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docs/platform-services/logging-monitoring/README.md

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+# Logging & Monitoring
+
+## Overview
+The Logging & Monitoring service will provide comprehensive observability capabilities for the stock-bot platform. It will collect, process, store, and visualize logs, metrics, and traces from all platform components, enabling effective operational monitoring, troubleshooting, and performance optimization.
+
+## Planned Features
+
+### Centralized Logging
+- **Log Aggregation**: Collection of logs from all services
+- **Structured Logging**: Standardized log format across services
+- **Log Processing**: Parsing, enrichment, and transformation
+- **Log Storage**: Efficient storage with retention policies
+- **Log Search**: Advanced search capabilities with indexing
+
+### Metrics Collection
+- **System Metrics**: CPU, memory, disk, network usage
+- **Application Metrics**: Custom application-specific metrics
+- **Business Metrics**: Trading and performance indicators
+- **SLI/SLO Tracking**: Service level indicators and objectives
+- **Alerting Thresholds**: Metric-based alert configuration
+
+### Distributed Tracing
+- **Request Tracing**: End-to-end tracing of requests
+- **Span Collection**: Detailed operation timing
+- **Trace Correlation**: Connect logs, metrics, and traces
+- **Latency Analysis**: Performance bottleneck identification
+- **Dependency Mapping**: Service dependency visualization
+
+### Alerting & Notification
+- **Alert Rules**: Multi-condition alert definitions
+- **Notification Channels**: Email, SMS, chat integrations
+- **Alert Grouping**: Intelligent alert correlation
+- **Escalation Policies**: Tiered notification escalation
+- **On-call Management**: Rotation and scheduling
+
+## Planned Integration Points
+
+### Data Sources
+- All platform microservices
+- Infrastructure components
+- Databases and storage systems
+- Message bus and event streams
+- External dependencies
+
+### Consumers
+- Operations team dashboards
+- Incident management systems
+- Capacity planning tools
+- Automated remediation systems
+
+## Planned Technical Implementation
+
+### Technology Stack
+- **Logging**: ELK Stack (Elasticsearch, Logstash, Kibana) or similar
+- **Metrics**: Prometheus and Grafana
+- **Tracing**: Jaeger or Zipkin
+- **Alerting**: AlertManager or PagerDuty
+- **Collection**: Vector, Fluentd, or similar collectors
+
+### Architecture Pattern
+- Centralized collection with distributed agents
+- Push and pull metric collection models
+- Sampling for high-volume telemetry
+- Buffering for resilient data collection
+
+## Development Guidelines
+
+### Instrumentation Standards
+- Logging best practices
+- Metric naming conventions
+- Trace instrumentation approach
+- Cardinality management
+
+### Performance Impact
+- Sampling strategies
+- Buffer configurations
+- Resource utilization limits
+- Batching recommendations
+
+### Data Management
+- Retention policies
+- Aggregation strategies
+- Storage optimization
+- Query efficiency guidelines
+
+## Implementation Roadmap
+1. Core logging infrastructure
+2. Basic metrics collection
+3. Critical alerting capability
+4. Distributed tracing
+5. Advanced analytics and visualization