How to Find the Instrument Key Associated with Stock, Option, Index and Commodity

Developer API
1

Team Upstox,
I am currently developing my personal app for algo trading, where I had a need to find the Instrument Key associated with a particular stock. I found it difficult. I need help on it.
And is there any API for searching the stocks which are listed on the market?

      I would appreciate your response

Thanks InAdvance

regards
PrabuSelvam Natarajan

2

To work with instrument data, you need to download the daily instrument JSON file from the following link:
Upstox Instruments JSON.

For searching a specific stock, you must know the segment, instrument_type, and a value for trading_symbol. Below are two examples of code that can help you filter and search the instrument data:

Exact Trading Symbol Search

The code below filters instruments based on exact matches for segment, instrument_type, and trading_symbol:

import json
import pandas as pd

def filter_instruments_with_pandas(file_path, segment, instrument_type, trading_symbol):
    """
    Filters instruments from a JSON file using Pandas and stores the output in a variable.
    
    Parameters:
    - file_path (str): Path to the JSON file.
    - segment (str): The segment to filter by (e.g., "NSE_EQ", "NSE_FO").
    - instrument_type (str): The instrument type to filter by (e.g.,  ""EQ", "CE", "PE").
    - trading_symbol (str): The trading symbol to filter by (e.g., "RELIANCE" , "TCS 3550 CE 26 DEC 24").
    
    Returns:
    - list: Filtered JSON data as a list of dictionaries.
    """
    try:
        # Load the JSON data from the file
        with open(file_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        # Convert the JSON data to a Pandas DataFrame
        df = pd.DataFrame(data)
        
        # Filter the DataFrame
        filtered_df = df[
            (df['segment'] == segment) & 
            (df['instrument_type'] == instrument_type) &
            (df['trading_symbol'] == trading_symbol)
        ]
        
        # Convert the filtered DataFrame back to a JSON-compatible list of dictionaries
        filtered_json = filtered_df.to_dict(orient='records')
        
        return filtered_json
    
    except Exception as e:
        print(f"Error: {e}")
        return []

# Example usage
file_path = "/path/to/your/file/NSE.json"  # Replace with your actual file path
segment = "NSE_EQ"  # Example segment
instrument_type = "EQ"  # Example instrument type
trading_symbol = "RELIANCE"  # Example trading symbol

# Store filtered data in a variable
filtered_data = filter_instruments_with_pandas(file_path, segment, instrument_type, trading_symbol)

# Print the filtered data
print(json.dumps(filtered_data, indent=4))

SQL-Like ā€œTrading Symbolā€ Search

If you want to search for a trading_symbol using a partial match (like a SQL LIKE query), you can use the following code:

import json
import pandas as pd

def filter_instruments_with_search(file_path, segment, instrument_type, trading_symbol_like):
    """
    Filters instruments from a JSON file using Pandas with SQL-like "LIKE" functionality.
    
    Parameters:
    - file_path (str): Path to the JSON file.
    - segment (str): The segment to filter by (e.g., "NSE_EQ",  "NSE_FO").
    - instrument_type (str): The instrument type to filter by (e.g., "EQ", "CE", "PE", ).
    - trading_symbol_like (str): A pattern to search for in trading symbols (e.g., "REL", "TATA").
    
    Returns:
    - list: Filtered JSON data as a list of dictionaries.
    """
    try:
        # Load the JSON data from the file
        with open(file_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        # Convert the JSON data to a Pandas DataFrame
        df = pd.DataFrame(data)
        
        # Filter the DataFrame with SQL-like conditions
        filtered_df = df[
            (df['segment'] == segment) & 
            (df['instrument_type'] == instrument_type) &
            (df['trading_symbol'].str.contains(trading_symbol_like, case=False, na=False))
        ]
        
        # Convert the filtered DataFrame back to a JSON-compatible list of dictionaries
        filtered_json = filtered_df.to_dict(orient='records')
        
        return filtered_json
    
    except Exception as e:
        print(f"Error: {e}")
        return []

# Example usage

file_path = "/path/to/your/file/NSE.json"  # Replace with your actual file path
segment = "NSE_EQ"  # Example segment
instrument_type = "EQ"  # Example instrument type
trading_symbol_like = "REL"  # Example trading symbol


# Store filtered data in a variable
filtered_data = filter_instruments_with_search(file_path, segment, instrument_type, trading_symbol_like)

# Print the filtered data
print(json.dumps(filtered_data, indent=4))

Browser Display Consideration

If you plan to display the results in a browser, creating a search interface that dynamically filters data from the instrument file is best. This way, you can handle input dynamically and display results efficiently.

1 Like