Process Automation
FinTech for options trading strategies
A fintech startup specializing in AI-driven trading strategies for options, faced significant challenges with their existing backtesting framework. Their custom backtesting logic, crucial for validating trading strategies and training their AI models, required approximately two days to process 20 years of options data. This inefficiency hindered their ability to rapidly iterate and improve their trading algorithms.
The project aimed to radically enhance the efficiency of the firm's backtesting process, enabling faster strategy development and more effective AI training. The objective was to redesign their backtesting framework using advanced, open-source technologies to expedite data fetching and execution times, ultimately reducing the backtest duration from two days to under 30 minutes.
Team
Software Developers specializing in data science, Project Manager, Systems Architect
Python for scripting and integration.
Specialized data storage solutions for efficient data retrieval.
Open-source libraries for data handling and computation.
Data fetching inefficiencies. Existing methods for data retrieval were slow and bottlenecked the backtesting process.
Backtest execution delays. The computation-heavy nature of options backtesting logic resulted in prolonged execution times.
AI integration. Needed a solution that not only optimized backtesting but also structured the output for efficient integration with their AI models.
Data handling optimization. Implemented open-source technologies to streamline data fetching, significantly reducing the time required to access historical options data.
Backtesting framework redesign. Redesigned the entire backtesting framework using Python, integrating high-performance computing techniques to handle extensive datasets more effectively.
AI-ready data outputs. Structured the backtesting outputs to be directly usable for AI training, ensuring that data fed into AI models was clean, structured, and aligned with training requirements.
Results
Reduced backtesting time. Successfully decreased the backtesting time from 48 hours to under 30 minutes for 20 years of data, drastically improving the turnaround time for strategy testing and refinement.
Enhanced AI model training. By optimizing the output data structure, AI models could be trained more effectively and with higher accuracy, leading to better-informed trading decisions.
Increased productivity. The faster backtesting cycle allowed the team to iterate more rapidly on trading strategies, significantly accelerating their research and development cycle.
Lessons
The project underscored the value of leveraging cutting-edge open-source technologies for complex financial data processing tasks.
Reinforced the idea that the quality and structure of output data are crucial for the effective training of AI models.
Demonstrated that system architecture needs to balance both scalability and efficiency to handle large-scale data processing effectively.