Kraken Trades API understanding: Historical Data Creation Guide
As a trader or analyst, it is very important to have access to reliable and precise historical data to make reasonable decisions on your investment. However, when it comes to trading platforms such as Kraken, providing such data It can be an important challenge. In this article, we will investigate the Kraken Trades API, allowing consumers to access historical trading data using the Python library with an open code.
Why do you need historical data?
Historical data is required for several reasons:
- To analyze price movement and establish trends
- Set purchase and sale signals under market conditions
- To optimize trading strategies
Without reliable historical data, it can be difficult to accurately anticipate future market changes.
API KRAKEN TRADES: Home
To start creating your own OHLC historical data from Kraken trades, you will need to do the following:
- Record the account : Create a free account on the Kraken site.
- Get access to API : Register on the developer account on Kraken Trades and get the API credentials.
Using API Kraken Trades with Python
After you have access to API credentials, you can start creating historical data using the following steps:
Step 1: Install the necessary libraries
To use Kraken Trades with Python, you will need to install the query library to send HTTP requests and the Pandas library to handle the data.
`Bash
PIP Installation Pandas
Step 2: Set the API connection
Create a new file called kraken_trades.py and add the following code:
Python
Import questions
Panda import as PD
Set -Set the API Kraken Trades credentials
Api_key = 'your_pi_key'
Api_secret = 'your_pi_secret'
Set the API ending point
Endpoint = f'https: //api.kraken.com/3/rads? Secret = {api_secret} & key = {api_key} & count = 1000 '
Send the request to the API result
Answer = questions.Get (contour)
Verify that the answer was successful
If the answer.status_code == 200:
Measure the JSON response to Dataframe
DF = pd.json_normalize (resege.json ())
Return DF
Otherwise:
Print (f'errror: {answer.text} ')
Don't return any
Step 3: Filter and clean the data
Once you have received the data, you will need to filter and clean them before importing into the desired data format.
Python
Filter any invalid or missing data
DF = DF [DF ['time']> 0]
#, If possible
DF ['open'] = pd.to_numeric (DF ['open'])
Step 4: Save and export data
Now you can save the cleaned and filtered data frame according to the desired file format.
Python
Import marinade
Save Datoframe in Murat file
with open ('kraken_trades.pkl', 'wb') as f:
Pickle.Dump (DF, F)
Example of case use
Here is an example of how you can use this code to create Historical Ohlc data from Kraken Trades:
` Python
Import kraken_trades
Get API credentials
Api_key = ‘your_pi_key’
Api_secret = ‘your_pi_secret’
Set the API ending point
Endpoint = f’https: //api.kraken.com/3/rads? Secret = {api_secret} & key = {api_key} & count = 1000 ‘
Send the request to Endpoint API and analyze the answer as dumframe
DF = KRAKEN_TRADESS.GET_TRADES_DATFRAM (result)
Filter any invalid or missing data
DF = DF [DF [‘time’]> 0]
#, If possible
DF [‘open’] = pd.to_numeric (DF [‘open’])
Save and Export Dataframe to Murat file
with open (‘kraken_trades.pkl’, ‘wb’) as f:
marinated.
