trading-signals

To generate confluence-driven trading signals, you provide crypto chart data to the framework, which then integrates Elliott Wave, Wyckoff, Fibonacci, Markov Regime, and Turtle Trading methodologies to identify aligned patterns and output actionable entry or exit levels.

Confluence scoring aggregates technical analysis signals across multiple methods like Wyckoff and Fibonacci to identify high-probability entries. It works by standardizing pattern recognition outputs and combining them to validate trading signals across crypto timeframes.

You combine Elliott Wave and Fibonacci retracements for crypto target projection by applying the modular pattern fusion framework. It processes chart data to identify wave structures and projects risk targets using Fibonacci levels without relying on external routing logic.

Yes, you can integrate technical analysis signals into backtesting and live trading pipelines. The framework is designed to plug directly into automation, DRL/LLM orchestration, and live trading pipelines without requiring external routing logic.

Yes, the framework works for regime identification across different crypto timeframes. It applies Markov Regime analysis to chart data to detect market states, enabling accurate pattern recognition and risk assessment for various trading projects.

The framework requires sufficient chart data to accurately fuse patterns for market regime identification. It does not rely on external routing logic, meaning users must supply complete crypto asset data and manage signal integration into their specific backtesting or live environments.