Deep-reach Analytics
Ditto has coined the term "deep-reach analytics" as a way to bring attention to insights and metrics that are often unused and not easily-accessible in researching. Deep-reach analytics are the result of extrapolating common metrics to a much deeper level and require a dual skillset of data engineering and on-chain knowledge; we believe that expanding upon common on-chain metrics and manipulating data to discover new insights is one of the most underrated and underused methods of determining profitable opportunities early.
Deep-reach analytics create an entirely new scope and perspective for a token, and when used in conjunction with other metrics, provide an unparalleled ability to accurately discover up-and-coming projects and tokens before the masses.
Some examples of deep-reach analytics that are used by Ditto for both research and trading include, but are not limited to:
Favorable-cluster tracking
Ditto records and saves individual addresses of each token and assigns a value to it; if multiple highly-rated wallets are detected in a token, Ditto pings it as a value point for the Ditto ranking system. Clusters often move as a unit due to the inherent nature of influencer groups moving together and Ditto stays ahead.
High-value-hop trading
In individual address ratings, one of the metrics used for the wallet score is time-in-token. Time-in-token is how early that wallet was in one of the discovered and promising tokens. Ditto records this wallet and tracks it for other early entries into successful tokens, with each time increasing the score. After a threshold is met, the wallet is weighted a bit more and their "hops" are given more credence and value in the overall ranking system.
Price deviation-from-mean
Token prices are pinged periodically for average prices with deviations from the standardized line leading to an increase/decrease in their value. Volume deviations play a critical role in identifying promising tokens.
Average wallet metrics
Average wallet metrics such as win rate, hold time, ROI and generalized wallet category are critical in identifying potential opportunities early; if the early wallets are those that tend to be highly-profitable traders, a greater value is assigned in the overall ranking system.
Per-wallet post-returns
A metric to identify wallets that often lead to a market cap increase after their entry is used to identify probable price increases.
Relevant keyword matching
All launches are indexed and project names and tickers are matched against popular account tweets to identify early metas.
Daisy-chaining
High-value quick rotations.
It is imperative to note that each of these deep-reach analytics have several individualized metrics that are fed into them and analyzed in a way to convert them into the aforementioned metrics. Additionally, it is also imperative to note that each of these deep-reach examples are a singular gear in a much larger machine; there are many other metrics that are fed into the AI-formulated ranking system to ensure a broader and more precise token profile.
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