Why not use QuantConnect or other platforms?

Cost ($50k/year) and data ownership concerns—open-source stack cost $10k total.

How did you ensure data quality?

Automated checks for missing values, outliers, and survivorship bias.

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.

Building First Quant Research Workflows

Executive Summary

A startup quant fund with no existing infrastructure needed to launch strategies quickly. Two junior quants built the entire research pipeline—data ingestion, backtesting, and visualization—reducing time-to-alpha from 6 months to 3 weeks and costing 80% less than vendor solutions.

Key Outcomes

▹ 6 months → 3 weeks time-to-first-alpha
▹ $500k saved vs commercial platforms
▹ 50+ strategies backtested in first 3 months

Client Situation

The fund had $50M AUM but zero quant infrastructure. Researchers manually downloaded data and ran Excel backtests.

Key Challenges

⚠ No automated data ingestion (6 hours daily manual work)
⚠ Excel backtests limited to 10-year history
⚠ No version control for research code

Existing Architecture

Excel spreadsheets, manual CSV downloads, and Jupyter notebooks. No shared code or data warehouse.

Data cleaning taking 80% of research time
Backtests non-reproducible
Single researcher knowledge silos

Solution Design

Lightweight research platform with automated data pipelines, vectorized backtester, and shared code repository.

Key Decisions

✓ Use open-source stack (no expensive vendors)
✓ Automated data ingestion from Bloomberg and FRED
✓ Airflow for workflow orchestration

PythonPandasAirflowPostgreSQLPlotlyGit

Implementation

Built incrementally starting with data pipeline, then backtester, finally production integration.

Phase 1: Phase 1: Data Pipeline
Automated daily ingestion of 500+ tickers from Bloomberg and FRED.
Phase 2: Phase 2: Backtester
Vectorized backtester handling 20-year history on 1000+ instruments in seconds.
Phase 3: Phase 3: Visualization
Interactive dashboards for performance attribution and risk metrics.

Technical Challenges

Data alignment across frequencies

Impact: Daily and intraday data mismatch causing backtest errors

Resolution: Standardized ETL with timestamps to millisecond precision

Backtest performance at scale

Impact: Initial pandas backtester took 30 minutes per run

Resolution: Vectorized operations and cached intermediate results (5-second runtime)

Results

Time to backtest new idea: Before2 days
After2 hours
Improvement92% reduction
Data preparation time: Before6 hours/day
After30 minutes
Improvement92% reduction
First alpha discovery: Before6 months
After3 weeks
Improvement88% reduction

Lessons Learned

📘 Open-source stack scaled to $200M AUM before needing upgrades
📘 Data quality issues cost more time than model development
📘 Reproducible backtests prevented 3 false positives

What We Would Do Differently

💡 Add data validation checks earlier
💡 Use DVC for data versioning from day one

Role Relevance

Junior quants built the foundation that scaled to $200M AUM, learning both research and engineering skills critical for systematic trading.

Critical Skills Demonstrated

Data engineeringBacktesting frameworksPython data stackWorkflow automation

Frequently Asked Questions

Why not use QuantConnect or other platforms?: Cost ($50k/year) and data ownership concerns—open-source stack cost $10k total.
How did you ensure data quality?: Automated checks for missing values, outliers, and survivorship bias.