Predicts market movements by identifying Fibonacci retracement and extension levels within historical stock price data. Focuses on detecting price reversals and trends, analyzing how these levels correlate with market volatility.

Scans historical Forex data to identify points where Fibonacci retracement levels align with price reversals. Visualizes these patterns and analyzes their predictive reliability.

Detects and quantifies fractal patterns in stock price movements that correlate with Fibonacci spirals. Assesses how these patterns can improve predictive models in finance.

Generates Fibonacci spiral overlays on daily market data to visualize momentum and trend shifts. Provides insights into how the golden ratio influences price action.

Creates a Fibonacci heatmap of market trends to identify correlations between Fibonacci ratios and market phases such as expansion or contraction.

Analyzes the potential for using AI to reduce "noise trading" by implementing Fibonacci-guided models. Explores the benefits of harmonizing human behavior with machine predictions in high-frequency markets.