# Sort the df by that index
df.sort_index(inplace=True)
# Convert dates to num for matplotlib
df['mpldate'] = df['transactTime'].map(mdates.date2num)
# Delete the old transactTime column
del df['transactTime']
df = df[start_datetime:end_datetime]
############################################################
"""
Get some bitcoin price data
"""
finex = BFX()
if start_datetime == None:
start_datetime = pd.to_datetime(df.index.values[0]).to_pydatetime()
now = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
if (end_datetime == None) or (end_datetime > now):
end_datetime = now
logger.info('Querying candles between: ' + str(start_datetime) + ' and ' +
str(end_datetime))
candles = finex.api_request_candles('1D', 'BTCUSD', start_datetime,
end_datetime)
df.set_index(df['transactTime'], inplace=True)
# Sort the df by that index
df.sort_index(inplace=True)
# Convert dates to num for matplotlib
df['mpldate'] = df['transactTime'].map(mdates.date2num)
# Delete the old transactTime column
del df['transactTime']
############################################################
"""
Get some bitcoin price data
"""
finex = BFX()
start_datetime = pd.to_datetime(df.index.values[0]).to_pydatetime()
end_datetime = datetime.now().replace(hour=0,
minute=0,
second=0,
microsecond=0)
candles_json = finex.api_request_candles('1D', start_datetime)
last_returned_from_api = datetime.utcfromtimestamp(
candles_json[len(candles_json) - 1][0] / 1000.0)
while last_returned_from_api < end_datetime:
# New api start date = the last returned date + 1 period
new_api_start_date = last_returned_from_api + datetime_timedelta(