# Architecture
At a high level, Hedgewater’s architecture consists of five interconnected components that work together to deliver autonomous trading capabilities: *** #### 1. User Interface & Agent Management (Frontend Hub) The primary interaction point where users design and configure their trading agents through an intuitive dashboard. * **Agent Configuration**: Users define agent parameters including agent name, trading style, value ranges, portfolio tokens, trading frequency, and custom instructions that guide the agent's behavior. * **Monitoring Dashboard**: Real-time visualization of agent performance, portfolio composition, trading history, and profitability metrics across all active agents. This component translates user intentions into structured agent configurations while offering ongoing visibility into autonomous trading operations. *** #### 2. Core Application Engine (Backend Intelligence) The central nervous system that manages all agent data, user authentication, and system orchestration. * **Agent Lifecycle Management**: Handles creation, modification, activation, and deactivation of trading agents with secure data persistence. * **Privy Wallet Integration**: Automatically provisions dedicated wallets for each agent, ensuring isolated fund management and secure transaction capabilities. * **API Gateway**: Provides secure endpoints for frontend communication and manages system-wide authentication and authorization. This component serves as the intelligent coordinator that maintains system state and enables secure operations across the platform. *** #### 3. Autonomous Execution Engine (Agent Worker) The scheduling and execution powerhouse that brings trading agents to life through continuous monitoring and activation. * **Smart Scheduler**: Continuously polls agent configurations to identify when agents are due for execution based on their defined frequencies (e.g., every 30 minutes, hourly, etc.). * **Parallel Processing**: Manages concurrent execution of multiple agents, handling resource allocation, error recovery, and execution logging. This component ensures that agents operate autonomously according to their schedules without manual intervention—providing the "always-on" trading capability. *** #### 4. Trading Intelligence System (AI Decision Engine) The analytical brain that processes market data and makes informed trading decisions for each agent. * **Technical Analysis Engine**: Accesses real-time technical indicators (e.g., MACD, RSI, VWAP, SMA, EMA) and portfolio analytics to understand current market conditions and agent positions. * **Decision Logic**: Processes agent instructions, trading style preferences, and market indicators through AI to determine optimal trading actions. * **Risk Management**: Enforces trading value limits and style constraints to ensure agents operate within defined parameters. This component transforms raw market data and user instructions into actionable trading decisions that align with each agent's unique strategy. *** #### 5. Transaction Execution Infrastructure (Trading Tools) The operational backbone that executes trading decisions and maintains portfolio integrity. * **Portfolio Analysis Tool**: Provides real-time insights into token holdings, portfolio composition, and current valuations across agent wallets. * **Market Data Integration**: Fetches live technical indicators and price feeds to support informed decision-making. * **Token Swap Engine**: Executes actual trades between tokens in agent portfolios, handling transaction signing, execution, and confirmation. This component bridges the gap between trading decisions and actual market execution, ensuring that AI-generated strategies are properly implemented in live trading environments. *** Hedgewater’s architecture creates an autonomous trading ecosystem where user intentions are transformed into intelligent, self-executing trading agents that operate continuously in cryptocurrency markets while maintaining security, transparency, and user control. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://hedgewaters-organization.gitbook.io/hedgewater-associates/architecture.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.