t-SNE: Stock Feature Similarity

Example 1: Stock Similarity (Feature-based t-SNE)

Stocks with similar financial characteristics should cluster together. Color by sector to see if the algorithm detects industry clusters.

Feature Selection for Distance Calculation:

Available Features:

Selected Features:

Rescaling: (Changes which variables determine similarity)

Color nodes by:

Perplexity: 15.0 (5-100, typically 15-50)

Learning Rate: 200.0 (10-1000, typically 100-500)

Convergence threshold:

Max iterations:

Early exaggeration iters:

Exaggeration factor:

Iteration: 0
         
Movement (last iteration): 0.000
Status: Ready

Aspect Ratio: 0.6

Zoom: 1.0x

Tip: Drag nodes to manually reposition. "Run to Convergence" uses early exaggeration for the configured number of iterations; "Step (small)" never uses exaggeration; "Exaggerated Step" always uses it.

Data: stock_data.csv

t-SNE Examples This page was created using JSPlots.jl v0.6.3.