AI Trading Platform: Adding CI/CD to Our Development Workflow

As the AI Trading Platform grew in complexity, we reached a point where manual code reviews could no longer keep pace. After our v0.3 exit strategy was frozen and the evaluation framework became operational, the codebase expanded to handle signal generation, position management, risk controls, and performance tracking.

This post documents why we added CI/CD, what types of issues it catches, and how it fits into our development workflow.

The Problem: Manual Review Limitations

During a recent code review session, we identified over 30 issues across the platform. Some were minor style problems. Others were logic errors that could affect trading calculations.

The manual review process had clear limitations:

• Uneven coverage — Critical modules received more attention while utility code was rushed
• Regression risk — Fixing one bug could accidentally break something else
• No enforcement — Nothing prevented problematic code from reaching the main branch
• Fatigue errors — Late-night sessions led to missed issues

Three categories of bugs stood out:

Missing Data Fields

Some data structures were missing fields that other parts of the system expected. These bugs only appeared during specific conditions, like viewing the dashboard during active paper trading. Standard testing did not trigger them.

Unhandled Edge Cases

External data sources sometimes return empty or delayed responses. Code that assumed data was always available would crash when the API was slow. These edge cases needed explicit handling.

Calculation Logic Errors

The two-stage exit strategy requires precise calculations. A bug in the trailing stop update logic meant runner positions were exiting too early. This affected performance but was hard to spot in code review.

Why CI/CD Matters for Trading Research

CI/CD (Continuous Integration / Continuous Deployment) automates code validation on every commit. For a trading platform, this is not just convenience — it directly affects the reliability of research results.

The AI Trading Platform runs causal paper trading where signals are processed bar by bar without future data. If a bug corrupts the signal queue or miscalculates position sizes, the paper trading results become invalid. We would not know whether poor performance came from the strategy or from a code error.

Automated testing catches these issues before they affect our data.

Pipeline Architecture

Our implementation uses GitHub Actions with the following stages:

Stage 1: Environment Setup

The pipeline creates a clean environment, installs Python, and restores cached dependencies. This ensures tests run consistently, not affected by local machine configurations.

Stage 2: Code Style Checking

We use Ruff to check all Python files for:

• Unused imports and variables
• Import ordering issues
• Common anti-patterns that make debugging difficult

Style checks help maintain readable code as the project grows.

Stage 3: Type Checking

Mypy validates type annotations across the codebase. In a trading system, type errors are especially dangerous. Passing a price as an integer instead of a float, or confusing different position metrics, could produce incorrect calculations.

Type checking catches these mismatches before runtime.

Stage 4: Test Execution

The test suite covers the core trading logic:

• Signal processing — Outputs are handled correctly
• Position calculations — Risk metrics match expected values
• Exit logic — The two-stage exit behaves as designed
• Data operations — Records are stored and retrieved correctly

All tests run in under three minutes.

Stage 5: Coverage Analysis

We set a minimum coverage threshold to ensure critical code paths have tests. Coverage reports identify areas where bugs could remain undetected.

Stage 6: Security Scanning

Following our security audit, we added security scanning to check for:

• Hardcoded credentials
• Unsafe data handling patterns
• Common security issues

Any high-severity finding fails the pipeline.

What We Learned from Implementation

Getting the pipeline to work required several iterations:

Dependency Management

Our first attempt failed because package version constraints were too loose. The dependency resolver could not find compatible versions. We fixed this by setting version bounds in our requirements file.

Lesson: Pin dependency versions or set upper bounds. Open-ended dependencies will break CI eventually.

Async Test Support

Tests for real-time data feeds failed because pytest does not support async functions by default. We added the appropriate plugin and markers.

Lesson: Check that your test framework supports all your testing patterns.

Type Annotation Gaps

Type checking revealed several functions with incorrect or missing type annotations. Parameters that could be None were not marked as optional, which prevented the type checker from verifying correct handling.

Lesson: Explicit type annotations help catch bugs early, especially in complex data structures.

Failure Handling

When any stage fails, the pipeline stops and marks the commit with a failure status. The code cannot merge until all checks pass.

This enforcement is the primary value of CI/CD: problematic code is blocked before it reaches the main branch.

For the AI Trading Platform, this means:

• Broken calculations cannot reach the paper trading system
• Security issues are caught immediately
• Logic bugs are detected before they corrupt research data

Integration with Development Workflow

The CI/CD pipeline now integrates with our broader workflow:

1. Feature development — New features are developed in branches
2. Automated validation — The pipeline runs on every push
3. Code review — Only validated code is reviewed
4. Merge to main — Reviewed and validated code reaches the main branch
5. Paper trading — The platform runs with tested code

This workflow ensures that paper trading results come from reliable, tested code.

Pre-Commit Hooks

In addition to CI/CD, we use pre-commit hooks to catch issues before code is committed. These hooks check for:

• Trailing whitespace and formatting issues
• Debug statements left in code
• Large files that should not be committed
• Patterns that look like credentials

Catching issues locally is faster than waiting for the CI pipeline.

Observed Results

After implementing CI/CD:

Next Steps

With CI/CD operational, planned improvements include:

1. Increase test coverage — Focus on exit and position sizing modules
2. Add performance benchmarks — Detect regressions in processing time
3. Automate deployment — Update systems automatically when tests pass

The CI/CD pipeline adds a layer of automated validation to the AI Trading Platform. Every commit now passes through style checking, type checking, tests, coverage analysis, and security scanning before reaching the main branch.

For a platform where code quality directly affects the validity of trading research, this level of automation is a practical requirement.

For a complete overview of the platform architecture and current statistics, visit the AI Trading Platform project page.

Previous: Exit Strategy Breakthrough

Automated testing cannot guarantee correctness, but it can guarantee that known issues are caught before they reach production.