Getting Binance Historical Data For Specific TimeZone - bitcoin

Question

I found this python script on the web, it gets OHLCV historical data from Binance api by wanted dates, assets and time intervals. The script currently returns the data for UTC time.
I want to modify it so it will return the data (daily/hourly) according to a specified timezone. I guess
it takes only to change one function or add an argument but I can't manage to do it correctly.
How can I change it so it will return data for UTC+2 (or any other time zone)?
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 )

Answer 1

In these lines you see the timezone being defined:
# get epoch value in UTC
epoch = datetime.utcfromtimestamp(0).replace(tzinfo=pytz.utc)
Just redefine the timezone there. For a list of timezones supported by pytz you can get a list using pytz.all_timezones.