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Understanding Kraken Trades API: A Guide to Building Historical Data

As a trader or analyst, having access to reliable and accurate historical data is crucial to making informed decisions about your investments. However, when it comes to trading platforms like Kraken, providing such data can be a significant challenge. In this article, we will explore the Kraken Trades API, which allows users to access historical trading data using an open-source Python library.

Why is Historical Data Needed?

Historical data is essential for several reasons:

• To analyze price movements and identify trends

• To set buy and sell signals based on market conditions

• To optimize trading strategies

Without reliable historical data, it can be difficult to make accurate predictions about future market movements.

Kraken Trades API: Getting Started

To start building your own historical OHLC data from Kraken trades, you’ll need to follow these steps:

• Register an account: Create a free account on the Kraken website.

• Obtain API access

  : Register for a developer account on the Kraken Trades API page and obtain your API credentials.

Using the Kraken Trades API with Python

Once you have access to your API credentials, you can start building historical data using the following steps:

Step 1: Install Required Libraries

To use the Kraken Trades API with Python, you will need to install the requests library for making HTTP requests and the pandas library for data manipulation.

pip install requests pandas

Step 2: Set Up Your API Connection

Create a new file named kraken_trades.py and add the following code:

import requests

import pandas as pd



Set up your Kraken Trades API credentials

api_key = 'YOUR_API_KEY'

api_secret = 'YOUR_API_SECRET'



Set up the API endpoint

endpoint = f' 



Send a GET request to the API endpoint

response = requests . get ( endpoint ) ;



Check if the response was successful

if response.status_code == 200:

    
Parse the JSON response into a DataFrame

    df = pd . json_normalize ( response . json ( ) )

    return df

else:

    print ( f ' Error : { response . text } ' )

    return None

Step 3: Filter and Clean the Data

Once you receive the data, you will need to filter and clean it before importing it into your preferred data format.

Filter out any invalid or missing data

df = df[df['time'] > 0]



Convert the 'open' column to a numeric type (float) if possible

df['open'] = pd.to_numeric(df['open'])

Step 4: Save and Export the Data

You can now save the cleaned and filtered DataFrame to your preferred file format.

import pickle



Save the DataFrame to a Pickle file

with open('kraken_trades.pkl','wb') as f:

    pickle . dump ( df , f )

Example Use Case

Here’s an example of how you can use this code to build historical OHLC data from Kraken trades:

“`python

import kraken_trades

Get your API credentials

api_key = ‘YOUR_API_KEY’

api_secret = ‘YOUR_API_SECRET’

Set up the API endpoint

endpoint = f’

Send a GET request to the API endpoint and parse the response as a DataFrame

df = crack_trades.get_trades_dataframe(endpoint);

Filter out any invalid or missing data

df = df[df[‘time’] > 0]

Convert the ‘open’ column to a numeric type (float) if possible

df[‘open’] = pd.to_numeric(df[‘open’])

Save and export the DataFrame to a Pickle file

with open(‘kraken_trades.pkl’,’wb’) as f:

pickle.