OctiFi Architecture Overview

High-level OctiFi architecture and decision flow

OctiFi’s decision stack is structured as eight cooperating layers:

Data Aggregation — multi-source ingestion from exchanges, on-chain data, order books, and liquidity pools
Sentiment Intelligence — NLP over news, social, and discourse streams
Predictive Forecasting — ensemble LSTM sequence models for directional signals
Model Fusion — stacking/boosting to cross-validate and de-noise predictions
Risk Management — position sizing, drawdown guards, Sharpe-optimized allocation
Strategy Orchestration — policy selects momentum, mean-reversion, arbitrage, hedging
Smart Execution — routing across DEXs with slippage and gas optimization; MEV-aware
Continuous Learning — reinforcement loop updates policies from realized outcomes

Data flows left-to-right, while feedback flows right-to-left. The result is a self-improving trading agent that is both precise and resilient.

Key properties

Consensus signals: No single model dominates; signals must clear fusion thresholds
Explicit risk guards: Exposure caps, stop triggers, and volatility-aware sizing
Market microstructure aware: Slippage estimates and pathfinding influence viability
Online adaptation: Recent performance shifts policy weights and exploration rate