この python スクリプトをウェブで見つけました。必要な日付、資産、時間間隔で Binance API から OHLCV 履歴データを取得します。スクリプトは現在、UTC 時間のデータを返します。
指定されたタイムゾーンに従ってデータ（毎日/毎時）を返すように変更したいと思います。1 つの関数を変更するか、引数を追加するだけでよいと思いますが、正しく行うことができません。
UTC+2 (または他のタイムゾーン) のデータを返すように変更するにはどうすればよいですか?
ありがとう！男
import time
import dateparser
import pytz
import os
from datetime import datetime
import binance
print(binance.__file__)
from binance.client import Client
import time
import pandas as pd
def date_to_milliseconds(date_str):
"""Convert UTC date to milliseconds
If using offset strings add "UTC" to date string e.g. "now UTC", "11 hours ago UTC"
See dateparse docs for formats http://dateparser.readthedocs.io/en/latest/
:param date_str: date in readable format, i.e. "January 01, 2018", "11 hours ago UTC", "now UTC"
:type date_str: str
"""
# get epoch value in UTC
epoch = datetime.utcfromtimestamp(0).replace(tzinfo=pytz.utc)
# parse our date string
d = dateparser.parse(date_str)
# if the date is not timezone aware apply UTC timezone
if d.tzinfo is None or d.tzinfo.utcoffset(d) is None:
d = d.replace(tzinfo=pytz.utc)
# return the difference in time
return int((d - epoch).total_seconds() * 1000.0)
def interval_to_milliseconds(interval):
"""Convert a Binance interval string to milliseconds
:param interval: Binance interval string 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w
:type interval: str
:return:
None if unit not one of m, h, d or w
None if string not in correct format
int value of interval in milliseconds
"""
ms = None
seconds_per_unit = {
"m": 60,
"h": 60 * 60,
"d": 24 * 60 * 60,
"w": 7 * 24 * 60 * 60
}
unit = interval[-1]
if unit in seconds_per_unit:
try:
ms = int(interval[:-1]) * seconds_per_unit[unit] * 1000
except ValueError:
pass
return ms
def GetUpdateData(kline):
Time = time.strftime("%a, %d %b %Y %H:%M:%S", time.gmtime(kline[0]/1000))
Open = kline[1]
High = kline[2]
Low = kline[3]
Close = kline[4]
Volume = kline[5]
Close_time = time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime(kline[6]/1000))
Quote_asset_volume = kline[7]
Number_of_trades = kline[8]
Taker_buy_base_asset_volume = kline[9]
Taker_buy_quote_asset_volume = kline[10]
return Time,Open,High,Low,Close,Volume,Close_time,Quote_asset_volume,Number_of_trades,Taker_buy_base_asset_volume,Taker_buy_quote_asset_volume
def get_historical_klines(symbol, interval, start_str, end_str=None):
"""Get Historical Klines from Binance
See dateparse docs for valid start and end string formats http://dateparser.readthedocs.io/en/latest/
If using offset strings for dates add "UTC" to date string e.g. "now UTC", "11 hours ago UTC"
:param symbol: Name of symbol pair e.g BNBBTC
:type symbol: str
:param interval: Biannce Kline interval
:type interval: str
:param start_str: Start date string in UTC format
:type start_str: str
:param end_str: optional - end date string in UTC format
:type end_str: str
:return: list of OHLCV values
"""
# create the Binance client, no need for api key
client = Client("", "")
# init our list
output_data = []
# setup the max limit
limit = 500
# convert interval to useful value in seconds
timeframe = interval_to_milliseconds(interval)
# convert our date strings to milliseconds
start_ts = date_to_milliseconds(start_str)
# if an end time was passed convert it
end_ts = None
if end_str:
end_ts = date_to_milliseconds(end_str)
idx = 0
# it can be difficult to know when a symbol was listed on Binance so allow start time to be before list date
symbol_existed = False
while True:
# fetch the klines from start_ts up to max 500 entries or the end_ts if set
temp_data = client.get_klines(
symbol=symbol,
interval=interval,
limit=limit,
startTime=start_ts,
endTime=end_ts
)
# handle the case where our start date is before the symbol pair listed on Binance
if not symbol_existed and len(temp_data):
symbol_existed = True
if symbol_existed:
# append this loops data to our output data
output_data += temp_data
# update our start timestamp using the last value in the array and add the interval timeframe
start_ts = temp_data[len(temp_data) - 1][0] + timeframe
else:
# it wasn't listed yet, increment our start date
start_ts += timeframe
idx += 1
# check if we received less than the required limit and exit the loop
if len(temp_data) < limit:
# exit the while loop
break
# sleep after every 3rd call to be kind to the API
if idx % 3 == 0:
time.sleep(1)
return output_data
start = "01 January, 2017"
end = "01 February, 2017"
symbols = ['ETHBTC']
interval = '1d'#Client.KLINE_INTERVAL_15MIN
for symbol in symbols:
klines = get_historical_klines(symbol, interval, start, end)
times = []
Opens = []
Highs = []
Lows = []
Closes = []
Volumes = []
Close_times = []
Quote_asset_volumes = []
Number_of_tradess = []
Taker_buy_base_asset_volumes = []
Taker_buy_quote_asset_volumes = []
for k in klines:
Time,Open,High,Low,Close,Volume,Close_time,Quote_asset_volume,Number_of_trades,Taker_buy_base_asset_volume,Taker_buy_quote_asset_volume = GetUpdateData(k)
times.append(Time)
Opens.append(Open)
Highs.append(High)
Lows.append(Low)
Closes.append(Close)
Volumes.append(Volume)
Close_times.append(Close_time)
Quote_asset_volumes.append(Quote_asset_volume)
Number_of_tradess.append(Number_of_trades)
Taker_buy_base_asset_volumes.append(Taker_buy_base_asset_volume)
Taker_buy_quote_asset_volumes.append(Taker_buy_quote_asset_volume)
DataStruct = pd.DataFrame()
DataStruct['time'] = times
DataStruct['Open'] = Opens
DataStruct['High'] = Highs
DataStruct['Low'] = Lows
DataStruct['Close'] = Closes
DataStruct['Volume'] = Volumes
DataStruct['Close_time'] = Close_times
DataStruct['Quote_asset_volume'] = Quote_asset_volumes
DataStruct['Number_of_trades'] = Number_of_tradess
DataStruct['Taker_buy_base_asset_volume'] = Taker_buy_base_asset_volumes
DataStruct['Taker_buy_quote_asset_volume'] = Taker_buy_quote_asset_volumes
FileName = symbol+ '_' + start+ '_' + end + ' .csv'
FileName = FileName.replace(' ','_')
FileName = FileName.replace(',','')
Path2Save = os.path.normpath(r'')
SaveStrFile = os.path.normpath(Path2Save+ '\\' +FileName)
#save FeatureWeights to CSV file
D_S_header = ['time','Open','High','Low','Close','Volume','Close_time','Quote_asset_volume','Number_of_trades','Taker_buy_base_asset_volume','Taker_buy_quote_asset_volume']
DataStruct.to_csv(path_or_buf = SaveStrFile, header = D_S_header )