The challenge
A quantitative hedge fund running systematic equity strategies relied on Bloomberg Terminal for fundamental data and backtesting. At $24,000 per seat per year, the cost scaled linearly with their research team. Worse, extracting data from the terminal for automated pipelines required brittle Excel add-in hacks and manual CSV exports. Their factor research was bottlenecked by data access.
The solution
The team migrated their entire factor research pipeline to Eulerpool's API. Using the Python SDK, they replaced manual Bloomberg queries with automated API calls that pull point-in-time fundamentals, price data, and financial ratios. The survivorship-bias-free historical data meant their backtests no longer suffered from look-ahead bias. Batch endpoints let them query 100 symbols in a single call, drastically reducing pipeline run times.
The results
Within two days, the team had fully migrated their backtesting infrastructure. Annual data costs dropped from $120,000 (5 seats) to $5,988 (1 Startup plan). Backtest accuracy improved thanks to point-in-time data, and pipeline execution time dropped by 60% due to batch API calls.
"We should have switched years ago. The data quality is identical, the API is infinitely better than scraping Bloomberg, and we saved six figures annually."
Head of Quantitative Research