A nimble quant hedge fund was missing opportunities because its data pipelines and models could not keep pace with the market. Every backtest took an entire trading day. Signal quality drifted as alternative data sources arrived. Analysts were juggling spreadsheets, R scripts, and brittle desktop setups. cloudlogic.dev partnered with the fund to overhaul its data foundations, unlock faster experimentation, and build the muscle to ship new strategies weekly.
The challenge
The fund’s edge depended on reacting quickly to new signals, yet their toolchain made iteration painfully slow. Eight-hour backtests meant traders went home before results landed. “Dirty data” polluted factors and forced analysts to spend weekends cleaning CSVs. The small engineering team lacked automation and cloud expertise, so infrastructure changes were risky and expensive. Leadership wanted measurable improvements in signal quality, execution speed, and team capability without inflating headcount.
What we did
- Cleaned data at the source: Built ingestion pipelines that profiled, cleansed, and enriched equities and alternative datasets. Automated data quality gates ensured signals stayed accurate.
- Accelerated backtesting on cloud: Moved R-based strategies to GCP, introduced parallel execution, and tuned infrastructure for cost-effective bursts—cutting runtime from eight hours to 20 minutes.
- Enhanced execution tooling: Integrated Bloomberg data flows, implemented version-controlled research environments, and delivered a Spring Boot portal for middle- and back-office needs.
- Uplifted the team: Ran targeted enablement on Git, Bash, GCP, and statistical coding practices so quants and engineers could co-own the platform after the engagement.
Impact
- 24× faster strategy backtesting, enabling daily research cycles instead of weekly results.
- Higher-confidence trading signals thanks to automated data quality checks and anomaly alerts.
- Analysts and engineers collaborating in the same Git-driven workflow, reducing operational risk.
- New signals moving from idea to production in weeks, not quarters, supporting growth plans.
“We finally had a research loop that matched our ambition. Ideas that would die on the whiteboard now hit the market in days.”
— CTO, Quant Hedge Fund
How the fund works today
The fund now operates a cloud-native research and trading platform. Data pipelines, backtests, and production models share the same automation, keeping costs predictable and releases safe. cloudlogic.dev continues to advise on advanced ML techniques, real-time signal processing, and the controls needed as the fund scales assets under management.