mirror of
https://github.com/Xe138/AI-Trader.git
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Bill
923cdec5ca
Add comprehensive suite of testing scripts for different workflows: - test.sh: Interactive menu for all testing operations - quick_test.sh: Fast unit test feedback (~10-30s) - run_tests.sh: Main test runner with full configuration options - coverage_report.sh: Coverage analysis with HTML/JSON/terminal reports - ci_test.sh: CI/CD optimized testing with JUnit/coverage XML output Features: - Colored terminal output with clear error messages - Consistent option flags across all scripts - Support for test markers (unit, integration, e2e, slow, etc.) - Parallel execution support - Coverage thresholds (default: 85%) - Virtual environment and dependency checks Documentation: - Update CLAUDE.md with testing section and examples - Expand docs/developer/testing.md with comprehensive guide - Add scripts/README.md with quick reference All scripts are tested and executable. This standardizes the testing process for local development, CI/CD, and pull request workflows.
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
import pytest
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import time
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from api.database import initialize_database
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from api.job_manager import JobManager
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from api.simulation_worker import SimulationWorker
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from unittest.mock import Mock, patch
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def test_worker_prepares_data_before_execution(tmp_path):
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"""Test that worker calls _prepare_data before executing trades."""
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db_path = str(tmp_path / "test.db")
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initialize_database(db_path)
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job_manager = JobManager(db_path=db_path)
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# Create job
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job_id = job_manager.create_job(
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config_path="configs/default_config.json",
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date_range=["2025-10-01"],
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models=["gpt-5"]
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)
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worker = SimulationWorker(job_id=job_id, db_path=db_path)
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# Mock _prepare_data to track call
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original_prepare = worker._prepare_data
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prepare_called = []
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def mock_prepare(*args, **kwargs):
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prepare_called.append(True)
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return (["2025-10-01"], [], {}) # Return available dates, no warnings, no completion skips
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worker._prepare_data = mock_prepare
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# Mock _execute_date to avoid actual execution
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worker._execute_date = Mock()
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# Run worker
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result = worker.run()
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# Verify _prepare_data was called
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assert len(prepare_called) == 1
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assert result["success"] is True
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def test_worker_handles_no_available_dates(tmp_path):
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"""Test worker fails gracefully when no dates are available."""
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db_path = str(tmp_path / "test.db")
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initialize_database(db_path)
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job_manager = JobManager(db_path=db_path)
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job_id = job_manager.create_job(
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config_path="configs/default_config.json",
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date_range=["2025-10-01"],
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models=["gpt-5"]
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)
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worker = SimulationWorker(job_id=job_id, db_path=db_path)
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# Mock _prepare_data to return empty dates
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worker._prepare_data = Mock(return_value=([], [], {}))
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# Run worker
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result = worker.run()
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# Should fail with descriptive error
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assert result["success"] is False
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assert "No trading dates available" in result["error"]
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# Job should be marked as failed
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job = job_manager.get_job(job_id)
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assert job["status"] == "failed"
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def test_worker_stores_warnings(tmp_path):
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"""Test worker stores warnings from prepare_data."""
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db_path = str(tmp_path / "test.db")
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initialize_database(db_path)
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job_manager = JobManager(db_path=db_path)
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job_id = job_manager.create_job(
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config_path="configs/default_config.json",
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date_range=["2025-10-01"],
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models=["gpt-5"]
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)
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worker = SimulationWorker(job_id=job_id, db_path=db_path)
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# Mock _prepare_data to return warnings
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warnings = ["Rate limited", "Skipped 1 date"]
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worker._prepare_data = Mock(return_value=(["2025-10-01"], warnings, {}))
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worker._execute_date = Mock()
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# Run worker
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result = worker.run()
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# Verify warnings in result
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assert result["warnings"] == warnings
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# Verify warnings stored in database
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import json
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job = job_manager.get_job(job_id)
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stored_warnings = json.loads(job["warnings"])
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assert stored_warnings == warnings
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