freqtrade_origin/freqtrade/misc.py

73 lines
1.8 KiB
Python

"""
Various tool function for Freqtrade and scripts
"""
import re
import json
import logging
from datetime import datetime
import numpy as np
logger = logging.getLogger(__name__)
def shorten_date(_date):
"""
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 #
############################################
def datesarray_to_datetimearray(dates):
"""
Convert an pandas-array of timestamps into
An numpy-array of datetimes
:return: numpy-array of datetime
"""
times = []
dates = dates.astype(datetime)
for index in range(0, dates.size):
date = dates[index].to_pydatetime()
times.append(date)
return np.array(times)
def common_datearray(dfs):
"""
Return dates from Dataframe
:param dfs: Dataframe
: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)
def file_dump_json(filename, data) -> None:
"""
Dump JSON data into a file
:param filename: file to create
:param data: JSON Data to save
:return:
"""
with open(filename, 'w') as file:
json.dump(data, file)