Fix OpenCV import for RL tests, mark slow RL tests, and document in README

- Add opencv-python-headless to requirements for RL compatibility
- Mark RL agent training tests as

@pytest
.mark.slow and reduce timesteps
- Update README with slow test instructions

README.md

@@ -386,7 +386,8 @@ tests/
 ├── test_strategy_agent.py
 ├── test_execution_agent.py
 ├── test_data_ingestion.py
-└── test_integration.py
+├── test_integration.py
+├── test_finrl_agent.py
 ```
 
 ### Test Categories
@@ -394,13 +395,15 @@ tests/
 - **Integration Tests**: Test complete workflows
 - **Performance Tests**: Test system performance and scalability
 - **Error Handling Tests**: Test error conditions and edge cases
+- **Slow RL Tests**: RL agent training tests are marked as `@pytest.mark.slow` and use minimal timesteps for speed. These are skipped by default unless explicitly run.
 
 ### Running Specific Tests
 
 ```bash
-# Run tests with specific markers
-pytest -m unit
-pytest -m integration
+# Run all fast tests (default)
+pytest
+
+# Run slow RL tests (FinRL agent training)
 pytest -m slow
 
 # Run tests with coverage

requirements.txt

@@ -12,3 +12,4 @@ stable-baselines3
 gymnasium
 tensorboard
 torch
+opencv-python-headless

tests/test_finrl_agent.py

@@ -228,6 +228,7 @@ class TestFinRLAgent:
         macd = agent._calculate_macd(prices)
         assert len(macd) == len(prices)
     
+    @pytest.mark.slow
     @patch('agentic_ai_system.finrl_agent.PPO')
     def test_training_ppo(self, mock_ppo, finrl_config, sample_data):
         """Test PPO training"""
@@ -236,12 +237,13 @@ class TestFinRLAgent:
         mock_ppo.return_value = mock_model
         
         agent = FinRLAgent(finrl_config)
-        result = agent.train(sample_data, total_timesteps=100)
+        result = agent.train(sample_data, total_timesteps=5)
         
         assert result['algorithm'] == 'PPO'
-        assert result['total_timesteps'] == 100
+        assert result['total_timesteps'] == 5
         mock_model.learn.assert_called_once()
     
+    @pytest.mark.slow
     @patch('agentic_ai_system.finrl_agent.A2C')
     def test_training_a2c(self, mock_a2c):
         """Test A2C training"""
@@ -258,7 +260,7 @@ class TestFinRLAgent:
             'volume': [1000, 1100, 1200]
         })
         
-        result = agent.train(sample_data, total_timesteps=100)
+        result = agent.train(sample_data, total_timesteps=5)
         
         assert result['algorithm'] == 'A2C'
         mock_model.learn.assert_called_once()