refactor: apply uv run ruff check pystock/ --fix

pystock/asset.py

@@ -14,6 +14,7 @@ def __init__(self, symbol: str, listed: bool = True) -> None:
         Args:
             symbol (str): Bloomberg ticker of the asset.
             listed (bool, optional): If the asset is listed on Yahoo Finance or not. Default to True.
+
         """
         self.symbol = symbol
         if listed:
@@ -35,6 +36,7 @@ def name(self) -> str:
 
         Returns:
             str: The name of the asset.
+
         """
         return self.ticker.info[cst.SHORT_NAME] if self.listed else self.symbol  # type: ignore
 
@@ -54,6 +56,7 @@ def fetch_historical_data(
 
         Returns:
             pd.DataFrame | None: The historical data of the asset.
+
         """
         if not self.listed:
             print(f"{self.name} is not listed! You should provide the data.")
@@ -75,6 +78,7 @@ def historical_data(self) -> pd.DataFrame | None:
 
         Returns:
             pd.DataFrame | None: The historical data of the asset.
+
         """
         return self.fetch_historical_data()
 
@@ -84,6 +88,7 @@ def closes(self) -> pd.Series | None:
 
         Returns:
             pd.Series | None: The closing prices of the asset.
+
         """
         if self._closes is None:
             self._closes = self.historical_data[cst.CLOSE]  # type: ignore
@@ -95,6 +100,7 @@ def daily_returns(self) -> np.ndarray | None:
 
         Returns:
             np.ndarray | None: The daily returns of the asset.
+
         """
         if self._daily_returns is None:
             closes = self.historical_data[cst.CLOSE]  # type: ignore
@@ -108,6 +114,7 @@ def daily_returns(self, data: list[float]) -> None:
 
         Args:
             data (list[float]): The daily returns of the asset.
+
         """
         self._daily_returns = data
 
@@ -117,6 +124,7 @@ def daily_log_returns(self) -> pd.Series | None:
 
         Returns:
             pd.Series | None: The daily log returns of the asset.
+
         """
         if self._daily_log_returns is None:
             closes = self.historical_data[cst.CLOSE]  # type: ignore
@@ -130,6 +138,7 @@ def daily_log_returns(self, data: list[float]) -> None:
 
         Args:
             data (list[float]): The daily log returns of the asset.
+
         """
         self._daily_log_returns = data
 
@@ -139,6 +148,7 @@ def expected_log_return(self) -> float | None:
 
         Returns:
             float | None: The expected log return of the asset.
+
         """
         if self._expected_log_return is None:
             if self._expected_return is not None:
@@ -155,6 +165,7 @@ def expected_log_return(self, value: float) -> None:
 
         Args:
             value (float): The expected log return of the asset.
+
         """
         self._expected_log_return = value
 
@@ -164,6 +175,7 @@ def expected_return(self) -> float | None:
 
         Returns:
             float | None: The expected return of the asset.
+
         """
         if self._expected_return is None:
             self._expected_return = np.exp(self.expected_log_return) - 1  # type: ignore
@@ -175,6 +187,7 @@ def expected_return(self, value: float) -> None:
 
         Args:
             value (float): The expected return of the asset.
+
         """
         self._expected_return = value
 

pystock/portfolio.py

@@ -35,6 +35,7 @@ def from_xlsx_file(cls, file_path: str) -> "Portfolio":
 
         Raises:
             ValueError: If the columns `SYMBOL_COL` and `WEIGHT_COL` are not present in the file.
+
         """
         df = pd.read_excel(file_path)
         if cst.SYMBOL_COL not in df.columns or cst.WEIGHT_COL not in df.columns:
@@ -52,6 +53,7 @@ def to_xlsx_file(self, file_path: str) -> None:
 
         Args:
             file_path (str): Path to the XLSX file.
+
         """
         data = {
             cst.SYMBOL_COL: [asset.symbol for asset in self.assets],
@@ -76,6 +78,7 @@ def cov_matrix(self) -> np.ndarray:
 
         Returns:
             np.ndarray: The covariance matrix.
+
         """
         if self._cov_matrix is None:
             daily_returns = [
@@ -113,6 +116,7 @@ def variance(self) -> float:
 
         Returns:
             float: The variance of the portfolio.
+
         """
         return np.dot(self.weights, np.dot(self.cov_matrix, self.weights))
 
@@ -152,6 +156,7 @@ def add_asset(self, asset: Asset, weight: float) -> None:
         Args:
             asset (Asset): The asset to add.
             weight (float): The weight of the asset in the portfolio.
+
         """
         self.assets.append(asset)
         self.weights = np.append(self.weights, weight)
@@ -167,6 +172,7 @@ def remove_asset(self, asset: Asset) -> None:
 
         Raises:
             ValueError: If the asset is not in the portfolio.
+
         """
         if asset not in self.assets:
             raise ValueError("The asset is not in the portfolio.")

pystock/quantitative.py

@@ -43,6 +43,7 @@ def minimize_variance_with_return(self, desired_return: float) -> np.ndarray:
 
         Returns:
             np.ndarray: Optimal portfolio weights.
+
         """
         self.model.objective = pyo.Objective(
             expr=pyo.quicksum(
@@ -100,6 +101,7 @@ def simulation(
 
         Returns:
             pd.DataFrame: Results of the simulation (returns, risk, Sharpe ratio, weights).
+
         """
         if num_simulations <= 0:
             raise ValueError("Number of simulations must be a positive integer.")
@@ -125,6 +127,7 @@ def get_gareto_front(df: pd.DataFrame) -> pd.DataFrame:
 
         Returns:
             pd.DataFrame: DataFrame containing Pareto-optimal portfolios.
+
         """
         return df.sort_values("Risk").drop_duplicates("Returns", keep="first")
 
@@ -137,6 +140,7 @@ def create_efficient_frontier(df: pd.DataFrame) -> go.Figure:
 
         Returns:
             px.scatter: Plotly scatter plot.
+
         """
         df["Weights"] = df["Weights"].apply(lambda x: str(x))
         fig = px.scatter(