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Ethereum: Understanding Kraken Trades API (market/limit)
Understanding of the API of Kraken Trades: a guide to the construction of historical data
As a seller or analyst, access to reliable and accurate historical data is crucial to make conscious decisions on your investments. However, when it comes to commercial platforms, such as Kraken, providing such data can be a significant challenge. In this article, we will examine the API Kraken Trades interface, which allows users to access historical commercial data using the Open Source Python library.
Why are historical data needed?
Historical data are necessary for various reasons:
- To analyze price movements and identify trends
- To set the purchase and sale signals based on market conditions
- To optimize commercial strategies
Without reliable historical data, it may be difficult to make accurate forecasts regarding future market movements.
API of Kraken Trades: first steps
To start building your OHLC historical data from Kraken Trades, you need to take these steps:
- Record an account : Create a free account on the Kraken website.
2.
With the help of the API, Kraken markets from Python
After accessing the API certificates, you can start building historical data using the following steps:
Step 1: Install the requested bookstores
To use the API Kraken Trades interface with Python, you need to install the “Request” library to send HTTP requests and the “Pandas” library to manipulate data.
`Bash
Pip installs Panda requests
`
passage 2: configure the API connection
Create a new file calledkraken_trades.pyis and add the following code:
`Python
Import requests
It matters Panda as PD
Configure the Cands API operations
Api_key = "Your_api_key"
Api_secret = 'Your_api_secret'
Configure the final point of the API
endpoint = f'https: //api.kraken.com/3/trades? Secret = {Api_Secret} & Key & Key = {Api_key} & Country = 1000 '
Send an GET request to the API's end point
Answer = Requests.get (end point)
Check if the answer has been successful
If the answer.status_code == 200:
Evapse the Json response to the data frame
DF = Pd.json_normalizza (Reply.json ())
DF
Otherwise:
Print (f'error: {Reply.text} ')
Do not return anyone
passage 3: filter and delete data
After receiving the data, it is necessary to filter and clean them before importing them into the favorite data format.
Python
Filter any incorrect or missing data
DF = DF [DF ['Time']> 0]
Convert the column "open" in a numerical type (float) if possible
DF ['Open'] = Pd.to_numeric (DF ['Open'])
passage 4: save and export data
Now you can save a clean and filtered frame of data in the favorite file format.
Python
Matters the marinade
Save the data frame on the Marinata file
With Open ("Krazen_Trades.pkl", "WB") as F:
Pickle.dump (DF, F)
Examples of use
Here is an example of how you can use this code to create historic OHLC data from Kraken Trades:
` Python
It matters Canner_trades
Get your API certificates
Api_key = “Your_api_key”
Api_secret = ‘Your_api_secret’
Configure the final point of the API
endpoint = f’https: //api.kraken.com/3/trades? Secret = {Api_Secret} & Key & Key = {Api_key} & Country = 1000 ‘
Send an GET request to the API’s end point and analyze the answer as a data setting
DF = kaken_trades.get_trades_dataframe (end point)
Filter any incorrect or missing data
DF = DF [DF [‘Time’]> 0]
Convert the column “open” in a numerical type (float) if possible
DF [‘Open’] = Pd.to_numeric (DF [‘Open’])
Save and export the data frame to the marinade file
With Open (“Krazen_Trades.pkl”, “WB”) as F:
marinated.