Kraken OHLC

For specified dates, pairs and frequencies, download trades from Kraken exchange public API, aggregate OHLC data and save it as CSV.

Output

Kraken OHLC will create two folders at specified path: trade_history and ohlc.

Structure example

data
└───ohlc
│   │   AAVEXBT_2021-05-04T13-00-00_2021-05-04T15-00-00_1H.csv
│   │   ADAXBT_2021-05-04T13-00-00_2021-05-04T15-00-00_1H.csv
│   
└───trade_history
    │   AAVEXBT_2021-05-04T13-00-00_2021-05-04T15-00-00.csv
    │   ADAXBT_2021-05-04T13-00-00_2021-05-04T15-00-00.csv

OHLC data

For specified pairs and frequencies in configuration files, OHLC dataframes will be created and saved as CSV in ohlc folder.
OHLC are saved with name PAIR_DOWNLOAD_START_DATE_DOWNLOAD_END_DATE_FREQUENCY.csv.
By default the volume is aggregated by the base asset, if you want to aggregate it with the quote asset set volume_in_quote_asset to True in configuration file. OHLC will then by savec in PAIR_DOWNLOAD_START_DATE_DOWNLOAD_END_DATE_FREQUENCY_quote. csv format. Dates are in YYYY-MM-DDTHH-MI-Sec format.

If OHLC file for specific configuration already exist, it will not be generated again.

OHLC CSV example

time	open	high	low	close	volume
2021-05-04 13:00:00	0.008945	0.009032	0.008909	0.008909	102.95793562
2021-05-04 14:00:00	0.008891	0.008891	0.008621	0.008697	46.800853210000014

Trade history data

If enabled in configuration file, downloaded trades will be saved as CSV in trade_history folder.
Trades are saved with name PAIR_DOWNLOAD_START_DATE_DOWNLOAD_END_DATE.csv.
Dates are in YYYY-MM-DDTHH-MI-Sec format.

If trade history file for specific configuration already exist, it will not be downloaded again.

Trade history CSV example

time	price	volume	buy/sell	market/limit	miscellaneous
2021-05-04 13:02:18.454200064	0.008945	0.08562438	s	l
2021-05-04 13:04:50.460200192	0.008927	0.08579703	s	l
2021-05-04 13:17:37.996599808	0.008975	0.08579703	b	l

Configuration file

Configuration file example: https://github.com/FuturBroke/kraken-ohlc/blob/main/config.yaml

Parameters:

• download_start_date: Trades download start date, must be in YYYY-MM-DD HH:MI:Sec format.
• download_end_date: Trades download end date, must be in YYYY-MM-DD HH:MI:Sec format.
• ohlc_frequencies: List OHLC frequencies aggregate trades data. Supported frequencies are: 1M, 3M, 5M, 15M, 30M, 1H, 2H, 4H, 6H, 8H, 12H, 1D, 3D, 1W.
• volume_in_quote_asset: true if you want to aggregate the volume by the quote asset.
• save_trade_history: True if you want to save downloaded trades as CSV in a trade_history folder, False otherwise.

download_all_associated_pairs:

• enabled: True if download all pairs associated to specified quote asset excepted excluded base assets, False otherwise.

• quote_assets: List of quote assets to download pairs.

• excluded_base_assets: List of quote assets to exclude from downloaded pairs.

• download_custom_pairs: List of pairs to download if enabled in download_all_associated_pairs is False.

How to run it

Docker image

You can download the image directly from Docker Hub using:

docker pull futurbroke/kraken-ohlc:latest

To start the container with restart as system reboot use:

docker run -v CONFIGURATION_FILE_PATH:/app/config.yaml \
 -v DATA_FOLDER_PATH:/app/data \
 --name kraken-ohlc \
 --restart=on-failure futurbroke/kraken-ohlc

• CONFIGURATION_FILE_PATH: Configuration folder filepath (e.g., ~/dev/config.yaml).
• DATA_FOLDER_PATH: Data folder for downloaded trades and OHLC CSV (e.g., ~/dev/data/).

To see container logs:

docker logs kraken-ohlc

To stop and delete the container:

docker kill kraken-ohlc
docker rm kraken-ohlc

Usage without Docker

You must specify your configuration in a config.yaml file in the Kraken-OHLC root folder.

Install python plugins requirements inside repository folder using:

python -m pip install -r requirements.txt

You can launch the program from the folder where you downloaded the repository folder using:

python kraken-ohlc

Or inside Kraken-OHLC repository directory using:

python __main__.py

License

GPL-3.0

How to contribute

Thanks for your interest in contributing to the project. You can contribute freely by creating an issue, fork or create a pull request. Before issuing a pull request, make sure the changes did not break any existing functionality by running unit tests in the base directory:

pytest