← Coins Grouping Hypothesis Team 1 Execution

Hierarchical Clustering Results

Used hierarchical clustering on scaled returns, creating groups.

5 Clusters → 3 Clusters → 4 Clusters

Testing different cluster counts revealed at least 2 distinct clusters:

  • Cluster 4: behavior similar to Cluster 3
  • Cluster 2: totally different behavior

Cluster Characteristics

Cluster Description
1 Low to middle-low volume
2 Shitcoins + stablecoin (DFUSDT, DEXEUSDT, HIVEUSDT, PHAUSDT, UXLINKUSDT, USDCUSDT)
4 Solid coins with big volumes

Time-Based Correlation Analysis

Explored changes in correlation between coins - better measure of evolving relationships than overall correlation trend.

Method: Monthly correlation changes over 12 months

Result: Very similar behavior across clusters - may not be significant when derived this way.

Cluster Statistics (Normalized)

Cluster Close Price Mean Std Volume Mean Std
1 -0.0 0.999 -0.0 0.999
2 -0.0 0.997 0.0 0.997
3 -0.0 0.999 0.0 0.999
4 0.0 0.997 -0.0 0.997

Conclusion

Insufficient time to implement proper coin grouping. Would require training multiple models per group with individual optimization - deprioritized in favor of other workflows.