stable/freqtrade/misc.py

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"""
Various tool function for Freqtrade and scripts
"""
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import json
import logging
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import re
from datetime import datetime
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from typing import Dict
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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"""
Trim the date so it fits on small screens
"""
new_date = re.sub('seconds?', 'sec', _date)
new_date = re.sub('minutes?', 'min', new_date)
new_date = re.sub('hours?', 'h', new_date)
new_date = re.sub('days?', 'd', new_date)
new_date = re.sub('^an?', '1', new_date)
return new_date
############################################
# Used by scripts #
# Matplotlib doesn't support ::datetime64, #
# so we need to convert it into ::datetime #
############################################
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def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
"""
Convert an pandas-array of timestamps into
An numpy-array of datetimes
:return: numpy-array of datetime
"""
times = []
dates = dates.astype(datetime)
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for index in range(0, dates.size):
date = dates[index].to_pydatetime()
times.append(date)
return np.array(times)
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def common_datearray(dfs: Dict[str, DataFrame]) -> np.ndarray:
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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
"""
alldates = {}
for pair, pair_data in dfs.items():
dates = datesarray_to_datetimearray(pair_data['date'])
for date in dates:
alldates[date] = 1
lst = []
for date, _ in alldates.items():
lst.append(date)
arr = np.array(lst)
return np.sort(arr, axis=0)
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def file_dump_json(filename, data) -> None:
"""
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Dump JSON data into a file
:param filename: file to create
:param data: JSON Data to save
:return:
"""
with open(filename, 'w') as fp:
json.dump(data, fp, default=str)
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def format_ms_time(date: str) -> str:
"""
convert MS date to readable format.
: epoch-string in ms
"""
return datetime.fromtimestamp(date/1000.0).strftime('%Y-%m-%dT%H:%M:%S')