66. Automated Strategy Evolution System¶

Overview¶

The Automated Strategy Evolution System enables machine-driven generation and optimization of trading strategies using genetic programming, hyperparameter search, and multi-objective optimization. Strategies evolve through generations, with each generation undergoing backtest evaluation, elite selection, crossover, and mutation—enabling continuous improvement and innovation.

Architecture & Module Breakdown¶

Microservice Directory¶

services/strategy-evolution-center/
├── src/
│   ├── main.py
│   ├── genepool/gene_manager.py
│   ├── generator/strategy_generator.py
│   ├── evaluator/strategy_evaluator.py
│   ├── evolution/evolution_engine.py
│   ├── recorder/evolution_recorder.py
│   ├── api/evolution_api.py
│   ├── config.py
│   └── requirements.txt
├── Dockerfile

Core Component Design¶

1. Gene Pool Manager

class GeneManager:
    def get_random_gene(self):
        return random_strategy_structure_and_parameters()

2. Strategy Generator

class StrategyGenerator:
    def generate_strategy(self):
        gene = gene_manager.get_random_gene()
        return build_strategy_from_gene(gene)

3. Strategy Evaluator

class StrategyEvaluator:
    def evaluate(self, strategy):
        results = backtest_strategy(strategy)
        score = compute_fitness_score(results)
        return score

4. Evolution Engine

class EvolutionEngine:
    def evolve(self, population):
        selected = select_top_k(population, k=20)
        next_generation = crossover_and_mutate(selected)
        return next_generation

5. Evolution Recorder

class EvolutionRecorder:
    def record_generation(self, generation_id, population_stats):
        save_to_db("evolution_history", generation_id=generation_id, stats=population_stats)

6. API Example

from fastapi import APIRouter

router = APIRouter()

@router.post("/evolution/start")
async def start_evolution_task(task_config: dict):
    return evolution_engine.start_new_task(task_config)

Frontend Integration¶

StrategyEvolutionView.tsx - Evolution progress chart (average score per generation) - Best strategy per generation - Genealogy tree (ancestor/descendant relationships) - Gene mutation visualization

Implementation Roadmap¶

• Phase 1: Core gene pool, generator, evaluator, and API
• Phase 2: Evolution engine, recorder, and frontend dashboard
• Phase 3: Co-evolution, ML-guided evolution, advanced visualization

System Integration¶

• Integrates with backtest engine for evaluation
• Supports launching and monitoring evolution experiments
• Provides genealogy and mutation history for research

Business & Technical Value¶

• Innovation: Machine-driven discovery of novel strategies
• Performance: Continuous optimization for returns, Sharpe, drawdown
• Scalability: Supports millions of strategies and generations
• Transparency: Full genealogy and evolution history
• Competitive Edge: AI-powered, self-improving quant research