Bulk Data Processor

This is a Streamlit web application designed to process bulk data from CSV files related to trading quantities of securities. The app analyzes the data, calculates the total quantities traded for each security, and allows users to download the processed data.

Features

• CSV File Upload: Users can upload a single CSV file containing trading data.
• Data Processing: The app processes the CSV to calculate the total quantities traded for each security based on buying and selling transactions.
• Download Processed Data: Users can download the processed data as a CSV file.

you can download bulk_data.csv file from website - https://www.nseindia.com/report-detail/display-bulk-and-block-deals or we have an example csv file also in our repo you can use it for example purpose.

Prerequisites

Make sure you have the following installed:

• Python 3.x

Required Libraries

You will need to install the following libraries:

• pandas: For data manipulation and analysis.
• streamlit: For building the web application.

Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder>

2. Install required dependencies:

   Create a requirements.txt file with the following content:

   pandas
   streamlit
   

   Then run:

   pip install -r requirements.txt

3. Run the Streamlit app:

   After installing the required libraries, run the application with the following command:

   streamlit run Bulk_Data_Analysis.py

4. Access the app:

   Open your browser and go to http://localhost:8501/.

CSV File Requirements

The uploaded CSV file must contain the following columns:

• Security Name
• Quantity Traded
• Buy / Sell

Usage

1. Upload CSV File: Use the file uploader to select a CSV file.
2. View Original DataFrame: The app displays the original data from the uploaded file.
3. Processed Data: The app displays the processed DataFrame with the total quantities traded for each security.
4. Download Processed Data: Click the download button to get the processed data as a CSV file.

Example CSV Data

Security Name	Quantity Traded
ABC Corp	1,000
XYZ Inc	500
ABC Corp	300

Acknowledgements

• Thanks to Streamlit for providing an easy framework for building interactive web apps.

Updates Made:

• Required Libraries: Added a section listing the necessary libraries (pandas and streamlit).
• How to Run: Expanded the instructions under the "Installation" section to provide a clear step on running the application after installation.