Forecasting Models

A detailed look at the four primary proprietary models that power EquiPredict Analytics — combining cycles, probability, and exogenous data to forecast market direction with exceptional accuracy.

EquiPredict Red Wave Forecasts

The Red Wave model is one of EquiPredict’s original forecasting systems. It detects repeating market cycle patterns within price data — identifying rhythm and phase alignment across major indices and equities. Red Wave forecasts are tied to time, not price points, meaning they highlight periods where bullish or bearish bias is most probable. This model is particularly useful for swing traders and macro analysts who seek to understand the underlying market rhythm.

Illustrative example: Red Wave forecasting a cyclical bullish phase and confirming result.

EquiPredict Green Wave Forecasts

The Green Wave model is a high-confidence directional forecast that identifies early-stage market momentum and quantifies the probability of continuation. Each signal is derived from a combination of trend persistence, inter-market validation, and proprietary probability scoring. Green Wave forecasts often include an expected range or ROI target over a defined time horizon. They are ideal for investors seeking high-probability directional trades.

Illustrative example: Green Wave identifying a high-probability bullish continuation and verified outcome.

EquiPredict Blue Wave Forecasts

The Blue Wave model integrates seasonal trends, natural cycles, and external (exogenous) variables to forecast long-term turning points in the market. It is primarily used to identify extended bullish or bearish phases with over 85–90% confidence. The Blue Wave model’s unique strength lies in its ability to merge cyclical recurrence with large-scale macro forces, making it valuable for long-term investors and institutional outlooks.

Illustrative example: Blue Wave projecting a long-term market inflection and verified reversal.

Exogenous Leading Data Driving Forecasts

The Exogenous Leading Data model examines data sets not directly tied to the underlying asset — such as commodities, global macro indicators, or correlated markets. These external drivers are used to anticipate future movement in related indices or equities by identifying lead-lag relationships that persist over time. For example, energy prices, credit spreads, or yield curve dynamics may provide early directional signals for the broader equity markets.

Illustrative example: Exogenous data (crude oil) leading Dow Jones movement several weeks in advance.