Alpha Research
Test orthogonal signals, conditioned returns, sentiment measures, and market-locator features within systematic research pipelines.
Systematic market intelligence, delivered as data.
Quant Atlas develops point-in-time market locators for tops and bottoms, volatility forecasts, drawdown-reduction and rebalancing strategies, market sentiment, and FX signals for institutional research, portfolio construction, and risk workflows.
3,000+ global marketsPoint-in-time constructionMachine-readable deliveryInstitutional evaluation available
Listed on institutional data platforms
Product Strategy Performance
YTD Performance
StrategyYTDMax DDBeta
SPY12.80%-8.88%1.00
50% SPY-Cyclex9.6%-5.10%0.64
Vertex HC Overlay15.0%-9.02%1.04
Apex HC Overlay19.41%-8.90%1.02
Vision FX 1.5x7.17%-8.02%-
Institutional Workflows
Designed to complement your existing research stack.
Quant Atlas data is built as an input, not a black-box investment recommendation, so teams can test, combine, and apply it within their own process.
Portfolio Construction
Use rankings, conviction measures, and sizing outputs as overlays for allocation, screening, and exposure decisions.
Risk Management
Use volatility forecasts, market locators, drawdown-reduction overlays, and rebalancing strategies within risk frameworks.
Machine Learning
Add documented, machine-readable market features to model training, signal validation, and feature-engineering workflows.
Delivery & Integration
Built to move into production workflows.
Choose a delivery method that fits your research environment, from exploratory historical files to scheduled production feeds.
API access for programmatic workflows
CSV, JSON, and Parquet file formats
Scheduled SFTP and cloud delivery
Documented schemas and field definitions
Historical files for research and evaluation
Custom coverage and delivery arrangements
1
Quant Atlas datasets
Timestamped model outputs
2
Quality-controlled delivery
Schema and field validation
3
Your research environment
API · SFTP · S3/R2 · Files
Research Methodology
A product must earn its place in the dataset suite.
We use a disciplined research and validation process before considering a product for institutional offering, and continue evaluating it after launch.
01
Thorough Benchmarking
Candidate models are evaluated against relevant baselines so that added information and practical utility can be measured clearly.
02
Extensive Stress-Testing
We test behaviour across instruments, market regimes, volatility environments, sample windows, and parameter assumptions.
03
Statistical Validation
Statistical significance, stability, robustness, and bias controls are examined before a dataset progresses toward release.
04
Shadow-Live Observation
Products operate in shadow-live conditions before they are considered for institutional offering, allowing us to observe real-time behaviour outside development samples.
05
Continuous Monitoring
Once launched, results are monitored continuously to identify drift, changing behaviour, or deterioration that requires investigation.
In Collaboration With
Institutional Evaluation
Evaluate Quant Atlas data in your research environment.
Review methodology, historical outputs, delivery options, and product fit with the Quant Atlas data team.