Existing Technologies and Their Limitations
Section titled “Existing Technologies and Their Limitations”The financial forecasting and algorithmic trading space contains many well-established tools, each with significant limitations. Key challenges include:
- Existing reinforcement learning libraries lack industry-specific customizations for high-frequency financial market data. Mean reversion strategies often fail to perform in volatile markets and during regime changes. Data collection, storage, and real-time adaptation present significant challenges, especially when scaling latency-sensitive trading algorithms.
Technical Requirements and Novel Approach
Section titled “Technical Requirements and Novel Approach”Victor investigated and integrated the Route28 API for dynamic data acquisition and trade execution. Route28, a Synthetic Institutional Exchange developed by Crossover Markets and Hidden Road Partners, enables efficient trading of perpetual swaps without a Central Limit Orderbook (CLO). This non-reliance on CLO could significantly improve trade efficiency through better slippage control and price optimization.
The project also incorporated TensorTrade, a reinforcement learning library, to enhance the trading models’ predictive accuracy and adaptability. Testing alternative mean reversion strategies, specifically customized for various market conditions, formed a crucial part of this development.
Systematic Investigation & Experimental Development
Section titled “Systematic Investigation & Experimental Development”Methodology and Hypothesis Testing
Section titled “Methodology and Hypothesis Testing”Victor enhanced the trading models through:
- API Integration: Implemented Route28 API for efficient data access and model testing. Testing Reinforcement Learning Models: Optimized TensorTrade algorithms for real-time predictions. Scenario Testing: Thoroughly tested mean reversion strategies to improve market predictions. Backtesting and Validation: Evaluated new strategies against Eon Labs’ trading goals using historical data. Data Synchronization: Resolved real-time data acquisition challenges during live environment transition. Model Stability: Fine-tuned reinforcement learning models for stable real-time execution. Latency Issues: Optimized execution speed while maintaining predictive accuracy. Integration with Existing Systems: Successfully merged new models and APIs with legacy systems.
Failed Approaches and Lessons Learned
Section titled “Failed Approaches and Lessons Learned”Several approaches proved inadequate:
- Insufficient Model Adaptability: Early models lacked rapid adaptation to market changes. Overcomplicated Data Structures: Initial complex structures reduced efficiency, requiring simplification. Inefficient Real-Time Integration: Early Route28 API implementation suffered from latency and update issues. Data Collection and Handling: Standard methods failed to meet high-frequency requirements, leading to custom solutions. These setbacks led to improved strategies focusing on model adaptability, streamlined data structures, and smooth live trading integration.