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54. Global Strategy Performance Ranking System

Overview

The Global Strategy Performance Ranking System provides comprehensive strategy evaluation and dynamic capital allocation through real-time collection of key performance metrics, intelligent scoring algorithms, and automated ranking mechanisms. The system continuously monitors strategy performance, generates dynamic rankings, and automatically adjusts capital allocation to optimize portfolio returns while implementing strategy lifecycle management and performance-based filtering.

Core Capabilities

  • Real-Time Performance Monitoring: Continuous collection of key performance indicators across all strategies
  • Intelligent Scoring System: Multi-factor weighted scoring with customizable metrics and weights
  • Dynamic Ranking Engine: Real-time strategy ranking with historical trend analysis
  • Automated Capital Allocation: Dynamic capital reallocation based on performance rankings
  • Strategy Lifecycle Management: Automatic identification and management of underperforming strategies
  • Performance Analytics: Comprehensive performance analysis and trend identification
  • Risk-Adjusted Evaluation: Sophisticated risk-adjusted performance metrics

System Architecture

Microservice: strategy-ranking-center

services/strategy-ranking-center/
├── src/
│   ├── main.py
│   ├── collector/
│   │   ├── performance_collector.py
│   │   ├── metrics_calculator.py
│   │   └── data_aggregator.py
│   ├── scorer/
│   │   ├── strategy_scorer.py
│   │   ├── scoring_engine.py
│   │   ├── weight_optimizer.py
│   │   └── benchmark_comparator.py
│   ├── ranker/
│   │   ├── strategy_ranker.py
│   │   ├── ranking_engine.py
│   │   ├── trend_analyzer.py
│   │   └── volatility_adjuster.py
│   ├── allocator/
│   │   ├── capital_allocator.py
│   │   ├── allocation_optimizer.py
│   │   ├── rebalancing_engine.py
│   │   └── risk_parity_calculator.py
│   ├── controller/
│   │   ├── strategy_freezer.py
│   │   ├── lifecycle_manager.py
│   │   ├── performance_filter.py
│   │   └── alert_manager.py
│   ├── analytics/
│   │   ├── performance_analyzer.py
│   │   ├── correlation_analyzer.py
│   │   ├── attribution_analyzer.py
│   │   └── backtest_validator.py
│   ├── api/
│   │   ├── ranking_api.py
│   ├── config.py
│   └── requirements.txt
├── Dockerfile
└── tests/

Core Components

1. Performance Collector

Real-time collection of strategy performance metrics:

class PerformanceCollector:
    def __init__(self, metrics_calculator, data_aggregator):
        self.metrics_calculator = metrics_calculator
        self.data_aggregator = data_aggregator
        self.performance_cache = {}
        self.collection_history = {}

    async def collect_all_strategies_performance(self) -> Dict:
        """Collect performance data for all strategies"""

        # Get all active strategies
        active_strategies = await self.get_active_strategies()

        performance_data = {}

        for strategy_id in active_strategies:
            try:
                # Collect performance metrics
                strategy_performance = await self.collect_strategy_performance(strategy_id)
                performance_data[strategy_id] = strategy_performance

                # Cache performance data
                self.performance_cache[strategy_id] = strategy_performance

            except Exception as e:
                print(f"Error collecting performance for {strategy_id}: {e}")
                continue

        # Record collection timestamp
        collection_time = datetime.now().isoformat()
        self.collection_history[collection_time] = {
            "strategies_count": len(performance_data),
            "collection_status": "completed"
        }

        return performance_data

    async def collect_strategy_performance(self, strategy_id: str) -> Dict:
        """Collect comprehensive performance metrics for a strategy"""

        # Get basic performance metrics
        basic_metrics = await self.get_basic_metrics(strategy_id)

        # Calculate advanced metrics
        advanced_metrics = await self.calculate_advanced_metrics(strategy_id)

        # Get risk metrics
        risk_metrics = await self.calculate_risk_metrics(strategy_id)

        # Get attribution metrics
        attribution_metrics = await self.calculate_attribution_metrics(strategy_id)

        # Combine all metrics
        performance_data = {
            "strategy_id": strategy_id,
            "timestamp": datetime.now().isoformat(),
            "basic_metrics": basic_metrics,
            "advanced_metrics": advanced_metrics,
            "risk_metrics": risk_metrics,
            "attribution_metrics": attribution_metrics,
            "composite_score": 0.0  # Will be calculated by scorer
        }

        return performance_data

    async def get_basic_metrics(self, strategy_id: str) -> Dict:
        """Get basic performance metrics"""

        # Get returns data
        returns_data = await self.get_strategy_returns(strategy_id)

        # Calculate basic metrics
        cumulative_return = self.calculate_cumulative_return(returns_data)
        annualized_return = self.calculate_annualized_return(returns_data)
        volatility = self.calculate_volatility(returns_data)

        return {
            "cumulative_return": cumulative_return,
            "annualized_return": annualized_return,
            "volatility": volatility,
            "total_trades": len(returns_data.get("trades", [])),
            "win_rate": self.calculate_win_rate(returns_data)
        }

    async def calculate_advanced_metrics(self, strategy_id: str) -> Dict:
        """Calculate advanced performance metrics"""

        returns_data = await self.get_strategy_returns(strategy_id)

        # Calculate Sharpe ratio
        sharpe_ratio = self.calculate_sharpe_ratio(returns_data)

        # Calculate Sortino ratio
        sortino_ratio = self.calculate_sortino_ratio(returns_data)

        # Calculate Calmar ratio
        calmar_ratio = self.calculate_calmar_ratio(returns_data)

        # Calculate information ratio
        information_ratio = self.calculate_information_ratio(returns_data)

        return {
            "sharpe_ratio": sharpe_ratio,
            "sortino_ratio": sortino_ratio,
            "calmar_ratio": calmar_ratio,
            "information_ratio": information_ratio,
            "profit_factor": self.calculate_profit_factor(returns_data),
            "max_consecutive_losses": self.calculate_max_consecutive_losses(returns_data)
        }

    async def calculate_risk_metrics(self, strategy_id: str) -> Dict:
        """Calculate risk metrics"""

        returns_data = await self.get_strategy_returns(strategy_id)

        # Calculate drawdown metrics
        max_drawdown = self.calculate_max_drawdown(returns_data)
        avg_drawdown = self.calculate_average_drawdown(returns_data)
        drawdown_duration = self.calculate_drawdown_duration(returns_data)

        # Calculate VaR metrics
        var_95 = self.calculate_var(returns_data, 0.95)
        var_99 = self.calculate_var(returns_data, 0.99)
        expected_shortfall = self.calculate_expected_shortfall(returns_data)

        # Calculate beta and correlation
        beta = self.calculate_beta(returns_data)
        correlation = self.calculate_correlation(returns_data)

        return {
            "max_drawdown": max_drawdown,
            "avg_drawdown": avg_drawdown,
            "drawdown_duration": drawdown_duration,
            "var_95": var_95,
            "var_99": var_99,
            "expected_shortfall": expected_shortfall,
            "beta": beta,
            "correlation": correlation,
            "downside_deviation": self.calculate_downside_deviation(returns_data)
        }

    async def calculate_attribution_metrics(self, strategy_id: str) -> Dict:
        """Calculate attribution metrics"""

        # Get benchmark data
        benchmark_returns = await self.get_benchmark_returns(strategy_id)
        strategy_returns = await self.get_strategy_returns(strategy_id)

        # Calculate alpha
        alpha = self.calculate_alpha(strategy_returns, benchmark_returns)

        # Calculate tracking error
        tracking_error = self.calculate_tracking_error(strategy_returns, benchmark_returns)

        # Calculate information ratio
        information_ratio = self.calculate_information_ratio(strategy_returns, benchmark_returns)

        return {
            "alpha": alpha,
            "tracking_error": tracking_error,
            "information_ratio": information_ratio,
            "r_squared": self.calculate_r_squared(strategy_returns, benchmark_returns)
        }

    def calculate_cumulative_return(self, returns_data: Dict) -> float:
        """Calculate cumulative return"""

        returns = returns_data.get("returns", [])
        if not returns:
            return 0.0

        cumulative_return = 1.0
        for ret in returns:
            cumulative_return *= (1 + ret)

        return cumulative_return - 1

    def calculate_sharpe_ratio(self, returns_data: Dict, risk_free_rate: float = 0.02) -> float:
        """Calculate Sharpe ratio"""

        returns = returns_data.get("returns", [])
        if not returns:
            return 0.0

        avg_return = np.mean(returns)
        std_return = np.std(returns)

        if std_return == 0:
            return 0.0

        # Annualize
        sharpe = (avg_return - risk_free_rate/252) / std_return * np.sqrt(252)
        return sharpe

    def calculate_max_drawdown(self, returns_data: Dict) -> float:
        """Calculate maximum drawdown"""

        returns = returns_data.get("returns", [])
        if not returns:
            return 0.0

        cumulative = np.cumprod(1 + np.array(returns))
        running_max = np.maximum.accumulate(cumulative)
        drawdown = (cumulative - running_max) / running_max

        return abs(np.min(drawdown))

    async def get_strategy_returns(self, strategy_id: str) -> Dict:
        """Get strategy returns data"""

        # This would integrate with performance database
        # For now, return placeholder data
        return {
            "returns": [0.001, -0.002, 0.003, 0.001, -0.001],
            "trades": [],
            "benchmark_returns": [0.0005, -0.001, 0.002, 0.0005, -0.0005]
        }

    async def get_active_strategies(self) -> List[str]:
        """Get list of active strategies"""

        # This would integrate with strategy management system
        # For now, return placeholder
        return ["strategy_001", "strategy_002", "strategy_003"]

2. Strategy Scorer

Intelligent scoring system for strategy evaluation:

class StrategyScorer:
    def __init__(self, scoring_engine, weight_optimizer):
        self.scoring_engine = scoring_engine
        self.weight_optimizer = weight_optimizer
        self.scoring_weights = {
            "return_weight": 0.25,
            "risk_weight": 0.25,
            "sharpe_weight": 0.20,
            "consistency_weight": 0.15,
            "attribution_weight": 0.15
        }

    def score_all_strategies(self, performance_data: Dict) -> Dict:
        """Score all strategies"""

        strategy_scores = {}

        for strategy_id, performance in performance_data.items():
            try:
                score = self.calculate_strategy_score(performance)
                strategy_scores[strategy_id] = score
            except Exception as e:
                print(f"Error scoring strategy {strategy_id}: {e}")
                strategy_scores[strategy_id] = 0.0

        return strategy_scores

    def calculate_strategy_score(self, performance: Dict) -> float:
        """Calculate comprehensive score for a strategy"""

        # Extract metrics
        basic_metrics = performance.get("basic_metrics", {})
        advanced_metrics = performance.get("advanced_metrics", {})
        risk_metrics = performance.get("risk_metrics", {})
        attribution_metrics = performance.get("attribution_metrics", {})

        # Calculate component scores
        return_score = self.calculate_return_score(basic_metrics)
        risk_score = self.calculate_risk_score(risk_metrics)
        sharpe_score = self.calculate_sharpe_score(advanced_metrics)
        consistency_score = self.calculate_consistency_score(basic_metrics, advanced_metrics)
        attribution_score = self.calculate_attribution_score(attribution_metrics)

        # Calculate weighted composite score
        composite_score = (
            return_score * self.scoring_weights["return_weight"] +
            risk_score * self.scoring_weights["risk_weight"] +
            sharpe_score * self.scoring_weights["sharpe_weight"] +
            consistency_score * self.scoring_weights["consistency_weight"] +
            attribution_score * self.scoring_weights["attribution_weight"]
        )

        # Normalize score to 0-100 range
        normalized_score = max(0, min(100, composite_score * 100))

        return normalized_score

    def calculate_return_score(self, basic_metrics: Dict) -> float:
        """Calculate return component score"""

        cumulative_return = basic_metrics.get("cumulative_return", 0)
        annualized_return = basic_metrics.get("annualized_return", 0)

        # Score based on annualized return
        if annualized_return > 0.20:  # > 20%
            return 1.0
        elif annualized_return > 0.10:  # > 10%
            return 0.8
        elif annualized_return > 0.05:  # > 5%
            return 0.6
        elif annualized_return > 0:  # > 0%
            return 0.4
        else:
            return 0.0

    def calculate_risk_score(self, risk_metrics: Dict) -> float:
        """Calculate risk component score"""

        max_drawdown = risk_metrics.get("max_drawdown", 1.0)
        var_95 = risk_metrics.get("var_95", 0.1)

        # Score based on risk metrics (lower risk = higher score)
        drawdown_score = max(0, 1 - max_drawdown * 2)  # Penalize high drawdowns
        var_score = max(0, 1 - var_95 * 10)  # Penalize high VaR

        return (drawdown_score + var_score) / 2

    def calculate_sharpe_score(self, advanced_metrics: Dict) -> float:
        """Calculate Sharpe ratio component score"""

        sharpe_ratio = advanced_metrics.get("sharpe_ratio", 0)
        sortino_ratio = advanced_metrics.get("sortino_ratio", 0)

        # Score based on Sharpe ratio
        if sharpe_ratio > 2.0:
            return 1.0
        elif sharpe_ratio > 1.5:
            return 0.9
        elif sharpe_ratio > 1.0:
            return 0.8
        elif sharpe_ratio > 0.5:
            return 0.6
        elif sharpe_ratio > 0:
            return 0.4
        else:
            return 0.0

    def calculate_consistency_score(self, basic_metrics: Dict, advanced_metrics: Dict) -> float:
        """Calculate consistency component score"""

        win_rate = basic_metrics.get("win_rate", 0.5)
        profit_factor = advanced_metrics.get("profit_factor", 1.0)
        max_consecutive_losses = advanced_metrics.get("max_consecutive_losses", 10)

        # Score based on consistency metrics
        win_rate_score = win_rate
        profit_factor_score = min(1.0, profit_factor / 2.0)
        consecutive_losses_score = max(0, 1 - max_consecutive_losses / 20)

        return (win_rate_score + profit_factor_score + consecutive_losses_score) / 3

    def calculate_attribution_score(self, attribution_metrics: Dict) -> float:
        """Calculate attribution component score"""

        alpha = attribution_metrics.get("alpha", 0)
        information_ratio = attribution_metrics.get("information_ratio", 0)
        r_squared = attribution_metrics.get("r_squared", 0)

        # Score based on attribution metrics
        alpha_score = max(0, min(1, alpha * 100))  # Normalize alpha
        ir_score = max(0, min(1, information_ratio / 2))  # Normalize IR
        r2_score = r_squared  # R-squared is already 0-1

        return (alpha_score + ir_score + r2_score) / 3

    def update_scoring_weights(self, new_weights: Dict):
        """Update scoring weights"""

        self.scoring_weights.update(new_weights)

        # Normalize weights to sum to 1
        total_weight = sum(self.scoring_weights.values())
        if total_weight > 0:
            for key in self.scoring_weights:
                self.scoring_weights[key] /= total_weight

3. Strategy Ranker

Dynamic ranking engine for strategies:

class StrategyRanker:
    def __init__(self, ranking_engine, trend_analyzer):
        self.ranking_engine = ranking_engine
        self.trend_analyzer = trend_analyzer
        self.ranking_history = {}
        self.ranking_changes = {}

    def generate_ranking(self, strategy_scores: Dict) -> List[Dict]:
        """Generate comprehensive strategy ranking"""

        # Sort strategies by score
        sorted_strategies = sorted(
            strategy_scores.items(), 
            key=lambda x: x[1], 
            reverse=True
        )

        # Generate ranking with additional metrics
        ranking = []
        for rank, (strategy_id, score) in enumerate(sorted_strategies, 1):
            ranking_entry = {
                "rank": rank,
                "strategy_id": strategy_id,
                "score": score,
                "score_percentile": self.calculate_percentile(rank, len(sorted_strategies)),
                "rank_change": self.get_rank_change(strategy_id, rank),
                "score_change": self.get_score_change(strategy_id, score),
                "trend": self.get_ranking_trend(strategy_id)
            }
            ranking.append(ranking_entry)

        # Store ranking history
        self.store_ranking_history(ranking)

        return ranking

    def calculate_percentile(self, rank: int, total_strategies: int) -> float:
        """Calculate percentile for a rank"""

        if total_strategies <= 1:
            return 100.0

        percentile = ((total_strategies - rank) / (total_strategies - 1)) * 100
        return percentile

    def get_rank_change(self, strategy_id: str, current_rank: int) -> int:
        """Get rank change from previous ranking"""

        if strategy_id in self.ranking_history:
            previous_rank = self.ranking_history[strategy_id].get("rank", current_rank)
            return previous_rank - current_rank  # Positive = improved, Negative = declined

        return 0

    def get_score_change(self, strategy_id: str, current_score: float) -> float:
        """Get score change from previous ranking"""

        if strategy_id in self.ranking_history:
            previous_score = self.ranking_history[strategy_id].get("score", current_score)
            return current_score - previous_score

        return 0.0

    def get_ranking_trend(self, strategy_id: str) -> str:
        """Get ranking trend for strategy"""

        # Analyze recent ranking history
        recent_rankings = self.get_recent_rankings(strategy_id, days=7)

        if len(recent_rankings) < 2:
            return "stable"

        # Calculate trend
        first_rank = recent_rankings[0]["rank"]
        last_rank = recent_rankings[-1]["rank"]

        if last_rank < first_rank:
            return "improving"
        elif last_rank > first_rank:
            return "declining"
        else:
            return "stable"

    def store_ranking_history(self, ranking: List[Dict]):
        """Store current ranking in history"""

        timestamp = datetime.now().isoformat()

        for entry in ranking:
            strategy_id = entry["strategy_id"]
            self.ranking_history[strategy_id] = {
                "rank": entry["rank"],
                "score": entry["score"],
                "timestamp": timestamp
            }

    def get_recent_rankings(self, strategy_id: str, days: int = 7) -> List[Dict]:
        """Get recent ranking history for strategy"""

        # This would integrate with historical data storage
        # For now, return placeholder
        return []

    def get_ranking_statistics(self) -> Dict:
        """Get ranking statistics"""

        if not self.ranking_history:
            return {}

        # Calculate statistics
        total_strategies = len(self.ranking_history)
        avg_score = sum(entry["score"] for entry in self.ranking_history.values()) / total_strategies

        return {
            "total_strategies": total_strategies,
            "average_score": avg_score,
            "top_performers": self.get_top_performers(),
            "bottom_performers": self.get_bottom_performers()
        }

    def get_top_performers(self, count: int = 5) -> List[Dict]:
        """Get top performing strategies"""

        sorted_strategies = sorted(
            self.ranking_history.items(),
            key=lambda x: x[1]["score"],
            reverse=True
        )

        return [
            {"strategy_id": strategy_id, "score": data["score"], "rank": data["rank"]}
            for strategy_id, data in sorted_strategies[:count]
        ]

    def get_bottom_performers(self, count: int = 5) -> List[Dict]:
        """Get bottom performing strategies"""

        sorted_strategies = sorted(
            self.ranking_history.items(),
            key=lambda x: x[1]["score"]
        )

        return [
            {"strategy_id": strategy_id, "score": data["score"], "rank": data["rank"]}
            for strategy_id, data in sorted_strategies[:count]
        ]

4. Capital Allocator

Dynamic capital allocation based on rankings:

class CapitalAllocator:
    def __init__(self, allocation_optimizer, rebalancing_engine):
        self.allocation_optimizer = allocation_optimizer
        self.rebalancing_engine = rebalancing_engine
        self.allocation_history = {}
        self.current_allocation = {}

    def calculate_allocation(self, ranking: List[Dict], total_capital: float) -> Dict:
        """Calculate capital allocation based on ranking"""

        # Get allocation weights
        allocation_weights = self.calculate_allocation_weights(ranking)

        # Calculate actual allocations
        allocations = {}
        for strategy_id, weight in allocation_weights.items():
            allocation = total_capital * weight
            allocations[strategy_id] = {
                "allocation": allocation,
                "weight": weight,
                "percentage": weight * 100
            }

        # Store allocation history
        self.store_allocation_history(allocations)

        return allocations

    def calculate_allocation_weights(self, ranking: List[Dict]) -> Dict:
        """Calculate allocation weights based on ranking"""

        weights = {}
        total_strategies = len(ranking)

        for entry in ranking:
            strategy_id = entry["strategy_id"]
            rank = entry["rank"]
            score = entry["score"]

            # Calculate weight based on rank and score
            # Higher rank and score = higher weight
            rank_weight = 1.0 / rank  # Inverse rank weighting
            score_weight = score / 100  # Normalize score to 0-1

            # Combine weights
            combined_weight = (rank_weight * 0.7 + score_weight * 0.3)
            weights[strategy_id] = combined_weight

        # Normalize weights to sum to 1
        total_weight = sum(weights.values())
        if total_weight > 0:
            for strategy_id in weights:
                weights[strategy_id] /= total_weight

        return weights

    def store_allocation_history(self, allocations: Dict):
        """Store allocation history"""

        timestamp = datetime.now().isoformat()

        for strategy_id, allocation_data in allocations.items():
            if strategy_id not in self.allocation_history:
                self.allocation_history[strategy_id] = []

            self.allocation_history[strategy_id].append({
                "allocation": allocation_data["allocation"],
                "weight": allocation_data["weight"],
                "timestamp": timestamp
            })

            # Keep history manageable
            if len(self.allocation_history[strategy_id]) > 1000:
                self.allocation_history[strategy_id] = self.allocation_history[strategy_id][-1000:]

    def get_allocation_trend(self, strategy_id: str, days: int = 30) -> Dict:
        """Get allocation trend for strategy"""

        if strategy_id not in self.allocation_history:
            return {"trend": "stable", "change_pct": 0.0}

        history = self.allocation_history[strategy_id]

        # Get recent history
        cutoff_time = datetime.now() - timedelta(days=days)
        recent_history = [
            entry for entry in history
            if datetime.fromisoformat(entry["timestamp"]) > cutoff_time
        ]

        if len(recent_history) < 2:
            return {"trend": "stable", "change_pct": 0.0}

        # Calculate trend
        initial_weight = recent_history[0]["weight"]
        final_weight = recent_history[-1]["weight"]

        change_pct = (final_weight - initial_weight) / initial_weight if initial_weight > 0 else 0

        if change_pct > 0.1:
            trend = "increasing"
        elif change_pct < -0.1:
            trend = "decreasing"
        else:
            trend = "stable"

        return {
            "trend": trend,
            "change_pct": change_pct,
            "initial_weight": initial_weight,
            "final_weight": final_weight
        }

API Design

Strategy Ranking API

@router.get("/ranking/current")
async def get_current_ranking():
    """Get current strategy ranking"""

    # Collect performance data
    performance_data = await performance_collector.collect_all_strategies_performance()

    # Score strategies
    strategy_scores = strategy_scorer.score_all_strategies(performance_data)

    # Generate ranking
    ranking = strategy_ranker.generate_ranking(strategy_scores)

    return {
        "ranking": ranking,
        "timestamp": datetime.now().isoformat(),
        "total_strategies": len(ranking)
    }

@router.get("/ranking/history/{strategy_id}")
async def get_ranking_history(strategy_id: str, days: int = 30):
    """Get ranking history for strategy"""

    history = strategy_ranker.get_recent_rankings(strategy_id, days)
    return {"strategy_id": strategy_id, "history": history}

@router.get("/allocation/current")
async def get_current_allocation():
    """Get current capital allocation"""

    # Get current ranking
    ranking = await get_current_ranking()

    # Calculate allocation
    total_capital = 1000000  # This would come from portfolio manager
    allocation = capital_allocator.calculate_allocation(ranking["ranking"], total_capital)

    return {
        "allocation": allocation,
        "total_capital": total_capital,
        "timestamp": datetime.now().isoformat()
    }

@router.get("/performance/{strategy_id}")
async def get_strategy_performance(strategy_id: str):
    """Get detailed performance for strategy"""

    performance = await performance_collector.collect_strategy_performance(strategy_id)
    return performance

Frontend Integration

Strategy Ranking Dashboard

const StrategyRankingView: React.FC = () => {
  const [currentRanking, setCurrentRanking] = useState<Ranking[]>([]);
  const [currentAllocation, setCurrentAllocation] = useState<Allocation | null>(null);
  const [performanceData, setPerformanceData] = useState<PerformanceData[]>([]);

  return (
    <div className="strategy-ranking-dashboard">
      {/* Current Ranking */}
      <StrategyRankingPanel 
        ranking={currentRanking}
        onStrategySelect={handleStrategySelect}
      />

      {/* Capital Allocation */}
      <CapitalAllocationPanel 
        allocation={currentAllocation}
      />

      {/* Performance Metrics */}
      <PerformanceMetricsPanel 
        performanceData={performanceData}
      />

      {/* Ranking Trends */}
      <RankingTrendsPanel 
        ranking={currentRanking}
      />

      {/* Strategy Comparison */}
      <StrategyComparisonPanel 
        ranking={currentRanking}
      />

      {/* Allocation History */}
      <AllocationHistoryPanel 
        allocation={currentAllocation}
      />

      {/* Performance Attribution */}
      <PerformanceAttributionPanel 
        performanceData={performanceData}
      />
    </div>
  );
};

Implementation Roadmap

Phase 1: Core Infrastructure (Weeks 1-2)

  • Set up performance collection framework
  • Implement basic scoring algorithms
  • Create ranking engine

Phase 2: Advanced Analytics (Weeks 3-4)

  • Develop comprehensive performance metrics
  • Implement trend analysis and attribution
  • Build allocation optimization

Phase 3: Capital Management (Weeks 5-6)

  • Create dynamic capital allocation
  • Implement rebalancing engine
  • Build strategy lifecycle management

Phase 4: Integration & Optimization (Weeks 7-8)

  • Integrate with existing strategy management
  • Develop comprehensive dashboard
  • Performance optimization and testing

Business Value

Strategic Benefits

  1. Performance Optimization: Automatic identification of best-performing strategies
  2. Capital Efficiency: Dynamic allocation to maximize returns
  3. Risk Management: Continuous monitoring and filtering of underperforming strategies
  4. Competitive Advantage: Data-driven strategy selection and optimization

Operational Benefits

  1. Automated Management: Systematic strategy evaluation and ranking
  2. Real-Time Monitoring: Continuous performance tracking and analysis
  3. Dynamic Allocation: Automatic capital reallocation based on performance
  4. Lifecycle Management: Proactive identification and management of strategy performance

Technical Specifications

Performance Requirements

  • Performance Collection: < 100ms for strategy metrics collection
  • Scoring Calculation: < 50ms for strategy scoring
  • Ranking Generation: < 20ms for ranking updates
  • Allocation Calculation: < 100ms for capital allocation

Analytics Requirements

  • Metric Accuracy: ±1% accuracy for performance calculations
  • Real-Time Updates: Sub-minute updates for critical metrics
  • Historical Analysis: Support for multi-year performance history
  • Scalability: Handle 1000+ concurrent strategies

This Global Strategy Performance Ranking System provides institutional-grade strategy evaluation and capital allocation capabilities, enabling sophisticated performance optimization and dynamic capital management, similar to the systems used by major quantitative asset managers like AQR and Man Group.