Real-Time Data Ingestion

At the core of Nexgent’s infrastructure is a real-time WebSocket connection to RPC nodes, subscribing directly to decentralized exchange (DEX) trade activity. The system captures live trade data and feeds it into an internal processing pipeline designed for high-throughput event handling.

Data Orchestration and Processing

Incoming trade data is first managed by the Data Orchestrator and In-Memory Buffer, which queues and batches transactions for efficient downstream processing. A Batch Processor organizes trades into logical sets, splitting them into buy and sell flows based on pre-defined time windows.

Trade batches are then fed into the Sliding Window Tracker, which aggregates trading activity across tokens, maintaining real-time rolling metrics such as total volume, number of trades, and price movements on a configurable sliding window basis.

Token Analysis and Strategy Execution

Batched and aggregated token data is continuously monitored by the Bucket Monitor, which triggers strategy evaluation once sufficient trade data is collected. Tokens are queued and analyzed at high frequency (every 10ms) to ensure near real-time responsiveness.

The Strategy Manager executes AI-driven and rule-based strategies on candidate tokens, evaluating trading opportunities based on a combination of volume patterns, price momentum, and market microstructure signals. Results are returned for further signal processing.

Trade Signal Generation

Validated strategy outcomes are funneled into the Signal Manager, which is responsible for generating, updating, and managing trade signals. Each trade signal adheres to a structured alert format that includes key attributes like balance change thresholds, buy/sell triggers, price changes, and trading volume shifts.

The Signal Manager performs deduplication, validation, and conflict resolution to ensure the quality and uniqueness of active trading signals.

Trade Execution and Risk Management

Upon generation of a valid trade signal, the system orchestrates trade execution and risk controls through multiple specialized modules:

• Token Price Manager: Validates token prices, ensures execution precision, and triggers stop-loss actions where applicable.

• Agent Trade Manager: Matches trade signals to eligible AI agents with sufficient on-chain balance, calculates position sizes, and issues trade instructions.

• Stop Loss Manager: Monitors trades post-execution to enforce risk controls such as fixed stop-loss and trailing stop-loss strategies.

• Position Calculator: Dynamically determines position sizing based on an agent’s available balance, implementing tiered systems to scale position size relative to asset pool.

Persistent Storage and Data Access Layer

All trade data, signal logs, and agent activities are persistently stored through the Database Layer, with clear separation of concerns via:

• Models: Define data structures and schemas.

• Repositories: Manage data access and business logic across trades, agents, and transaction histories.

This architecture ensures a scalable, modular backend capable of supporting millions of daily trades, real-time signal generation, and comprehensive trade lifecycle management.

Design Philosophy

Nexgent’s platform architecture emphasizes:

• High-frequency, low-latency data ingestion to capture and react to market movements in real-time.

• Modular design for scalability, maintainability, and future cross-chain expansion.

• Strong separation of concerns between data orchestration, strategy evaluation, and execution management.

• Advanced risk management built into every stage of the signal-to-execution flow.

This robust and flexible foundation powers Nexgent’s mission to deliver scalable, intelligent, and secure autonomous trading infrastructure for decentralized markets.