File size: 3,782 Bytes
b3e9d26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
"""
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"
|