Style: Black formatting

pystocktopus/__main__.py

@@ -1,3 +1,3 @@
 from .GUI import main
 
-main()
+main()

pystocktopus/config.py

@@ -3,6 +3,7 @@
 
 pystocktopus.
 """
+
 from __future__ import annotations
 
 import os

pystocktopus/core.py

@@ -27,7 +27,7 @@ def ticker_data_collection(
         timespan: str,
         multiplier: int,
         user_date: str,
-        days: int
+        days: int,
     ) -> list[float]:
         """Extracts stock data closing price from Polygon.io.
 
@@ -80,7 +80,11 @@ def ticker_data_collection(
 
                     # Store the close_list in the dictionary with ticker as the key
                     ticker_data[ticker] = close_list
-                    logging.info("Collected %d data points for ticker: %s", len(close_list), ticker)
+                    logging.info(
+                        "Collected %d data points for ticker: %s",
+                        len(close_list),
+                        ticker,
+                    )
                 else:
                     logging.warning("No results found for ticker: %s", ticker)
 
@@ -111,8 +115,12 @@ def CalculateDate(start_date_str: str, days_lag: int):
             # Calculate the start_date by subtracting the days_lag from the end_date
             start_date = end_date - datetime.timedelta(days=days_lag)
 
-            logging.info("Calculated start date: %s from end date: %s with a lag of %d days",
-                         start_date.strftime("%Y-%m-%d"), start_date_str, days_lag)
+            logging.info(
+                "Calculated start date: %s from end date: %s with a lag of %d days",
+                start_date.strftime("%Y-%m-%d"),
+                start_date_str,
+                days_lag,
+            )
 
             # Return the start_date as a string in the format "YYYY-MM-DD"
             return start_date.strftime("%Y-%m-%d")

pystocktopus/main.py

@@ -8,31 +8,40 @@
 
 # Set up logging configuration
 logging.basicConfig(
-    filename="Demo_file_logs.log",  
-    level=logging.INFO,             
-    format="%(asctime)s - %(levelname)s - %(message)s"
+    filename="Demo_file_logs.log",
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
 )
 
+
 def main():
     try:
-        data = StockExtractor.ticker_data_collection(ticker_values=ticker_values, timespan=timespan, multiplier=multiplier, user_date=user_date,days=days)
+        data = StockExtractor.ticker_data_collection(
+            ticker_values=ticker_values,
+            timespan=timespan,
+            multiplier=multiplier,
+            user_date=user_date,
+            days=days,
+        )
         logging.info(f"Stock data collected for tickers: {ticker_values}")
     except Exception as e:
         logging.error(f"Error collecting stock data: {str(e)}")
 
     try:
         CSVDataHandler.close_list_csv(data)
         logging.info("CSV data list successfully closed and saved.")
-
-        result = CSVDataHandler.combine_data_csv(data_values=ticker_values, close_list=data)
+
+        result = CSVDataHandler.combine_data_csv(
+            data_values=ticker_values, close_list=data
+        )
         logging.info(f"CSV data combined successfully: {result}")
     except Exception as e:
         logging.error(f"Error handling CSV data: {str(e)}")
 
     try:
         df = pd.read_csv("Your-CSV-FilePath")
         logging.info("CSV file loaded successfully.")
-        
+
         # Store the second column name in a variable
         second_column_name = df.columns[1]
         logging.info(f"Second column name: {second_column_name}")
@@ -43,7 +52,7 @@ def main():
         prediction = ModelStockData.create_fit_train_rnn(
             csv_file="GUI_GENERATED_DATA.csv",
             epochs=400,
-            stock_closing_price_column_name="Your-column-name-for-analysis-and-training"
+            stock_closing_price_column_name="Your-column-name-for-analysis-and-training",
         )
         logging.info(f"Model training and prediction complete: {prediction}")
     except Exception as e:
@@ -52,19 +61,22 @@ def main():
     try:
         result = News.new_data_extract(ticker_values=Article, predict_date=predict)
         logging.info(f"News data extracted for analysis: {result}")
-        
+
         ans = News.news_predict_analysis(result)
         logging.info(f"News analysis result: {ans}")
     except Exception as e:
         logging.error(f"Error in news analysis: {str(e)}")
 
     try:
-        News.create_csv_with_predictions(csv_filename="stock_data.csv", analysis_results=ans)
+        News.create_csv_with_predictions(
+            csv_filename="stock_data.csv", analysis_results=ans
+        )
         logging.info("CSV file created with predictions.")
     except Exception as e:
         logging.error(f"Error creating CSV with predictions: {str(e)}")
 
-if __name__=="__main__":
+
+if __name__ == "__main__":
     config.api_key = "your-polygon-api-key"
     config.news_api = "your-news-api-key"
 
@@ -74,7 +86,7 @@ def main():
     multiplier = 2
     user_date = "2023-10-01"
     predict = "2024-10-01"
-    days=340
+    days = 340
 
     # Calling the func
-    main(ticker_values,Article,timespan,multiplier,user_date,predict,days)
+    main(ticker_values, Article, timespan, multiplier, user_date, predict, days)

pystocktopus/news_analysis.py

@@ -20,6 +20,7 @@
     level=logging.INFO,  # Log level
 )
 
+
 class News:
     """Class for handling news data."""
 
@@ -64,7 +65,9 @@ def _combine_news_article(all_articles):
     @staticmethod
     def new_data_extract(ticker_values, predict_date, days: int = 10):
         """Extracts news articles for a given list of tickers and date range."""
-        logging.info(f"Extracting news data for tickers: {ticker_values} from {predict_date} for {days} days.")
+        logging.info(
+            f"Extracting news data for tickers: {ticker_values} from {predict_date} for {days} days."
+        )
         load_dotenv()
 
         try:
@@ -85,11 +88,13 @@ def new_data_extract(ticker_values, predict_date, days: int = 10):
                     from_param=start_date,
                     to=end_date,
                     language="en",
-                    sort_by="relevancy"
+                    sort_by="relevancy",
                 )
 
                 # Log the number of articles fetched
-                logging.info(f"Fetched {len(all_articles.get('articles', []))} articles for {ticker}.")
+                logging.info(
+                    f"Fetched {len(all_articles.get('articles', []))} articles for {ticker}."
+                )
 
                 # Store the result in the dictionary
                 results_dict[ticker] = all_articles
@@ -128,8 +133,12 @@ def news_predict_analysis(data: dict[str, str]) -> dict[str, str]:
                         positive += 1
 
                 # Store the analysis results in the dictionary
-                analysis_results[ticker] = "NEGATIVE" if negative >= positive else "POSITIVE"
-                logging.info(f"Sentiment for {ticker}: {'NEGATIVE' if negative >= positive else 'POSITIVE'}")
+                analysis_results[ticker] = (
+                    "NEGATIVE" if negative >= positive else "POSITIVE"
+                )
+                logging.info(
+                    f"Sentiment for {ticker}: {'NEGATIVE' if negative >= positive else 'POSITIVE'}"
+                )
 
             return analysis_results
 

pystocktopus/stock_csv.py

@@ -33,7 +33,9 @@ def csv_data_reader(csv_file, csv_stock_column_name: str) -> list[str]:
         data_values: list[str] = []
 
         try:
-            logging.info(f"Attempting to read column '{csv_stock_column_name}' from CSV file: {csv_file}")
+            logging.info(
+                f"Attempting to read column '{csv_stock_column_name}' from CSV file: {csv_file}"
+            )
             with open(csv_file) as file:
                 csv_reader = csv.reader(file)
 
@@ -51,15 +53,19 @@ def csv_data_reader(csv_file, csv_stock_column_name: str) -> list[str]:
                 # Check if the column was found
                 if data_column_index == -1:
                     raise ValueError(f"No {csv_stock_column_name} column found.")
-
-                logging.info(f"Column '{csv_stock_column_name}' found at index {data_column_index}.")
+
+                logging.info(
+                    f"Column '{csv_stock_column_name}' found at index {data_column_index}."
+                )
 
                 # Read the data from the specified column
                 for row in csv_reader:
                     if data_column_index < len(row):
                         data_values.append(row[data_column_index])
 
-                logging.info(f"Successfully read {len(data_values)} rows from column '{csv_stock_column_name}'.")
+                logging.info(
+                    f"Successfully read {len(data_values)} rows from column '{csv_stock_column_name}'."
+                )
 
         except FileNotFoundError:
             logging.error(f"File not found: {csv_file}")
@@ -88,7 +94,9 @@ def _getValue(closing_price: dict[float, float]):
                 last_value = value_list[-1]
                 last_values.append(last_value)
 
-        logging.info(f"Extracted {len(last_values)} values from closing price dictionary.")
+        logging.info(
+            f"Extracted {len(last_values)} values from closing price dictionary."
+        )
         return last_values
 
     @staticmethod
@@ -101,7 +109,7 @@ def combine_data_csv(
         results: list[float] = []
         for bought, closing_price in zip(data_values, data_extractor):
             results.append(float(bought) * float(closing_price))
-        
+
         logging.info(f"Combined data into {len(results)} result values.")
         return results
 
@@ -111,14 +119,18 @@ def update_csv(
     ) -> None:
         """Updates the CSV file with a new column for the calculated values."""
         try:
-            logging.info(f"Updating CSV file: {csv_path} with new column '{new_column_name}'.")
-
+            logging.info(
+                f"Updating CSV file: {csv_path} with new column '{new_column_name}'."
+            )
+
             df = pd.read_csv(csv_path)
             df[new_column_name] = results
 
             df.to_csv(csv_path, index=False)
 
-            logging.info(f"Successfully added '{new_column_name}' column with {len(results)} values to the CSV file.")
+            logging.info(
+                f"Successfully added '{new_column_name}' column with {len(results)} values to the CSV file."
+            )
 
         except FileNotFoundError:
             logging.error(f"CSV file not found: {csv_path}")
@@ -131,12 +143,12 @@ def update_csv(
     def close_list_csv(
         ticker_data: dict[str, list[float]],
         closing_data_fieldname: list[str] | None = None,
-        csv_file_name="stock_data.csv"
+        csv_file_name="stock_data.csv",
     ) -> None:
         """Stores the closing list stock results in a CSV file."""
         if closing_data_fieldname is None:
             closing_data_fieldname = ["closing_stock_data"]
-        
+
         if not ticker_data:
             logging.warning("Ticker data is empty. No CSV file will be created.")
             return
@@ -145,7 +157,9 @@ def close_list_csv(
             logging.info(f"Writing closing stock data to CSV file: {csv_file_name}")
             with open(csv_file_name, "w", newline="") as csvfile:
                 fieldnames = ["Date"] + [
-                    f"{field}_{ticker}" for ticker in ticker_data for field in closing_data_fieldname
+                    f"{field}_{ticker}"
+                    for ticker in ticker_data
+                    for field in closing_data_fieldname
                 ]
                 writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
                 writer.writeheader()
@@ -158,7 +172,9 @@ def close_list_csv(
                             data_row[f"{field}_{ticker}"] = close_list[i]
                     writer.writerow(data_row)
 
-            logging.info(f"CSV file '{csv_file_name}' created successfully with {num_rows} rows.")
+            logging.info(
+                f"CSV file '{csv_file_name}' created successfully with {num_rows} rows."
+            )
             file_path = os.path.abspath(csv_file_name)
             logging.info(f"CSV file saved at: {file_path}")