Adaptive Portfolio of Strategies Engine¶

36.1 System Overview¶

The Adaptive Portfolio of Strategies Engine automatically combines different strategies (trend, arbitrage, mean reversion, market making) into an optimal "super portfolio" based on real-time performance metrics. It implements professional portfolio management techniques for strategy allocation, enabling multi-objective optimization and correlation constraints.

36.1.1 Core Objectives¶

• Strategy Performance Tracking: Real-time monitoring of returns, volatility, drawdown, Sharpe, and Sortino ratios
• Dynamic Portfolio Optimization: Multi-objective optimization (maximize returns, minimize volatility)
• Correlation Constraints: Prevent high positive correlations between strategies
• Automatic Rebalancing: Periodic portfolio rebalancing (daily/weekly)
• Meta-Strategy Management: Professional portfolio management for all strategies

36.2 Architecture Design¶

36.2.1 Microservice Architecture¶

Strategy Portfolio Center Service:

services/strategy-portfolio-center/
├── src/
│   ├── main.py
│   ├── tracker/
│   │   ├── strategy_tracker.py
│   ├── optimizer/
│   │   ├── portfolio_optimizer.py
│   ├── executor/
│   │   ├── rebalance_executor.py
│   ├── monitor/
│   │   ├── portfolio_monitor.py
│   ├── api/
│   │   ├── portfolio_api.py
│   ├── config.py
│   ├── requirements.txt
├── Dockerfile

36.2.2 Core Components¶

• Strategy Performance Tracker: Real-time tracking of strategy performance metrics
• Portfolio Optimizer: Dynamic calculation of optimal portfolio weights
• Rebalance Executor: Adjusts strategy fund allocation based on new weights
• Portfolio Monitor: Real-time monitoring of overall portfolio performance
• API Interface: Query current portfolio composition and weights
• Frontend Dashboard: Portfolio weight visualization and performance tracking

36.3 Module Design¶

36.3.1 Strategy Performance Tracker (strategy_tracker.py)¶

• Collects and updates strategy performance data

class StrategyTracker:
    def __init__(self):
        self.performance_data = {}

    def update_performance(self, strategy_id, daily_return, volatility, max_drawdown):
        self.performance_data[strategy_id] = {
            "return": daily_return,
            "volatility": volatility,
            "max_drawdown": max_drawdown
        }

36.3.2 Portfolio Optimizer (portfolio_optimizer.py)¶

• Implements mean-variance optimization and custom optimization methods

import numpy as np
import cvxpy as cp

class PortfolioOptimizer:
    def optimize(self, returns, covariance_matrix):
        n = len(returns)
        weights = cp.Variable(n)
        objective = cp.Maximize(returns @ weights - 0.5 * cp.quad_form(weights, covariance_matrix))
        constraints = [cp.sum(weights) == 1, weights >= 0]
        prob = cp.Problem(objective, constraints)
        prob.solve()
        return weights.value

36.3.3 Rebalance Executor (rebalance_executor.py)¶

• Executes portfolio rebalancing based on new weights

class RebalanceExecutor:
    def rebalance(self, new_weights):
        for strategy_id, weight in new_weights.items():
            adjust_strategy_fund_allocation(strategy_id, weight)

36.3.4 Portfolio Monitor (portfolio_monitor.py)¶

• Monitors overall portfolio performance metrics

class PortfolioMonitor:
    def compute_portfolio_metrics(self, strategy_returns, strategy_weights):
        portfolio_return = np.dot(strategy_returns, strategy_weights)
        return portfolio_return

36.3.5 API Interface (portfolio_api.py)¶

• FastAPI endpoints for portfolio queries and management

from fastapi import APIRouter
router = APIRouter()

@router.get("/portfolio/weights")
async def get_current_portfolio_weights():
    return optimizer.current_weights

36.3.6 Frontend Dashboard¶

• Strategy portfolio weight pie chart
• Portfolio historical return curve
• Rebalancing history table
• Real-time portfolio Sharpe ratio and max drawdown

36.4 Portfolio Optimization Flow Example¶

1. Strategy daily performance updates → strategy-tracker records metrics
2. Portfolio optimizer recalculates optimal weights
3. Rebalance executor adjusts strategy fund allocation
4. Portfolio monitor tracks overall performance
5. Frontend dashboard visualizes portfolio changes and optimization effects

36.5 Technology Stack¶

• Python (FastAPI, numpy, cvxpy): Service implementation and optimization
• Redis: Real-time performance data caching
• Docker: Containerization
• React/TypeScript: Frontend dashboard
• Prometheus: Portfolio performance metrics

36.6 API Design¶

• GET /portfolio/weights: Get current portfolio weights
• GET /portfolio/performance: Get portfolio performance metrics
• POST /portfolio/rebalance: Trigger manual rebalancing
• GET /portfolio/history: Get rebalancing history
• GET /portfolio/optimization: Get optimization parameters and results

36.7 Frontend Integration¶

• Real-time portfolio weight visualization
• Performance tracking and historical analysis
• Rebalancing management and optimization controls

36.8 Implementation Roadmap¶

• Phase 1: Strategy performance tracking and basic optimization
• Phase 2: Advanced optimization algorithms and rebalancing
• Phase 3: Correlation constraints and advanced portfolio management

36.9 Integration with Existing System¶

• Integrates with strategy engine, risk center, and execution layer
• Provides portfolio management for all strategies and accounts

36.10 Business Value¶