DumpViz

Objective: The primary goal is to visually represent the relationships between different cryptocurrency wallets and the timing of sales transactions. This includes showing how wallets are interconnected through transactions, the frequency and volume of sales, and any patterns that emerge from these activities.

Data Collection: Gathering data from blockchain transactions, including wallet addresses, transaction timestamps, amounts, and tokens involved. This requires accessing blockchain explorers or APIs that provide detailed transaction histories.

Link Analysis: Analyzing the transaction data to identify links between wallets. This involves determining which wallets have transacted with each other and the nature of these transactions (e.g., direct transfers, sales, purchases). The analysis can also extend to multi-hop transactions, where funds move through several wallets before reaching their final destination.

Timing Analysis: Examining the timing of transactions to identify patterns. This includes analyzing the frequency of transactions, identifying peak trading times, and correlating market events with transaction volumes. Timing analysis can reveal behaviors like regular accumulation or distribution of assets, timed responses to market news, or coordinated actions among multiple wallets.

Visualization Techniques:

Network Graphs: Using nodes to represent wallets and edges to represent transactions between them. Nodes can be sized or colored based on transaction volumes, wallet balances, or other metrics. Edges can be weighted to indicate the volume of transactions.
Timeline Visualizations: Plotting transactions over time to identify patterns in sales timing. This could include histograms, line charts, or heat maps to show activity levels at different times.

Objective: The primary goal is to visually represent the relationships between different cryptocurrency wallets and the timing of sales transactions. This includes showing how wallets are interconnected through transactions, the frequency and volume of sales, and any patterns that emerge from these activities.

Data Collection: Gathering data from blockchain transactions, including wallet addresses, transaction timestamps, amounts, and tokens involved. This requires accessing blockchain explorers or APIs that provide detailed transaction histories.

Link Analysis: Analyzing the transaction data to identify links between wallets. This involves determining which wallets have transacted with each other and the nature of these transactions (e.g., direct transfers, sales, purchases). The analysis can also extend to multi-hop transactions, where funds move through several wallets before reaching their final destination.

Timing Analysis: Examining the timing of transactions to identify patterns. This includes analyzing the frequency of transactions, identifying peak trading times, and correlating market events with transaction volumes. Timing analysis can reveal behaviors like regular accumulation or distribution of assets, timed responses to market news, or coordinated actions among multiple wallets.

Visualization Techniques:

Network Graphs: Using nodes to represent wallets and edges to represent transactions between them. Nodes can be sized or colored based on transaction volumes, wallet balances, or other metrics. Edges can be weighted to indicate the volume of transactions.
Timeline Visualizations: Plotting transactions over time to identify patterns in sales timing. This could include histograms, line charts, or heat maps to show activity levels at different times.