Zerodha API Integration for Stock Market Data Retrieval

This Python script provides functionalities to interact with Zerodha API for accessing stock market data, placing orders, and retrieving historical data.

Prerequisites

Ensure you have the required Python libraries installed:

• requests
• dateutil

You can install these dependencies using pip:

pip install requests python-dateutil

Setup

1. API Authentication:

   • Obtain your Zerodha API credentials (user_id, password, twofa) to authenticate API requests.

2. Installation:

   • Clone or download the script to your local machine.

3. Run the Script:

   • Open the script in your Python environment and execute it.

Script Overview

The script provides the following functionalities:

• Authentication:

  • Authenticate user credentials and obtain the enctoken for subsequent API calls.

• Zerodha API Wrapper:

  • Implements a KiteApp class to interact with Zerodha API endpoints.
  • Provides methods for fetching instruments, quotes, historical data, margins, orders, and positions.

• Data Retrieval:

  • Retrieve historical market data for specified stock symbols within a date range.
  • Supports multi-threading for efficient data retrieval.

Usage

1. Initialize Zerodha API:

   • Obtain the enctoken from Zerodha web and initialize the KiteApp instance.

2. Retrieve Historical Data:

   • Modify the symbol_list and date parameters to specify the stocks and date range for data retrieval.
   • Run the script to retrieve and store historical data in CSV format.

3. Customization:

   • Customize the script by adjusting API endpoints, adding new functionalities, or modifying data processing logic.

File Structure

• zerodha_api_integration.py: Main script for Zerodha API integration.
• nifty.csv: CSV file containing stock symbols and IDs for data retrieval.
• Data/: Directory to store retrieved market data in CSV format.

Notes

• Ensure you have valid Zerodha API credentials and permissions to access the required endpoints.
• Customize the script as per your specific requirements and data retrieval preferences.
• Use multi-threading for efficient data retrieval across multiple stock symbols and date ranges.

Resampling Stock Market Data into 5-Minute Intervals

This Python script allows you to resample stock market data stored in CSV files into 5-minute intervals (OHLCV format) and save the resampled data to separate CSV files.

Prerequisites

Before using this script, ensure you have the following installed:

• Python (3.x recommended)
• Required Python libraries (pandas, numpy, glob)

Install the required libraries using pip:

pip install pandas numpy

Script Overview

The script performs the following tasks:

1. Import Libraries:

   • Import necessary libraries including pandas, numpy, glob, datetime, and warnings.

2. Set Paths:

   • Define the paths for reading original data (path) and saving resampled data (path_to_save_data).

3. Read Files:

   • Use glob.glob() to retrieve a list of file paths for the original data.

4. Resampling Process:

   • Loop through each file path and read the CSV file into a pandas DataFrame.
   • Convert the 'Date' and 'TIME1' columns to datetime format and set the 'Datetime' column as the index.
   • Resample the data to 5-minute intervals ("5T"), aggregating OHLCV values.
   • Filter the resampled data to include only market hours (between 09:15 AM and 03:25 PM) and remove any NaN values.

5. Save Resampled Data:

   • Rename columns for consistency (e.g., 'CLOSE' to 'Close').
   • Define the file save path for the resampled data.
   • Save the resampled data to a CSV file without including the index.

Usage

1. Specify Paths:

   • Modify path_to_save_data and path variables according to your directory structure.

2. Run the Script:

   • Execute the script in your Python environment.

3. Output:

   • The resampled data will be saved as separate CSV files in the specified path_to_save_data directory.

Notes

• Ensure that the original data files are in CSV format with columns for 'Date', 'TIME1', 'Open', 'High', 'Low', 'CLOSE', and 'VOLUME'.
• Customize the script as needed to handle different data formats or adjust the resampling parameters.
• Use this script to preprocess and organize stock market data for further analysis and modeling.