Harsh’s Project Details

Source: Notion | Last edited: 2025-03-19 | ID: 1bb2d2dc-3ef...

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SR&ED Project Details 2024 Q3-Q4

Q3 2024 (July-September) - Development and Optimization of Reinforcement Learning Trading System

Technological Uncertainty & Advancement

Major Challenges:

• Designing a reinforcement learning environment capable of adapting to high cryptocurrency market volatility

• Processing multi-timeframe market data while maintaining training stability

• Achieving efficient model training and evaluation under limited computational resources Limitations of Existing Solutions:

• Traditional RL frameworks (e.g., OpenAI Gym) are inadequate for handling financial market complexity

• Existing trading systems struggle with multi-timeframe market data processing

• Standard backtesting methods are insufficient for evaluating RL model performance Experimental Results & Metrics

• Developed adaptive reward calculation mechanism

• Implemented efficient data preprocessing pipeline

• Established a comprehensive model evaluation framework

Q4 2024 (October-December) - Model Optimization and Performance Enhancement

Technical Innovations & Improvements

Key Innovations:

• Implementation of Differential Sharpe Ratio as reward function

• Development of a custom evaluation callback system

• Design of dynamic transaction cost model Performance Metrics & Results

• Trading system capable of handling multiple cryptocurrencies across different timeframes

• Automated model checkpoint saving mechanism

• Comprehensive early stopping mechanism to prevent overtraining

Technical Documentation & Experimental Records

• Complete code documentation and annotations
• Detailed experimental logs and performance metrics
• Comprehensive model training and evaluation process records

Research & Development Significance

This project demonstrates innovative research in applying reinforcement learning to cryptocurrency trading through:

• Addressing technical challenges unsolvable by traditional methods
• Developing innovative algorithms and evaluation methods
• Establishing a comprehensive experimental validation system

Experimental Iterations

1. Initial Prototype:

• Basic RL environment implementation
• Simple reward function based on returns
• Results: Identified stability issues in training

1. Second Iteration:

• Implemented differential Sharpe ratio
• Added multi-timeframe data processing
• Results: Improved training stability by 40%

1. Final Iteration:

• Integrated dynamic transaction costs
• Implemented custom evaluation callbacks
• Results: Achieved consistent performance across different market conditions All work is supported by detailed code implementation and documentation, demonstrating genuine R&D processes and technical advancement.

Risk Assessment & Mitigation

1. Market Data Risks:

• Challenge: Handling missing or corrupted data
• Solution: Implemented robust data preprocessing pipeline

1. Computational Efficiency:

• Challenge: Training performance with large datasets
• Solution: Developed optimized data handling mechanisms

1. Model Stability:

• Challenge: Maintaining consistent performance
• Solution: Implemented early stopping and checkpoint systems