Implement Multi-Agent Architecture v3.0 - Foundation

Core infrastructure for Claude Code-equivalent multi-agent system:

## 1. MCP Integration for QuantConnect ✅
- quantcoder/mcp/quantconnect_mcp.py: Full MCP client/server
- Real-time code validation against QC API
- Backtest execution via MCP
- Live deployment support
- API documentation lookup

## 2. Parallel Execution Framework ✅
- quantcoder/execution/parallel_executor.py
- Execute multiple agents simultaneously (like Claude Code)
- Dependency resolution for task chains
- Async tool execution
- Expected 3-5x speedup for multi-file generation

## 3. Multi-LLM Support ✅
- quantcoder/llm/providers.py
- Anthropic (Sonnet 4.5) - best reasoning/coordination
- Mistral (Devstral 2) - code generation specialist
- DeepSeek - cost-effective alternative
- OpenAI (GPT-4o) - fallback option
- LLMFactory for easy switching

## 4. Architecture Documentation ✅
- docs/ARCHITECTURE_V3_MULTI_AGENT.md
- Complete multi-agent system design
- Agent specifications (Universe, Alpha, Risk, etc.)
- Execution workflow examples
- Performance projections (3-5x speedup)

## Next Steps:
- Create specialized agents
- Build coordinator agent
- Implement multi-file code generation
- Add async tool wrappers
- End-to-end testing

This lays the foundation for production-grade multi-agent
QuantConnect algorithm generation with parallel execution.

Author: claude