fix: resolve test failures and dependency issues

- Remove pypfopt dependency causing compatibility issues
- Lower version requirements for better Python 3.8-3.11 compatibility
- Simplify GitHub Actions workflow to focus on Python 3.9-3.11
- Add basic functionality test to verify module imports work
- Improve test output with success indicators

Author: IsmailMoudden

.github/workflows/test.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v4
@@ -26,21 +26,34 @@ jobs:
         python -m pip install --upgrade pip
         pip install -r requirements.txt
     
-    - name: Test imports
+    - name: Test basic imports
       run: |
-        python -c "import pandas, numpy, matplotlib, yfinance, scipy, sklearn; print('All imports successful!')"
+        python -c "import pandas, numpy, matplotlib, yfinance, scipy, sklearn; print('✅ All basic imports successful!')"
     
     - name: Test data utilities
       run: |
-        python -c "from src.data_utils import download_prices; print('Data utils import successful!')"
+        python -c "from src.data_utils import download_prices; print('✅ Data utils import successful!')"
     
     - name: Test optimization modules
       run: |
-        python -c "from src.markowitz import markowitz_weights; print('Markowitz module import successful!')"
-        python -c "from src.risk_parity import risk_parity_weights; print('Risk parity module import successful!')"
-        python -c "from src.monte_carlo import monte_carlo_simulation; print('Monte Carlo module import successful!')"
-        python -c "from src.black_litterman import black_litterman_weights; print('Black-Litterman module import successful!')"
-        python -c "from src.ml_predictor import ml_predictor; print('ML predictor module import successful!')"
-        python -c "from src.hybrid_model import hybrid_weights; print('Hybrid model module import successful!')"
-        python -c "from src.custom_metrics_opt import custom_metrics_weights; print('Custom metrics module import successful!')"
-        python -c "from src.walkforward_backtest import walkforward_backtest; print('Walkforward backtest module import successful!')"
+        python -c "from src.markowitz import markowitz_weights; print('✅ Markowitz module import successful!')"
+        python -c "from src.risk_parity import risk_parity_weights; print('✅ Risk parity module import successful!')"
+        python -c "from src.monte_carlo import monte_carlo_simulation; print('✅ Monte Carlo module import successful!')"
+        python -c "from src.black_litterman import black_litterman_weights; print('✅ Black-Litterman module import successful!')"
+        python -c "from src.ml_predictor import ml_predictor; print('✅ ML predictor module import successful!')"
+        python -c "from src.hybrid_model import hybrid_weights; print('✅ Hybrid model module import successful!')"
+        python -c "from src.custom_metrics_opt import custom_metrics_weights; print('✅ Custom metrics module import successful!')"
+        python -c "from src.walkforward_backtest import walkforward_backtest; print('✅ Walkforward backtest module import successful!')"
+    
+    - name: Test basic functionality
+      run: |
+        python -c "
+        import numpy as np
+        from src.risk_parity import risk_parity_weights
+        
+        # Test basic functionality
+        cov_matrix = np.array([[0.01, 0.005], [0.005, 0.01]])
+        weights = risk_parity_weights(cov_matrix)
+        print(f'✅ Risk parity weights calculated: {weights}')
+        print('✅ Basic functionality test passed!')
+        "

requirements.txt

@@ -1,11 +1,10 @@
-numpy>=1.26.0
-pandas>=2.1.0
-scipy>=1.11.0
-matplotlib>=3.8.0
-seaborn>=0.13.0
-scikit-learn>=1.3.0
-yfinance>=0.2.40
-ta>=0.10.2
-pypfopt>=1.5.5
+numpy>=1.21.0
+pandas>=1.5.0
+scipy>=1.9.0
+matplotlib>=3.5.0
+seaborn>=0.11.0
+scikit-learn>=1.1.0
+yfinance>=0.2.0
+ta>=0.10.0