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Verify TA Bot Metrics & Document Baseline

Alex Johnson
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Verify TA Bot Metrics & Document Baseline

Introduction: Ensuring Reliable Production Monitoring

Ensuring the reliability of production monitoring is paramount in the fast-paced world of trading bots. After the deployment of new TA Bot business metrics, it's not enough to simply merge and deploy; rigorous verification is essential. This article outlines the necessary steps to verify these metrics in the production environment and document a baseline for future comparisons. This proactive approach ensures that the new metrics function correctly, the Grafana dashboard provides actionable insights, and that we can trust the data for critical production monitoring. It also safeguards the business impact, as it prevents relying on potentially flawed metrics, thus maintaining the integrity of our trading bot’s operations. By following the outlined steps, we can confirm the accuracy of our data and the reliability of our monitoring systems.

Why Verification Matters

The primary goal of this process is to confirm that the deployed metrics are functioning as expected and that the Grafana dashboard correctly reflects the collected data. Inadequate verification can lead to several issues, including the failure of the dashboard to display the anticipated data, inaccurate metric dimensions, and the absence of a baseline for future comparisons. If these problems arise, the business may not be able to rely on the new metrics for production monitoring, which will delay the detection of any future issues that might arise. This, in turn, can negatively impact operational confidence. This detailed verification process ensures that the metrics are not just present but are also providing actionable insights for the business. This method not only validates the functionality of the metrics but also establishes a foundation for efficient monitoring and troubleshooting.

Prerequisites: Setting the Stage for Success

Before diving into the verification process, it is essential to ensure that all prerequisites are in place. This includes confirming that no duplicate issues exist and verifying that the necessary files are in the repository. The existence of these files is crucial to begin the testing. Additionally, verifying that the problem or gap identified still exists within the most recent version of the code is important. The identification of a specific repository is also essential because it specifies where we'll focus our efforts, which ultimately guides the entire process. Confirming clear, testable acceptance criteria is also crucial before starting, because these criteria ensure that the testing outcomes are measurable and aligned with the desired results. Finally, examples of code are necessary to ensure the tests run smoothly.

Checklist of Essential Preparations

  • Search Existing Issues: Ensure no duplicate efforts are underway.
  • Check Git History: Confirm no prior completion of the work.
  • Verify Affected Files: Ensure the required files are present in the repository.
  • Confirm Problem/Gap: Verify the issue persists in the latest code.
  • Identify Repository: Specify the target repository for the work.
  • Define Acceptance Criteria: Establish clear, testable success metrics.
  • Code Examples: Use actual code from the codebase to test.

Problem Statement: Addressing the Risk of Unverified Metrics

The core issue is that the TA Bot business metrics, as introduced in a previous project, have been deployed without thorough verification. This lack of verification poses a significant risk to the reliability of our monitoring system. This issue stems from metrics that may be present but non-functional. The Grafana dashboard might not accurately display the expected data, and there is no baseline for future comparisons. This absence can impact the business, as new metrics cannot be relied upon for production monitoring until they are verified. This means we will not have the information needed to monitor and analyze the system. Therefore, the team will not have the insights to ensure the smooth operation of the trading bots. It is crucial to confirm the metrics’ proper functionality and accuracy.

The Impact of Unverified Metrics

  • Metric Emissions: Metrics might not be sent to Prometheus.
  • Dashboard Issues: The Grafana dashboard might not import or display data correctly.
  • Accuracy Concerns: Metric dimensions might be inaccurate.
  • Baseline Absence: Baseline metric values are not documented.
  • Insight Deficiencies: The dashboard might not offer useful insights.

Estimation: Planning the Verification Effort

To effectively manage the verification process, a detailed estimation is essential. This involves considering the size, priority, complexity, and overall time required. The verification is estimated as a small task, taking approximately one to two hours to complete. Given its importance, the priority is set to high, emphasizing its criticality for production confidence. The complexity of the task is considered low, primarily because it involves straightforward verification and documentation tasks. The estimated time frame includes a total of 120 minutes, allocated for all required actions. This meticulous planning ensures that the verification process is efficient, effective, and completed within a reasonable timeframe.

Breakdown of the Verification Effort

  • Size: Small (1-2 hours) - The scope of the task is manageable.
  • Priority: High (Critical for production confidence) - Prioritize for business impact.
  • Estimate: 120 minutes - Allocate appropriate time for completion.
  • Complexity: Low (verification and documentation) - Streamline the process.

Acceptance Criteria: Defining Success

To ensure a successful verification process, it's essential to define clear acceptance criteria. These criteria provide measurable goals that, when met, confirm that the verification process has achieved its objectives. Each criterion specifies a key aspect of the metrics and dashboard that needs to be validated. These criteria involve confirming that all metrics are sent to Prometheus, importing the Grafana dashboard, testing the increment of a signal generation counter, verifying the recording of processing latency, and testing the configuration change counter via the runtime API. The documentation of the baseline metrics, the creation of a verification runbook section, and the capture of dashboard screenshots are also critical for the verification process. Meeting these criteria ensures that the implemented metrics are reliable and provide useful data for production monitoring.

Key Success Factors

  • Prometheus Metrics: Verify that all metrics are emitted to Prometheus.
  • Dashboard Import: Import the Grafana dashboard and verify all panels show data.
  • Counter Verification: Test that the signal generation counter increments correctly.
  • Latency Testing: Test that the processing latency histogram records values.
  • Config Changes: Test the config change counter via runtime API.
  • Baseline Documentation: Document the baseline metrics (current values).
  • Runbook Creation: Create a verification runbook section.
  • Screenshots: Capture screenshots of the dashboard for documentation.

Technical Details: Implementation Steps

The technical details provide specific instructions and code snippets to guide the verification process. This includes specifying the affected components, current behavior, and the proposed solution. The affected components include the petrosa-bot-ta-analysis repository and specific files within it, such as docs/RUNBOOK.md, docs/METRICS_BASELINE.md, and the dashboards/screenshots/ directory. These are critical components that need to be verified. The current behavior describes the state where metrics have been deployed but not verified. The proposed solution involves a step-by-step process to verify Prometheus metrics, import the dashboard, test metrics, and document the baseline. Each step includes detailed instructions and code examples to facilitate accurate verification.

Component Breakdown

  • Repository: petrosa-bot-ta-analysis - The primary repository for the verification work.
  • Files: Includes key files for documentation and dashboard integration.
    • docs/RUNBOOK.md: Add a metrics verification section.
    • docs/METRICS_BASELINE.md: Document current values.
    • dashboards/screenshots/: Capture dashboard screenshots.

Detailed Technical Procedures

Step 1: Verify Prometheus Metrics

This step involves using kubectl and promtool to check that the TA Bot is running and that all the new metrics are correctly emitted to Prometheus. The code examples query Prometheus to verify each metric's existence. The use of specific commands to verify the metrics makes sure the verification is accurate. The execution of each check, including the queries and the output verification, is fundamental to establishing the functionality of these metrics. The purpose of these tests is to confirm whether or not all of the necessary metrics are being sent to Prometheus so they can be monitored and analyzed.

# Check TA Bot is running
kubectl --kubeconfig=k8s/kubeconfig.yaml get pods -n petrosa-apps -l app=ta-bot

# Query Prometheus for new metrics
kubectl --kubeconfig=k8s/kubeconfig.yaml exec -it deployment/prometheus -n monitoring -- \
  promtool query instant http://localhost:9090 \
  'ta_bot_signals_generated_total'

# Verify all metrics exist
for metric in \
  ta_bot_signals_generated_total \
  ta_bot_signal_processing_duration \
  ta_bot_strategies_run_total \
  ta_bot_strategy_executions_total \
  ta_bot_config_changes_total; do
  echo "Checking $metric..."
  kubectl exec -it deployment/prometheus -n monitoring -- \
    promtool query instant http://localhost:9090 "$metric" | grep -q "Element" && echo "✓ Found" || echo "✗ Missing"
done

Step 2: Import Dashboard

This involves using curl and the Grafana API to import the TA Bot business metrics dashboard. The script first retrieves Grafana credentials from a secret and then uses these credentials to import the dashboard. This action confirms that the dashboard can be imported into Grafana and that the connection is working. After the import, the dashboard’s URL is echoed, facilitating the confirmation of its accessibility and integration within the Grafana environment. The script is also responsible for ensuring that the proper data is being collected and accessible for viewing.

# Get Grafana credentials from secret
GRAFANA_URL=$(kubectl get secret grafana-credentials -n monitoring -o jsonpath='{.data.url}' | base64 --decode)
GRAFANA_API_KEY=$(kubectl get secret grafana-credentials -n monitoring -o jsonpath='{.data.api-key}' | base64 --decode)

# Import dashboard
curl -X POST "${GRAFANA_URL}/api/dashboards/db" \
  -H "Authorization: Bearer ${GRAFANA_API_KEY}" \
  -H "Content-Type: application/json" \
  -d @dashboards/ta-bot-business-metrics.json

# Get dashboard URL
echo "Dashboard: ${GRAFANA_URL}/d/ta-bot-business-metrics"

Step 3: Test Metrics

This stage focuses on testing the functionality of specific metrics by triggering actions in the TA Bot and then verifying the results in Prometheus. This step includes triggering signal generation, waiting for metrics to propagate, and then querying Prometheus to confirm that the signal counter has incremented correctly. The team will also test the config change counter, which confirms that the metrics reflect changes made through the runtime API. The tests serve to validate the accurate tracking of key events, thus verifying that the metrics are capturing the data as expected.

# Trigger signal generation
kubectl --kubeconfig=k8s/kubeconfig.yaml exec -it deployment/ta-bot -n petrosa-apps -- \
  curl -X POST http://localhost:8080/api/v1/test/analyze \
  -H "Content-Type: application/json" \
  -d '{"symbol":"BTCUSDT","timeframe":"1h"}'

# Wait 30 seconds for metrics to propagate
sleep 30

# Verify signal counter incremented
kubectl exec -it deployment/prometheus -n monitoring -- \
  promtool query instant http://localhost:9090 \
  'ta_bot_signals_generated_total{symbol="BTCUSDT"}'

# Test config change counter
curl -X PUT http://ta-bot:8080/api/v1/strategies/rsi/config/TESTBTC \
  -H "Content-Type: application/json" \
  -d '{"period": 15, "overbought": 72}'

# Verify config change metric
kubectl exec -it deployment/prometheus -n monitoring -- \
  promtool query instant http://localhost:9090 \
  'ta_bot_config_changes_total{action="update"}'

Step 4: Document Baseline

This involves creating a METRICS_BASELINE.md file to document current metric values. This file serves as a reference for future comparisons. This document is essential for understanding the typical behavior of the TA Bot and for detecting any significant changes over time. By documenting the baseline, the team can quickly identify and address anomalies that might indicate operational issues or changes in system performance. The information documented includes key metrics such as signal generation rates, processing latency, strategy execution details, and configuration change frequencies. This provides a comprehensive overview of the bot's performance under normal operating conditions.

# TA Bot Business Metrics Baseline

Captured: 2025-10-26

## Signal Generation
- Avg signals/hour: ~15
- Top strategy: golden_trend_sync (40% of signals)
- BUY/SELL ratio: 55/45

## Processing Latency
- p50: 850ms
- p95: 2100ms
- p99: 3500ms

## Strategy Execution
- Success rate: 98.5%
- Error rate: 1.5%
- Most reliable: momentum_pulse (100%)

## Configuration Changes
- Changes/week: 3-5
- Most changed: rsi, macd

Suggestions: Alternative Verification Methods

Option 1: Manual Verification (Recommended)

This manual method involves executing a step-by-step checklist. The team will thoroughly verify each aspect of the metrics and dashboard. This method has the advantage of thoroughness and allows the capture of baseline data and the creation of detailed documentation. The main advantage is that it provides a detailed and accurate method for gathering data. The main drawback is that it might take more time to complete because each step must be executed manually.

Option 2: Automated Verification Script

This process involves creating a script to automatically verify all metrics. The benefits of automated scripting are its repeatability and speed. However, it might miss edge cases that could be detected through manual inspection. Although the speed of the script is helpful, it is important to remember to include manual checks.

Option 3: Grafana Only

This is a quick validation method that involves simply importing the dashboard and visually verifying the data. It's a faster way of validating the metrics. The team will have an immediate visual understanding of the data, but it will not include any baseline documentation or Prometheus verification. This makes the data less reliable.

References: Supporting Documentation and Resources

Metrics from #93

This section provides the sources related to the metrics, including the implementation details in the ta_bot/core/signal_engine.py file. It also references the dashboard file dashboards/ta-bot-business-metrics.json and the pull request (PR) link for context. All these resources collectively provide comprehensive information about the development and use of the metrics and dashboard, providing transparency and accessibility for all relevant stakeholders.

Documentation

This provides links to Prometheus and Grafana documentation for detailed guidance. It provides comprehensive information on these tools, which is helpful to the verification process.

Context: Background and Motivation

This section outlines the context of the verification work. This includes how the work was discovered, the impact, any dependencies, and the estimated effort. The verification work was discovered while completing the #93 implementation. It has a high impact because verifying the production deployment is necessary. It is not blocked by any other tasks and is dependent on #93. The estimated effort is small, with the process taking approximately one to two hours to complete. This context ensures that all team members understand the importance and urgency of the task. Understanding the context helps prioritize and manage the resources efficiently.

  • Discovered While: Completing #93 implementation
  • Impact: HIGH - Need to verify production deployment
  • Blocked By: None - metrics already deployed
  • Depends On: #93 (merged and deployed)
  • Sprint/Iteration: Current sprint (urgent)
  • Estimated Effort: Small (1-2 hours)

Testing Requirements: Verifying Success

To ensure the verification process is successful, specific testing requirements are set. This includes verifying the presence of each metric in Prometheus, testing the dashboard's functionality, and verifying the reasonableness of the metric values. The testing also requires real-time metric updates and capturing a baseline for future comparison. These requirements ensure that the system functions correctly and provides a reliable foundation for monitoring the TA Bot’s performance. These requirements must be met before considering the verification successful.

  • Verify each metric appears in Prometheus.
  • Test dashboard loads without errors.
  • Verify metric values are reasonable.
  • Test metric updates in real-time.
  • Capture baseline for future comparison.

Success Metrics: Measuring the Outcome

This outlines the specific criteria that define the success of the verification process. This includes all 5 metrics found in Prometheus and the dashboard showing data in all 8 panels. The documented baseline metrics, the creation of the verification runbook, and the capture of screenshots for reference are also success metrics. Meeting these metrics confirms that the verification is complete and that the new business metrics are correctly deployed and ready for ongoing monitoring. The successful completion of these metrics confirms the verification and that the team has achieved the goals set.

  • All 5 metrics found in Prometheus
  • Dashboard shows data in all 8 panels
  • Baseline metrics documented
  • Verification runbook created
  • Screenshots captured for reference

For more in-depth information on Prometheus, visit the official Prometheus documentation on Prometheus.

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