OmniPulse (Fixed Version)

OmniPulse (Fixed Version) – Description
OmniPulse is a multi-indicator framework designed to combine three core oscillators—RSI, Stochastic, and Momentum—at various lookback lengths, then refine their signals using placeholder features such as machine learning forecasting, adaptive cycle detection, and neural network filtering. While some of these advanced features are not natively supported in Pine Script, they are represented here in simplified forms to illustrate how a more sophisticated system could be structured.

Key Components:
Multi-Length Oscillator Arrays

RSI (calcrsi() function)
Stochastic (placeholder via ta.sma() on a typical price average)
Momentum (ta.roc())
These are calculated for multiple lengths defined by the rsiLengths, stochLengths, and momentumLengths arrays.
Dual-Threshold Convergence

Compares each oscillator’s value to user-defined upper/lower thresholds (threshold1, threshold2) to identify bullish or bearish conditions.
Summarizes results in a convergence score.
Placeholder Machine Learning Forecast

Demonstrates a simple averaging of oscillator values as a “forecast” when toggled on.
Adaptive Cycle Detection (Placeholder)

Introduces a static cycle period (e.g., 20.0) as a placeholder for more advanced transforms.
Neural Network Filter (Placeholder)

Averages convergence, forecast, and cyclePeriod into a single filteredSignal.
Signal Plotting

Plots the filtered signal on the chart.
Highlights potential bullish or bearish extremes with shape markers based on percentile thresholds.
Practical Use & Extension:
Real Multi-Timeframe Analysis: Replace placeholders with request.security() for each timeframe.
Advanced Forecasting: Incorporate custom or external machine learning models.
Genuine Cycle Detection: Implement more sophisticated logic or user-defined cycle detection tools.
Neural Network Heuristics: Expand the placeholder step into a deeper filtering or weighting system.
Overall, OmniPulse serves as an adaptable blueprint for traders and developers, showcasing how multiple indicators and advanced concepts might be combined into a cohesive, signal-generating framework.