📄High Level Overview

High Level Overview on the architecture behind our ETH20-RNN V1 Model.

This section offers a comprehensive high-level explanation of how Smart AI leverages its proprietary technology to generate significant Alpha, showcasing its potential to revolutionize trading strategies through advanced data analysis and machine learning techniques. By delving into the mechanics behind our innovative algorithms, we aim to illustrate the sophisticated approach Smart AI employs to harness the power of blockchain data, ultimately empowering traders with actionable insights and a competitive edge in the market.

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1 - Data Gathering

Smart AI aggregates data from a diverse array of sources. It begins by retrieving raw data through our cluster of Ethereum nodes. Subsequently, this information is indexed to streamline subsequent stages of processing. Once indexed, pertinent data undergoes various processing methods to glean additional insights.

2 - Feature Extraction

In the feature extraction phase, Smart AI employs autoencoders, a specialized type of neural networks, to perform dimensionality reduction on the collected data. The primary aim of utilizing autoencoders in this context is to distill the vast datasets into a more manageable form by identifying and isolating the most pertinent features for analysis. This process significantly enhances the efficiency and effectiveness of subsequent analytical tasks.

3 - Wallet Categorization

Following feature extraction, we employ clustering algorithms to analyze wallet data, aiming to uncover intrinsic groupings. The objective is to discern distinct types of wallet behaviors, effectively identifying categories such as 'Smart Snipers' or 'Smart Money' wallets based on their transactional characteristics.

4 - Token Success Prediction

In our analysis, we employ advanced techniques starting with feature engineering to develop time-series features and aggregate metrics capturing key aspects like token transaction volumes and deployer interaction patterns. We then utilize sequence prediction models to discern and forecast temporal trends in token activities, leveraging historical data to predict future movements.

5 - Hybrid Integration and Continuous Learning

At this juncture, we integrate outputs from diverse predictive models to enhance the precision and robustness of our decisions. For instance, we merge the insights derived from the wallet classification clustering model with those from the token success prediction model. This multidimensional approach facilitates a more comprehensive and nuanced decision-making process. Furthermore, we have designed and implemented a systematic mechanism for the periodic retraining and updating of our models, ensuring they evolve in tandem with new data inflows.