Automated Strategy Evolution Engine¶

39.1 System Overview¶

The Automated Strategy Evolution Engine implements genetic algorithms and evolutionary computation to automatically generate, optimize, and evolve trading strategies. It uses historical data and real-time performance to create new strategies, optimize existing parameters, and eliminate underperforming strategies through machine learning and genetic evolution.

39.1.1 Core Objectives¶

• Strategy Generation: Automatically generate new strategies using genetic algorithms and evolutionary computation
• Parameter Optimization: Automatically tune existing strategy parameters
• Performance Evaluation: Assess strategy performance through backtesting and live trading
• Strategy Selection: Eliminate underperforming strategies and select optimal ones
• Machine Learning Integration: Support reinforcement learning and supervised learning for strategy design
• Genetic Evolution: Implement selection, crossover, and mutation for strategy evolution

39.2 Architecture Design¶

39.2.1 Microservice Architecture¶

Strategy Evolution Center Service:

services/strategy-evolution-center/
├── src/
│   ├── main.py
│   ├── encoding/
│   │   ├── strategy_genome.py
│   ├── generator/
│   │   ├── strategy_generator.py
│   ├── evaluator/
│   │   ├── strategy_evaluator.py
│   ├── evolver/
│   │   ├── evolution_controller.py
│   ├── archiver/
│   │   ├── strategy_archiver.py
│   ├── api/
│   │   ├── evolution_api.py
│   ├── config.py
│   ├── requirements.txt
├── Dockerfile

39.2.2 Core Components¶

• Strategy Genome Encoder: Converts strategies into genetic representations
• Strategy Generator: Creates new strategies through crossover and mutation
• Strategy Evaluator: Backtests and evaluates strategy performance
• Evolution Controller: Manages selection, breeding, mutation, and iteration
• Strategy Archiver: Stores each generation's strategies and performance
• API Interface: Manual evolution triggers and status queries
• Frontend Dashboard: Strategy evolution genealogy and evolution logs

39.3 Module Design¶

39.3.1 Strategy Genome Encoder (strategy_genome.py)¶

• Converts strategies into genetic representations

class StrategyGenome:
    def encode(self, strategy):
        return {
            "moving_avg_period": strategy.moving_avg_period,
            "rsi_threshold": strategy.rsi_threshold,
            "stop_loss_pct": strategy.stop_loss_pct
        }

    def decode(self, genome):
        # Returns a Strategy instance
        return Strategy(**genome)

39.3.2 Strategy Generator (strategy_generator.py)¶

• Generates new strategies through crossover and mutation

import random

class StrategyGenerator:
    def crossover(self, parent1, parent2):
        child = {}
        for key in parent1:
            child[key] = random.choice([parent1[key], parent2[key]])
        return child

    def mutate(self, genome, mutation_rate=0.1):
        for key in genome:
            if random.random() < mutation_rate:
                genome[key] *= random.uniform(0.8, 1.2)  # Small adjustment
        return genome

39.3.3 Strategy Evaluator (strategy_evaluator.py)¶

• Backtests and evaluates strategy performance

class StrategyEvaluator:
    def backtest(self, strategy):
        # Call backtest-engine
        results = run_backtest(strategy)
        return {
            "return": results.total_return,
            "sharpe": results.sharpe_ratio,
            "max_drawdown": results.max_drawdown
        }

39.3.4 Evolution Controller (evolution_controller.py)¶

• Manages the entire evolution process

class EvolutionController:
    def evolve(self, population):
        evaluated = [(s, self.evaluator.backtest(s)) for s in population]
        evaluated.sort(key=lambda x: x[1]["sharpe"], reverse=True)
        survivors = [e[0] for e in evaluated[:len(evaluated)//2]]
        new_population = []
        while len(new_population) < len(population):
            p1, p2 = random.sample(survivors, 2)
            child = self.generator.crossover(p1, p2)
            mutated_child = self.generator.mutate(child)
            new_population.append(mutated_child)
        return new_population

39.3.5 Strategy Archiver (strategy_archiver.py)¶

• Archives each generation's strategies and performance

class StrategyArchiver:
    def save_generation(self, generation_id, strategies_with_scores):
        # Save to database, file system, etc.
        ...

39.3.6 API Interface (evolution_api.py)¶

• FastAPI endpoints for evolution control and status

from fastapi import APIRouter
router = APIRouter()

@router.post("/evolution/start")
async def start_evolution(generations: int = 10):
    evolution_controller.start_evolution(generations)
    return {"message": "Evolution started"}

39.3.7 Frontend Dashboard¶

• Strategy evolution genealogy tree
• Performance curves for each generation
• Current optimal strategy display
• Evolution log table (generation, best return, best Sharpe)

39.4 Evolution Flow Example¶

1. Initial strategy population generation → StrategyGenerator initialization
2. Each generation backtest and scoring → StrategyEvaluator assessment
3. Survival of the fittest + genetic crossover/mutation → EvolutionController generates new generation
4. Multiple iterations → Strategy performance continuous optimization
5. StrategyArchiver saves each generation's results

39.5 Technology Stack¶

• Python (FastAPI, numpy, scipy): Service implementation and genetic algorithms
• Redis: Real-time evolution data caching
• Docker: Containerization
• React/TypeScript: Frontend dashboard
• PostgreSQL: Strategy and performance data storage

39.6 API Design¶

• POST /evolution/start: Start evolution process
• GET /evolution/status: Get evolution status
• GET /evolution/generations: Get generation history
• GET /evolution/best_strategies: Get current best strategies
• POST /evolution/deploy: Deploy selected strategy

39.7 Frontend Integration¶

• Real-time evolution progress visualization
• Strategy genealogy and performance tracking
• Evolution control and strategy deployment

39.8 Implementation Roadmap¶

• Phase 1: Basic genetic encoding and strategy generation
• Phase 2: Evolution controller and evaluation system
• Phase 3: Advanced machine learning integration and automated deployment

39.9 Integration with Existing System¶

• Integrates with backtest engine, strategy engine, and execution layer
• Provides automated strategy evolution for all trading strategies

39.10 Business Value¶