🤖 LLM-TradeBot

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.

🌐 Web App (Recommended)

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

✨ Key Features

🕵️ Perception First: Unlike strict indicator-based systems, this framework prioritizes judging "IF we should trade" before deciding "HOW to trade".
🤖 17-Agent Collaboration: 17 highly specialized Agents (3 core + 14 optional) with LLM and Local variants for flexible deployment.
🎛️ Agent Configuration: Enable/disable optional agents via Dashboard, environment variables, or config file for customized strategy.
🎰 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

Data Flow Architecture

The system uses a Multi-Layer Agent Architecture where data flows through specialized agents:

📡 Data Layer: SymbolSelectorAgent → DataSyncAgent (5m/15m/1h market data)
📊 Analysis Layer: QuantAnalystAgent, RegimeDetectorAgent, TriggerDetectorAgent, PositionAnalyzerAgent
🔮 ML Prediction: PredictAgent with LightGBM model
🧠 Semantic Strategy: TrendAgent (1h) + SetupAgent (15m) + TriggerAgent (5m)
⚖️ Decision Layer: DecisionCoreAgent with Bull vs Bear adversarial debate
🛡️ Risk Audit: RiskAuditAgent with absolute veto power
🚀 Execution: ExecutionEngine manages orders
🪞 Learning: ReflectionAgent analyzes trade history for continuous improvement

Detailed Flowchart

📖 Detailed Docs: See Data Flow Analysis for complete mechanisms.

🤝 Supported Ecosystem

Supported Exchanges

CEX (Centralized Exchanges)

Exchange	Status	Register (Fee Discount)
Binance	✅ Supported	Register
Bybit	🗓️ Coming Soon	Register
OKX	🗓️ Coming Soon	Register
Bitget	🗓️ Coming Soon	Register

Perp-DEX (Decentralized Perpetual Exchanges)

Exchange	Status	Register (Fee Discount)
Hyperliquid	🗓️ Coming Soon	Register
Aster DEX	🗓️ Coming Soon	Register
Lighter	🗓️ Coming Soon	Register

Supported AI Models

AI Model	Status	Get API Key
DeepSeek	✅ Supported	Get API Key
Qwen	✅ Supported	Get API Key
OpenAI (GPT)	✅ Supported	Get API Key
Claude	✅ Supported	Get API Key
Gemini	✅ Supported	Get API Key
Grok	🗓️ Coming Soon	Get API Key
Kimi	🗓️ Coming Soon	Get API Key

📚 What You Need to Know

For Complete Beginners:

This is an automated trading bot that trades cryptocurrency futures on Binance
It uses AI (LLM) and machine learning to make trading decisions
Test mode lets you practice with virtual money before risking real funds
The bot runs 24/7 and makes decisions based on market analysis

Technical Level: Intermediate Python knowledge recommended but not required for basic usage.

🚀 Quick Start (One-Click Installation)

🎯 Recommended: One-Click Installation

No need to manually configure Python environment! Use our automated installation scripts:

Method 1: Local Installation (Development)

# 1. Clone the project
git clone <your-repo-url>
cd LLM-TradeBot

# 2. One-click install
chmod +x install.sh
./install.sh

# 3. Configure API keys
vim .env  # Edit and add your API keys

# 4. One-click start
./start.sh

Visit Dashboard: http://localhost:8000

Method 2: Docker Deployment (Production)

# 1. Clone the project
git clone <your-repo-url>
cd LLM-TradeBot

# 2. Configure environment
cp .env.example .env
vim .env  # Edit and add your API keys

# 3. One-click start
cd docker && docker-compose up -d

📖 Detailed Guide: See QUICKSTART.md

⚙️ Prerequisites

Before you start, make sure you have:

For One-Click Installation (Recommended)

✅ Git installed (Download here)
✅ Python 3.11+ OR Docker (installation script will check)

For Test Mode (Beginners)

✅ Nothing else needed! Test mode uses virtual balance

For Live Trading (Advanced)

✅ Binance Account (Sign up here)
✅ Binance Futures API Keys with trading permissions
✅ USDT in Futures Wallet (minimum $100 recommended)
⚠️ Risk Warning: Only trade with money you can afford to lose

🧠 LLM Configuration (Multi-Provider Support)

The bot supports 5 LLM providers. Configure via environment variables or Dashboard Settings:

Supported Providers

Provider	Model	Cost	Speed	Get API Key
DeepSeek (Recommended)	deepseek-chat	💰 Low	⚡ Fast	platform.deepseek.com
OpenAI	gpt-4o, gpt-4o-mini	💰💰💰 High	⚡ Fast	platform.openai.com
Claude	claude-3-5-sonnet	💰💰 Medium	⚡ Fast	console.anthropic.com
Qwen	qwen-turbo, qwen-plus	💰 Low	⚡ Fast	dashscope.console.aliyun.com
Gemini	gemini-1.5-pro	💰 Low	⚡ Fast	aistudio.google.com

Configuration Methods

Method 1: Environment Variables (Recommended)

Edit your .env file:

# Select LLM Provider (required)
LLM_PROVIDER=deepseek  # Options: deepseek, openai, claude, qwen, gemini

# Configure API Key for your selected provider
DEEPSEEK_API_KEY=sk-xxx     # if using DeepSeek
OPENAI_API_KEY=sk-xxx       # if using OpenAI
CLAUDE_API_KEY=sk-xxx       # if using Claude
QWEN_API_KEY=sk-xxx         # if using Qwen
GEMINI_API_KEY=xxx          # if using Gemini

Method 2: Dashboard Settings

Open Dashboard at http://localhost:8000
Click ⚙️ Settings → API Keys tab
Select your preferred LLM provider and enter API key
Click Save - changes apply on next trading cycle

Method 3: Config File (config.yaml)

llm:
  provider: "deepseek"  # or: openai, claude, qwen, gemini
  model: "deepseek-chat"  # provider-specific model
  temperature: 0.3
  max_tokens: 2000
  api_keys:
    deepseek: "sk-xxx"
    openai: "sk-xxx"
    # ... other providers

📖 Manual Installation (Advanced)

If you prefer manual setup:

1. Install Dependencies

pip install -r requirements.txt

2. Configure Environment

# Copy environment variable template
cp .env.example .env

# Set API Keys
./set_api_keys.sh

3. Configure Trading Parameters

# Copy config template
cp config.example.yaml config.yaml

Edit config.yaml to set parameters:

Trading pair (symbol)
Max position size (max_position_size)
Leverage (leverage)
Stop loss/Take profit % (stop_loss_pct, take_profit_pct)

⚙️ Dashboard Settings

You can also configure all settings from the Dashboard:

Settings Modal with 4 tabs: API Keys (LLM Provider), Accounts (Multi-Account), Trading, Strategy (Prompt)

4. Start the Bot

Built-in modern real-time monitoring dashboard.

🧪 Test Mode (Recommended for beginners)

Simulates trading with virtual balance ($1000). No real trades executed.

# Start with test mode
python main.py --test --mode continuous

🖥️ CLI Headless Mode (For Servers)

Run the bot without Web Dashboard, perfect for headless servers or terminal-only environments.

# Basic CLI mode (manual start required)
python main.py --test --headless

# Auto-start mode (trading begins immediately)
python main.py --test --headless --auto-start

# Custom interval (1 minute cycles)
python main.py --test --headless --auto-start --interval 1

Features:

✅ No Web UI - runs entirely in terminal
✅ Rich formatted output with colors and tables
✅ Real-time price updates and trading decisions
✅ Account summary panel after each cycle
✅ Graceful shutdown with session statistics (Ctrl+C)
✅ Optimized log output (93% less verbose than Web mode)

Output Example:

╔═══════════════════════════════════════════════════════╗
║ 🤖 LLM-TradeBot CLI - TEST MODE                      ║
╚═══════════════════════════════════════════════════════╝

─────────── Cycle #1 | LINKUSDT, NEARUSDT ────────────
  🔍 Analyzing LINKUSDT...
  ✅ Data ready: $13.29
  
  ⏸️  HOLD | Confidence: 45.0%
     Reason: No clear 1h trend

╭─────────────── Account Summary ───────────────────╮
│ 💰 Equity:    $1,000.00                           │
│ 📊 Available:   $900.00                           │
│ 📈 PnL:          $0.00 (0.00%)                    │
╰───────────────────────────────────────────────────╯

  ⏳ Next cycle in 1.0 minutes...

🚀 Simplified CLI Mode (Live Trading)

For production live trading, use the simplified CLI script that skips non-essential components:

# Activate virtual environment first
source venv/bin/activate

# Test mode - single run
python simple_cli.py --mode once

# Test mode - continuous (3-minute intervals)
python simple_cli.py --mode continuous --interval 3

# LIVE mode - continuous trading (⚠️ REAL MONEY)
python simple_cli.py --mode continuous --interval 3 --live

# Custom symbols (overrides .env)
python simple_cli.py --mode continuous --symbols BTCUSDT,ETHUSDT --live

# AUTO3 mode - automatic symbol selection
python simple_cli.py --mode continuous --symbols AUTO3 --live

Features:

✅ Minimal footprint - only core trading components loaded
✅ Production-ready - designed for stable 24/7 operation
✅ AUTO3 support - automatic best symbol selection via backtest
✅ LLM integration - full multi-agent decision system
✅ Risk management - built-in risk audit and position limits
✅ Graceful shutdown - Ctrl+C for clean exit

Configuration:

The script reads trading symbols from .env file by default:

# In your .env file
TRADING_SYMBOLS=BTCUSDT,ETHUSDT
# Or use AUTO3 for automatic selection
TRADING_SYMBOLS=AUTO3

⚠️ Live Trading Prerequisites:

Valid Binance Futures API keys in .env
Sufficient USDT balance in Futures wallet
API permissions: Read + Futures Trading enabled
DeepSeek/OpenAI API key for LLM decisions

🔴 Live Trading Mode (Web Dashboard)

⚠️ WARNING: Executes real trades on Binance Futures!

# Start live trading
python main.py --mode continuous

Prerequisites for Live Trading:

Valid Binance Futures API keys configured in .env

Sufficient USDT balance in Futures wallet

API permissions: Read + Futures Trading enabled

After startup, visit: http://localhost:8000 (or use our Cloud Hosting)

Dashboard Features:

🧪💰 Test/Live Mode Toggle: Quick switch between paper trading and real trading with visual confirmation
📈 K-Line Chart: TradingView Lightweight Charts with real-time candlestick updates, auto-synced to current symbol
💰 Account Summary Panel: Real-time display of wallet balance, available balance, equity, initial capital, PnL, and position details
🤖 Multi-Agent Decision Framework: Visual flow diagram showing all 15 agents organized by layer:
- 📡 Data Layer: DataSync Agent, Symbol Selector
- 📊 Analysis Layer: Quant Analyst, Regime Detector, Trigger Detector, Position Analyzer, Predict Agent
- 🧠 LLM Strategy Layer: Trend Agent, Trigger Agent, AI Filter
- ⚖️ Decision Layer: Decision Core with Bull/Bear debate visualization
- 🛡️ Execution Layer: Risk Audit, Final Output, Reflection Agent
🎛️ Agent Selection Panel: Configure optional agents via Settings → Agents tab with checkboxes
📡 Agent Activity Feed: Real-time event stream showing agent status updates
📜 Trade History: Complete record of all trades with Open/Close cycles and PnL statistics
📋 Live Log Output: Real-time scrolling logs with agent documentation sidebar, simplified/detailed mode toggle

📋 Recent Decisions Indicator Guide

All indicators use semantic icons and two-line display format for quick visual scanning:

📊 System Columns

Time: Decision timestamp
Cycle: Trading cycle number
Symbol: Trading pair (e.g., BTCUSDT)

⚖️ Critic (Decision Core)

Result: Final action (LONG/SHORT/WAIT)
Conf: Decision confidence (0-100%)
Reason: Decision rationale (hover for full text)

👨‍🔬 Strategist (Quant Analysis)

1h/15m/5m: Multi-timeframe signals
- Format: T:UP (Trend) / O:DN (Oscillator)
- Colors: Green (UP), Red (DN), Gray (NEU)
Sent: Sentiment score with icon (📈/📉/➖)

🔮 Prophet (ML Prediction)

Format: 🔮↗ + 65%
Direction: ↗UP (>55%), ➖NEU (45-55%), ↘DN (<45%)

🐂🐻 Bull/Bear (Adversarial Analysis)

Bull: ↗Bull / 🔥Bull + confidence %
Bear: ↘Bear / 🔥Bear + confidence %
Stance: 🔥Strong, ↗Slight, ➖Neutral, ❓Unclear

🌍 Context (Market State)

Regime: 📈UP / 📉DN / 〰️CHOP
Position: 🔝HIGH / ➖MID / 🔻LOW + percentage

🛡️ Guardian (Risk Control)

Risk: ✅SAFE / ⚠️WARN / 🚨DANGER
Guard: ✅PASS / ⛔BLOCK (with reason on hover)
Aligned: ✅ Multi-period aligned / ➖ Not aligned

5. Common Operations

# Stop the bot
pkill -f "python main.py"

# Restart the bot (Test Mode)
pkill -f "python main.py"; sleep 2; python main.py --test --mode continuous

# View running processes
ps aux | grep "python main.py"

# View logs in terminal (if running in background)
tail -f logs/trading_$(date +%Y%m%d).log

📁 Project Structure

Directory Tree

Directory Description

LLM-TradeBot/
├── src/                    # Core Source Code
│   ├── agents/            # Multi-Agent Definitions (DataSync, Quant, Decision, Risk)
│   ├── api/               # Binance API Client
│   ├── data/              # Data Processing (processor, validator)
│   ├── exchanges/         # 🆕 Multi-Account Exchange Abstraction
│   │   ├── base.py       # BaseTrader ABC + Data Models
│   │   ├── binance_trader.py  # Binance Futures Implementation
│   │   ├── factory.py    # Exchange Factory
│   │   └── account_manager.py # Multi-Account Manager
│   ├── execution/         # Order Execution Engine
│   ├── features/          # Feature Engineering
│   ├── llm/               # 🆕 Multi-LLM Interface
│   │   ├── base.py       # BaseLLMClient ABC
│   │   ├── openai_client.py  # OpenAI Implementation
│   │   ├── deepseek_client.py # DeepSeek Implementation
│   │   ├── claude_client.py  # Anthropic Claude
│   │   ├── qwen_client.py    # Alibaba Qwen
│   │   ├── gemini_client.py  # Google Gemini
│   │   └── factory.py    # LLM Factory
│   ├── monitoring/        # Monitoring & Logging
│   ├── risk/              # Risk Management
│   ├── strategy/          # LLM Decision Engine
│   └── utils/             # Utilities (DataSaver, TradeLogger, etc.)
│
├── docs/                  # Documentation
│   ├── data_flow_analysis.md          # Data Flow Analysis
│   └── *.png                          # Architecture & Flow Diagrams
│
├── data/                  # Structured Data Storage (Archived by Date)
│   ├── market_data/       # Raw K-Line Data
│   ├── indicators/        # Technical Indicators
│   ├── features/          # Feature Snapshots
│   ├── decisions/         # Final Decision Results
│   └── execution/         # Execution Records
│
├── config/                # Configuration Files
│   └── accounts.example.json  # 🆕 Multi-Account Config Template
│
├── logs/                  # System Runtime Logs
├── tests/                 # Unit Tests
│
├── main.py                # Main Entry Point (Multi-Agent Loop)
├── config.yaml            # Trading Parameters
├── .env                   # API Key Configuration
└── requirements.txt       # Python Dependencies

🎯 Core Architecture

17-Agent Collaborative Framework + Four-Layer Strategy

The system uses a Four-Layer Strategy Filter architecture with 17 specialized Agents collaborating to make trading decisions. Core agents are always enabled, while optional agents can be configured via Dashboard or config.yaml.

Key Feature: Agents have LLM and Local variants - LLM versions use AI for semantic analysis, while Local versions use fast rule-based heuristics.

Core Agents (Always Enabled)

Agent	Role	Responsibility
🕵️ DataSyncAgent	The Oracle	Async concurrent fetch of 5m/15m/1h K-lines, ensuring snapshot consistency
👨‍🔬 QuantAnalystAgent	The Strategist	Generates trend scores, oscillators, sentiment, and OI Fuel (Volume Proxy)
🛡️ RiskAuditAgent	The Guardian	Risk audit with absolute veto power on all trades

Symbol Selection Layer (Optional)

Agent	Role	Responsibility
🎰 SymbolSelectorAgent	AUTO1/3 Selector	Two-stage backtest selection: AI500 Top10 + Majors → Top 5 (1h) → Top 2 (15m)

Prediction & Analysis Layer (Optional)

Agent	Role	Responsibility
🎯 PredictAgent	The Prophet	Predicts price probability using LightGBM ML model (auto-retrain every 2h)
🤖 AIPredictionFilterAgent	AI Validator	AI-Trend alignment verification with veto power
🔮 RegimeDetectorAgent	Regime Analyzer	Detects market state (Trending/Choppy/Ranging) and ADX strength
📍 PositionAnalyzerAgent	Position Tracker	Price position analysis (High/Mid/Low zone) and S/R level detection
⚡ TriggerDetectorAgent	Entry Scanner	5m pattern detection and trigger signal scoring

Semantic Analysis Layer (LLM or Local)

Agent	LLM Version	Local Version	Responsibility
📈 TrendAgent	`TrendAgentLLM`	`TrendAgent`	1h trend semantic analysis (UPTREND/DOWNTREND)
📊 SetupAgent	`SetupAgentLLM`	`SetupAgent`	15m setup zone analysis (KDJ, Bollinger Bands, entry zones)
🔥 TriggerAgent	`TriggerAgentLLM`	`TriggerAgent`	5m trigger signal analysis (CONFIRMED/WAITING)

Decision & Execution Layer

Agent	Role	Responsibility
⚖️ DecisionCoreAgent	The Critic	LLM Bull/Bear debate decision engine with confidence scoring
🚀 ExecutionEngine	The Executor	Precision order execution and state management
🪞 ReflectionAgent	The Philosopher	Trade reflection every 10 trades (LLM or Local variant)

Agent Configuration

Agents can be configured in multiple ways (priority order):

Dashboard Settings → Agents tab with checkboxes for each optional agent
Environment Variables → AGENT_<NAME>=true/false (e.g., AGENT_PREDICT_AGENT=false)
config.yaml → agents: section

# config.yaml example
agents:
  # Prediction & Analysis
  predict_agent: true              # ML probability prediction
  ai_prediction_filter_agent: true # AI veto mechanism
  regime_detector_agent: true      # Market state detection
  position_analyzer_agent: false   # Price position analysis
  trigger_detector_agent: true     # 5m pattern detection
  
  # Semantic Analysis - LLM variants (expensive, disabled by default)
  trend_agent_llm: false           # 1h trend LLM analysis
  setup_agent_llm: false           # 15m setup LLM analysis
  trigger_agent_llm: false         # 5m trigger LLM analysis
  
  # Semantic Analysis - Local variants (fast, enabled by default)
  trend_agent_local: true          # 1h trend rule-based analysis
  setup_agent_local: true          # 15m setup rule-based analysis
  trigger_agent_local: true        # 5m trigger rule-based analysis
  
  # Reflection
  reflection_agent_llm: false      # Trade reflection via LLM
  reflection_agent_local: true     # Trade reflection via rules
  
  # Symbol Selection
  symbol_selector_agent: true      # AUTO symbol selection

Four-Layer Strategy Filter

Layer 1: Trend + Fuel (1h EMA + Volume Proxy)
    ↓ PASS/FAIL
Layer 2: AI Filter (PredictAgent direction alignment)
    ↓ PASS/VETO
Layer 3: Setup (15m KDJ + Bollinger Bands entry zone)
    ↓ READY/WAIT
Layer 4: Trigger (5m Pattern + RVOL volume confirmation)
    ↓ CONFIRMED/WAITING
    ↓
🧠 LLM Decision (DeepSeek Bull/Bear Debate)
    ↓
🛡️ Risk Audit (Veto Power)
    ↓
🚀 Execution

Data Flow Diagrams

📖 See System Architecture Overview section above for visual diagrams.

📐 Mermaid Diagram (Interactive)

graph TB
    subgraph "1️⃣ Data Collection Layer"
        A["🕵️ DataSyncAgent<br/>(The Oracle)"] --> MS["MarketSnapshot<br/>5m/15m/1h K-lines"]
    end
    
    subgraph "2️⃣ Quant Analysis Layer"
        MS --> QA["👨‍🔬 QuantAnalystAgent<br/>(The Strategist)"]
        QA --> TS["📈 TrendSubAgent"]
        QA --> OS["📊 OscillatorSubAgent"]
        QA --> SS["💹 SentimentSubAgent"]
        TS & OS & SS --> QR["Quant Signals"]
    end

    subgraph "3️⃣ Prediction Layer"
        MS --> PA["🔮 PredictAgent<br/>(The Prophet)"]
        PA --> ML["LightGBM Model<br/>Auto-Train 2h"]
        ML --> PR["P_Up Prediction"]
    end

    subgraph "4️⃣ Bull/Bear Adversarial Layer"
        MS --> BULL["🐂 Bull Agent<br/>(The Optimist)"]
        MS --> BEAR["🐻 Bear Agent<br/>(The Pessimist)"]
        BULL --> BP["Bull Perspective"]
        BEAR --> BRP["Bear Perspective"]
    end
    
    subgraph "5️⃣ Reflection Layer"
        TH["📜 Trade History<br/>Last 10 Trades"] --> REF["🧠 ReflectionAgent<br/>(The Philosopher)"]
        REF --> RI["Reflection Insights<br/>Patterns & Recommendations"]
    end
    
    subgraph "6️⃣ Decision Layer"
        QR & PR & BP & BRP & RI --> DC["⚖️ DecisionCoreAgent<br/>(The Critic)"]
        DC --> RD["RegimeDetector"]
        DC --> POS["PositionAnalyzer"]
        RD & POS --> VR["VoteResult<br/>Action + Confidence"]
    end
    
    subgraph "7️⃣ Risk Audit Layer"
        VR --> RA["🛡️ RiskAuditAgent<br/>(The Guardian)"]
        RA --> AR["AuditResult<br/>Risk Level + Guard"]
    end
    
    subgraph "8️⃣ Execution Layer"
        AR --> EE["🚀 ExecutionEngine<br/>(The Executor)"]
        EE -.->|"Trade Complete"| TH
    end
    
    %% Styling for Agent Nodes
    style A fill:#4A90E2,color:#fff,stroke:#2563EB,stroke-width:2px
    style QA fill:#7ED321,color:#fff,stroke:#059669,stroke-width:2px
    style PA fill:#BD10E0,color:#fff,stroke:#9333EA,stroke-width:2px
    style BULL fill:#F8E71C,color:#333,stroke:#CA8A04,stroke-width:2px
    style BEAR fill:#F8E71C,color:#333,stroke:#CA8A04,stroke-width:2px
    style REF fill:#00CED1,color:#fff,stroke:#0891B2,stroke-width:2px
    style DC fill:#F5A623,color:#fff,stroke:#EA580C,stroke-width:2px
    style RA fill:#D0021B,color:#fff,stroke:#DC2626,stroke-width:2px
    style EE fill:#9013FE,color:#fff,stroke:#7C3AED,stroke-width:2px
    
    %% Styling for Output Nodes
    style MS fill:#1E3A5F,color:#fff
    style QR fill:#1E3A5F,color:#fff
    style PR fill:#1E3A5F,color:#fff
    style BP fill:#1E3A5F,color:#fff
    style BRP fill:#1E3A5F,color:#fff
    style RI fill:#1E3A5F,color:#fff
    style VR fill:#1E3A5F,color:#fff
    style AR fill:#1E3A5F,color:#fff
    style TH fill:#1E3A5F,color:#fff

📖 Detailed Docs: See Data Flow Analysis for complete mechanisms.

🧪 Backtesting

Professional-grade backtesting system for strategy validation before live trading:

Features:

📊 Multi-Tab Parallel Backtests: Run up to 5 backtests simultaneously with independent configurations
📈 Real-time Progress: Live equity curve, drawdown chart, and trade markers
🎯 LLM-Enhanced Mode: Test the full multi-agent decision system including DeepSeek analysis
📅 Flexible Date Ranges: Quick presets (1/3/7/14/30 days) or custom date selection
⚙️ Advanced Parameters: Configurable leverage, stop-loss, take-profit, and trailing stops
📋 Detailed Metrics: Total return, Sharpe/Sortino ratios, win rate, max drawdown, and more
💾 Full Logging: All decisions and LLM interactions saved for analysis

Access: Visit http://localhost:8000/backtest after starting the bot.

📄 Full-Link Data Auditing

Data Storage Structure

Storage Organization

The system automatically records intermediate processes for each cycle in the data/ directory, organized by date for easy review and debugging:

data/
├── market_data/           # Raw Multi-Timeframe K-Lines
│   └── {date}/
│       ├── BTCUSDT_5m_{timestamp}.json
│       ├── BTCUSDT_5m_{timestamp}.csv
│       ├── BTCUSDT_5m_{timestamp}.parquet
│       ├── BTCUSDT_15m_{timestamp}.json
│       └── BTCUSDT_1h_{timestamp}.json
│
├── indicators/            # Full Technical Indicators DataFrames
│   └── {date}/
│       ├── BTCUSDT_5m_{snapshot_id}.parquet
│       ├── BTCUSDT_15m_{snapshot_id}.parquet
│       └── BTCUSDT_1h_{snapshot_id}.parquet
│
├── features/              # Feature Snapshots
│   └── {date}/
│       ├── BTCUSDT_5m_{snapshot_id}_v1.parquet
│       ├── BTCUSDT_15m_{snapshot_id}_v1.parquet
│       └── BTCUSDT_1h_{snapshot_id}_v1.parquet
│
├── context/               # Quant Analysis Summary
│   └── {date}/
│       └── BTCUSDT_quant_analysis_{snapshot_id}.json
│
├── llm_logs/              # LLM Input Context & Voting Process
│   └── {date}/
│       └── BTCUSDT_{snapshot_id}.md
│
├── decisions/             # Final Weighted Vote Results
│   └── {date}/
│       └── BTCUSDT_{snapshot_id}.json
│
└── execution/             # Execution Tracking
    └── {date}/
        └── BTCUSDT_{timestamp}.json

Data Formats

JSON: Human-readable, used for configuration and decision results
CSV: High compatibility, easy for Excel import
Parquet: Efficient compression, used for large-scale time-series data

🛡️ Safety Warning

⚠️ Important Safety Measures:

API Keys: Keep them safe, DO NOT commit to version control.
Test First: Use --test argument to run simulations first.
Risk Control: Set reasonable stop-loss and position limits in config.yaml.
Minimal Permissions: Grant only necessary Futures Trading permissions to API keys.
Monitoring: Regularly check the logs/ directory for anomalies.

📚 Documentation Navigation

Document	Description
README.md	Project Overview & Quick Start
Data Flow Analysis	Complete Data Flow Mechanisms
API Key Guide	API Key Configuration Guide
Config Example	Trading Parameters Template
Env Example	Environment Variables Template

🎉 Latest Updates

2026-01-19:

✅ Dashboard UI Enhancement: Modernized dashboard with gold-themed NoFX design.
- K-Line Chart: TradingView Lightweight Charts integration for real-time candlestick display
- Account Summary Panel: Real-time balance, equity, PnL, and position tracking
- Test/Live Mode Toggle: Quick switch with visual confirmation and safety warnings
- Agent Selection Panel: Configure optional agents via Settings → Agents tab
- Current Symbol Display: Shows AUTO1 selected symbol in agent framework header
✅ 17-Agent Framework: Expanded to 17 specialized agents (3 core + 14 optional) with LLM and Local variants.
- 3 Core agents (DataSync, QuantAnalyst, RiskAudit) always enabled
- 14 Optional agents: LLM variants for AI analysis, Local variants for fast rule-based heuristics
- Added SetupAgent for 15m setup zone analysis (KDJ, Bollinger Bands, entry zones)

2026-01-07:

✅ AUTO3 Two-Stage Symbol Selection: Enhanced SymbolSelectorAgent with two-stage filtering.
- Stage 1 (Coarse Filter): 1h backtest on AI500 Top10 + Major coins (~16 symbols) → Top 5
- Stage 2 (Fine Filter): 15m backtest on Top 5 → Top 2 performers
- Expanded candidate pool: AI500 (30+ AI/Data coins) + Majors (BTC, ETH, SOL, BNB, XRP, DOGE)
- Auto-refresh every 6 hours with smart caching
✅ BacktestAgentRunner Parity: Full consistency between backtest and live trading environments.
- Risk Audit Agent integrated into backtest flow
- Four-Layer Strategy Filter applied in backtests
- Position analysis and regime detection enabled
✅ Enhanced Backtest CLI: python backtest.py with support for:
- Multi-symbol backtesting
- Agent strategy mode (--strategy-mode agent)
- LLM enhancement option (--use-llm)
- Detailed HTML reports with equity curves

2025-12-31:

✅ Full Chinese Internationalization (i18n): Complete bilingual support with language toggle button.
- Dashboard UI elements (headers, tables, buttons) fully translated
- Agent documentation sidebar with Chinese descriptions
- Seamless language switching without page reload

2025-12-28:

✅ Dashboard Log Mode Toggle: Switch between Simplified (agent summaries) and Detailed (full debug) log views.
✅ Net Value Curve Enhancement: Smart x-axis labels that adapt to data volume while preserving first cycle timestamp.

2025-12-25:

✅ ReflectionAgent (The Philosopher): New agent that analyzes every 10 trades and provides insights to improve future decisions.
✅ Trading Retrospection: Automatic pattern detection, confidence calibration, and actionable recommendations.
✅ Decision Integration: Reflection insights are injected into Decision Agent prompts for continuous learning.

2025-12-24:

✅ Multi-LLM Support: Added support for 5 LLM providers (DeepSeek, OpenAI, Claude, Qwen, Gemini) with unified interface.
✅ Dashboard LLM Settings: Switch LLM provider and API keys directly from Dashboard Settings.
✅ Multi-Account Architecture: New src/exchanges/ module with BaseTrader abstraction for multi-exchange support.
✅ Account Manager: Manage multiple trading accounts via Dashboard or config/accounts.json.

2025-12-21:

✅ ML Model Upgrade: Upgraded PredictAgent to use LightGBM machine learning model.
✅ Auto-Training: Implemented automatic model retraining every 2 hours to adapt to market drifts.
✅ Dashboard Refinement: Enhanced dashboard with auto-scrolling logs, robust scrollbars, and ML probability display.

2025-12-20:

✅ Adversarial Decision Framework: Introduced PositionAnalyzer and RegimeDetector.
✅ Confidence Score Refactor: Implemented dynamic confidence penalties.
✅ Full-Link Auditing: Implemented complete intermediate state archiving.

❓ Frequently Asked Questions (FAQ)

For Beginners

Q: Is this safe to use? Will I lose money? A: Test mode is 100% safe - it uses virtual money. For live trading, only use funds you can afford to lose. Cryptocurrency trading is risky.

Q: Do I need to know Python to use this? A: No! Just follow the Quick Start guide. You only need Python installed, not programming knowledge.

Q: How much money do I need to start? A: Test mode is free. For live trading, minimum $100 USDT recommended, but start small while learning.

Q: Will the bot trade 24/7? A: Yes, once started in continuous mode, it runs non-stop analyzing markets and making decisions.

Q: How do I know if it's working? A: Open http://localhost:8000 in your browser to see the real-time dashboard with live logs and charts.

Technical Questions

Q: Which exchanges are supported? A: Currently only Binance Futures. Spot trading and other exchanges are not supported.

Q: Can I customize the trading strategy? A: Yes! Edit config.yaml for basic parameters. Advanced users can modify agent logic in src/ directory.

Q: What's the difference between Test and Live mode? A: Test mode simulates trading with $1000 virtual balance. Live mode executes real trades on Binance.

Q: How do I stop the bot? A: Press Ctrl+C in the terminal, or run pkill -f "python main.py"

Q: Why is the dashboard not loading? A: Make sure the bot is running and visit http://localhost:8000. Check firewall settings if issues persist.

Troubleshooting

Q: "ModuleNotFoundError" when starting A: Run pip install -r requirements.txt to install all dependencies.

Q: "API Key invalid" error A: Check your .env file has correct Binance API keys. For test mode, API keys are optional.

Q: Bot keeps saying "WAIT" and not trading A: This is normal! The bot is conservative and only trades when conditions are favorable. Check the dashboard logs for reasoning.

Q: How do I update to the latest version? A: Run git pull origin main then restart the bot.

🤝 Contribution

Issues and Pull Requests are welcome!

This project is licensed under the GNU Affero General Public License v3.0. See the LICENSE file for details.

Empowered by AI, Focused on Precision, Starting a New Era of Intelligent Quant! 🚀

Name		Name	Last commit message	Last commit date
Latest commit History 331 Commits
config		config
docker		docker
docs		docs
research		research
scripts		scripts
src		src
tests		tests
web		web
.dockerignore		.dockerignore
.env.auto5.example		.env.auto5.example
.env.example		.env.example
.gitignore		.gitignore
Dockerfile		Dockerfile
LICENSE		LICENSE
Procfile		Procfile
QUICKSTART.md		QUICKSTART.md
README.md		README.md
README_CN.md		README_CN.md
backtest.py		backtest.py
cleanup_data_dirs.sh		cleanup_data_dirs.sh
compare_strategies.py		compare_strategies.py
config.example.yaml		config.example.yaml
install.sh		install.sh
main.py		main.py
optimize_backtest.py		optimize_backtest.py
package-lock.json		package-lock.json
requirements.txt		requirements.txt
run_multi_symbol_backtest.py		run_multi_symbol_backtest.py
set_api_keys.sh		set_api_keys.sh
simple_cli.py		simple_cli.py
start.sh		start.sh
switch_to_production.sh		switch_to_production.sh

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EthanAlgoX/LLM-TradeBot

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🤖 LLM-TradeBot

🌐 Web App (Recommended)

✨ Key Features

🏗️ System Architecture Overview

Data Flow Architecture

Detailed Flowchart

🤝 Supported Ecosystem

Supported Exchanges

CEX (Centralized Exchanges)

Perp-DEX (Decentralized Perpetual Exchanges)

Supported AI Models

📚 What You Need to Know

🚀 Quick Start (One-Click Installation)

🎯 Recommended: One-Click Installation

Method 1: Local Installation (Development)

Method 2: Docker Deployment (Production)

⚙️ Prerequisites

For One-Click Installation (Recommended)

For Test Mode (Beginners)

For Live Trading (Advanced)

🧠 LLM Configuration (Multi-Provider Support)

Supported Providers

Configuration Methods

📖 Manual Installation (Advanced)

1. Install Dependencies

2. Configure Environment

3. Configure Trading Parameters

⚙️ Dashboard Settings

4. Start the Bot

🧪 Test Mode (Recommended for beginners)

🖥️ CLI Headless Mode (For Servers)

🚀 Simplified CLI Mode (Live Trading)

🔴 Live Trading Mode (Web Dashboard)

📋 Recent Decisions Indicator Guide

5. Common Operations

📁 Project Structure

Directory Tree

Directory Description

🎯 Core Architecture

17-Agent Collaborative Framework + Four-Layer Strategy

Core Agents (Always Enabled)

Symbol Selection Layer (Optional)

Prediction & Analysis Layer (Optional)

Semantic Analysis Layer (LLM or Local)

Decision & Execution Layer

Agent Configuration

Four-Layer Strategy Filter

Data Flow Diagrams

🧪 Backtesting

📄 Full-Link Data Auditing

Data Storage Structure

Storage Organization

Data Formats

🛡️ Safety Warning

📚 Documentation Navigation

🎉 Latest Updates

❓ Frequently Asked Questions (FAQ)

For Beginners

Technical Questions

Troubleshooting

🤝 Contribution

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