How many CPU cores needed for 200 strategies?

500 cores on AWS—execution time 6 hours (vs 2 weeks manual).

What validation metrics do you track?

Sharpe, Calmar, max drawdown, win rate, profit factor, parameter stability.

How does this case study work?

Raise a request, talk to experts, fund the project, expert works, review and approve payment. All remote, all through our platform.

Validating Multi-Asset Trading Systems at Scale

Executive Summary

A global macro fund's strategy validation took 2 weeks per strategy—manual, error-prone, and impossible to scale. Building a distributed walk-forward validation platform reduced validation time to 6 hours per 200 strategies, enabling weekly strategy iteration and catching 30% of failures before deployment.

Key Outcomes

▹ 2 weeks → 6 hours per 200 strategies
▹ 30% of strategy failures caught pre-deployment
▹ 500+ cores utilized for parallel validation

Client Situation

The fund had 200+ strategies across equities, FX, rates, and commodities. Manual validation by quants took 2 weeks each, bottlenecking deployment.

Key Challenges

⚠ 2 weeks per strategy validation
⚠ Inconsistent validation across asset classes
⚠ No centralized historical validation results

Existing Architecture

Manual strategy validation in Jupyter notebooks, Excel tracking, no automation.

Validation not reproducible
No cross-asset consistency checks
Cannot run on historical data changes

Solution Design

Distributed walk-forward validation platform with parallel processing, result database, and automated reporting.

Key Decisions

✓ Ray distributed computing (500+ cores)
✓ Unified validation framework across asset classes
✓ Result database with backtest artifact storage

PythonRayDaskPostgreSQLAirflowGrafana

Implementation

Pilot with 20 strategies, then scaled to 200+, adding asset classes incrementally.

Phase 1: Phase 1: Framework
Built unified validation engine supporting all 10 asset classes.
Phase 2: Phase 2: Parallelization
Ray distributed computing—200 strategies in 6 hours.
Phase 3: Phase 3: Automation
Airflow DAG running weekly validation on new strategy versions.

Technical Challenges

Cross-asset data alignment

Impact: Different tick sizes, market hours, and calendars

Resolution: Unified time index with alignment rules per asset class

Distributed state management

Impact: Ray task failures causing partial results

Resolution: Idempotent task design + result persistence after each strategy

Results

Strategy validation time: Before2 weeks/strategy
After6 hours/200 strategies
Improvement99.9% reduction
Strategies validated monthly: Before2
After50+
Improvement25x increase
Pre-deployment failures caught: Before10%
After30%
Improvement3x more issues caught

Lessons Learned

📘 Ray's task parallelism perfect for embarrassingly parallel strategy validation
📘 Standardized reporting across asset classes reduced analyst time 80%
📘 Automated re-validation on data changes caught 15 latent bugs

What We Would Do Differently

💡 Use Apache Spark for larger datasets (>10TB)
💡 Implement live strategy monitoring dashboards earlier

Role Relevance

Validation experts built the distributed platform that scaled strategy validation 100x, enabling weekly strategy iteration for a global macro fund.

Critical Skills Demonstrated

Distributed computing (Ray)Multi-asset data alignmentValidation pipeline designResult database architecture

Frequently Asked Questions

How many CPU cores needed for 200 strategies?: 500 cores on AWS—execution time 6 hours (vs 2 weeks manual).
What validation metrics do you track?: Sharpe, Calmar, max drawdown, win rate, profit factor, parameter stability.