feat: Initial setup improvements and architecture planning - Add .env file support with automatic environment loading - Create comprehensive architecture planning document - Improve main.py with user-friendly CLI interface - Fix hardcoded paths in default_config.py - Set optimal testing defaults (gpt-4o-mini, offline mode) - Add python-dotenv dependency for secure API key management - Update .gitignore to exclude .env files - Add 6-month development roadmap from testing to production - Ready for cost-efficient architecture testing
This commit is contained in:
Marc Platt
parent
0fdfd35867
commit
2af03ecf69
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@ -6,3 +6,4 @@ src/
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eval_results/
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eval_data/
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*.egg-info/
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.env
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@ -0,0 +1,442 @@
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# TradingAgents: Master Architecture & Development Plan
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## 📋 Project Overview
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**TradingAgents** is a multi-agent LLM framework for financial trading that simulates real-world trading firms through specialized AI agents collaborating on market analysis and trading decisions.
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**Current Status**: ✅ Basic Setup Complete | 🧪 Architecture Testing Phase
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**Last Updated**: 2025-01-27
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**Version**: v0.1.0-alpha
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---
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## 🏗️ Current Architecture
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```mermaid
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graph TB
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subgraph "Input Layer"
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A[Market Data APIs]
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B[News Sources]
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C[Social Media]
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end
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subgraph "Data Processing Layer"
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D[FinnHub API]
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E[Yahoo Finance]
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F[Reddit API]
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G[Google News]
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end
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subgraph "Agent Framework (LangGraph)"
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H[Analyst Team]
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I[Research Team]
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J[Trading Team]
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K[Risk Management]
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end
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subgraph "Analyst Team"
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H1[Market Analyst]
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H2[Sentiment Analyst]
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H3[News Analyst]
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H4[Fundamentals Analyst]
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end
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subgraph "Research Team"
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I1[Bull Researcher]
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I2[Bear Researcher]
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I3[Research Manager]
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end
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subgraph "Trading Team"
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J1[Trader Agent]
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end
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subgraph "Risk Management"
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K1[Risk Analyst]
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K2[Portfolio Manager]
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end
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subgraph "LLM Backend"
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L[OpenAI API]
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M[gpt-4o-mini]
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end
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subgraph "Memory & State"
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N[Financial Memory]
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O[Agent State]
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P[Trading History]
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end
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subgraph "Output Layer"
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Q[Trading Decisions]
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R[Risk Assessments]
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S[Reports]
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end
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A --> D
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B --> G
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C --> F
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D --> H
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E --> H
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F --> H
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G --> H
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H --> I
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I --> J
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J --> K
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H1 --> H
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H2 --> H
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H3 --> H
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H4 --> H
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I1 --> I
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I2 --> I
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I3 --> I
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J1 --> J
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K1 --> K
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K2 --> K
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L --> H
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L --> I
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L --> J
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L --> K
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N --> H
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N --> I
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N --> J
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N --> K
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K --> Q
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K --> R
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K --> S
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```
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### 🔧 Technical Stack
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- **Framework**: LangGraph for agent orchestration
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- **LLMs**: OpenAI GPT-4o-mini (testing), planned DeepSeek (production)
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- **Data Sources**: FinnHub, Yahoo Finance, Reddit, Google News
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- **Memory**: Custom FinancialSituationMemory
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- **Environment**: Python 3.9+, .env configuration
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---
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## 📊 Current State Assessment
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### ✅ Completed Components
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- [x] **Environment Setup**: API keys, dependencies, .env configuration
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- [x] **Core Agent Framework**: LangGraph-based multi-agent system
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- [x] **Data Integration**: FinnHub, Yahoo Finance, Reddit APIs
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- [x] **Basic Agent Types**: Market, Sentiment, News, Fundamentals analysts
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- [x] **Memory System**: Agent memory for learning from past decisions
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- [x] **CLI Interface**: Interactive command-line interface
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### 🔄 In Progress
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- [ ] **Architecture Testing**: Validating agent interactions and decision flow
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- [ ] **Cost Optimization**: Minimizing API calls for testing phase
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- [ ] **Error Handling**: Robust error handling for API failures
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### ❌ Pending
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- [ ] **Historical Backtesting**: Testing against historical market data
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- [ ] **Performance Metrics**: Quantitative evaluation of trading decisions
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- [ ] **Local Model Integration**: DeepSeek model deployment
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- [ ] **Scalability Testing**: Multi-asset, multi-timeframe analysis
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- [ ] **Production Infrastructure**: Raspberry Pi cluster setup
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---
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## 📝 Changelog
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### v0.1.0-alpha (2025-01-27)
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- **Added**: Initial project setup and environment configuration
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- **Added**: .env file support for secure API key management
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- **Added**: Complete dependency installation and verification
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- **Added**: LangGraph-based agent framework
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- **Added**: Multi-agent analyst team (Market, Sentiment, News, Fundamentals)
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- **Added**: Research team with Bull/Bear researchers
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- **Added**: Trading and Risk Management agents
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- **Added**: CLI interface for interactive testing
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- **Fixed**: Environment variable loading issues
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- **Changed**: Model configuration to use gpt-4o-mini for cost efficiency
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---
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## 🧪 Phase 1: Architecture Testing & Validation
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### Immediate Next Steps (Week 1-2)
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1. **OpenAI API Resolution**
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- [ ] Add payment method to OpenAI account
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- [ ] Verify API quota and rate limits
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- [ ] Test minimal API calls for functionality
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2. **Agent Flow Testing**
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- [ ] Test single agent execution (Market Analyst)
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- [ ] Test agent-to-agent communication
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- [ ] Validate decision propagation through the pipeline
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- [ ] Test memory persistence between runs
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3. **Data Pipeline Validation**
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- [ ] Test offline data sources (cached financial data)
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- [ ] Validate data format consistency
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- [ ] Test error handling for missing data
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- [ ] Verify date range handling
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4. **Cost Optimization Testing**
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- [ ] Measure token usage per agent
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- [ ] Optimize prompt efficiency
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- [ ] Test reduced debate rounds (current: 1)
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- [ ] Implement request batching where possible
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### Testing Scenarios (Week 2-3)
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1. **Single Stock Analysis**
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- [ ] Test NVDA analysis (current test case)
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- [ ] Test AAPL analysis for comparison
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- [ ] Test volatile stock (e.g., meme stock)
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- [ ] Test stable stock (e.g., utility)
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2. **Market Condition Testing**
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- [ ] Bull market scenario
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- [ ] Bear market scenario
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- [ ] Sideways market scenario
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- [ ] High volatility events
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3. **Decision Quality Assessment**
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- [ ] Track decision consistency
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- [ ] Measure reasoning quality
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- [ ] Test risk assessment accuracy
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- [ ] Validate portfolio management logic
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---
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## 🚀 Major Project Phases
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### Phase 2: Local Model Integration (Month 1-2)
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**Objective**: Replace OpenAI API with local DeepSeek models
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#### 2.1 Local Model Setup
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- [ ] **Research DeepSeek Model Variants**
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- Evaluate DeepSeek-R1, DeepSeek-V3 for financial tasks
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- Compare model sizes vs. performance trade-offs
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- Test quantization options for Raspberry Pi deployment
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- [ ] **Local Inference Setup**
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- Install and configure Ollama or similar framework
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- Test model performance on development machine
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- Optimize inference parameters for speed/quality balance
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- [ ] **API Compatibility Layer**
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- Create OpenAI-compatible API wrapper
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- Implement model switching configuration
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- Test seamless transition between local/remote models
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#### 2.2 Raspberry Pi Cluster Architecture
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- [ ] **Hardware Planning**
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- Calculate compute requirements for multi-agent system
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- Plan Raspberry Pi cluster configuration
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- Design power, cooling, and networking setup
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- [ ] **Distributed Processing**
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- Design agent-to-Pi assignment strategy
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- Implement load balancing for inference requests
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- Create failover mechanisms for hardware failures
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### Phase 3: MCP Server Integration (Month 2-3)
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**Objective**: Implement Model Context Protocol for enhanced capabilities
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#### 3.1 MCP Server Setup
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- [ ] **Server Architecture Design**
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- Design MCP server for financial data access
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- Plan secure API endpoints for agent communication
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- Implement authentication and rate limiting
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- [ ] **Financial Data MCP Tools**
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- Real-time market data feeds
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- Economic calendar integration
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- News sentiment analysis tools
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- Technical indicator calculators
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- [ ] **Agent-MCP Integration**
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- Modify agents to use MCP tools
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- Implement tool discovery and capability negotiation
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- Add error handling for MCP communication
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#### 3.2 Enhanced Capabilities
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- [ ] **Advanced Data Sources**
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- SEC filing analysis
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- Earnings call transcripts
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- Insider trading data
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- Options flow data
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- [ ] **Real-time Processing**
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- Streaming market data integration
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- Event-driven analysis triggers
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- Real-time risk monitoring
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### Phase 4: Agent Specialization & Expansion (Month 3-4)
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**Objective**: Create more specialized and sophisticated agents
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#### 4.1 Specialized Analyst Agents
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- [ ] **Technical Analysis Specialists**
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- Chart pattern recognition agent
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- Options flow analysis agent
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- Momentum/trend analysis agent
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- Support/resistance level agent
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- [ ] **Fundamental Analysis Specialists**
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- Earnings analysis agent
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- Sector rotation agent
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- Economic indicator agent
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- Company comparison agent
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- [ ] **Alternative Data Agents**
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- Satellite imagery analysis (retail foot traffic)
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- Social media sentiment (beyond Reddit)
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- Patent filing analysis
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- Supply chain analysis
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#### 4.2 Advanced Trading Agents
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- [ ] **Strategy Specialists**
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- Pairs trading agent
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- Arbitrage opportunity agent
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- Options strategy agent
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- Swing trading agent
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- [ ] **Risk Management Specialists**
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- VaR calculation agent
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- Correlation analysis agent
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- Portfolio optimization agent
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- Black swan detection agent
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### Phase 5: Historical Backtesting & Validation (Month 4-5)
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**Objective**: Comprehensive testing against historical market data
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#### 5.1 Backtesting Infrastructure
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- [ ] **Data Pipeline**
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- Historical data ingestion (10+ years)
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- Data quality validation and cleaning
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- Event timeline reconstruction
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- News/sentiment historical matching
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- [ ] **Simulation Engine**
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- Multi-timeframe simulation capability
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- Transaction cost modeling
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- Slippage and market impact simulation
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- Portfolio rebalancing logic
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#### 5.2 Performance Analysis
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- [ ] **Quantitative Metrics**
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- Sharpe ratio calculation
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- Maximum drawdown analysis
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- Win/loss ratio tracking
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- Risk-adjusted returns
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- [ ] **Comparative Analysis**
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- Benchmark comparison (S&P 500, etc.)
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- Strategy performance across market regimes
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- Agent contribution analysis
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- Decision quality metrics
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### Phase 6: Production Deployment (Month 5-6)
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**Objective**: Deploy robust, scalable production system
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#### 6.1 Infrastructure Scaling
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- [ ] **Multi-Asset Support**
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- Stocks, ETFs, options, futures
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- Multiple market coverage (US, EU, Asia)
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- Currency and commodity analysis
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- Crypto market integration
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- [ ] **High Availability Setup**
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- Redundant system architecture
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- Automatic failover mechanisms
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- Data backup and recovery
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- Monitoring and alerting systems
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#### 6.2 Real-World Integration
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- [ ] **Broker Integration**
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- Paper trading implementation
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- Real money trading (small scale)
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- Order management system
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- Trade execution optimization
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- [ ] **Regulatory Compliance**
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- Trading regulations adherence
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- Risk management requirements
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- Audit trail implementation
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- Compliance monitoring
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---
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## 🎯 Success Metrics
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### Testing Phase Metrics
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- **System Stability**: >95% uptime during testing
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- **Decision Consistency**: <10% variance in similar scenarios
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- **Cost Efficiency**: <$0.10 per analysis cycle
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- **Response Time**: <2 minutes for complete analysis
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### Production Phase Metrics
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- **Return Performance**: Target 15%+ annual return
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- **Risk Management**: Maximum 10% drawdown
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- **Sharpe Ratio**: Target >1.5
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- **Win Rate**: Target >55% profitable trades
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---
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## 🔧 Development Workflow
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### Testing Protocol
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1. **Feature Branch Development**: All new features in separate branches
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2. **Unit Testing**: Each agent component tested individually
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3. **Integration Testing**: Full pipeline testing before merge
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4. **Performance Testing**: Token usage and response time monitoring
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### Documentation Standards
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- **Code Documentation**: Inline comments for all agent logic
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- **API Documentation**: Complete endpoint documentation
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- **User Guides**: Setup and usage instructions
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- **Architecture Updates**: This document updated with each major change
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---
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## 🚨 Risk Management
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### Technical Risks
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- **Model Reliability**: Implement multiple model fallbacks
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- **Data Quality**: Comprehensive data validation
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- **System Failures**: Redundant infrastructure planning
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- **Security**: Secure API key and data handling
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### Financial Risks
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- **Backtesting Limitations**: Over-optimization awareness
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- **Market Regime Changes**: Adaptive strategy implementation
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- **Regulatory Changes**: Compliance monitoring
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- **Capital Protection**: Strict risk limits and stop-losses
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---
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## 📞 Next Actions Summary
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### This Week (High Priority)
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1. 🔴 **Resolve OpenAI API access** - Add payment method
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2. 🟡 **Complete agent flow testing** - Validate end-to-end pipeline
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3. 🟡 **Implement cost monitoring** - Track and optimize token usage
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4. 🟢 **Document test results** - Update this plan with findings
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### Next Week (Medium Priority)
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1. **Expand testing scenarios** - Multiple stocks and market conditions
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2. **Performance optimization** - Reduce latency and costs
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3. **Error handling** - Robust failure recovery
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4. **Local model research** - DeepSeek evaluation and setup planning
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---
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*This document serves as the living roadmap for TradingAgents development. Update regularly as progress is made and new insights are gained.*
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# Load environment variables from .env file
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import tradingagents.env_loader
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from typing import Optional
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import datetime
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import typer
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|
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83
main.py
83
main.py
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#!/usr/bin/env python3
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"""
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TradingAgents - Multi-Agent Financial Trading System
|
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Main entry point for running standard trading analysis.
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Usage:
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python main.py [TICKER] [DATE]
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Examples:
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python main.py # Analyze NVDA on default date
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python main.py AAPL # Analyze AAPL on default date
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python main.py TSLA 2024-01-15 # Analyze TSLA on specific date
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"""
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import sys
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from datetime import datetime, timedelta
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# Load environment variables from .env file
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import tradingagents.env_loader
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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from tradingagents.default_config import DEFAULT_CONFIG
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# Create a custom config
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config = DEFAULT_CONFIG.copy()
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config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model
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config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model
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config["max_debate_rounds"] = 1 # Increase debate rounds
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config["online_tools"] = True # Increase debate rounds
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# Initialize with custom config
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ta = TradingAgentsGraph(debug=True, config=config)
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def main():
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"""Run TradingAgents analysis with command line arguments."""
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# Parse command line arguments
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ticker = sys.argv[1] if len(sys.argv) > 1 else "NVDA"
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# Default to a recent trading date if none provided
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if len(sys.argv) > 2:
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trade_date = sys.argv[2]
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else:
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# Use a date from a few days ago (to ensure market data is available)
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default_date = datetime.now() - timedelta(days=3)
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trade_date = default_date.strftime("%Y-%m-%d")
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print(f"🚀 TradingAgents Analysis")
|
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print(f"📊 Ticker: {ticker}")
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print(f"📅 Date: {trade_date}")
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print("=" * 50)
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try:
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# Initialize TradingAgents system
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ta = TradingAgentsGraph(debug=False, config=DEFAULT_CONFIG)
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print("🤖 Running multi-agent analysis...")
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# Run the complete analysis pipeline
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final_state, decision = ta.propagate(ticker, trade_date)
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# Display results
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print("\n" + "=" * 50)
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print("📋 FINAL TRADING DECISION")
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print("=" * 50)
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print(decision)
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# Optional: Save results for later review
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# ta.reflect_and_remember(1000) # Implement after backtesting
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print(f"\n✅ Analysis complete for {ticker} on {trade_date}")
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except Exception as e:
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||||
print(f"❌ Error during analysis: {str(e)}")
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||||
print("\n💡 Troubleshooting:")
|
||||
print(" - Check your API keys in .env file")
|
||||
print(" - Verify internet connection for data sources")
|
||||
print(" - Try: python -m cli.main for interactive mode")
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
# forward propagate
|
||||
_, decision = ta.propagate("NVDA", "2024-05-10")
|
||||
print(decision)
|
||||
|
||||
# Memorize mistakes and reflect
|
||||
# ta.reflect_and_remember(1000) # parameter is the position returns
|
||||
if __name__ == "__main__":
|
||||
exit_code = main()
|
||||
|
|
|
|||
|
|
@ -22,3 +22,4 @@ redis
|
|||
chainlit
|
||||
rich
|
||||
questionary
|
||||
python-dotenv
|
||||
|
|
|
|||
|
|
@ -1,19 +1,19 @@
|
|||
import os
|
||||
|
||||
# Get the project root directory (TradingAgents folder)
|
||||
PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
|
||||
"data_cache_dir": os.path.join(
|
||||
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"dataflows/data_cache",
|
||||
),
|
||||
"project_dir": PROJECT_ROOT,
|
||||
"data_dir": os.path.join(PROJECT_ROOT, "data"), # Use local data directory
|
||||
"data_cache_dir": os.path.join(PROJECT_ROOT, "tradingagents", "dataflows", "data_cache"),
|
||||
# LLM settings
|
||||
"deep_think_llm": "o4-mini",
|
||||
"deep_think_llm": "gpt-4o-mini", # Fixed model name
|
||||
"quick_think_llm": "gpt-4o-mini",
|
||||
# Debate and discussion settings
|
||||
"max_debate_rounds": 1,
|
||||
"max_risk_discuss_rounds": 1,
|
||||
"max_recur_limit": 100,
|
||||
# Tool settings
|
||||
"online_tools": True,
|
||||
"online_tools": False, # Start with offline for testing
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,49 @@
|
|||
"""
|
||||
Environment variable loader for TradingAgents.
|
||||
Automatically loads environment variables from .env file if it exists.
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
def load_environment():
|
||||
"""Load environment variables from .env file if it exists."""
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Find the project root (where .env should be located)
|
||||
current_dir = Path(__file__).parent
|
||||
project_root = current_dir.parent # Go up one level from tradingagents/
|
||||
env_file = project_root / ".env"
|
||||
|
||||
if env_file.exists():
|
||||
load_dotenv(env_file)
|
||||
print(f"✅ Environment variables loaded from {env_file}")
|
||||
|
||||
# Verify required API keys are loaded
|
||||
required_keys = ["FINNHUB_API_KEY", "OPENAI_API_KEY"]
|
||||
missing_keys = []
|
||||
|
||||
for key in required_keys:
|
||||
if not os.getenv(key) or os.getenv(key) == f"your_{key.lower()}_here":
|
||||
missing_keys.append(key)
|
||||
|
||||
if missing_keys:
|
||||
print(f"⚠️ Warning: The following API keys need to be set in your .env file:")
|
||||
for key in missing_keys:
|
||||
print(f" - {key}")
|
||||
print(f" Please edit {env_file} and replace the placeholder values with your actual API keys.")
|
||||
else:
|
||||
print("✅ All required API keys are configured!")
|
||||
|
||||
else:
|
||||
print(f"ℹ️ No .env file found at {env_file}")
|
||||
print(" You can create one with your API keys or use environment variables directly.")
|
||||
|
||||
except ImportError:
|
||||
print("⚠️ python-dotenv not installed. Install it with: pip install python-dotenv")
|
||||
except Exception as e:
|
||||
print(f"❌ Error loading .env file: {e}")
|
||||
|
||||
# Call load_environment when this module is imported
|
||||
load_environment()
|
||||
Loading…
Reference in New Issue