CI/CD Pipelines for Trading Systems

What CI/CD Actually Means

CI/CD stands for Continuous Integration and Continuous Deployment (or Delivery). In plain terms: every time a developer pushes code, automated systems verify it works, and — if everything passes — deploy it to production without manual intervention.

This might sound risky, especially for financial systems where bugs can be expensive. But the alternative is worse: manual deployments are slow, error-prone, and create bottlenecks. Teams that deploy manually tend to deploy infrequently, which means each deployment is a large, risky batch of changes. Teams with good CI/CD deploy small changes frequently, making each deployment low-risk and easy to roll back.

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Continuous Integration: Catch Problems Early

CI is the practice of automatically building and testing code every time someone pushes to the repository. The goal is simple: find bugs within minutes, not days.

A Typical CI Pipeline

# .github/workflows/ci.yml (GitHub Actions)
name: CI Pipeline
on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'

      - name: Install dependencies
        run: |
          pip install -r requirements.txt
          pip install -r requirements-dev.txt

      - name: Run linting
        run: |
          ruff check .
          mypy src/

      - name: Run tests
        run: pytest tests/ -v --cov=src --cov-report=xml

      - name: Check test coverage
        run: |
          coverage report --fail-under=80

This runs automatically on every push and pull request. If any step fails — linting errors, type check violations, test failures, coverage drops — the pipeline fails and the team is notified immediately.

What to Include in CI

The key principle: fast feedback. If a developer has to wait 30 minutes for CI results, they context-switch and the feedback loop breaks. Keep your CI pipeline under 10 minutes — parallelise test suites, cache dependencies, and avoid unnecessary steps.

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Continuous Deployment: From Code to Production

CD extends CI by automatically deploying code that passes all checks. In finance, this usually means Continuous Delivery (code is deployable but a human approves the final push) rather than fully automated deployment to production.

Deployment Strategies

Blue-Green Deployment — maintain two identical environments. Deploy to the idle one, verify it works, then switch traffic. If something goes wrong, switch back instantly.

Canary Deployment — route a small percentage of traffic (say 5%) to the new version. Monitor error rates and performance. If everything looks good, gradually increase. If not, roll back.

Rolling Deployment — update instances one at a time. If instance #1 is healthy after update, proceed to #2, and so on.

For trading systems, blue-green is common because the switchover is near-instant and the rollback is trivial.

Environment Promotion

A typical flow for financial software:

Developer branch → CI tests pass → Merge to develop
                                          ↓
                                   Deploy to DEV environment
                                          ↓
                                   Automated regression tests
                                          ↓
                                   Deploy to UAT (user acceptance)
                                          ↓
                                   Manual sign-off by business
                                          ↓
                                   Deploy to PRODUCTION

Each environment mirrors production as closely as possible. The further along the pipeline, the more rigorous the testing. This multi-stage approach is part of broader SDLC best practices.

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Infrastructure as Code

Modern CI/CD does not just deploy application code — it manages infrastructure too. Tools like Terraform, CloudFormation, and Pulumi let you define servers, databases, and networking in version-controlled files:

# Terraform example: define a database
resource "aws_rds_instance" "trading_db" {
  engine         = "postgres"
  engine_version = "15.4"
  instance_class = "db.r6g.xlarge"
  storage_type   = "gp3"

  backup_retention_period = 30
  multi_az               = true
  storage_encrypted      = true
}

This means your infrastructure changes go through the same review, testing, and deployment process as your application code. No more "someone changed a server config and nobody knows what they did." For more on this, see our guides on Docker containers and cloud infrastructure.

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Monitoring and Observability

A pipeline that deploys without monitoring is a pipeline that deploys blind. You need to know — quickly — if a deployment caused problems:

• Health checks — is the application responding?
• Error rate monitoring — has the error rate increased since deployment?
• Latency tracking — are response times degraded?
• Business metrics — are trades still flowing? Are P&L calculations completing?

Automated rollbacks triggered by anomaly detection are the gold standard: if error rates spike within 15 minutes of deployment, automatically revert to the previous version.

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Getting Started

You do not need to build a perfect pipeline from day one. Start with:

1. Automated tests running on every push
2. Linting and type checking in CI
3. A manual but documented deployment process

Then iterate: add coverage thresholds, security scanning, automated deployments to staging, and eventually production deployment automation. Each step reduces risk and increases team velocity.

The investment in CI/CD infrastructure pays dividends from the first week. Bugs are caught earlier, deployments are less stressful, and the team can move faster with confidence that the safety nets are in place.