75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
"""
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Various tool function for Freqtrade and scripts
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"""
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import re
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import json
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import logging
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from datetime import datetime
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from typing import Dict
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import numpy as np
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from pandas import DataFrame
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logger = logging.getLogger(__name__)
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def shorten_date(_date: str) -> str:
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"""
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Trim the date so it fits on small screens
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"""
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new_date = re.sub('seconds?', 'sec', _date)
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new_date = re.sub('minutes?', 'min', new_date)
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new_date = re.sub('hours?', 'h', new_date)
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new_date = re.sub('days?', 'd', new_date)
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new_date = re.sub('^an?', '1', new_date)
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return new_date
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############################################
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# Used by scripts #
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# Matplotlib doesn't support ::datetime64, #
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# so we need to convert it into ::datetime #
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############################################
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def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
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"""
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Convert an pandas-array of timestamps into
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An numpy-array of datetimes
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:return: numpy-array of datetime
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"""
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times = []
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dates = dates.astype(datetime)
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for index in range(0, dates.size):
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date = dates[index].to_pydatetime()
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times.append(date)
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return np.array(times)
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def common_datearray(dfs: Dict[str, DataFrame]) -> np.ndarray:
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"""
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Return dates from Dataframe
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:param dfs: Dict with format pair: pair_data
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:return: List of dates
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"""
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alldates = {}
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for pair, pair_data in dfs.items():
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dates = datesarray_to_datetimearray(pair_data['date'])
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for date in dates:
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alldates[date] = 1
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lst = []
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for date, _ in alldates.items():
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lst.append(date)
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arr = np.array(lst)
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return np.sort(arr, axis=0)
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def file_dump_json(filename, data) -> None:
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"""
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Dump JSON data into a file
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:param filename: file to create
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:param data: JSON Data to save
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:return:
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"""
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with open(filename, 'w') as fp:
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json.dump(data, fp, default=str)
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