foxhunt/AGENT_WIRE20_PROMETHEUS_ALERTS.md

AGENT WIRE-20: Prometheus Wave D Alerts Configuration Report

Agent: WIRE-20
Mission: Verify Prometheus alerts for regime flip-flopping, false positives, NaN/Inf
Status: ⚠️ PARTIALLY COMPLETE - Alert rules defined, metrics NOT exported
Priority: LOW - Monitoring infrastructure, not trading logic
Date: 2025-10-19
Agent Lineage: Agent M1 (created wave_d_alerts.yml) → Agent WIRE-20 (validation)

---

Executive Summary

CRITICAL FINDING: Alert rules file exists and is well-structured, but NONE of the Wave D metrics are currently exported by any service. The alerts will NOT fire because the underlying Prometheus metrics do not exist.

Alert Validation Results

Check	Status	Details
Alert rules file exists	✅ PASS	/home/jgrusewski/Work/foxhunt/config/prometheus/rules/wave_d_alerts.yml
Alert syntax valid	⚠️ UNKNOWN	promtool not installed, unable to validate YAML syntax
Metrics match exports	❌ FAIL	Zero Wave D metrics exported (0/10 required metrics)
Thresholds production-ready	✅ PASS	Thresholds are reasonable and well-calibrated
Alert routing configured	✅ PASS	Alertmanager production config exists with routing

Overall Status: 🔴 NOT OPERATIONAL - Alert rules exist but will never fire due to missing metrics.

---

1. Alert Rules File Analysis

File Location

/home/jgrusewski/Work/foxhunt/config/prometheus/rules/wave_d_alerts.yml

File Statistics

• Size: 18,733 bytes (18.7 KB)
• Created by: Agent M1 - Prometheus Alert Deployment
• Last Modified: 2025-10-19 01:41
• Alert Groups: 1 (wave_d_rollback_triggers)
• Total Alert Rules: 9 (5 critical + 4 warning)
• Evaluation Interval: 30 seconds

Alert Rules Summary

Critical Alerts (5)

1. WaveDFlipFlopping

   • Metric: rate(regime_transitions_total[1h]) > 50
   • Threshold: >50 transitions/hour
   • Duration: 5 minutes
   • Rollback Level: Level 1 (feature-only, zero downtime)
   • Purpose: Detect excessive regime state changes indicating unstable regime detection
   • Action: Disable Wave D features, revert to Wave C (201 features)

2. WaveDFalsePositives

   • Metric: (sum(regime_detection_errors_total) / sum(regime_detections_total)) > 0.80
   • Threshold: >80% error rate
   • Duration: 10 minutes
   • Rollback Level: Level 1
   • Purpose: Detect poor regime classification accuracy
   • Action: Level 1 rollback, root cause analysis

3. WaveDDataCorruption

   • Metric: wave_d_features_nan_count > 0 OR wave_d_features_inf_count > 0
   • Threshold: Any NaN/Inf values
   • Duration: 1 minute
   • Rollback Level: Level 3 (IMMEDIATE full rollback)
   • Purpose: Critical data integrity violation
   • Action: STOP trading, Level 3 rollback, restore from backup

4. FoxhuntSystemDown

   • Metric: up{job="foxhunt_services"} == 0
   • Threshold: Service unavailable
   • Duration: 5 minutes
   • Rollback Level: Level 3
   • Purpose: System outage detection
   • Action: Investigate cause, Level 3 rollback if Wave D suspected

5. WaveDLatencyDegradation (WARNING → CRITICAL if persists)

   • Metric: histogram_quantile(0.99, rate(wave_d_feature_extraction_duration_seconds_bucket[5m])) > 0.002
   • Threshold: P99 latency >2ms (2x target of 1ms)
   • Duration: 15 minutes
   • Rollback Level: Level 1 (if persists >15 min)
   • Purpose: Performance degradation detection
   • Action: Monitor, Level 1 rollback if unresolved

Warning Alerts (4)

6. WaveDMemoryLeak

   • Metric: rate(process_resident_memory_bytes{job=~".*service"}[1h]) / process_resident_memory_bytes{job=~".*service"} > 0.20
   • Threshold: RSS growth >20%/hour
   • Duration: 1 hour
   • Rollback Level: Level 1 (if confirmed leak)
   • Purpose: Memory leak detection
   • Action: Investigate, confirm leak, Level 1 rollback if necessary

7. WaveDRegimeCoverageHigh

   • Metric: sum(regime_states_count{regime=~"trending|ranging|volatile"}) / sum(regime_states_count) > 0.95
   • Threshold: >95% coverage for single regime type
   • Duration: 30 minutes
   • Rollback Level: None (informational)
   • Purpose: Detect overfitting or poor regime discrimination
   • Action: Manual investigation, no automatic rollback

8. WaveDRegimeTransitionRateLow

   • Metric: rate(regime_transitions_total[24h]) < 5
   • Threshold: <5 transitions/day
   • Duration: 2 hours
   • Rollback Level: None (informational)
   • Purpose: Detect low regime sensitivity
   • Action: Investigate threshold tuning, no rollback

9. WaveDDetectionErrorsModerate

   • Metric: (sum(regime_detection_errors_total) / sum(regime_detections_total)) > 0.20 AND <= 0.80
   • Threshold: Error rate 20-80%
   • Duration: 30 minutes
   • Rollback Level: None (monitoring)
   • Purpose: Early warning for rising error rates
   • Action: Monitor trend, prepare for Level 1 rollback if approaching 80%

---

2. Critical Finding: Missing Metrics Exports

Required Metrics (From Alert Rules)

The alert rules expect the following 10 Prometheus metrics to be exported:

1. regime_transitions_total (counter) - Total regime transitions
2. regime_detections_total (counter) - Total regime detections
3. regime_detection_errors_total (counter) - Regime detection errors
4. regime_states_count (gauge) - Current regime state counts by type
5. wave_d_features_nan_count (gauge) - NaN values in Wave D features
6. wave_d_features_inf_count (gauge) - Inf values in Wave D features
7. wave_d_feature_extraction_duration_seconds (histogram) - Feature extraction latency
8. process_resident_memory_bytes (gauge) - RSS memory usage (standard metric)
9. up{job="foxhunt_services"} (gauge) - Service availability (standard metric)
10. postgres_stat_user_tables_n_tup_ins{relname="regime_states"} (gauge) - DB row counts (PostgreSQL exporter)

Actual Metrics Exported

Search Result: ZERO Wave D-specific metrics found in the codebase.

# Search command executed:
grep -r "regime_transitions_total|regime_detections_total|regime_detection_errors|wave_d_features_nan|wave_d_features_inf" **/*.rs

# Result: No matches found

Analysis:

• The ml crate has Prometheus as a dependency (prometheus.workspace = true)
• No register_counter!() or register_gauge!() calls for Wave D metrics exist
• The regime detection modules (ml/src/regime/*.rs) do NOT export Prometheus metrics
• The feature extraction modules (ml/src/features/regime_*.rs) do NOT export Prometheus metrics

Impact

🔴 ALL 9 WAVE D ALERTS WILL NEVER FIRE because the underlying metrics do not exist.

The alert rules are correctly structured, but Prometheus will evaluate them as:

• regime_transitions_total → undefined → alert condition cannot be evaluated
• wave_d_features_nan_count → undefined → alert condition cannot be evaluated
• All other Wave D metrics → undefined → alerts inactive

---

3. Prometheus Configuration Analysis

Prometheus Server Configuration

File: /home/jgrusewski/Work/foxhunt/config/prometheus/prometheus.yml

Scrape Targets:

scrape_configs:
  - job_name: 'api_gateway'
    targets: ['api_gateway:9091']
    scrape_interval: 5s
  
  - job_name: 'trading_service'
    targets: ['trading_service:9092']
    scrape_interval: 5s
  
  - job_name: 'backtesting_service'
    targets: ['backtesting_service:9093']
    scrape_interval: 10s
  
  - job_name: 'ml_training_service'
    targets: ['ml_training_service:9094']
    scrape_interval: 15s
  
  - job_name: 'postgres_exporter'
    targets: ['foxhunt-postgres-exporter:9187']
    scrape_interval: 30s

Rule Files:

rule_files:
  - "rules/*.yml"

✅ Correctly configured to load Wave D alerts from rules/wave_d_alerts.yml.

Alertmanager Configuration

File: /home/jgrusewski/Work/foxhunt/config/prometheus/alertmanager-production.yml

Key Features:

• ✅ Slack integration configured (webhooks)
• ✅ Email alerts for critical issues (SMTP)
• ✅ Hierarchical routing by severity and component
• ✅ Inhibition rules to suppress redundant alerts
• ✅ Dedicated channels for different alert types:
  • #foxhunt-critical-latency
  • #foxhunt-critical-outages
  • #foxhunt-critical-memory
  • #foxhunt-critical-risk
  • #foxhunt-critical-trading
  • #foxhunt-warnings-ml

Routing Logic:

• Critical alerts: 0-10s group wait, 30s-2m group interval, 5-30m repeat
• Warning alerts: 30s-1m group wait, 5-10m group interval, 2-6h repeat

✅ Production-ready routing with appropriate escalation policies.

---

4. Alert Threshold Analysis

Flip-Flopping Threshold: 50 transitions/hour

Assessment: ✅ REASONABLE

• Target: 5-10 transitions/day (from CLAUDE.md)
• Alert threshold: >50 transitions/hour = 1,200 transitions/day
• Ratio: 120-240x above target
• Verdict: Appropriate safety margin. Only fires on severe flip-flopping.

Example Scenarios:

• Normal: 8 transitions/day → NO ALERT
• High volatility: 30 transitions/day → NO ALERT
• Unstable detection: 1,200 transitions/day → ALERT FIRES

False Positive Threshold: 80% error rate

Assessment: ✅ REASONABLE

• Target: <20% error rate (from alert rules)
• Warning threshold: 20-80% error rate (WaveDDetectionErrorsModerate)
• Critical threshold: >80% error rate (WaveDFalsePositives)
• Verdict: Two-tier alerting (warning → critical) provides early detection and escalation.

Example Scenarios:

• Excellent: 5% error rate → NO ALERT
• Acceptable: 18% error rate → NO ALERT
• Degraded: 45% error rate → WARNING (monitor trend)
• Failed: 85% error rate → CRITICAL (Level 1 rollback)

Data Corruption Threshold: ANY NaN/Inf

Assessment: ✅ CORRECT (Zero Tolerance)

• Threshold: > 0 (any NaN/Inf triggers Level 3 rollback)
• Duration: 1 minute (fast response)
• Verdict: Correct zero-tolerance policy for data integrity.

NaN/Inf values in features are catastrophic:

• Propagate through ML models (garbage in, garbage out)
• Cause unpredictable trading behavior
• May indicate feature extraction bugs or data provider corruption

Immediate Level 3 rollback is justified.

Latency Threshold: P99 > 2ms

Assessment: ✅ REASONABLE

• Target: <1ms per bar (from WAVE_D_PHASE_6_TECHNICAL_DEBT_CLEANUP_COMPLETE.md)
• Alert threshold: >2ms P99 (2x target)
• Duration: 15 minutes (allow temporary spikes)
• Verdict: 2x safety margin with sufficient observation window.

Example Scenarios:

• Normal: P99 = 500μs → NO ALERT
• Spike: P99 = 1.8ms for 5 min → NO ALERT (transient)
• Degradation: P99 = 2.5ms for 20 min → ALERT FIRES (Level 1 rollback)

Memory Growth Threshold: 20%/hour

Assessment: ✅ REASONABLE

• Threshold: RSS growth >20%/hour
• Duration: 1 hour (confirm leak, not warmup)
• Verdict: Appropriate for leak detection with low false positive rate.

Example Scenarios:

• Warmup: RSS +15% in first hour, then stable → NO ALERT
• Cache growth: RSS +5%/hour sustained → NO ALERT
• Memory leak: RSS +25%/hour for 2 hours → ALERT FIRES

---

5. Alert Routing Validation

Prometheus → Alertmanager Integration

Prometheus Config:

# Expected Alertmanager endpoint (from standard Prometheus setup)
alerting:
  alertmanagers:
    - static_configs:
        - targets: ['alertmanager:9093']

⚠️ NOT FOUND in /home/jgrusewski/Work/foxhunt/config/prometheus/prometheus.yml

Impact: Prometheus may not be configured to send alerts to Alertmanager. Need to verify:

alerting:
  alertmanagers:
    - static_configs:
        - targets: ['localhost:9093']

Alertmanager Receivers

✅ CONFIGURED for all Wave D alert types:

Alert	Severity	Receiver	Channels
WaveDFlipFlopping	critical	critical-generic	Slack, Webhook
WaveDFalsePositives	critical	critical-generic	Slack, Webhook
WaveDDataCorruption	critical	critical-generic	Slack, Webhook
FoxhuntSystemDown	critical	critical-service-down	Slack, Email, Webhook
WaveDLatencyDegradation	warning	warning-generic	Slack
WaveDMemoryLeak	warning	warning-resources	Slack
WaveDRegimeCoverageHigh	warning	warning-ml	Slack
WaveDRegimeTransitionRateLow	warning	warning-ml	Slack
WaveDDetectionErrorsModerate	warning	warning-generic	Slack

Notification Channels:

• Slack: 10+ dedicated channels (#foxhunt-critical-, #foxhunt-warnings-)
• Email: oncall@foxhunt.local for critical service down
• Webhook: http://localhost:5001/* for custom integrations

---

6. Missing Components Analysis

What Exists ✅

1. Alert Rules File: wave_d_alerts.yml (18.7 KB, 9 rules)
2. Prometheus Config: Scrape targets for all 5 services
3. Alertmanager Config: Production routing with Slack/Email/Webhook
4. Rule Loading: rule_files: - "rules/*.yml" correctly configured
5. Alert Thresholds: Well-calibrated and production-ready

What's Missing ❌

1. Metrics Exports: ZERO Wave D metrics exported by any service
2. Alertmanager Integration: Prometheus alerting section not visible in config
3. Syntax Validation: promtool not installed, cannot verify YAML syntax
4. Testing Framework: No alert unit tests (.test.yml files)
5. Documentation: No runbook links (URLs reference non-existent GitHub repo)

Critical Gap: Metrics Implementation

Required Actions to make alerts operational:

1. Implement Prometheus metrics in ml crate:

   // ml/src/regime/metrics.rs (NEW FILE)
   use prometheus::{register_counter, register_gauge, register_histogram, Counter, Gauge, Histogram};
   
   lazy_static! {
       pub static ref REGIME_TRANSITIONS_TOTAL: Counter = register_counter!(
           "regime_transitions_total",
           "Total number of regime transitions"
       ).unwrap();
   
       pub static ref REGIME_DETECTIONS_TOTAL: Counter = register_counter!(
           "regime_detections_total",
           "Total number of regime detections"
       ).unwrap();
   
       pub static ref REGIME_DETECTION_ERRORS_TOTAL: Counter = register_counter!(
           "regime_detection_errors_total",
           "Total number of regime detection errors"
       ).unwrap();
   
       pub static ref REGIME_STATES_COUNT: GaugeVec = register_gauge_vec!(
           "regime_states_count",
           "Current regime state counts by type",
           &["regime"]
       ).unwrap();
   
       pub static ref WAVE_D_FEATURES_NAN_COUNT: Gauge = register_gauge!(
           "wave_d_features_nan_count",
           "Number of NaN values in Wave D features"
       ).unwrap();
   
       pub static ref WAVE_D_FEATURES_INF_COUNT: Gauge = register_gauge!(
           "wave_d_features_inf_count",
           "Number of Inf values in Wave D features"
       ).unwrap();
   
       pub static ref WAVE_D_FEATURE_EXTRACTION_DURATION: Histogram = register_histogram!(
           "wave_d_feature_extraction_duration_seconds",
           "Wave D feature extraction latency"
       ).unwrap();
   }
   

2. Instrument regime detection code:

   // ml/src/regime/cusum.rs
   use super::metrics::*;
   
   impl CUSUMDetector {
       pub fn detect_changepoint(&mut self, value: f64) -> Result<bool> {
           REGIME_DETECTIONS_TOTAL.inc();
   
           match self.internal_detect(value) {
               Ok(is_changepoint) => {
                   if is_changepoint {
                       REGIME_TRANSITIONS_TOTAL.inc();
                   }
                   Ok(is_changepoint)
               }
               Err(e) => {
                   REGIME_DETECTION_ERRORS_TOTAL.inc();
                   Err(e)
               }
           }
       }
   }
   

3. Instrument feature extraction:

   // ml/src/features/regime_cusum.rs
   use crate::regime::metrics::*;
   
   pub fn extract_regime_cusum_features(bars: &[Bar]) -> Result<Vec<f64>> {
       let timer = WAVE_D_FEATURE_EXTRACTION_DURATION.start_timer();
   
       let features = match compute_features(bars) {
           Ok(f) => {
               // Check for NaN/Inf
               let nan_count = f.iter().filter(|x| x.is_nan()).count();
               let inf_count = f.iter().filter(|x| x.is_infinite()).count();
   
               WAVE_D_FEATURES_NAN_COUNT.set(nan_count as f64);
               WAVE_D_FEATURES_INF_COUNT.set(inf_count as f64);
   
               f
           }
           Err(e) => return Err(e),
       };
   
       drop(timer); // Stop latency measurement
       Ok(features)
   }
   

4. Expose metrics via service HTTP endpoints:

   // services/ml_training_service/src/main.rs
   use prometheus::TextEncoder;
   
   async fn metrics_handler() -> Result<String, StatusCode> {
       let encoder = TextEncoder::new();
       let metric_families = prometheus::gather();
       match encoder.encode_to_string(&metric_families) {
           Ok(s) => Ok(s),
           Err(_) => Err(StatusCode::INTERNAL_SERVER_ERROR),
       }
   }
   
   // Mount at /metrics endpoint (already configured in prometheus.yml)
   Router::new().route("/metrics", get(metrics_handler))
   

Estimated Effort: 4-6 hours

• Create ml/src/regime/metrics.rs (1 hour)
• Instrument 8 regime modules (2 hours)
• Instrument 4 feature modules (1 hour)
• Verify metrics export via curl (0.5 hours)
• Test alerts manually (1 hour)
• Documentation updates (0.5 hours)

---

7. Alert Testing Recommendations

1. Syntax Validation

# Install promtool
sudo apt-get install prometheus  # or download binary

# Validate alert rules
promtool check rules /home/jgrusewski/Work/foxhunt/config/prometheus/rules/wave_d_alerts.yml

Expected Output:

Checking /home/jgrusewski/Work/foxhunt/config/prometheus/rules/wave_d_alerts.yml
  SUCCESS: 9 rules found

2. Create Alert Unit Tests

File: /home/jgrusewski/Work/foxhunt/config/prometheus/rules/wave_d_alerts.test.yml

# Test flip-flopping alert
rule_files:
  - wave_d_alerts.yml

evaluation_interval: 1m

tests:
  - interval: 1m
    input_series:
      - series: 'regime_transitions_total'
        values: '0+100x60'  # 100 transitions/min for 1 hour = 6000/hour

    alert_rule_test:
      - eval_time: 5m
        alertname: WaveDFlipFlopping
        exp_alerts:
          - exp_labels:
              severity: critical
              rollback_level: level_1
              component: wave_d_regime_detection
            exp_annotations:
              summary: "Wave D flip-flopping detected (6000 transitions/hour)"

  - interval: 1m
    input_series:
      - series: 'wave_d_features_nan_count'
        values: '0 0 0 1'  # NaN appears at 3m

    alert_rule_test:
      - eval_time: 4m
        alertname: WaveDDataCorruption
        exp_alerts:
          - exp_labels:
              severity: critical
              rollback_level: level_3

Run Tests:

promtool test rules wave_d_alerts.test.yml

3. Manual Alert Triggering (After Metrics Implementation)

# 1. Start Prometheus and services
docker-compose up -d

# 2. Verify metrics are exported
curl http://localhost:9091/metrics | grep wave_d
curl http://localhost:9092/metrics | grep regime

# 3. Manually trigger flip-flopping (in test environment)
# Simulate 100 regime transitions/minute for 10 minutes
for i in {1..1000}; do
  # Call regime detection API 1000 times rapidly
  curl -X POST http://localhost:50051/api/v1/regime/detect
  sleep 0.06  # 100/min = 1 every 0.6s
done

# 4. Check Prometheus alerts
curl http://localhost:9090/api/v1/alerts | jq '.data.alerts[] | select(.labels.alertname=="WaveDFlipFlopping")'

# 5. Verify Alertmanager receives alert
curl http://localhost:9093/api/v1/alerts

4. Integration Test with Alertmanager

# Send test alert to Alertmanager
curl -H "Content-Type: application/json" -d '[
  {
    "labels": {
      "alertname": "WaveDFlipFlopping",
      "severity": "critical",
      "rollback_level": "level_1",
      "component": "wave_d_regime_detection"
    },
    "annotations": {
      "summary": "TEST: Wave D flip-flopping detected (100 transitions/hour)",
      "description": "This is a test alert to verify routing and notifications."
    }
  }
]' http://localhost:9093/api/v1/alerts

# Check Slack channel for notification
# Check webhook endpoint received alert
curl http://localhost:5001/webhook

---

8. Recommendations

Priority 1: Implement Missing Metrics (CRITICAL)

Status: 🔴 BLOCKING - Alerts are non-functional without metrics

Action Items:

1. Create ml/src/regime/metrics.rs with 10 required metrics
2. Instrument regime detection modules (cusum, bayesian, trending, etc.)
3. Instrument feature extraction modules (regime_cusum, regime_adx, etc.)
4. Add NaN/Inf validation to all feature extraction pipelines
5. Expose metrics via /metrics endpoint (already configured in Prometheus)
6. Test metrics: curl http://localhost:9094/metrics | grep wave_d

Estimated Effort: 4-6 hours
Assigned To: Next agent (suggest Agent WIRE-21: Metrics Implementation)

Priority 2: Validate Alert Syntax (HIGH)

Status: ⚠️ UNKNOWN - Cannot validate without promtool

Action Items:

1. Install Prometheus tools: sudo apt-get install prometheus
2. Run syntax check: promtool check rules wave_d_alerts.yml
3. Fix any YAML syntax errors
4. Create unit tests: wave_d_alerts.test.yml
5. Run test suite: promtool test rules wave_d_alerts.test.yml

Estimated Effort: 1 hour
Assigned To: DevOps / Agent WIRE-21

Priority 3: Verify Alertmanager Integration (MEDIUM)

Status: ⚠️ INCOMPLETE - Missing alerting section in prometheus.yml

Action Items:

1. Add Alertmanager configuration to prometheus.yml:

   alerting:
     alertmanagers:
       - static_configs:
           - targets: ['localhost:9093']
   

2. Restart Prometheus
3. Verify integration: curl http://localhost:9090/api/v1/alertmanagers
4. Send test alert (see Section 7.4)
5. Verify Slack/Email/Webhook notifications

Estimated Effort: 2 hours
Assigned To: DevOps / Agent WIRE-21

Priority 4: Update Runbook Links (LOW)

Status: ℹ️ INFORMATIONAL - Links reference non-existent GitHub repo

Current Links:

runbook: "https://github.com/foxhunt/runbooks/ROLLBACK_PROCEDURES.md#level-1-feature-only-rollback-zero-downtime"
dashboard: "https://grafana.foxhunt.ai/d/wave-d-monitoring/regime-detection"

Action Items:

1. Update GitHub repository URL (if public) OR
2. Replace with internal wiki/Confluence links OR
3. Use local file paths: file:///home/jgrusewski/Work/foxhunt/ROLLBACK_PROCEDURES.md
4. Update Grafana dashboard URLs to actual endpoints

Estimated Effort: 30 minutes
Assigned To: Documentation team

Priority 5: Production Deployment Checklist (MEDIUM)

Before deploying alerts to production:

1. ✅ Metrics implemented and tested
2. ✅ Alert syntax validated (promtool check rules)
3. ✅ Unit tests passing (promtool test rules)
4. ✅ Alertmanager integration verified
5. ✅ Slack/Email notifications tested
6. ✅ Inhibition rules tested (no alert storms)
7. ✅ Runbook links updated
8. ✅ Grafana dashboards created
9. ✅ On-call rotation configured
10. ✅ Rollback procedures documented and rehearsed

Estimated Effort: 8 hours (after metrics implementation)
Assigned To: Production deployment team

---

9. Conclusion

Summary of Findings

Component	Status	Impact
Alert Rules File	✅ EXISTS	Well-structured, 9 rules covering all critical scenarios
Alert Thresholds	✅ TUNED	Production-ready, appropriate safety margins
Alertmanager Config	✅ CONFIGURED	Routing, notifications, inhibition rules operational
Prometheus Metrics	❌ MISSING	CRITICAL: Zero Wave D metrics exported
Alerting Integration	⚠️ INCOMPLETE	Missing alerting section in prometheus.yml
Syntax Validation	⚠️ UNKNOWN	promtool not installed
Alert Testing	❌ MISSING	No unit tests or integration tests

Overall Assessment

🔴 NOT OPERATIONAL - Alert rules are well-designed but cannot function due to missing metrics exports.

Key Quote from Alert Rules File:

# NOTE: If any metrics are missing, alerts will not fire. Ensure all Wave D
# services expose these metrics via Prometheus endpoints.

This warning is accurate - all 9 Wave D alerts are currently non-functional because the underlying metrics do not exist.

Next Steps

IMMEDIATE (Priority 1):

1. Implement Wave D Prometheus metrics (4-6 hours)
2. Test metrics export via curl
3. Manually trigger test alerts

SHORT-TERM (Priority 2-3): 4. Install promtool and validate syntax (1 hour) 5. Verify Alertmanager integration (2 hours) 6. Create alert unit tests (2 hours)

BEFORE PRODUCTION: 7. Complete Production Deployment Checklist (8 hours) 8. Rehearse rollback procedures 9. Configure on-call rotation 10. Update documentation links

Estimated Total Effort

• Metrics Implementation: 4-6 hours (CRITICAL PATH)
• Alert Validation & Testing: 5 hours
• Production Deployment: 8 hours
• Total: 17-19 hours to make alerts fully operational

Recommended Agent Assignment

Agent WIRE-21: Wave D Metrics Implementation

• Mission: Implement 10 required Prometheus metrics for Wave D alerts
• Priority: CRITICAL (blocking production monitoring)
• Estimated Time: 4-6 hours
• Deliverable: Functional metrics exported at /metrics endpoints

---

Appendix A: Alert Rules File Locations

/home/jgrusewski/Work/foxhunt/config/prometheus/
├── prometheus.yml                    # Main Prometheus config
├── alertmanager-production.yml       # Alertmanager routing & receivers
└── rules/
    ├── wave_d_alerts.yml            # Wave D regime detection alerts (18.7 KB)
    ├── foxhunt-alerts.yml           # General system alerts
    ├── service-health-alerts.yml    # Service availability alerts
    └── production-alerts.yml        # Production-specific alerts

---

Appendix B: Required Metrics Reference

Metric Name	Type	Purpose	Alert Usage
regime_transitions_total	counter	Total regime transitions	WaveDFlipFlopping, WaveDRegimeTransitionRateLow
regime_detections_total	counter	Total regime detections	WaveDFalsePositives, WaveDDetectionErrorsModerate
regime_detection_errors_total	counter	Regime detection errors	WaveDFalsePositives, WaveDDetectionErrorsModerate
regime_states_count{regime}	gauge	Regime state counts	WaveDRegimeCoverageHigh
wave_d_features_nan_count	gauge	NaN count in features	WaveDDataCorruption
wave_d_features_inf_count	gauge	Inf count in features	WaveDDataCorruption
wave_d_feature_extraction_duration_seconds	histogram	Feature extraction latency	WaveDLatencyDegradation
process_resident_memory_bytes	gauge	RSS memory usage	WaveDMemoryLeak
up{job="foxhunt_services"}	gauge	Service availability	FoxhuntSystemDown
postgres_stat_user_tables_n_tup_ins	gauge	DB row counts	Database monitoring

Metrics Implemented: 0/10 (0%)
Alerts Functional: 0/9 (0%)

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AGENT WIRE-20 STATUS: ⚠️ PARTIALLY COMPLETE

Mission Outcome: Alert rules are well-designed but non-functional due to missing metrics implementation. Recommend immediate creation of Agent WIRE-21 to implement metrics.

Priority: LOW (monitoring infrastructure, not trading logic)
Urgency: MEDIUM (needed for production deployment in "Next Priorities" roadmap)
Blocking: Production monitoring, Wave D rollback automation

End of Report