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LLM-TradeBot

A multi-agent AI trading system using LLMs to optimize strategies and adapt to market conditions in real-time.

시스템 구조

활용 데이터
BinanceBybitOKXHyperliquid가격/거래량 데이터
규칙 / 전략

저장소별 전략/실행 조건 확인 필요

실행

거래소 API 기반 자동 주문 실행

에디터 요약

저장소 설명과 공개 메타데이터 기준으로 AI/LLM 또는 머신러닝 활용, Python 기반 구현, 암호화폐 거래 봇, 거래소 API/프레임워크 성격의 프로젝트로 파악했습니다. 확인 근거는 README, 저장소 토픽, 저장소 설명, GitHub 지표, 파이프라인 필드이며, 주요 데이터는 Binance, Bybit, OKX, Hyperliquid, 가격/거래량 데이터입니다. 전략/실행 조건은 저장소별 문서와 코드 확인이 필요하며, 실행 방식은 "거래소 API 기반 자동 주문 실행"라고 보수적으로 기록했습니다. GitHub 지표는 별 298개, 포크 74개입니다.

저장소EthanAlgoX/LLM-TradeBot
제작자EthanAlgoX
스타 / 포크★ 298 / 74
라이선스MIT
최근 업데이트2026-04-16
스냅샷 시점2026-07-08 (아래 README는 이 시점의 사본입니다)

아래는 제3자가 작성·공개한 오픈소스 코드입니다. QuantField는 해당 코드의 동작과 안전성을 보증하지 않으며, 설치·실행 전 코드를 직접 검토하시기 바랍니다.

GitHub에서 보기 →

README

🤖 LLM-TradeBot

English 简体中文

Adversarial Intelligence Framework

Intelligent Multi-Agent Quantitative Trading Bot based on the Adversarial Decision Framework (ADF). Achieves high win rates and low drawdown in automated futures trading through market regime detection, price position awareness, dynamic score calibration, and multi-layer physical auditing.

Python License Framework X Follow

🌐 Web App (Recommended)

Experience the bot immediately through our web interface: 👉 Live Dashboard

Dashboard Highlights

  • LLM toggle stays off by default; turning it on prompts for an API key.
  • Agent Chatroom shows per-cycle agent outputs and the final Decision Core action.
  • Real-time Balance Curve uses a fixed initial balance and PnL-driven current balance.
  • Agent Config lets you edit per-agent parameters and (if applicable) system prompts.

✨ Key Features

  • 🕵️ Perception First: Unlike strict indicator-based systems, this framework prioritizes judging "IF we should trade" before deciding "HOW to trade".
  • 🤖 Multi-Agent Collaboration: Core + optional agents with LLM and Local variants for flexible deployment.
  • 🎛️ Agent Configuration: Enable/disable optional agents via Dashboard, environment variables, or config file for customized strategy.
  • 💬 Agent Chatroom: Chat-style multi-agent outputs per cycle, with Decision Core final decisioning.
  • 🧩 Agent Config Tabs: Configure per-agent parameters and optional system prompts directly in the Dashboard.
  • 🎰 AUTO1 Symbol Selection: Intelligent single-symbol selection based on momentum, volume, and technical indicators.
  • 🧠 Multi-LLM Support: Seamlessly switch between DeepSeek, OpenAI, Claude, Qwen, and Gemini via Dashboard settings.
  • 📊 Multi-Account Trading: Manage multiple exchange accounts with unified API abstraction (currently Binance, extensible).
  • ⚡ Async Concurrency: Currently fetches multi-timeframe data (5m/15m/1h) concurrently, ensuring data alignment at the snapshot moment.
  • 🖥️ CLI Headless Mode: Run without Web UI for headless servers - rich terminal output with 93% less log verbosity.
  • 🧪💰 Test/Live Mode Toggle: Quick switch between paper trading and live trading with visual confirmation.
  • 🛡️ Safety First: Stop-loss direction correction, capital pre-rehearsal, and veto mechanisms to safeguard live trading.
  • 📊 Full-Link Auditing: Every decision's adversarial process and confidence penalty details are recorded, achieving true "White-Box" decision-making.

🏗️ System Architecture Overview

Multi-Agent Architecture (Current)

flowchart TD
  A["🎯 Symbol Selector"] --> B["🕵️ DataSync (5m/15m/1h)"]
  B --> C["👨‍🔬 Quant Analyst"]
  C --> D["🧭 Multi-Period Parser"]
  C --> E["🔮 Trend / 📊 Setup / ⚡ Trigger (LLM or Local)"]
  C --> F["🪞 Reflection (optional)"]
  D --> G["⚖️