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fix: resolve test failures and dependency issues #2

fix: resolve test failures and dependency issues

fix: resolve test failures and dependency issues #2

Workflow file for this run

name: Tests
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
jobs:
test:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.9", "3.10", "3.11"]
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Test basic imports
run: |
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!')"
- 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!')"
- 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!')
"