700 lines
20 KiB
Markdown
700 lines
20 KiB
Markdown
# TradingAgents Configuration and Prompt Modification Guide
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## 📖 Overview
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This document provides a comprehensive guide for new users to modify configurations and customize prompts in the TradingAgents project. Through this guide, you will learn:
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- How to modify system configuration parameters
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- How to configure multi-market support (US stocks and China A-shares)
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- How to setup database integration (MongoDB and Redis)
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- How to configure multiple LLM providers (DashScope, OpenAI, Google, Anthropic)
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- How to customize prompts for various agents
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- How to add new features and configurations
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## 🌟 New Features Overview
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### 🇨🇳 China A-Share Market Support
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- **TongDaXin API Integration**: Real-time A-share data access
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- **Market Selection**: Interactive CLI market selection
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- **Exchange Support**: Shanghai, Shenzhen, ChiNext, STAR Market
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- **Intelligent Caching**: Optimized data retrieval and storage
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### 🤖 DashScope (Alibaba Cloud) Integration
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- **Qwen Model Series**: qwen-turbo, qwen-plus, qwen-max, qwen-max-longcontext
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- **Embedding Service**: DashScope embeddings for memory system
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- **Intelligent Fallback**: Automatic fallback to OpenAI when unavailable
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### 🗄️ Database Integration
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- **MongoDB**: Persistent data storage and analytics
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- **Redis**: High-performance caching
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- **Adaptive Cache**: Intelligent cache management with automatic fallback
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## 🔧 Configuration File Locations and Descriptions
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### 1. Main Configuration Files
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#### 📁 `tradingagents/default_config.py`
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**Purpose**: Core configuration file defining all default parameters
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```python
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DEFAULT_CONFIG = {
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# Directory configuration
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"project_dir": "Project root directory path",
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"results_dir": "Results output directory",
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"data_dir": "Data storage directory",
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"data_cache_dir": "Cache directory",
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# LLM model configuration
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"llm_provider": "dashscope", # LLM provider: "dashscope", "openai", "google", "anthropic"
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"deep_think_llm": "qwen-plus", # Deep thinking model
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"quick_think_llm": "qwen-turbo", # Quick thinking model
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"backend_url": "https://dashscope.aliyuncs.com/api/v1", # API backend URL
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# Debate and discussion settings
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"max_debate_rounds": 1, # Maximum debate rounds
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"max_risk_discuss_rounds": 1, # Maximum risk discussion rounds
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"max_recur_limit": 100, # Maximum recursion limit
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# Tool settings
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"online_tools": True, # Whether to use online tools
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}
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```
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**Modification Method**:
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1. Directly edit the `tradingagents/default_config.py` file
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2. Modify the corresponding configuration values
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3. Restart the application for changes to take effect
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#### 📁 `main.py`
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**Purpose**: Runtime configuration override, allows temporary parameter adjustments without modifying default config
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```python
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# Create custom configuration
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config = DEFAULT_CONFIG.copy()
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config["llm_provider"] = "google" # Use Google models
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config["backend_url"] = "https://generativelanguage.googleapis.com/v1"
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config["deep_think_llm"] = "gemini-2.0-flash" # Deep thinking model
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config["quick_think_llm"] = "gemini-2.0-flash" # Quick thinking model
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config["max_debate_rounds"] = 2 # Increase debate rounds
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config["online_tools"] = True # Enable online tools
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```
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**Modification Method**:
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1. Edit the config section in `main.py`
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2. Add or modify configuration items to override
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3. Save and run
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### 2. Dynamic Configuration Management
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#### 📁 `tradingagents/dataflows/config.py`
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**Purpose**: Provides dynamic configuration get/set functionality
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```python
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# Get current configuration
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config = get_config()
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# Dynamically modify configuration
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set_config({
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"llm_provider": "anthropic",
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"max_debate_rounds": 3
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})
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```
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## 🌟 New Features Configuration
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### 1. Environment Variables Configuration (`.env`)
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#### 📁 `.env` File Setup
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**Purpose**: Configure API keys and database settings
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**Required API Keys**:
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**For US Stock Analysis**:
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```env
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# Choose one LLM provider
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OPENAI_API_KEY=your_openai_api_key_here
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# OR
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GOOGLE_API_KEY=your_google_api_key_here
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# OR
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ANTHROPIC_API_KEY=your_anthropic_api_key_here
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# FinnHub - Required for financial data
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FINNHUB_API_KEY=your_finnhub_api_key_here
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```
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**For China A-Share Analysis**:
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```env
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# DashScope - Required for Chinese stock analysis
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DASHSCOPE_API_KEY=your_dashscope_api_key_here
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# FinnHub - Required for financial data
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FINNHUB_API_KEY=your_finnhub_api_key_here
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```
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**For DashScope LLM Provider**:
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```env
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# DashScope - Required for Qwen models
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DASHSCOPE_API_KEY=your_dashscope_api_key_here
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# FinnHub - Required for financial data
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FINNHUB_API_KEY=your_finnhub_api_key_here
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```
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**Optional API Keys**:
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```env
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# OpenAI - Optional fallback
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OPENAI_API_KEY=your_openai_api_key_here
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# Google AI - For Gemini models
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GOOGLE_API_KEY=your_google_api_key_here
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# Anthropic - For Claude models
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ANTHROPIC_API_KEY=your_anthropic_api_key_here
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```
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**Database Configuration (Optional)**:
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```env
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# MongoDB - For persistent data storage
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MONGODB_ENABLED=false
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MONGODB_HOST=localhost
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MONGODB_PORT=27018
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MONGODB_USERNAME=admin
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MONGODB_PASSWORD=your_mongodb_password
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MONGODB_DATABASE=tradingagents
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# Redis - For high-performance caching
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REDIS_ENABLED=false
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REDIS_HOST=localhost
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REDIS_PORT=6380
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REDIS_PASSWORD=your_redis_password
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REDIS_DB=0
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```
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### 2. Market Selection Configuration
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#### 📁 CLI Market Selection
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**Purpose**: Configure supported markets and data sources
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**Supported Markets**:
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1. **US Stock Market**
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- Format: 1-5 letter symbols (e.g., AAPL, SPY)
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- Data Source: Yahoo Finance
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- Validation: `^[A-Z]{1,5}$`
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2. **China A-Share Market**
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- Format: 6-digit codes (e.g., 000001, 600036)
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- Data Source: TongDaXin API
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- Validation: `^\d{6}$`
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- Exchanges: Shanghai (60xxxx), Shenzhen (00xxxx), ChiNext (30xxxx), STAR (68xxxx)
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**Configuration in Code**:
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```python
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# Market-specific configuration
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market_config = {
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"us_stock": {
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"data_source": "yahoo_finance",
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"pattern": r'^[A-Z]{1,5}$'
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},
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"china_a_share": {
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"data_source": "tongdaxin",
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"pattern": r'^\d{6}$'
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}
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}
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```
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### 3. Database Integration Configuration
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#### 📁 MongoDB Configuration
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**Purpose**: Persistent data storage and analytics
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**Setup Steps**:
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1. **Start MongoDB**:
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```bash
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docker run -d -p 27017:27017 --name mongodb mongo
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```
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2. **Enable in .env**:
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```env
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MONGODB_ENABLED=true
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```
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3. **Configuration Options**:
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```python
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mongodb_config = {
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"host": "localhost",
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"port": 27018,
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"database": "tradingagents",
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"username": "admin",
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"password": "your_password"
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}
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```
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#### 📁 Redis Configuration
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**Purpose**: High-performance caching
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**Setup Steps**:
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1. **Start Redis**:
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```bash
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docker run -d -p 6379:6379 --name redis redis
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```
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2. **Enable in .env**:
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```env
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REDIS_ENABLED=true
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```
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3. **Configuration Options**:
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```python
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redis_config = {
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"host": "localhost",
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"port": 6380,
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"password": "your_password",
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"db": 0
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}
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```
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### 4. LLM Provider Configuration
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#### 📁 DashScope (Alibaba Cloud) Configuration
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**Purpose**: Chinese-optimized LLM provider
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**Supported Models**:
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- `qwen-turbo`: Fast response, suitable for quick tasks
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- `qwen-plus`: Balanced performance and cost (Recommended)
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- `qwen-max`: Best performance for complex analysis
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- `qwen-max-longcontext`: Ultra-long context support
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**Configuration Example**:
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```python
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dashscope_config = {
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"llm_provider": "dashscope",
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"deep_think_llm": "qwen-plus",
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"quick_think_llm": "qwen-turbo",
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"backend_url": "https://dashscope.aliyuncs.com/api/v1"
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}
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```
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**API Key Setup**:
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1. Visit: https://dashscope.aliyun.com/
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2. Register Alibaba Cloud account
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3. Enable DashScope service
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4. Get API key
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5. Set in .env: `DASHSCOPE_API_KEY=your_key`
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#### 📁 Multi-LLM Fallback Configuration
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**Purpose**: Intelligent fallback between LLM providers
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**Fallback Priority**:
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1. Primary: DashScope (if configured)
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2. Secondary: OpenAI (if configured)
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3. Tertiary: Google AI (if configured)
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4. Fallback: Anthropic (if configured)
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**Configuration**:
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```python
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fallback_config = {
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"primary_provider": "dashscope",
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"fallback_providers": ["openai", "google", "anthropic"],
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"auto_fallback": True,
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"retry_attempts": 3
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}
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```
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## 🤖 Agent Prompt Modification Guide
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### 1. Analyst Prompts
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#### 📁 Market Analyst (`tradingagents/agents/analysts/market_analyst.py`)
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**Location**: `system_message` variable at lines 24-50
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**Current Prompt**:
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```python
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system_message = (
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"""You are a trading assistant tasked with analyzing financial markets.
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Your role is to select the **most relevant indicators** for a given market
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condition or trading strategy from the following list..."""
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)
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```
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**Modification Example**:
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```python
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system_message = (
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"""You are a professional market analyst specializing in financial market analysis.
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Your task is to select the most relevant indicators from the following list,
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providing analysis for specific market conditions or trading strategies.
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Goal: Choose up to 8 indicators that provide complementary insights without redundancy..."""
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)
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```
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#### 📁 Fundamentals Analyst (`tradingagents/agents/analysts/fundamentals_analyst.py`)
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**Location**: `system_message` variable at lines 23-26
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**Key Modification Points**:
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- Analysis depth requirements
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- Report format requirements
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- Focus financial metrics
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#### 📁 News Analyst (`tradingagents/agents/analysts/news_analyst.py`)
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**Location**: `system_message` variable at lines 20-23
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**Key Modification Points**:
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- News source preferences
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- Analysis time range
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- Types of news to focus on
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#### 📁 Social Media Analyst (`tradingagents/agents/analysts/social_media_analyst.py`)
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**Location**: `system_message` variable at lines 19-22
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**Key Modification Points**:
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- Sentiment analysis depth
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- Social media platform preferences
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- Sentiment weight settings
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### 2. Researcher Prompts
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#### 📁 Bull Researcher (`tradingagents/agents/researchers/bull_researcher.py`)
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**Location**: `prompt` variable at lines 25-43
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**Current Prompt Structure**:
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```python
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prompt = f"""You are a Bull Analyst advocating for investing in the stock.
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Key points to focus on:
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- Growth Potential: Highlight market opportunities, revenue projections, and scalability
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- Competitive Advantages: Emphasize unique products, strong branding, or market dominance
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- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence
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- Bear Counterpoints: Critically analyze bear arguments with specific data and sound reasoning
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"""
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```
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**Modification Suggestions**:
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- Adjust analysis focus
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- Modify argumentation strategy
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- Customize rebuttal logic
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#### 📁 Bear Researcher (`tradingagents/agents/researchers/bear_researcher.py`)
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**Key Modification Points**:
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- Risk identification focus
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- Pessimistic scenario analysis
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- Strategy for countering bull arguments
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### 3. Trader Prompts
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#### 📁 Trader (`tradingagents/agents/trader/trader.py`)
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**Location**: System message in `messages` array at lines 30-36
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**Current Prompt**:
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```python
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{
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"role": "system",
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"content": f"""You are a trading agent analyzing market data to make
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investment decisions. Based on your analysis, provide a specific
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recommendation to buy, sell, or hold. End with a firm decision and
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always conclude your response with 'FINAL TRANSACTION PROPOSAL:
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**BUY/HOLD/SELL**' to confirm your recommendation.""",
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}
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```
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**Modification Example**:
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```python
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{
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"role": "system",
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"content": f"""You are a professional trading agent responsible for analyzing
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market data and making investment decisions.
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Decision Requirements:
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1. Provide detailed analysis reasoning
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2. Consider risk management
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3. Must end with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**'
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Historical Lessons: {past_memory_str}""",
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}
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```
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### 4. Risk Management Prompts
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#### 📁 Conservative Debater (`tradingagents/agents/risk_mgmt/conservative_debator.py`)
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#### 📁 Aggressive Debater (`tradingagents/agents/risk_mgmt/aggresive_debator.py`)
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#### 📁 Neutral Debater (`tradingagents/agents/risk_mgmt/neutral_debator.py`)
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**Key Modification Points**:
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- Risk tolerance settings
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- Debate style adjustments
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- Decision weight allocation
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### 5. Reflection System Prompts
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#### 📁 Reflection System (`tradingagents/graph/reflection.py`)
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**Location**: `_get_reflection_prompt` method at lines 15-47
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**Current Prompt Structure**:
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```python
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return """
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You are an expert financial analyst tasked with reviewing trading
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decisions/analysis and providing a comprehensive, step-by-step analysis.
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1. Reasoning: Analyze whether each trading decision was correct
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2. Improvement: Propose revisions for incorrect decisions
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3. Summary: Summarize lessons learned from successes and failures
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4. Query: Extract key insights into concise sentences
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"""
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```
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## 🎯 Prompt Modification Best Practices
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### 1. Pre-modification Preparation
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1. **Backup Original Files**:
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```bash
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cp tradingagents/agents/trader/trader.py tradingagents/agents/trader/trader.py.backup
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```
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2. **Understand Agent Roles**: Ensure modifications align with expected agent functionality
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3. **Prepare Test Environment**: Validate modifications in test environment
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### 2. Prompt Modification Techniques
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#### 🔍 **Structured Prompts**
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```python
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system_message = f"""
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Role Definition: You are a {role_name}
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Main Tasks:
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1. {task_1}
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2. {task_2}
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3. {task_3}
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Analysis Requirements:
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- Depth: {analysis_depth}
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- Format: {output_format}
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- Focus: {focus_areas}
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Output Format:
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{output_template}
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Constraints:
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- {constraint_1}
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- {constraint_2}
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"""
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```
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#### ⚙️ **Parameterized Prompts**
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```python
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def create_analyst_prompt(
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role="Market Analyst",
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analysis_depth="Detailed",
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time_horizon="1 week",
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risk_tolerance="Moderate",
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output_language="English"
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):
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return f"""
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You are a professional {role}, please analyze based on the following parameters:
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Analysis Depth: {analysis_depth}
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Time Horizon: {time_horizon}
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Risk Preference: {risk_tolerance}
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Output Language: {output_language}
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Please provide corresponding market analysis and investment recommendations based on these parameters.
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"""
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```
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### 3. Common Modification Scenarios
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#### 📈 **Adjusting Analysis Focus**
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```python
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# Original: General market analysis
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system_message = "Analyze overall market trends..."
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# Modified: Focus on specific industry
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system_message = "Analyze technology stock market trends, focusing on AI, semiconductor, and cloud computing industries..."
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```
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#### 🎯 **Modifying Decision Style**
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```python
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# Original: Conservative
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"provide conservative investment recommendations..."
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# Modified: Aggressive
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"provide aggressive growth-oriented investment recommendations with higher risk tolerance..."
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```
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## 🔧 New Configuration Items
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### 1. Cache Configuration (`tradingagents/dataflows/cache_manager.py`)
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```python
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# Add new cache configuration in cache_manager.py
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self.cache_config = {
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'us_stock_data': {
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'ttl_hours': 2, # US stock data cached for 2 hours
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'description': 'US stock historical data'
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},
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'china_stock_data': {
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'ttl_hours': 1, # A-share data cached for 1 hour
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'description': 'A-share historical data'
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},
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# Add new cache type
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'crypto_data': {
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'ttl_hours': 0.5, # Crypto data cached for 30 minutes
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'description': 'Cryptocurrency data'
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}
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}
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```
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### 2. API Configuration
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```python
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# Add new API configuration in default_config.py
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DEFAULT_CONFIG = {
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# Existing configuration...
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# New API configuration
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"api_keys": {
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"finnhub": "your_finnhub_api_key",
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"alpha_vantage": "your_alpha_vantage_key",
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"polygon": "your_polygon_key"
|
|
},
|
|
|
|
# API limit configuration
|
|
"api_limits": {
|
|
"finnhub_calls_per_minute": 60,
|
|
"alpha_vantage_calls_per_minute": 5,
|
|
"polygon_calls_per_minute": 100
|
|
}
|
|
}
|
|
```
|
|
|
|
## 🚀 Quick Start Examples
|
|
|
|
### 1. Switch to Google Models
|
|
|
|
```python
|
|
# Edit main.py
|
|
config = DEFAULT_CONFIG.copy()
|
|
config["llm_provider"] = "google"
|
|
config["backend_url"] = "https://generativelanguage.googleapis.com/v1"
|
|
config["deep_think_llm"] = "gemini-2.0-flash"
|
|
config["quick_think_llm"] = "gemini-2.0-flash"
|
|
```
|
|
|
|
#### 🚀 Supported Google Models
|
|
|
|
**Fast Thinking Models (Quick Analysis)**:
|
|
- `gemini-2.0-flash-lite` - Cost efficiency and low latency
|
|
- `gemini-2.0-flash` - Next generation features, speed, and thinking ⭐ **Recommended**
|
|
- `gemini-2.5-flash-preview-05-20` - Adaptive thinking, cost efficiency
|
|
|
|
**Deep Thinking Models (Complex Analysis)**:
|
|
- `gemini-2.0-flash-lite` - Cost efficiency and low latency
|
|
- `gemini-2.0-flash` - Next generation features, speed, and thinking ⭐ **Current Default**
|
|
- `gemini-2.5-flash-preview-05-20` - Adaptive thinking, cost efficiency
|
|
- `gemini-2.5-pro-preview-06-05` - Professional-grade performance
|
|
|
|
#### 🔑 Google API Key Setup
|
|
|
|
**Method 1: Environment Variable (Recommended)**
|
|
```bash
|
|
export GOOGLE_API_KEY="your_google_api_key_here"
|
|
```
|
|
|
|
**Method 2: In Code**
|
|
```python
|
|
import os
|
|
os.environ["GOOGLE_API_KEY"] = "your_google_api_key_here"
|
|
```
|
|
|
|
**Method 3: .env File**
|
|
```
|
|
# Create .env file in project root
|
|
GOOGLE_API_KEY=your_google_api_key_here
|
|
```
|
|
|
|
#### 📋 Model Selection Examples
|
|
|
|
**High Performance Setup**:
|
|
```python
|
|
config["deep_think_llm"] = "gemini-2.5-pro-preview-06-05" # Best reasoning
|
|
config["quick_think_llm"] = "gemini-2.0-flash" # Fast response
|
|
```
|
|
|
|
**Cost-Optimized Setup**:
|
|
```python
|
|
config["deep_think_llm"] = "gemini-2.0-flash-lite" # Economical
|
|
config["quick_think_llm"] = "gemini-2.0-flash-lite" # Economical
|
|
```
|
|
|
|
**Balanced Setup (Current Default)**:
|
|
```python
|
|
config["deep_think_llm"] = "gemini-2.0-flash" # Good performance
|
|
config["quick_think_llm"] = "gemini-2.0-flash" # Good speed
|
|
```
|
|
|
|
### 2. Add Risk Control
|
|
|
|
```python
|
|
# Edit tradingagents/agents/trader/trader.py
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": f"""You are a professional trading agent with strict risk control awareness.
|
|
|
|
Trading Principles:
|
|
1. Risk first, returns second
|
|
2. Strict stop-loss, protect capital
|
|
3. Diversified investment, reduce risk
|
|
4. Data-driven, rational decisions
|
|
|
|
Decision Process:
|
|
1. Analyze market trends and technical indicators
|
|
2. Assess fundamental and news impact
|
|
3. Calculate risk-reward ratio
|
|
4. Set stop-loss and take-profit points
|
|
5. Make final trading decision
|
|
|
|
Output Requirements:
|
|
- Must include risk assessment
|
|
- Must set stop-loss points
|
|
- Must end with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**'
|
|
|
|
Historical Experience: {past_memory_str}""",
|
|
},
|
|
context,
|
|
]
|
|
```
|
|
|
|
## 📝 Important Notes
|
|
|
|
1. **Backup Important**: Always backup original files before modification
|
|
2. **Test Validation**: Validate modifications in test environment
|
|
3. **Version Control**: Use Git to manage configuration changes
|
|
4. **Documentation Updates**: Update related documentation promptly
|
|
5. **Team Collaboration**: Sync configuration changes with team members
|
|
|
|
## 🔗 Quick File Index
|
|
|
|
| Function | File Path | Description |
|
|
|----------|-----------|-------------|
|
|
| Main Config | `tradingagents/default_config.py` | System default configuration |
|
|
| Runtime Config | `main.py` | Runtime configuration override |
|
|
| Dynamic Config | `tradingagents/dataflows/config.py` | Configuration management interface |
|
|
| Market Analyst | `tradingagents/agents/analysts/market_analyst.py` | Technical analysis prompts |
|
|
| Fundamentals Analyst | `tradingagents/agents/analysts/fundamentals_analyst.py` | Fundamental analysis prompts |
|
|
| News Analyst | `tradingagents/agents/analysts/news_analyst.py` | News analysis prompts |
|
|
| Social Media Analyst | `tradingagents/agents/analysts/social_media_analyst.py` | Sentiment analysis prompts |
|
|
| Bull Researcher | `tradingagents/agents/researchers/bull_researcher.py` | Bull analysis prompts |
|
|
| Bear Researcher | `tradingagents/agents/researchers/bear_researcher.py` | Bear analysis prompts |
|
|
| Trader | `tradingagents/agents/trader/trader.py` | Trading decision prompts |
|
|
| Reflection System | `tradingagents/graph/reflection.py` | Reflection analysis prompts |
|
|
| Cache Config | `tradingagents/dataflows/cache_manager.py` | Cache management configuration |
|
|
|
|
Through this guide, you should be able to easily modify the TradingAgents project's configuration and prompts to meet your specific needs.
|