"""Strategy Optimization Agent for Jesse trading platform."""
from jesse_mcp.agents.base import BaseJesseAgent
class StrategyOptimizationAgent(BaseJesseAgent):
"""Agent specialized in analyzing and improving trading strategies.
This agent focuses on:
- Analyzing backtest results for performance weaknesses
- Identifying under-performing trading pairs
- Recommending improvements to strategy logic
- Suggesting position sizing adjustments
- Tracking optimization history and effectiveness
"""
@property
def system_prompt(self) -> str:
"""Return the specialized system prompt for strategy optimization."""
return """You are a Strategy Optimization Expert for the Jesse trading platform.
Your expertise includes:
- Deep analysis of backtest results (profit factor, win rate, drawdown, Sharpe ratio)
- Identifying profitable vs unprofitable trading pairs
- Optimizing entry/exit logic and indicator parameters
- Adjusting position sizing and risk management
- Detecting market regime changes affecting strategy performance
- Suggesting concrete improvements with expected impact estimates
When analyzing strategies:
1. First, run comprehensive backtests across multiple timeframes
2. Identify the top and bottom performing pairs
3. Analyze what makes successful pairs different
4. Suggest specific, testable improvements
5. Recommend A/B testing new ideas against current baseline
6. Track optimization iterations and their results
Always provide:
- Current performance metrics
- Identified weaknesses or opportunities
- Specific, actionable recommendations
- Expected improvements if recommendations are implemented
- Testing methodology to validate improvements
Focus on sustainable improvements that work across different market conditions."""
def suggest_improvements(self, strategy_name: str, pair: str) -> str:
"""Suggest specific improvements for a strategy/pair combination.
Args:
strategy_name: Name of the strategy to optimize
pair: Trading pair to analyze (e.g., 'BTCUSDT')
Returns:
Analysis and recommendations for improvement
"""
message = (
f"Analyze the strategy '{strategy_name}' on pair '{pair}' and "
f"suggest specific improvements. Include: "
f"1) Current performance metrics, "
f"2) Identified weaknesses, "
f"3) Specific changes to test, "
f"4) Expected improvements"
)
self.add_to_history("user", message)
return message
def compare_strategies(self, strategy1: str, strategy2: str) -> str:
"""Compare two strategies to identify best practices.
Args:
strategy1: First strategy name
strategy2: Second strategy name
Returns:
Comparative analysis and insights
"""
message = (
f"Compare '{strategy1}' and '{strategy2}' strategies. "
f"Analyze: 1) Performance metrics comparison, "
f"2) Risk profile differences, "
f"3) Which performs better in different market conditions, "
f"4) Key differences in logic/parameters that drive performance"
)
self.add_to_history("user", message)
return message
def optimize_pair_selection(self, strategy_name: str) -> str:
"""Analyze which pairs to trade for a given strategy.
Args:
strategy_name: Strategy to optimize pair selection for
Returns:
Analysis of best-performing pairs and recommendations
"""
message = (
f"Analyze which trading pairs work best with '{strategy_name}'. "
f"Provide: 1) Performance ranking of pairs, "
f"2) Common characteristics of profitable pairs, "
f"3) Pairs to avoid, "
f"4) Recommended pair selection for future trading"
)
self.add_to_history("user", message)
return message
def analyze_optimization_impact(self, change_description: str) -> str:
"""Estimate the impact of a proposed change.
Args:
change_description: Description of the proposed change
Returns:
Impact analysis and testing recommendations
"""
message = (
f"Analyze the potential impact of this change: {change_description}. "
f"Provide: 1) Expected benefits and risks, "
f"2) Testing methodology, "
f"3) Key metrics to monitor, "
f"4) Success criteria"
)
self.add_to_history("user", message)
return message
