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The Inner Workings of the Dakko 'Critical Mass' Algorithm

At its core, the 'Critical Mass' algorithm is a powerful amalgamation of AI technology and advanced statistical techniques. It predicts market trends, tailors trading strategies, and effectively manages risk.

The algorithm functions through a four-step process:

Step 1: Under the hood - Identification of Influential Nodes:

The first step is network analysis. In network analysis, picture nodes as individual traders and their wallet activity on a public blockchain network. Out of all the activity and transactions taking place, some nodes will emerge as more crucial than others because if they were removed, it would greatly affect the overall market capitalization of the cryptocurrency.
The 'Critical Mass' algorithm finds these important nodes by looking at their influence within the network. It considers both the connections they receive and the ones they send and their importance. By pinpointing these influential nodes, traders can see where the 'smart money' is going and use this information to shape their trading strategies.
The process of identifying dominant Nodes operates through a unique, innovative method that we have developed, which resides securely under the hood of our system. The system’s trade secret is deeply rooted in its algorithmic ability to scan and discern the influential nodes within a network, focusing not merely on node removal but rather on a systematic process that goes beyond traditional methods.
Our technique innovatively prioritizes nodes based on our proprietary network resilience and impact analysis. By examining the effects on the overall network's resilience when nodes flex their influence, our system can ascertain which nodes have significant influence, branding them as pivotal nodes.
These Pivotal Nodes have the capability to sway the market capitalization of tokens by buying or selling in substantial volumes. Our algorithm (although far from perfect) tracks the wallets of the most dominant traders, grouping them into clusters of data fragments associated with specific temporal sequences. These data fragments form a directed weighted graph, with nodes symbolizing diverse token-holding wallets, and connections assigned weights between them.
This measure of significance outlines the importance of a node within the network, initiating the algorithm to systematically remove nodes, commencing with the node with the highest significance.
Upon removal of each node, the algorithm assesses the impact on network resilience by scrutinizing the change in the weighted measure of connectivity. The largest resiliently connected component (LRCC) metric allows us to monitor the network's evolution after each node removal.
By establishing an influential threshold or selecting the most dominant nodes, our algorithm can identify key market influencers for the specific fungible token in question. These market influencers are the nodes that have the most impact on the overall market capitalization of the token.
In concert with this group of market influencers, the algorithm performs tests to examine the impact of specific events. For instance, it can ascertain whether the actions of these market influencers directly affect the token's price. Moreover, by calculating cross-correlations among different groups of market influencers, the algorithm can analyze the interrelationships between their actions.
These tests allow our users to construct predictive models that categorize individuals based on their strong positive or negative effect on the token's price. By periodically applying the algorithm within different temporal windows, we can acquire invaluable indicators for trading and making astute decisions.
The identification of these dominant nodes provides us with an enhanced understanding of how specific events or actions impact the network and, subsequently, the market. This knowledge enables traders to foresee potential outcomes and make informed trading decisions based on a more detailed analysis of market dynamics.

Step 2: Implementation of the Granger Causality Test

Once the influential nodes are identified, the 'Critical Mass' algorithm employs the Granger causality test. This is a statistical hypothesis test developed by Nobel Laureate Clive Granger that ascertains whether a one-time series of events is predictive of another. In cryptocurrency trading, this tool helps project market trends based on the behavior of influential traders in the past and present. This powerful tool is invaluable in predicting market trends and aligning trading strategies accordingly.

Step 3: Community Detection Using Wallet On-Chain Clusters

Traditionally, community detection in trading networks relied heavily on social media data, which often led to inaccuracies due to the noise of misleading information. The 'Critical Mass' algorithm, on the other hand, utilizes wallet on-chain clusters for community detection, offering a more accurate, transparent, and comprehensive analysis of various tokens. This approach allows traders to better understand the dynamics of different token markets and adjust their trading strategies to the prevailing trends.

Step 4: Generation of Trading Indicators

The 'Critical Mass' algorithm generates critical trading indicators. These indicators help identify potential bullish or bearish market phases, assess the duration of these phases, and evaluate the associated risk levels. By anticipating these market phases and understanding their potential risks, traders gain a significant advantage in managing their investments and optimizing profitability.