monitoring.md

Monitoring System Guide

Overview

This guide explains how to use DeepChain's monitoring system to track and analyze your trading strategies.

Table of Contents

1. Metrics Collection
2. Alert Management
3. Performance Tracking
4. System Health
5. Visualization

Metrics Collection

Set up metrics collection:

from deepchain.core.monitoring import MetricsCollector

collector = MetricsCollector()

# Record metrics
collector.record_latency(100)  # ms
collector.record_memory_usage(512)  # MB
collector.record_prediction(prediction_data)
collector.record_error(error_data)

# Get statistics
stats = collector.get_statistics()
print(f"Average latency: {stats['avg_latency']}ms")
print(f"Max memory usage: {stats['max_memory']}MB")

Alert Management

Configure and manage alerts:

from deepchain.core.monitoring import AlertManager

manager = AlertManager()

# Set thresholds
manager.set_threshold(
    metric="latency",
    warning=200,  # ms
    critical=500  # ms
)

# Add alert handler
def alert_handler(alert):
    print(f"Alert: {alert.message}")
    
manager.add_handler(alert_handler)

# Check metrics
manager.check_metrics(metrics_data)

Performance Tracking

Track strategy performance:

from deepchain.core.monitoring import PerformanceTracker

tracker = PerformanceTracker()

# Set baseline
tracker.set_baseline({
    'win_rate': 0.6,
    'sharpe_ratio': 1.5,
    'max_drawdown': 0.2
})

# Record metrics
tracker.record_metrics(current_metrics)

# Get performance report
report = tracker.get_performance_report()
print(f"Model drift: {report['drift_score']}")
print(f"Performance score: {report['performance_score']}")

System Health

Monitor system health:

from deepchain.core.monitoring import HealthChecker

checker = HealthChecker()

# Check system health
health = checker.check_health()
print(f"System status: {health['status']}")
print(f"Components: {health['components']}")

# Get detailed report
report = checker.get_health_report()
for component, status in report.items():
    print(f"{component}: {status}")

Visualization

Create monitoring dashboards:

from deepchain.core.monitoring import Dashboard

dashboard = Dashboard()

# Add metrics panels
dashboard.add_panel(
    title="Latency",
    metric="latency",
    chart_type="line"
)

dashboard.add_panel(
    title="Memory Usage",
    metric="memory",
    chart_type="gauge"
)

# Generate dashboard
dashboard.render()

Best Practices

1. Regular metric collection
2. Appropriate alert thresholds
3. Performance baseline updates
4. System health checks
5. Resource monitoring
6. Alert response procedures