How to Fetch Stock Data Using World Trading Data API in Python

Install Required Libraries

• You will primarily need the `requests` library to interact with the World Trading Data API. You can install it via pip:

   
  

  ```bash
  pip install requests
  ```

 

• Optionally, you might want to use `pandas` to handle and manipulate the stock data efficiently:

   
  

  ```bash
  pip install pandas
  ```

Create a Configuration File

• It's a good practice to store configuration details like API keys separately to keep your script clean and secure. Create a configuration file, say `config.py`, to store your API key:

   
  

  ```python
  API_KEY = 'your_api_key_here'
  ```

Fetch Stock Data

• Define a function to fetch stock data from the World Trading Data API. Use the `requests` library to handle the request and response:

   
  

  ```python
  import requests
  from config import API_KEY

  def fetch_stock_data(symbol):
  # Define the API endpoint
  endpoint = 'https://api.worldtradingdata.com/api/v1/stock'
  # Set the parameters for the request
  params = {
  'symbol': symbol,
  'api_token': API_KEY
  }
  # Make the request to the World Trading Data API
  response = requests.get(endpoint, params=params)
  # Parse the response JSON
  data = response.json()

    if 'data' in data:
        return data['data']
    else:
        raise Exception(f"Error fetching data: {data.get('message', 'Unknown Error')}")
  

  ```

Handle Errors & Exceptions

• Ensure your application can handle potential errors gracefully, such as network failures or incorrect parameters:

   
  

  ```python
  try:
  stock_data = fetch_stock_data('AAPL')
  print(stock_data)
  except Exception as e:
  print(f"Failed to fetch stock data: {e}")
  ```

• Consider logging any errors for future troubleshooting or audits.

Process the Stock Data

• Once you've successfully fetched the data, you can process it using `pandas` for further analysis:

   
  

  ```python
  import pandas as pd

  def process_stock_data(stock_data):
  # Create a DataFrame from the stock data
  df = pd.DataFrame(stock_data)
  # Perform operations with the dataframe,
  # like converting string dates to datetime
  df['date'] = pd.to_datetime(df['date'])
  return df

  stock_data_df = process_stock_data(stock_data)
  print(stock_data_df.head())
  ```

Enhancements and Future Work

• Add functionalities to store fetched data into a database or CSV file for persistent storage.

 

• Implement automated tasks to fetch data at regular intervals, if needed.

 

• Enhance the script to support bulk fetching or multiple symbols at once.