fix: add fallback imports for direct module execution

- Add try/except blocks for relative imports in all modules
- Fallback to absolute imports when relative imports fail
- Ensures modules work both as package and when executed directly
- Resolves 'attempted relative import with no known parent package' error

Author: IsmailMoudden

src/__init__.py

@@ -2,7 +2,10 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+try:
+    from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+except ImportError:
+    from data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
 
 
 def run_black_litterman(tickers=None, start="2020-01-01", end="2023-12-31") -> pd.Series:

src/black_litterman.py

@@ -3,7 +3,10 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 from scipy.optimize import minimize
-from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+try:
+    from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+except ImportError:
+    from data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
 
 
 def sharpe_ratio(returns: pd.Series, rf: float = 0.0) -> float:

src/custom_metrics_opt.py

@@ -2,8 +2,12 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from .data_utils import download_prices, compute_indicators, ensure_figures_dir, DEFAULT_TICKERS
-from .risk_parity import risk_parity_weights
+try:
+    from .data_utils import download_prices, compute_indicators, ensure_figures_dir, DEFAULT_TICKERS
+    from .risk_parity import risk_parity_weights
+except ImportError:
+    from data_utils import download_prices, compute_indicators, ensure_figures_dir, DEFAULT_TICKERS
+    from risk_parity import risk_parity_weights
 from sklearn.linear_model import Ridge
 from sklearn.preprocessing import StandardScaler
 from sklearn.pipeline import Pipeline

src/hybrid_model.py

@@ -2,7 +2,10 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+try:
+    from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+except ImportError:
+    from data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
 
 
 def random_portfolios(mu: np.ndarray, cov: np.ndarray, n: int = 20000, seed: int = 42):

src/markowitz.py

@@ -7,7 +7,10 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.pipeline import Pipeline
 from sklearn.model_selection import TimeSeriesSplit
-from .data_utils import download_prices, compute_indicators, ensure_figures_dir, DEFAULT_TICKERS
+try:
+    from .data_utils import download_prices, compute_indicators, ensure_figures_dir, DEFAULT_TICKERS
+except ImportError:
+    from data_utils import download_prices, compute_indicators, ensure_figures_dir, DEFAULT_TICKERS
 
 
 def prepare_ml_dataset(start="2020-01-01", end="2023-12-31", tickers=None) -> Tuple[pd.DataFrame, pd.Series, pd.Series]:

src/ml_predictor.py

@@ -10,13 +10,22 @@
 from sklearn.pipeline import Pipeline
 from sklearn.metrics import r2_score, mean_absolute_error
 
-from .data_utils import (
-    download_prices,
-    compute_indicators,
-    align_features_and_targets,
-    ensure_figures_dir,
-    DEFAULT_TICKERS,
-)
+try:
+    from .data_utils import (
+        download_prices,
+        compute_indicators,
+        align_features_and_targets,
+        ensure_figures_dir,
+        DEFAULT_TICKERS,
+    )
+except ImportError:
+    from data_utils import (
+        download_prices,
+        compute_indicators,
+        align_features_and_targets,
+        ensure_figures_dir,
+        DEFAULT_TICKERS,
+    )
 
 
 def compute_sign_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:

src/ml_training_report.py

@@ -2,7 +2,10 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+try:
+    from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+except ImportError:
+    from data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
 
 
 def simulate_portfolios(returns: pd.DataFrame, n_portfolios: int = 10000, seed: int = 42):

src/monte_carlo.py

@@ -3,7 +3,10 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 from scipy.optimize import minimize
-from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+try:
+    from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+except ImportError:
+    from data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
 
 
 def risk_contribution(weights: np.ndarray, cov: np.ndarray) -> np.ndarray:

src/risk_parity.py

@@ -4,8 +4,12 @@
 from datetime import datetime
 from typing import Dict, Callable, Tuple
 
-from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
-from .metrics_utils import sharpe_ratio, calmar_ratio, sortino_ratio, weight_stability, turnover
+try:
+    from .data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+    from .metrics_utils import sharpe_ratio, calmar_ratio, sortino_ratio, weight_stability, turnover
+except ImportError:
+    from data_utils import download_prices, ensure_figures_dir, DEFAULT_TICKERS
+    from metrics_utils import sharpe_ratio, calmar_ratio, sortino_ratio, weight_stability, turnover
 
 
 def compute_weights_markowitz(prices: pd.DataFrame) -> np.ndarray: