Auto Strategy Review Center Design¶
12.1 Overview¶
The Auto Strategy Review Center represents the quality control and governance component that ensures all strategies meet institutional-grade standards before live trading deployment. This system provides systematic strategy review, automated backtesting, comprehensive metrics evaluation, and standardized approval processes to maintain portfolio quality and risk management standards.
🎯 Core Capabilities¶
| Capability | Description |
|---|---|
| Systematic Backtesting | Automated backtesting with standardized parameters |
| Comprehensive Metrics | Sharpe ratio, max drawdown, win rate, profit factor evaluation |
| Quality Standards | Configurable review criteria and thresholds |
| Automated Decision | Automated approve/reject decision making |
| Multi-stage Review | Multi-environment review process (DEV/QA/PROD) |
| Audit Trail | Complete review history and decision tracking |
12.2 System Architecture¶
12.2.1 Strategy Review Center Service Microservice Design¶
New Microservice: strategy-review-center
services/strategy-review-center/
├── src/
│ ├── main.py # FastAPI application entry point
│ ├── evaluator/
│ │ ├── backtest_runner.py # Automated backtest execution
│ │ ├── metrics_evaluator.py # Performance metrics calculation
│ │ ├── risk_evaluator.py # Risk metrics evaluation
│ │ └── quality_evaluator.py # Strategy quality assessment
│ ├── review/
│ │ ├── review_engine.py # Main review decision engine
│ │ ├── criteria_manager.py # Review criteria management
│ │ ├── approval_workflow.py # Multi-stage approval workflow
│ │ └── decision_validator.py # Decision validation and audit
│ ├── registry/
│ │ ├── strategy_registry.py # Strategy registration management
│ │ ├── version_control.py # Strategy version management
│ │ └── metadata_manager.py # Strategy metadata management
│ ├── api/
│ │ ├── review_api.py # Review management endpoints
│ │ ├── registry_api.py # Strategy registry endpoints
│ │ └── metrics_api.py # Metrics and reporting endpoints
│ ├── models/
│ │ ├── strategy_model.py # Strategy configuration models
│ │ ├── review_model.py # Review process models
│ │ ├── metrics_model.py # Performance metrics models
│ │ └── approval_model.py # Approval workflow models
│ ├── config.py # Configuration management
│ └── requirements.txt # Python dependencies
├── Dockerfile # Container definition
└── docker-compose.yml # Local development setup
12.2.2 Strategy Review Architecture Layers¶
Layer 1: Strategy Registration - Strategy Metadata: Strategy information and configuration - Version Control: Strategy version management - Dependency Tracking: Strategy dependencies and requirements - Documentation: Strategy documentation and specifications
Layer 2: Automated Testing - Backtest Execution: Automated backtest execution - Performance Analysis: Comprehensive performance analysis - Risk Assessment: Risk metrics calculation and evaluation - Quality Assessment: Strategy quality and robustness testing
Layer 3: Review Decision - Criteria Evaluation: Review criteria application - Decision Engine: Automated decision making - Workflow Management: Multi-stage approval workflow - Validation: Decision validation and audit
Layer 4: Deployment Control - Environment Management: Multi-environment deployment control - Approval Tracking: Approval status tracking - Deployment Coordination: Strategy deployment coordination - Monitoring Integration: Post-deployment monitoring setup
12.3 Core Components Design¶
12.3.1 Strategy Registry Module¶
Purpose: Manage strategy registration and metadata
Key Functions: - Strategy Registration: Register new strategies for review - Metadata Management: Manage strategy metadata and documentation - Version Control: Track strategy versions and changes - Dependency Management: Manage strategy dependencies
Strategy Registry Implementation:
from datetime import datetime
from typing import Dict, List, Optional
from enum import Enum
class StrategyStatus(Enum):
DRAFT = "draft"
SUBMITTED = "submitted"
UNDER_REVIEW = "under_review"
APPROVED = "approved"
REJECTED = "rejected"
DEPLOYED = "deployed"
ARCHIVED = "archived"
class StrategyType(Enum):
TREND_FOLLOWING = "trend_following"
MEAN_REVERSION = "mean_reversion"
ARBITRAGE = "arbitrage"
MARKET_MAKING = "market_making"
STATISTICAL_ARBITRAGE = "statistical_arbitrage"
MOMENTUM = "momentum"
class StrategyRegistry:
def __init__(self):
self.strategies = {}
self.next_strategy_id = 1
def register_strategy(self, strategy_data: Dict) -> str:
"""Register a new strategy for review"""
strategy_id = f"strategy_{self.next_strategy_id:06d}"
self.next_strategy_id += 1
strategy_record = {
"strategy_id": strategy_id,
"name": strategy_data["name"],
"type": strategy_data["type"],
"description": strategy_data.get("description", ""),
"author": strategy_data["author"],
"version": strategy_data.get("version", "1.0.0"),
"parameters": strategy_data.get("parameters", {}),
"dependencies": strategy_data.get("dependencies", []),
"status": StrategyStatus.DRAFT,
"created_at": datetime.now(),
"updated_at": datetime.now(),
"review_history": [],
"deployment_history": []
}
self.strategies[strategy_id] = strategy_record
return strategy_id
def submit_for_review(self, strategy_id: str) -> bool:
"""Submit strategy for review"""
if strategy_id not in self.strategies:
return False
strategy = self.strategies[strategy_id]
strategy["status"] = StrategyStatus.SUBMITTED
strategy["updated_at"] = datetime.now()
return True
def update_strategy(self, strategy_id: str, updates: Dict) -> bool:
"""Update strategy information"""
if strategy_id not in self.strategies:
return False
strategy = self.strategies[strategy_id]
for key, value in updates.items():
if key in ["name", "description", "parameters", "dependencies"]:
strategy[key] = value
strategy["updated_at"] = datetime.now()
return True
def get_strategy(self, strategy_id: str) -> Optional[Dict]:
"""Get strategy information"""
return self.strategies.get(strategy_id)
def list_strategies(self, status: Optional[StrategyStatus] = None) -> List[Dict]:
"""List strategies with optional status filter"""
strategies = list(self.strategies.values())
if status:
strategies = [s for s in strategies if s["status"] == status]
return strategies
12.3.2 Backtest Runner Module¶
Purpose: Execute automated backtests for strategy review
Key Functions: - Backtest Execution: Automated backtest execution - Parameter Management: Standardized backtest parameters - Result Collection: Backtest result collection and storage - Integration: Integration with backtest engine
Backtest Runner Implementation:
import asyncio
from typing import Dict, List, Optional
from datetime import datetime, timedelta
class BacktestRunner:
def __init__(self, backtest_engine_url: str = "http://backtest-engine:8000"):
self.backtest_engine_url = backtest_engine_url
self.standard_parameters = {
"start_date": "2023-01-01",
"end_date": "2024-12-20",
"initial_capital": 100000,
"commission": 0.001,
"slippage": 0.0005,
"data_frequency": "1min"
}
async def run_backtest(self, strategy_id: str, strategy_params: Dict) -> Dict:
"""Run automated backtest for strategy review"""
try:
# Prepare backtest request
backtest_request = {
"strategy_id": strategy_id,
"strategy_parameters": strategy_params,
"backtest_parameters": self.standard_parameters,
"request_id": f"review_{strategy_id}_{int(datetime.now().timestamp())}"
}
# Execute backtest (this would integrate with actual backtest engine)
backtest_result = await self._execute_backtest(backtest_request)
# Validate backtest result
if not self._validate_backtest_result(backtest_result):
raise ValueError("Invalid backtest result")
return backtest_result
except Exception as e:
return {
"success": False,
"error": str(e),
"strategy_id": strategy_id,
"timestamp": datetime.now()
}
async def _execute_backtest(self, request: Dict) -> Dict:
"""Execute backtest via backtest engine"""
# This would make HTTP call to backtest engine
# For now, return mock result
return {
"success": True,
"strategy_id": request["strategy_id"],
"performance_metrics": {
"total_return": 0.15,
"annualized_return": 0.12,
"sharpe_ratio": 1.8,
"max_drawdown": 0.08,
"win_rate": 0.65,
"profit_factor": 1.5,
"calmar_ratio": 1.5,
"sortino_ratio": 2.1
},
"risk_metrics": {
"volatility": 0.12,
"var_95": 0.02,
"cvar_95": 0.025,
"beta": 0.8,
"correlation": 0.3
},
"trade_metrics": {
"total_trades": 1250,
"winning_trades": 812,
"losing_trades": 438,
"avg_trade_duration": 2.5,
"avg_win": 0.008,
"avg_loss": 0.005
},
"execution_metrics": {
"total_slippage": 0.002,
"avg_slippage": 0.0008,
"execution_cost": 0.0012,
"fill_rate": 0.98
},
"timestamp": datetime.now()
}
def _validate_backtest_result(self, result: Dict) -> bool:
"""Validate backtest result"""
required_fields = ["success", "strategy_id", "performance_metrics"]
return all(field in result for field in required_fields) and result["success"]
def get_standard_parameters(self) -> Dict:
"""Get standard backtest parameters"""
return self.standard_parameters.copy()
def update_standard_parameters(self, new_params: Dict):
"""Update standard backtest parameters"""
self.standard_parameters.update(new_params)
12.3.3 Metrics Evaluator Module¶
Purpose: Evaluate strategy performance metrics against review criteria
Key Functions: - Metrics Calculation: Calculate comprehensive performance metrics - Criteria Evaluation: Evaluate metrics against review criteria - Risk Assessment: Assess strategy risk characteristics - Quality Scoring: Generate overall quality score
Metrics Evaluator Implementation:
from typing import Dict, List, Tuple
import numpy as np
class MetricsEvaluator:
def __init__(self, review_criteria: Dict):
self.review_criteria = review_criteria
self.metric_weights = {
"sharpe_ratio": 0.25,
"max_drawdown": 0.20,
"win_rate": 0.15,
"profit_factor": 0.15,
"calmar_ratio": 0.10,
"sortino_ratio": 0.10,
"var_95": 0.05
}
def evaluate_strategy(self, backtest_result: Dict) -> Dict:
"""Evaluate strategy against review criteria"""
if not backtest_result.get("success", False):
return {
"overall_score": 0,
"status": "FAILED",
"reason": "Backtest failed",
"evaluation_details": {}
}
performance_metrics = backtest_result["performance_metrics"]
risk_metrics = backtest_result["risk_metrics"]
# Calculate individual metric scores
metric_scores = {}
failed_criteria = []
for metric, threshold in self.review_criteria.items():
if metric in performance_metrics:
value = performance_metrics[metric]
score, passed = self._evaluate_metric(metric, value, threshold)
metric_scores[metric] = {
"value": value,
"threshold": threshold,
"score": score,
"passed": passed
}
if not passed:
failed_criteria.append(f"{metric}: {value} vs {threshold}")
# Calculate overall score
overall_score = self._calculate_overall_score(metric_scores)
# Determine status
status, reason = self._determine_status(metric_scores, failed_criteria)
return {
"overall_score": overall_score,
"status": status,
"reason": reason,
"evaluation_details": metric_scores,
"failed_criteria": failed_criteria
}
def _evaluate_metric(self, metric: str, value: float, threshold: float) -> Tuple[float, bool]:
"""Evaluate individual metric against threshold"""
if metric == "max_drawdown":
# For max_drawdown, lower is better
if value <= threshold:
score = 1.0
passed = True
else:
score = max(0, 1 - (value - threshold) / threshold)
passed = False
else:
# For other metrics, higher is better
if value >= threshold:
score = 1.0
passed = True
else:
score = max(0, value / threshold)
passed = False
return score, passed
def _calculate_overall_score(self, metric_scores: Dict) -> float:
"""Calculate overall evaluation score"""
total_score = 0
total_weight = 0
for metric, score_data in metric_scores.items():
weight = self.metric_weights.get(metric, 0.1)
total_score += score_data["score"] * weight
total_weight += weight
return total_score / total_weight if total_weight > 0 else 0
def _determine_status(self, metric_scores: Dict, failed_criteria: List[str]) -> Tuple[str, str]:
"""Determine overall review status"""
if not failed_criteria:
return "APPROVED", "All criteria passed"
# Check if any critical criteria failed
critical_metrics = ["sharpe_ratio", "max_drawdown"]
critical_failures = [criteria for criteria in failed_criteria
if any(metric in criteria for metric in critical_metrics)]
if critical_failures:
return "REJECTED", f"Critical criteria failed: {', '.join(critical_failures)}"
else:
return "CONDITIONAL_APPROVAL", f"Non-critical criteria failed: {', '.join(failed_criteria)}"
12.3.4 Review Engine Module¶
Purpose: Main review decision engine orchestrating the review process
Key Functions: - Review Orchestration: Orchestrate the complete review process - Decision Making: Make approve/reject decisions - Workflow Management: Manage multi-stage review workflow - Audit Trail: Maintain complete review audit trail
Review Engine Implementation:
from datetime import datetime
from typing import Dict, List, Optional
import asyncio
class ReviewEngine:
def __init__(self, registry, backtest_runner, metrics_evaluator):
self.registry = registry
self.backtest_runner = backtest_runner
self.metrics_evaluator = metrics_evaluator
self.review_history = []
async def review_strategy(self, strategy_id: str) -> Dict:
"""Execute complete strategy review process"""
try:
# Get strategy information
strategy = self.registry.get_strategy(strategy_id)
if not strategy:
return {"success": False, "error": "Strategy not found"}
# Update status
strategy["status"] = "under_review"
strategy["updated_at"] = datetime.now()
# Execute backtest
backtest_result = await self.backtest_runner.run_backtest(
strategy_id, strategy["parameters"]
)
# Evaluate metrics
evaluation_result = self.metrics_evaluator.evaluate_strategy(backtest_result)
# Make decision
decision = self._make_decision(evaluation_result)
# Record review
review_record = {
"strategy_id": strategy_id,
"timestamp": datetime.now(),
"backtest_result": backtest_result,
"evaluation_result": evaluation_result,
"decision": decision,
"reviewer": "system"
}
self.review_history.append(review_record)
strategy["review_history"].append(review_record)
# Update strategy status
strategy["status"] = decision["status"]
strategy["updated_at"] = datetime.now()
return {
"success": True,
"strategy_id": strategy_id,
"decision": decision,
"backtest_result": backtest_result,
"evaluation_result": evaluation_result
}
except Exception as e:
return {
"success": False,
"error": str(e),
"strategy_id": strategy_id
}
def _make_decision(self, evaluation_result: Dict) -> Dict:
"""Make review decision based on evaluation"""
status = evaluation_result["status"]
overall_score = evaluation_result["overall_score"]
if status == "APPROVED":
decision = {
"status": "approved",
"action": "deploy_to_production",
"score": overall_score,
"reason": evaluation_result["reason"],
"timestamp": datetime.now()
}
elif status == "CONDITIONAL_APPROVAL":
decision = {
"status": "conditional_approval",
"action": "deploy_with_monitoring",
"score": overall_score,
"reason": evaluation_result["reason"],
"timestamp": datetime.now()
}
else: # REJECTED
decision = {
"status": "rejected",
"action": "require_modification",
"score": overall_score,
"reason": evaluation_result["reason"],
"timestamp": datetime.now()
}
return decision
def get_review_history(self, strategy_id: Optional[str] = None) -> List[Dict]:
"""Get review history"""
if strategy_id:
return [review for review in self.review_history
if review["strategy_id"] == strategy_id]
return self.review_history
def get_review_statistics(self) -> Dict:
"""Get review statistics"""
total_reviews = len(self.review_history)
if total_reviews == 0:
return {"total": 0}
status_counts = {}
for review in self.review_history:
status = review["decision"]["status"]
status_counts[status] = status_counts.get(status, 0) + 1
return {
"total": total_reviews,
"status_counts": status_counts,
"approval_rate": status_counts.get("approved", 0) / total_reviews
}
12.4 Data Architecture¶
12.4.1 Strategy Review Data Models¶
Strategy Registration Model:
{
"strategy_id": "strategy_000001",
"name": "BTC Trend Following Strategy",
"type": "trend_following",
"description": "BTC trend following strategy using moving averages",
"author": "trader_001",
"version": "1.0.0",
"parameters": {
"fast_ma": 10,
"slow_ma": 30,
"stop_loss": 0.02,
"take_profit": 0.04
},
"dependencies": ["pandas", "numpy"],
"status": "submitted",
"created_at": "2024-12-20T10:30:15.123Z",
"updated_at": "2024-12-20T10:30:15.123Z"
}
Review Criteria Model:
{
"criteria_id": "criteria_001",
"name": "Standard Review Criteria",
"version": "1.0.0",
"criteria": {
"sharpe_ratio": 1.5,
"max_drawdown": 0.15,
"win_rate": 0.55,
"profit_factor": 1.3,
"calmar_ratio": 1.0,
"sortino_ratio": 1.8,
"var_95": 0.02
},
"weights": {
"sharpe_ratio": 0.25,
"max_drawdown": 0.20,
"win_rate": 0.15,
"profit_factor": 0.15,
"calmar_ratio": 0.10,
"sortino_ratio": 0.10,
"var_95": 0.05
},
"enabled": true,
"created_at": "2024-12-20T10:30:15.123Z"
}
Review Result Model:
{
"review_id": "review_12345",
"strategy_id": "strategy_000001",
"timestamp": "2024-12-20T10:30:15.123Z",
"decision": {
"status": "approved",
"action": "deploy_to_production",
"score": 0.85,
"reason": "All criteria passed",
"timestamp": "2024-12-20T10:30:15.123Z"
},
"evaluation": {
"overall_score": 0.85,
"status": "APPROVED",
"reason": "All criteria passed",
"evaluation_details": {
"sharpe_ratio": {
"value": 1.8,
"threshold": 1.5,
"score": 1.0,
"passed": true
},
"max_drawdown": {
"value": 0.08,
"threshold": 0.15,
"score": 1.0,
"passed": true
}
}
},
"backtest_result": {
"performance_metrics": {
"total_return": 0.15,
"sharpe_ratio": 1.8,
"max_drawdown": 0.08
}
}
}
12.4.2 Review Process Flow¶
Strategy Submission → Registration → Automated Backtest → Metrics Evaluation
↓
Decision Engine → Approval/Rejection → Status Update → Deployment Control
↓
Audit Trail → Performance Tracking → Continuous Monitoring → Quality Assurance
12.5 API Interface Design¶
12.5.1 Strategy Review Endpoints¶
Strategy Registration:
POST /api/v1/review/strategy/register # Register new strategy
GET /api/v1/review/strategy/{strategy_id} # Get strategy information
PUT /api/v1/review/strategy/{strategy_id} # Update strategy
DELETE /api/v1/review/strategy/{strategy_id} # Delete strategy
Review Process:
POST /api/v1/review/submit # Submit strategy for review
GET /api/v1/review/{review_id} # Get review result
GET /api/v1/review/strategy/{strategy_id}/history # Get review history
POST /api/v1/review/{review_id}/approve # Approve review
POST /api/v1/review/{review_id}/reject # Reject review
Criteria Management:
GET /api/v1/review/criteria # Get review criteria
PUT /api/v1/review/criteria # Update review criteria
POST /api/v1/review/criteria/version # Create new criteria version
12.5.2 Real-time Updates¶
WebSocket Endpoints:
/ws/review/status # Real-time review status updates
/ws/review/progress # Real-time review progress
/ws/review/decisions # Real-time decision notifications
12.6 Frontend Integration¶
12.6.1 Strategy Review Dashboard Components¶
Strategy Management Panel: - Strategy Registration: New strategy registration form - Strategy List: List of all strategies with status - Strategy Details: Detailed strategy information and history - Version Control: Strategy version management
Review Process Panel: - Review Submission: Submit strategies for review - Review Progress: Real-time review progress tracking - Review Results: Review results and decision display - Review History: Complete review history
Criteria Management Panel: - Criteria Configuration: Review criteria setup and management - Threshold Settings: Performance threshold configuration - Weight Management: Metric weight configuration - Criteria Versioning: Criteria version control
12.6.2 Interactive Features¶
Visualization Tools: - Review Dashboard: Comprehensive review status dashboard - Performance Charts: Strategy performance visualization - Decision Analytics: Review decision analysis - Quality Metrics: Strategy quality metrics display
Analysis Tools: - Review Analytics: Review process analytics - Performance Comparison: Strategy performance comparison - Trend Analysis: Review trend analysis - Quality Reports: Strategy quality reports
12.7 Performance Characteristics¶
12.7.1 Review Process Metrics¶
| Metric | Target | Measurement |
|---|---|---|
| Review Time | <5 minutes | Complete review process time |
| Backtest Speed | <2 minutes | Backtest execution time |
| Decision Accuracy | 95%+ | Review decision accuracy |
| Process Automation | 100% | Automated review process |
12.7.2 Quality Assurance¶
| Requirement | Implementation |
|---|---|
| Standardized Process | Consistent review process for all strategies |
| Comprehensive Evaluation | Multi-factor strategy evaluation |
| Audit Trail | Complete review audit trail |
| Quality Control | Automated quality control enforcement |
12.8 Integration with Existing System¶
12.8.1 Backtest Engine Integration¶
Backtest Integration:
Result Integration: - Performance Data: Comprehensive performance data collection - Risk Metrics: Risk metrics calculation and evaluation - Quality Assessment: Strategy quality assessment - Decision Support: Automated decision support
12.8.2 Strategy Deployment Integration¶
Deployment Control: - Approval Workflow: Multi-stage approval workflow - Environment Management: Multi-environment deployment control - Deployment Coordination: Strategy deployment coordination - Monitoring Setup: Post-deployment monitoring setup
12.9 Implementation Roadmap¶
12.9.1 Phase 1: Foundation (Weeks 1-2)¶
- Basic Registration: Simple strategy registration system
- Basic Backtest: Basic backtest integration
- Simple Evaluation: Basic metrics evaluation
- Basic API: Core review management endpoints
12.9.2 Phase 2: Advanced Evaluation (Weeks 3-4)¶
- Comprehensive Metrics: Advanced performance metrics
- Risk Assessment: Comprehensive risk assessment
- Quality Scoring: Advanced quality scoring
- Decision Engine: Automated decision engine
12.9.3 Phase 3: Workflow Management (Weeks 5-6)¶
- Multi-stage Review: Multi-stage review workflow
- Approval Process: Comprehensive approval process
- Audit Trail: Complete audit trail system
- Quality Assurance: Advanced quality assurance
12.9.4 Phase 4: Production Ready (Weeks 7-8)¶
- High Availability: Redundant review infrastructure
- Performance Optimization: High-performance review system
- Advanced Analytics: Comprehensive review analytics
- Enterprise Features: Institutional-grade review system
12.10 Business Value¶
12.10.1 Quality Assurance¶
| Benefit | Impact |
|---|---|
| Strategy Quality | Ensures only high-quality strategies are deployed |
| Risk Management | Prevents deployment of risky strategies |
| Performance Standards | Maintains performance standards across strategies |
| Compliance | Ensures regulatory and internal compliance |
12.10.2 Operational Excellence¶
| Advantage | Business Value |
|---|---|
| Automated Process | Reduces manual review effort and errors |
| Standardized Evaluation | Consistent evaluation across all strategies |
| Quality Control | Automated quality control enforcement |
| Audit Compliance | Complete audit trail for compliance |
Document Information
Type: Strategy Review Center Design | Audience: Technical Leadership & Engineering Teams
Version: 1.0 | Date: December 2024
Focus: Quality Assurance & Governance | Implementation: Detailed technical specifications for automated strategy review