Proprietary temporal graph algorithms identify statistically significant transactional recurrence patterns across mempools and confirmed ledgers — forecasting loop reactivation with Bayesian confidence intervals.
Each detected loop is stress-tested against 7 years of market microstructure data. Outputs include Amplification Factor (σ), Expected Basis Point Movement (Δbp), and Liquidity Shock Probability.
Generates gas-efficient, slippage-minimized arbitrage pathways across 22 DEX/CEX venues. Simulates execution under variable latency conditions and recommends optimal order splitting strategies.
Real-time ingestion from 14 chain nodes (Ethereum, Solana, Arbitrum, Base, etc.) normalized into unified temporal hypergraph schema. Timestamp precision: ±50ns using TAI64N with NTP stratum-1 synchronization.
Modified Transformer-XL with spatio-temporal attention layers identifies isomorphic subgraphs. Trained on 8.4PB of historical chain data. Statistical significance threshold: p < 0.005 (Bonferroni-corrected).
Each loop receives dual scoring: Probability of Reoccurrence (0–100%) and Market Impact Magnitude (1–10x). Only signals exceeding 92% combined confidence are surfaced to users.
10,000-iteration simulation per loop. Outputs: Sharpe Ratio, Value-at-Risk (95% CI), Kelly-optimal position sizing, and latency-adjusted entry/exit triggers. Backtested accuracy: 92.7%.
Perpetual access to full predictive engine, priority alert queue, API integration, and all future model upgrades. License cryptographically bound to your wallet address.
“Integrating CryptoLoops increased our quarterly arbitrage ROI by 22%. Its ability to predict micro-liquidity cascades is unmatched. We’ve discontinued three competing analytics platforms.”
“The ‘loop’ abstraction is genius. It’s not technical analysis — it’s behavioral topology. We caught a 4.7% ETH/USDC opportunity 11 minutes before liquidity providers reacted.”