def create_file(pair, granularity, api):
candle_count = 2000
time_step = INCREMENTS[granularity] * candle_count
end_date = utils.get_utc_dt_from_string("2020-12-31 23:59:59")
date_from = utils.get_utc_dt_from_string("2018-01-01 00:00:00")
candle_dfs = []
date_to = date_from
while date_to < end_date:
date_to = date_from + dt.timedelta(minutes=time_step)
if date_to > end_date:
date_to = end_date
code, df = api.fetch_candles(pair,
granularity=granularity,
date_from=date_from,
date_to=date_to,
as_df=True)
if df is not None and df.empty == False:
candle_dfs.append(df)
elif code != 200:
print("ERROR", pair, granularity, date_from, date_to)
break
date_from = date_to
final_df = pd.concat(candle_dfs)
final_df.drop_duplicates(subset='time', inplace=True)
final_df.sort_values(by='time', inplace=True)
final_df.to_pickle(utils.get_his_data_filename(pair, granularity))
print(
f"{pair} {granularity} {final_df.iloc[0].time} {final_df.iloc[-1].time}"
)
def create_file(pair, granularity, api):
candle_count = 2000
time_step = INCREMENTS[granularity] * candle_count
end_date = utils.get_utc_dt_from_string("2020-12-31 23:59:59")
date_from = utils.get_utc_dt_from_string("2018-01-01 00:00:00")
candle_dfs = []
date_to = date_from
while date_to < end_date:
date_to = date_from + dt.timedelta(minutes=time_step)
if date_to > end_date:
date_to = end_date
date_from = date_to
def candles_to_df(cls, json_data):
prices = ['mid', 'bid', 'ask']
ohlc = ['o', 'h', 'l', 'c']
our_data = []
for candle in json_data:
if candle['complete'] == False:
continue
new_dict = {}
new_dict['time'] = candle['time']
new_dict['volume'] = candle['volume']
for price in prices:
for oh in ohlc:
new_dict[f"{price}_{oh}"] = float(candle[price][oh])
our_data.append(new_dict)
df = pd.DataFrame.from_dict(our_data)
df["time"] = [parse(x) for x in df.time]
return df
if __name__ == "__main__":
api = OandaAPI()
#api.save_instruments()
date_from = utils.get_utc_dt_from_string("2019-05-05 18:00:00")
date_to = utils.get_utc_dt_from_string("2019-05-10 18:00:00")
res, df = api.fetch_candles("EUR_USD",
date_from=date_from,
date_to=date_to,
as_df=True)
print(df.info())