How many experiments actually made it to production?

~5% of experiments showed alpha, 1% deployed—from 500 promising signals weekly.

What's the cost of running 10k experiments daily?

$3,500 per day ($0.35/experiment × 10k), generating 3-5 production strategies yearly worth $100M+.

How does this case study work?

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Scaling Research Pipelines for Systematic Funds

Executive Summary

Quant researchers at a systematic fund were bottlenecked by manual experiment execution and limited compute. A distributed research platform with Ray and experiment tracking scaled throughput from 10 to 10,000 experiments daily, reducing alpha discovery time from weeks to hours.

Key Outcomes

▹ 10 → 10,000 experiments/day (1,000x increase)
▹ Alpha discovery time from 2 weeks to 4 hours
▹ Researcher satisfaction score 3.2 → 4.8/5

Client Situation

Fifteen quant researchers shared a single JupyterHub server. Each experiment took 2-4 hours, forcing prioritization and wasted time.

Key Challenges

⚠ GPU/CPU contention causing 4-hour wait times
⚠ No experiment tracking—duplicate work common
⚠ Results stored inconsistently across researchers

Existing Architecture

Jupyter notebooks on shared EC2 instances. Manual result logging to Excel. No version control for experiments.

Sequential execution of experiments
No reproducibility across research team
Wasted compute on duplicate experiments

Solution Design

Distributed research platform with Ray for compute, MLflow for tracking, and standardized experiment templates.

Key Decisions

✓ Use Ray for distributed hyperparameter search
✓ MLflow for experiment tracking and model registry
✓ Preemptible EC2 spot instances for 70% cost reduction

RayMLflowAWSDockerPostgreSQLS3

Implementation

Pilot with 2 researchers, then full rollout to 15 researchers over 3 months with training and onboarding.

Phase 1: Phase 1: Infrastructure Setup
Deployed Ray cluster with autoscaling (10-200 nodes) and MLflow tracking server.
Phase 2: Phase 2: Experiment Templates
Standardized factor research, backtesting, and ML templates reducing boilerplate.
Phase 3: Phase 3: Full Adoption
All researchers migrated, with daily 10k experiments running in parallel.

Technical Challenges

Ray task serialization overhead

Impact: 2-second per-task latency limiting throughput

Resolution: Batched experiment parameters (100 per task) reducing overhead by 99%

MLflow scalability at 10k experiments/day

Impact: PostgreSQL backend saturated, query latency 30 seconds

Resolution: Added read replicas and partitioned metadata storage

Results

Experiments per day: Before10
After10,000
Improvement1,000x increase
Alpha discovery time: Before2 weeks
After4 hours
Improvement98% reduction
Compute cost per experiment: Before$8.50
After$0.35
Improvement96% reduction

Lessons Learned

📘 Researcher adoption required standardized templates and documentation
📘 Spot instances saved 70% cost with 5% interruption rate (acceptable for research)
📘 Experiment tracking enabled post-hoc analysis of 100k+ past runs

What We Would Do Differently

💡 Implement automated experiment prioritization based on potential impact
💡 Add model performance monitoring for production-bound experiments

Role Relevance

Quant researchers with engineering mindset built the platform themselves, understanding exactly what features would accelerate their discovery process.

Critical Skills Demonstrated

Distributed computing (Ray)Experiment tracking (MLflow)Cloud infrastructure (AWS)Researcher workflow automation

Frequently Asked Questions

How many experiments actually made it to production?: ~5% of experiments showed alpha, 1% deployed—from 500 promising signals weekly.
What's the cost of running 10k experiments daily?: $3,500 per day ($0.35/experiment × 10k), generating 3-5 production strategies yearly worth $100M+.