# quant-trade: A Full-Stack Cryptocurrency Quantitative Trading Agent System Based on Hermes-Agent

> quant-trade is a fully functional cryptocurrency quantitative trading agent integrating 40 tools, 14 skills, and 19 FRED macro indicators. It supports multi-exchange access, multi-channel integration, and manual confirmation workflows. This article provides an in-depth analysis of its architecture design, tool system, and practical application scenarios.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-18T05:42:25.000Z
- 最近活动: 2026-04-18T05:50:26.773Z
- 热度: 163.9
- 关键词: quant-trade, 加密货币, 量化交易, Hermes-Agent, AI Agent, MCP, ccxt, 宏观指标, FRED, 飞书
- 页面链接: https://www.zingnex.cn/en/forum/thread/quant-trade-hermes-agentagent
- Canonical: https://www.zingnex.cn/forum/thread/quant-trade-hermes-agentagent
- Markdown 来源: floors_fallback

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## Main Floor: Introduction to the quant-trade Full-Stack Cryptocurrency Quantitative Trading Agent System

quant-trade is a full-stack cryptocurrency quantitative trading agent system based on the Hermes-Agent framework. It integrates 40 tools, 14 skills, and 19 FRED macro indicators, supporting multi-exchange access, multi-channel interaction (CLI, HTTP API, Feishu Bot, etc.), and manual confirmation workflows. It builds an end-to-end trading system that balances automation and risk control.

## Background: Challenges of Cryptocurrency Quantitative Trading and Application of AI Agents

The cryptocurrency market features 24/7 trading, high volatility, and global liquidity, which provides opportunities for quantitative trading but imposes high requirements on systems. Traditional quantitative systems have high barriers (requiring professional programming and infrastructure setup). quant-trade redefines cryptocurrency quantitative trading with the AI Agent paradigm, introducing manual confirmation workflows to strike a balance between automation and risk control.

## Architecture Design: Layered Architecture Ensures Flexibility and Clarity

quant-trade adopts a layered architecture: The access layer supports CLI, HTTP API, Feishu Bot, MCP Server, and scheduled tasks; the core layer is the Hermes Agent decision-making center; the skill layer includes 14 skills responsible for strategy formulation and other tasks; the tool layer has 40 tools covering multiple modules; the execution layer connects to more than 10 mainstream exchanges (Binance, OKX, etc.) via ccxt, balancing flexibility and responsibility boundaries.

## Tool System: Covering Multi-Dimensional Trading Needs

The quant-trade tool system includes 15 modules: market data (K-lines, real-time prices, etc.) and technical indicators (MA, RSI, etc.); transaction execution (order placement, cancellation, etc.) and derivatives (funding rates, etc.); sentiment (fear and greed index, news sentiment); on-chain (TVL, whale transactions); macro (19 FRED indicators: interest rates, inflation, employment, etc.). Data sources include ccxt, Alternative.me, CoinGecko, DeFiLlama, FRED, etc.

## Skill System: Mechanism for Converting Data to Decisions

The 14 skills are divided into four categories: data acquisition (market-data, news-filter with two-level filtering); signal generation (signal-generator with multi-dimensional fusion, 5 professional analyst agents); execution and risk control (risk-manager with hard-coded rules, trade-executor with 6-step process, confirm-tools for manual confirmation); review and optimization (strategy-review for log analysis, backtest guidance, alert-notifier for warnings).

## Manual Confirmation Workflow: Risk Control Balance Point for Human-Machine Collaboration

Workflow: Agent generates a signal → creates a trading proposal → pushes to Feishu (including currency, direction, reasons, etc.) → user confirms/rejects; if confirmed, automatic order placement, stop-loss setting, and log recording are performed; if rejected, the reason is recorded to optimize the strategy. Feishu supports natural language interaction, lowering the operation threshold and balancing automation and human decision-making rights.

## Multi-Channel Access and Visualization: Flexible Interaction and Intuitive Monitoring

Supports CLI (terminal), HTTP API (FastAPI port 8899), Feishu Bot (group chat interaction), MCP Server (client call), and scheduled tasks (periodic scanning/review); the web visualization dashboard (ECharts) displays indicators such as net worth curves, K-lines (buy/sell points), profit and loss distribution, and real-time balances.

## Testing and Conclusion: System Reliability and Exploration of Agent-Based Quantification

The system includes 81 unit tests and a 6-stage end-to-end joint debugging pipeline (covering market data acquisition, macro integration, etc.) to ensure reliability; quant-trade is a deep case of vertical application of AI Agents, not a simple ChatGPT wrapper, but a full-life-cycle trading system. The macro module and manual confirmation are worth attention, providing a reference implementation for developers/traders.