new version

BharatFinTrack/__init__.py

@@ -1,11 +1,13 @@
 from .nse_product import NSEProduct
+from .nse_index import NSEIndex
 from .nse_tri import NSETRI
 
 
 __all__ = [
     'NSEProduct',
+    'NSEIndex',
     'NSETRI'
 ]
 
 
-__version__ = '0.1.0'
+__version__ = '0.1.1'

BharatFinTrack/nse_index.py

@@ -0,0 +1,194 @@
+import os
+import tempfile
+import typing
+import datetime
+import dateutil.relativedelta
+import pandas
+import requests
+import bs4
+import matplotlib
+from .nse_product import NSEProduct
+from .core import Core
+
+
+class NSEIndex:
+
+    '''
+    Download and analyze NSE index price data.
+    '''
+
+    def all_equity_index_cagr_from_inception(
+        self,
+        excel_file: str,
+        http_headers: typing.Optional[dict[str, str]] = None
+    ) -> pandas.DataFrame:
+
+        '''
+        Returns a DataFrame with the CAGR(%) of all NSE equity indices
+        (excluding dividend reinvestment) from inception.
+
+        Parameters
+        ----------
+        excel_file : str
+            Path to an Excel file to save the DataFrame.
+
+        http_headers : dict, optional
+            HTTP headers for the web request. Defaults to
+            :attr:`BharatFinTrack.core.Core.default_http_headers` if not provided.
+
+        Returns
+        -------
+        DataFrame
+            A multi-index DataFrame with the CAGR(%) for all NSE equity indices from inception,
+            sorted in descending order by CAGR(%) within each index category.
+        '''
+
+        # web request headers
+        headers = Core().default_http_headers if http_headers is None else http_headers
+
+        # download data
+        main_url = 'https://www.niftyindices.com'
+        csv_url = main_url + '/reports/daily-reports'
+        response = requests.get(csv_url, headers=headers)
+        soup = bs4.BeautifulSoup(response.content, 'html.parser')
+        for anchor in soup.find_all('a'):
+            if anchor['href'].endswith('.csv') and anchor['id'] == 'dailysnapOneDaybefore':
+                csv_link = main_url + anchor['href']
+            else:
+                pass
+        response = requests.get(csv_link, headers=headers)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            daily_file = os.path.join(tmp_dir, 'daily.csv')
+            with open(daily_file, 'wb') as daily_data:
+                daily_data.write(response.content)
+            daily_df = pandas.read_csv(daily_file)
+
+        # processing downloaded data
+        date_string = datetime.datetime.strptime(
+            daily_df.loc[0, 'Index Date'], '%d-%m-%Y'
+        )
+        daily_date = date_string.date()
+        daily_df = daily_df[['Index Name', 'Index Date', 'Closing Index Value']]
+        daily_df.columns = ['Index Name', 'Date', 'Close']
+        daily_df['Index Name'] = daily_df['Index Name'].apply(lambda x: x.upper())
+        exclude_word = [
+            'G-SEC',
+            '1D RATE',
+            'INDIA VIX',
+            'DIVIDEND POINTS'
+        ]
+        exclude_index = daily_df['Index Name'].apply(
+            lambda x: any(word in x for word in exclude_word)
+        )
+        daily_df = daily_df[~exclude_index].reset_index(drop=True)
+        daily_df['Date'] = daily_date
+
+        # processing base DataFrame
+        base_df = NSEProduct()._dataframe_equity_index
+        base_df = base_df.reset_index()
+        category = list(base_df['Category'].unique())
+        base_df = base_df.drop(columns=['ID', 'API TRI'])
+        base_df['Base Date'] = base_df['Base Date'].apply(lambda x: x.date())
+
+        # checking absent indices in both files
+        base_unmatch = {
+            'NIFTY 50 FUTURES INDEX': 'NIFTY 50 FUTURES PR',
+            'NIFTY 50 FUTURES TR INDEX': 'NIFTY 50 FUTURES TR',
+            'NIFTY HEALTHCARE INDEX': 'NIFTY HEALTHCARE'
+        }
+        daily_df['Index Name'] = daily_df['Index Name'].apply(
+            lambda x: base_unmatch.get(x, x)
+        )
+        # base_index = base_df['Index Name']
+        # daily_index = daily_df['Index Name']
+        # unavailable_base = list(base_index[~base_index.isin(daily_index)])
+        # print(f'Base indices {unavailable_base} are not available in daily indices.')
+        # unavailable_daily = list(daily_index[~daily_index.isin(base_index)])
+        # print(f'Daily indices {unavailable_daily} are not available in base indices.')
+
+        # merging data
+        cagr_df = base_df.merge(daily_df)
+        cagr_df['Return(1 INR)'] = (cagr_df['Close'] / cagr_df['Base Value']).round(2)
+        cagr_df['Years'] = list(
+            map(
+                lambda x: dateutil.relativedelta.relativedelta(daily_date, x).years, cagr_df['Base Date']
+            )
+        )
+        cagr_df['Days'] = list(
+            map(
+                lambda x, y: (daily_date - x.replace(year=x.year + y)).days, cagr_df['Base Date'], cagr_df['Years']
+            )
+        )
+        total_years = cagr_df['Years'] + (cagr_df['Days'] / 365)
+        cagr_df['CAGR(%)'] = 100 * (pow(cagr_df['Close'] / cagr_df['Base Value'], 1 / total_years) - 1)
+
+        # Convert 'Category' column to categorical data types with a defined order
+        cagr_df['Category'] = pandas.Categorical(
+            cagr_df['Category'],
+            categories=category,
+            ordered=True
+        )
+
+        # Sort the dataframe
+        cagr_df = cagr_df.sort_values(
+            by=['Category', 'CAGR(%)', 'Years', 'Days'],
+            ascending=[True, False, False, False]
+        )
+
+        # output
+        dfs_category = map(lambda x: cagr_df[cagr_df['Category'] == x], category)
+        dataframes = list(
+            map(
+                lambda x: x.drop(columns=['Category']).reset_index(drop=True), dfs_category
+            )
+        )
+        output = pandas.concat(
+            dataframes,
+            keys=[word.upper() for word in category],
+            names=['Category', 'ID']
+        )
+
+        # saving the DataFrame
+        excel_ext = Core()._excel_file_extension(excel_file)
+        if excel_ext != '.xlsx':
+            raise Exception(
+                f'Input file extension "{excel_ext}" does not match the required ".xlsx".'
+            )
+        else:
+            with pandas.ExcelWriter(excel_file, engine='xlsxwriter') as excel_writer:
+                output.to_excel(excel_writer, index=True)
+                workbook = excel_writer.book
+                worksheet = excel_writer.sheets['Sheet1']
+                # number of columns for DataFrame indices
+                index_cols = len(output.index.names)
+                # format columns
+                worksheet.set_column(0, index_cols - 1, 15)
+                worksheet.set_column(index_cols, index_cols, 60)
+                worksheet.set_column(index_cols + 1, index_cols + output.shape[1] - 2, 15)
+                worksheet.set_column(
+                    index_cols + output.shape[1] - 1,
+                    index_cols + output.shape[1] - 1, 15,
+                    workbook.add_format({'num_format': '#,##0.0'})
+                )
+                # Dataframe colors
+                get_colormap = matplotlib.colormaps.get_cmap('Pastel2')
+                colors = [
+                    get_colormap(count / len(category)) for count in range(len(category))
+                ]
+                hex_colors = [
+                    '{:02X}{:02X}{:02X}'.format(*[int(num * 255) for num in color]) for color in colors
+                ]
+                # coloring of DataFrames
+                start_col = index_cols - 1
+                end_col = index_cols + len(output.columns) - 1
+                start_row = 1
+                for df, color in zip(dataframes, hex_colors):
+                    color_format = workbook.add_format({'bg_color': color})
+                    end_row = start_row + len(df) - 1
+                    worksheet.conditional_format(
+                        start_row, start_col, end_row, end_col,
+                        {'type': 'no_blanks', 'format': color_format}
+                    )
+                    start_row = end_row + 1
+
+        return output

BharatFinTrack/nse_tri.py

@@ -8,7 +8,8 @@
 class NSETRI:
 
     '''
-    Download and analyze NSE TRI (Total Return Index) data.
+    Download and analyze NSE TRI (Total Return Index) data,
+    including both price index and dividend reinvestment.
     '''
 
     @property

README.md

@@ -6,7 +6,8 @@ It is designed to simplify the process of downloading and analyzing financial da
 * [Nifty Indices](https://www.niftyindices.com/)
 
     - Provides access to the characteristics of NSE equity indices.
-    - Facilitates downloading TRI (Total Return Index) data for all NSE equity indices.
+    - Calculates the CAGR(%) of all NSE equity indices (excluding dividend reinvestment) from inception.
+    - Facilitates downloading Total Return Index, including both price and dividend reinvestment, data for all NSE equity indices.
 
 
 ## Roadmap

docs/changelog.rst

@@ -2,22 +2,35 @@
 Release Notes
 ===============
 
+
+Version 0.1.1
+--------------
+
+* **Release date:** 02-Oct-2024
+
+* **Feature Additions:** Introduced the :class:`BharatFinTrack.NSEIndex` class, which currently calculates the CAGR(%) of all NSE equity indices
+  (excluding dividend reinvestment) from inception. Additional features are planned for future releases.
+
+* **Documentation:** Updated to reflect the newly introduced features.
+
+* **Development Status:** Upgraded from Pre-Alpha to Alpha.
+
+
 Version 0.1.0
 ---------------
 
 * **Release date:** 30-Sep-2024.
 
 * **Feature Additions:** Introduced :class:`BharatFinTrack.NSETRI` class, which facilitates downloading Total Return Index (TRI) data for all NSE equity indices.
-
-
+
 * **Changes:** 
 
     * Renamed class :class:`BharatFinTrack.NSETrack` to :class:`BharatFinTrack.NSEProduct` for improved clarity.
     * Updated and renamed methods in the new class :class:`BharatFinTrack.NSEProduct`.
 
 * **Documentation:** Added a tutorial on how to use the newly introduced features.
 
-* **Development status:** Upgraded to Pre-Alpha from Planning.
+* **Development status:** Upgraded from Planning to Pre-Alpha.
 
 
 Version 0.0.3

docs/functionality.rst

@@ -0,0 +1,29 @@
+===============
+Functionality
+===============
+
+
+Index CAGR(%)
+---------------
+Save the CAGR(%) of all NSE equity indices (excluding dividend reinvestment) from inception to an Excel file. 
+
+.. code-block:: python
+
+    import BharatFinTrack
+    nse_index = BharatFinTrack.NSEIndex()
+    nse_index.all_index_cagr_from_inception(
+        excel_file=r"C:\Users\Username\Folder\file.xlsx"
+    )
+
+
+
+
+
+
+
+
+    
+
+
+    
+    

docs/index.rst

@@ -13,7 +13,7 @@ Welcome to BharatFinTrack's documentation!
    introduction
    installation
    quickstart
-   data_download
+   functionality
    modules
    changelog
 

docs/quickstart.rst

@@ -90,4 +90,35 @@ Expected output:
 .. code-block:: text
 
     '03-Nov-1995'
-    1000.0
+    1000.0
+    
+    
+    
+Download Data
+---------------
+
+Total Return Index (TRI)
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Download historical daily TRI data, including both price and dividend reinvestment, for the NIFTY 50 index:
+
+.. code-block:: python
+
+    import BharatFinTrack
+    nse_tri = BharatFinTrack.NSETRI()
+    nse_tri.download_historical_daily_data(
+        index='NIFTY 50',
+    	start_date='23-SEP-2024',
+    	end_date='27-SEP-2024'	
+    )
+
+
+Expected output:
+
+.. code-block:: text
+
+	      Date         Close
+    0	2024-09-23	38505.51
+    1	2024-09-24	38507.55
+    2	2024-09-25	38602.21
+    3	2024-09-26	38916.76
+    4	2024-09-27	38861.64

docs/requirements-docs.txt

@@ -3,5 +3,7 @@ pandas>=2.2.2
 requests>=2.32.3
 openpyxl>=3.1.5
 xlsxwriter>=3.2.0
+beautifulsoup4>=4.12.3
+matplotlib>=3.9.2