L 2. Use pandas rolling correlation functions Forward Returns (Task 1.1) - Target Variables for Part 20 ### Task 1.3: Correlation Analysis 3. Set appropriate min_periods parameter 4. Return time series of correlation coefficientse Volatility Analysis (Task 1.2) - Market Understanding #### 'calculate_correlation_matrix () ' - Correlation Matrix # TODO: STUDENT IMPLEMENTATION REQUIRED Correlation Analysis (Task 1.3) - Cross-sectional Structured Computation passe `python def calculate_correlation_matrix (daily_returns: pd. DataFrame, Foundation for Factor Engineering (Part 2) - method: str = 'pearson' ) -> pd. DataFrame:+ #### 'plot_correlation_heatmap () ' - Correlation Heatmap Calculate pairwise correlation matrix of stock returns.~ Visualization # Testing and Validation python Implementation Points:~ def plot_correlation_heatmap (correlation_matrix: pd. DataFrame, ### Running Tests+ "bashe 1. Use pandas corr () function with specified methods title: str = "Stock Returns Correlation Matrix", cmap: str = 2. Handle missing data appropriately (pairwise deletion) + RdBu_r', figsize: Tuple [int, int] = (12, 10), save_path: # Run all Part 1 testsf 3. Ensure symmetric matrix with diagonal = 1. 06 Optional [str] = None) -> None:" python -m pytest tests/test_parti/ -ve 4. Support different correlation methods (pearson, spearman, Create correlation matrix heatmap visualization.~ kendall) + # Run specific task testsf python -m pytest tests/test_parti/test_taski.py -v # Return # TODO: STUDENT IMPLEMENTATION REQUIRED- Implementation Points:" calculation 1. Use seaborn heatmap function passe python -m pytest tests/test_parti/test_task2.py -v # Volatility 2. Apply appropriate color scheme and formatting analysis 3. Add title and axis labels python -m pytest tests/test_parti/test_task3.py -v # Correlation #### 'calculate_rolling_correlation() ' - Rolling Correlation 4. Handle large matrices with proper scaling analysis Analysis ``python # TODO: STUDENT IMPLEMENTATION REQUIRED # Run simple verification def calculate_rolling_correlation (stockl_returns: pd. Series, passe python test_parti_simple.pye stock2_returns: pd. Series, window: int = 60, min_periods: Optional [int] = None) -> pd. Series:" #* Data Flow and Dependencies~ ### Validation Checkpoints Calculate rolling correlation between two stocks.~ #### Task 1.1: Return Calculations Implementation Points:+ Raw K-bar Data (Part 1) + - [ ] Forward returns shape matches input data dimensions 1. Align two return series by index - [ ] Weekly returns properly aggregated to weekly frequency