Institutional Data

A reversal-detection dataset designed to identify potential market turning points. It supports machine learning workflows, timing overlays, risk filters, and research into market tops, bottoms, and regime transitions.

A seven-component market sentiment dataset built from exogenous correlation, trend quality, regime-aware features, and related market pressure indicators, designed to augment alpha research, feed machine learning models, and support signal validation.

Monthly equity signal data designed to support both market-neutral alpha generation and tactical equity risk management. Cyclex Alpha ranks equities through a proprietary conditioned return framework to identify long/short opportunities with low benchmark dependence, while Cyclex Risk adjusts equity exposure using systematic temporal, trend, and volatility-regime signals.

A point-in-time volatility forecasting dataset covering global assets, with high-conviction forecasts generated from a multi-factor framework that combines pattern recognition, smoothing techniques, and proprietary indicators.

Structural supply and demand zones across global assets. The methodology combines topology-inspired mapping, smoothing techniques, swing structure, volatility analysis, and pattern recognition to produce strong conviction market zones.

Hourly and daily timestamped FX signal data generated from a multi-factor framework combining momentum, volatility, trend structure, pattern recognition, and proprietary indicators. Outputs include signal direction, sizing multiplier, estimated holding period, and conviction score.