"""
QuantitativeAnalystExpert: A specialized expert that blends math, finance, and code domains
for solving complex quantitative analysis tasks.
"""

from typing import Dict, Any, Optional
from experts.base_expert import BaseExpert
from experts.domain import DomainBase
from experts.tools import ToolBase
from experts.shared import shared
from experts.chain_tracker import ChainTracker
from experts.shared_memory import SharedMemory

class QuantitativeAnalystExpert(BaseExpert):
    """Expert specialized in quantitative analysis tasks."""
    
    def __init__(self):
        super().__init__()
        self.domains = ["math", "finance", "code"]
        self.math_module = shared.math_module
        self.finance_module = shared.finance_module
        self.codegen_tool = shared.codegen_tool
        self.shared_memory = shared.memory
        self.chain_tracker = ChainTracker()
        
    def handle_task(self, task: Dict[str, Any], context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """
        Handle a quantitative analysis task by blending math, finance, and code domains.
        
        Args:
            task: Task description and parameters
            context: Optional context information
            
        Returns:
            Dict containing the blended analysis results
        """
        # Query shared memory for relevant context
        memory_context = self.shared_memory.query(task)
        
        # Analyze math components
        math_analysis = self.math_module.analyze(task)
        
        # Evaluate financial aspects
        finance_evaluation = self.finance_module.evaluate(task)
        
        # Generate necessary code
        code_generation = self.codegen_tool.generate_code(task)
        
        # Combine results using blended reasoning
        combined_result = self._blend_results(
            math_analysis,
            finance_evaluation,
            code_generation,
            memory_context
        )
        
        # Log the chain of reasoning
        self.chain_tracker.log_chain(
            task=task,
            math_result=math_analysis,
            finance_result=finance_evaluation,
            code_result=code_generation,
            final_result=combined_result
        )
        
        return combined_result
    
    def _blend_results(self, math: Dict, finance: Dict, code: Dict, memory: Dict) -> Dict:
        """Combine results from different domains."""
        # Implementation of result blending logic
        # This would typically involve:
        # 1. Weighting different domain results
        # 2. Integrating memory context
        # 3. Generating comprehensive analysis
        
        blended_result = {
            "math_analysis": math,
            "financial_evaluation": finance,
            "generated_code": code,
            "context": memory,
            "final_recommendation": self._generate_recommendation(math, finance)
        }
        
        return blended_result
    
    def _generate_recommendation(self, math: Dict, finance: Dict) -> str:
        """Generate a final recommendation based on math and finance analysis."""
        # This would be implemented based on specific use cases
        return """Based on the quantitative analysis:
        - Mathematical soundness: {}
        - Financial implications: {}
        - Recommended action: {}""".format(
            math.get("confidence", "N/A"),
            finance.get("impact", "N/A"),
            self._determine_action(math, finance)
        )
    
    def _determine_action(self, math: Dict, finance: Dict) -> str:
        """Determine appropriate action based on analysis."""
        # Placeholder for action determination logic
        return "Analyze results and determine appropriate action"