ADCT is a validated approach to cryptocurrency trading that leverages the power of attention metrics to identify high-potential tokens before they gain mainstream traction. In the noisy crypto market, our fundamental philosophy is simple: Trade attention, not speculation.
By systematically quantifying attention across social platforms, analytics tools, and on-chain activity, ADCT provides a data-driven edge for early position entry.
Our attention scoring engine analyzes signals from dozens of sources to identify the rare tokens with genuine momentum — the 1% that matters among thousands of daily launches.
Our system quantifies attention through multiple channels, creating a comprehensive scoring mechanism that outperforms traditional indicators. When attention spikes occur across multiple sources, we identify high-potential opportunities within seconds.
Continuously monitor new token launches across Ethereum, BNB, Base, and Solana chains in real-time. Our system automatically indexes every new token as it appears, creating baseline attention profiles.
Apply our scoring algorithm to aggregate attention signals across all data channels. Advanced machine learning models normalize and weight each signal based on historical correlation with successful outcomes.
Analyze attention scores against dynamic thresholds. High-scoring tokens trigger automatic alerts for early position entry. The vast majority (99%) of tokens with low attention scores are filtered out.
Enter positions according to predefined parameters when attention thresholds are crossed. Our system monitors performance in real-time and automatically executes profit-taking strategies based on attention decay indicators.
ADCT is built on a high-performance, scalable architecture designed for sub-minute reaction times. By processing massive data streams in real-time on our high-end dedicated nodes, our system can identify attention spikes faster than manual traders or conventional algorithms.
Real-time data ingestion from social media APIs, blockchain nodes, and web scrapers for tools lacking public APIs.
Highly parallel ETL pipelines process raw data streams and calculate standardized metrics for attention scoring.
Machine learning models calculate comprehensive attention scores and detect anomalies in real-time.
Automated trading systems execute strategies based on attention signals while dashboard tools provide real-time performance monitoring.
We're accepting a limited number of participants for our closed beta program. Get early access to ADCT and take advantage of attention-driven trading strategies ahead of the crowd.