def download_symbol(cls,
symbol: tp.Label,
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now',
freq: tp.Union[None, str, pd.DateOffset] = None,
date_range_kwargs: tp.KwargsLike = None,
**kwargs) -> tp.SeriesFrame:
"""Download the symbol.
Generates datetime index and passes it to `SyntheticData.generate_symbol` to fill
the Series/DataFrame with generated data."""
if date_range_kwargs is None:
date_range_kwargs = {}
index = pd.date_range(
start=to_tzaware_datetime(start, tz=get_utc_tz()),
end=to_tzaware_datetime(end, tz=get_utc_tz()),
freq=freq,
**date_range_kwargs
)
if len(index) == 0:
raise ValueError("Date range is empty")
return cls.generate_symbol(symbol, index, **kwargs)
keep='last',
drop_redundant=True,
ignore_default=True
),
array_wrapper=dict(
column_only_select=False,
group_select=True,
freq=None,
silence_warnings=False
),
datetime=dict(
naive_tz=get_local_tz(),
to_py_timezone=True
),
data=dict(
tz_localize=get_utc_tz(),
tz_convert=get_utc_tz(),
missing_index='nan',
missing_columns='raise',
binance=Config( # flex
dict(
api_key=None,
api_secret=None
)
),
ccxt=Config( # flex
dict(
enableRateLimit=True
)
),
stats=Config(), # flex
def download_symbol(cls,
symbol: str,
timeframe: str = '1d',
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now UTC',
adjustment: tp.Optional[str] = 'all',
limit: int = 500,
exchange: tp.Optional[str] = 'CBSE',
**kwargs) -> tp.Frame:
"""Download the symbol.
Args:
symbol (str): Symbol.
timeframe (str): Timeframe of data.
Must be integer multiple of 'm' (minute), 'h' (hour) or 'd' (day). i.e. '15m'.
See https://alpaca.markets/data.
!!! note
Data from the latest 15 minutes is not available with a free data plan.
start (any): Start datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
end (any): End datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
adjustment (str): Specifies the corporate action adjustment for the stocks.
Allowed are `raw`, `split`, `dividend` or `all`.
limit (int): The maximum number of returned items.
exchange (str): For crypto symbols. Which exchange you wish to retrieve data from.
Allowed are `FTX`, `ERSX`, `CBSE`
For defaults, see `data.alpaca` in `vectorbt._settings.settings`.
"""
from vectorbt._settings import settings
from alpaca_trade_api.rest import TimeFrameUnit, TimeFrame, REST
alpaca_cfg = settings['data']['alpaca']
client_kwargs = dict()
for k in get_func_kwargs(REST):
if k in kwargs:
client_kwargs[k] = kwargs.pop(k)
client_kwargs = merge_dicts(alpaca_cfg, client_kwargs)
client = REST(**client_kwargs)
_timeframe_units = {'d': TimeFrameUnit.Day, 'h': TimeFrameUnit.Hour, 'm': TimeFrameUnit.Minute}
if len(timeframe) < 2:
raise ValueError("invalid timeframe")
amount_str = timeframe[:-1]
unit_str = timeframe[-1]
if not amount_str.isnumeric() or unit_str not in _timeframe_units:
raise ValueError("invalid timeframe")
amount = int(amount_str)
unit = _timeframe_units[unit_str]
_timeframe = TimeFrame(amount, unit)
start_ts = to_tzaware_datetime(start, tz=get_utc_tz()).isoformat()
end_ts = to_tzaware_datetime(end, tz=get_utc_tz()).isoformat()
def _is_crypto_symbol(symbol):
return len(symbol) == 6 and "USD" in symbol
if _is_crypto_symbol(symbol):
df = client.get_crypto_bars(
symbol=symbol,
timeframe=_timeframe,
start=start_ts,
end=end_ts,
limit=limit,
exchanges=exchange
).df
else:
df = client.get_bars(
symbol=symbol,
timeframe=_timeframe,
start=start_ts,
end=end_ts,
adjustment=adjustment,
limit=limit
).df
# filter for OHLCV
# remove extra columns
df.drop(['trade_count', 'vwap'], axis=1, errors='ignore', inplace=True)
# capitalize
df.rename(columns={
'open': 'Open',
'high': 'High',
'low': 'Low',
'close': 'Close',
'volume': 'Volume',
'exchange': 'Exchange'
}, inplace=True)
df['Open'] = df['Open'].astype(float)
df['High'] = df['High'].astype(float)
df['Low'] = df['Low'].astype(float)
df['Close'] = df['Close'].astype(float)
df['Volume'] = df['Volume'].astype(float)
return df
def _ts_to_str(ts):
return str(pd.Timestamp(to_tzaware_datetime(ts, tz=get_utc_tz())))
def download_symbol(cls,
symbol: str,
exchange: tp.Union[str, "ExchangeT"] = 'binance',
config: tp.Optional[dict] = None,
timeframe: str = '1d',
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now UTC',
delay: tp.Optional[float] = None,
limit: tp.Optional[int] = 500,
retries: int = 3,
show_progress: bool = True,
params: tp.Optional[dict] = None,
tqdm_kwargs: tp.KwargsLike = None) -> tp.Frame:
"""Download the symbol.
Args:
symbol (str): Symbol.
exchange (str or object): Exchange identifier or an exchange object of type
`ccxt.base.exchange.Exchange`.
config (dict): Config passed to the exchange upon instantiation.
Will raise an exception if exchange has been already instantiated.
timeframe (str): Timeframe supported by the exchange.
start (any): Start datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
end (any): End datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
delay (float): Time to sleep after each request (in milliseconds).
!!! note
Use only if `enableRateLimit` is not set.
limit (int): The maximum number of returned items.
retries (int): The number of retries on failure to fetch data.
show_progress (bool): Whether to show the progress bar.
tqdm_kwargs (dict): Keyword arguments passed to `tqdm`.
params (dict): Exchange-specific key-value parameters.
For defaults, see `data.ccxt` in `vectorbt._settings.settings`.
"""
import ccxt
from vectorbt._settings import settings
ccxt_cfg = settings['data']['ccxt']
if config is None:
config = {}
if tqdm_kwargs is None:
tqdm_kwargs = {}
if params is None:
params = {}
if isinstance(exchange, str):
if not hasattr(ccxt, exchange):
raise ValueError(f"Exchange {exchange} not found")
# Resolve config
default_config = {}
for k, v in ccxt_cfg.items():
# Get general (not per exchange) settings
if k in ccxt.exchanges:
continue
default_config[k] = v
if exchange in ccxt_cfg:
default_config = merge_dicts(default_config, ccxt_cfg[exchange])
config = merge_dicts(default_config, config)
exchange = getattr(ccxt, exchange)(config)
else:
if len(config) > 0:
raise ValueError("Cannot apply config after instantiation of the exchange")
if not exchange.has['fetchOHLCV']:
raise ValueError(f"Exchange {exchange} does not support OHLCV")
if timeframe not in exchange.timeframes:
raise ValueError(f"Exchange {exchange} does not support {timeframe} timeframe")
if exchange.has['fetchOHLCV'] == 'emulated':
warnings.warn("Using emulated OHLCV candles", stacklevel=2)
def _retry(method):
@wraps(method)
def retry_method(*args, **kwargs):
for i in range(retries):
try:
return method(*args, **kwargs)
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
if i == retries - 1:
raise e
if delay is not None:
time.sleep(delay / 1000)
return retry_method
@_retry
def _fetch(_since, _limit):
return exchange.fetch_ohlcv(
symbol,
timeframe=timeframe,
since=_since,
limit=_limit,
params=params
)
# Establish the timestamps
start_ts = datetime_to_ms(to_tzaware_datetime(start, tz=get_utc_tz()))
try:
first_data = _fetch(0, 1)
first_valid_ts = first_data[0][0]
next_start_ts = start_ts = max(start_ts, first_valid_ts)
except:
next_start_ts = start_ts
end_ts = datetime_to_ms(to_tzaware_datetime(end, tz=get_utc_tz()))
def _ts_to_str(ts):
return str(pd.Timestamp(to_tzaware_datetime(ts, tz=get_utc_tz())))
# Iteratively collect the data
data: tp.List[list] = []
with tqdm(disable=not show_progress, **tqdm_kwargs) as pbar:
pbar.set_description(_ts_to_str(start_ts))
while True:
# Fetch the klines for the next interval
next_data = _fetch(next_start_ts, limit)
if len(data) > 0:
next_data = list(filter(lambda d: next_start_ts < d[0] < end_ts, next_data))
else:
next_data = list(filter(lambda d: d[0] < end_ts, next_data))
# Update the timestamps and the progress bar
if not len(next_data):
break
data += next_data
pbar.set_description("{} - {}".format(
_ts_to_str(start_ts),
_ts_to_str(next_data[-1][0])
))
pbar.update(1)
next_start_ts = next_data[-1][0]
if delay is not None:
time.sleep(delay / 1000) # be kind to api
# Convert data to a DataFrame
df = pd.DataFrame(data, columns=[
'Open time',
'Open',
'High',
'Low',
'Close',
'Volume'
])
df.index = pd.to_datetime(df['Open time'], unit='ms', utc=True)
del df['Open time']
df['Open'] = df['Open'].astype(float)
df['High'] = df['High'].astype(float)
df['Low'] = df['Low'].astype(float)
df['Close'] = df['Close'].astype(float)
df['Volume'] = df['Volume'].astype(float)
return df
def _ts_to_str(ts: tp.DatetimeLike) -> str:
return str(pd.Timestamp(to_tzaware_datetime(ts, tz=get_utc_tz())))
def download_symbol(cls,
symbol: str,
client: tp.Optional["ClientT"] = None,
interval: str = '1d',
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now UTC',
delay: tp.Optional[float] = 500,
limit: int = 500,
show_progress: bool = True,
tqdm_kwargs: tp.KwargsLike = None) -> tp.Frame:
"""Download the symbol.
Args:
symbol (str): Symbol.
client (binance.client.Client): Binance client of type `binance.client.Client`.
interval (str): Kline interval.
See `binance.enums`.
start (any): Start datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
end (any): End datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
delay (float): Time to sleep after each request (in milliseconds).
limit (int): The maximum number of returned items.
show_progress (bool): Whether to show the progress bar.
tqdm_kwargs (dict): Keyword arguments passed to `tqdm`.
For defaults, see `data.binance` in `vectorbt._settings.settings`.
"""
if client is None:
raise ValueError("client must be provided")
if tqdm_kwargs is None:
tqdm_kwargs = {}
# Establish the timestamps
start_ts = datetime_to_ms(to_tzaware_datetime(start, tz=get_utc_tz()))
try:
first_data = client.get_klines(
symbol=symbol,
interval=interval,
limit=1,
startTime=0,
endTime=None
)
first_valid_ts = first_data[0][0]
next_start_ts = start_ts = max(start_ts, first_valid_ts)
except:
next_start_ts = start_ts
end_ts = datetime_to_ms(to_tzaware_datetime(end, tz=get_utc_tz()))
def _ts_to_str(ts: tp.DatetimeLike) -> str:
return str(pd.Timestamp(to_tzaware_datetime(ts, tz=get_utc_tz())))
# Iteratively collect the data
data: tp.List[list] = []
with tqdm(disable=not show_progress, **tqdm_kwargs) as pbar:
pbar.set_description(_ts_to_str(start_ts))
while True:
# Fetch the klines for the next interval
next_data = client.get_klines(
symbol=symbol,
interval=interval,
limit=limit,
startTime=next_start_ts,
endTime=end_ts
)
if len(data) > 0:
next_data = list(filter(lambda d: next_start_ts < d[0] < end_ts, next_data))
else:
next_data = list(filter(lambda d: d[0] < end_ts, next_data))
# Update the timestamps and the progress bar
if not len(next_data):
break
data += next_data
pbar.set_description("{} - {}".format(
_ts_to_str(start_ts),
_ts_to_str(next_data[-1][0])
))
pbar.update(1)
next_start_ts = next_data[-1][0]
if delay is not None:
time.sleep(delay / 1000) # be kind to api
# Convert data to a DataFrame
df = pd.DataFrame(data, columns=[
'Open time',
'Open',
'High',
'Low',
'Close',
'Volume',
'Close time',
'Quote volume',
'Number of trades',
'Taker base volume',
'Taker quote volume',
'Ignore'
])
df.index = pd.to_datetime(df['Open time'], unit='ms', utc=True)
del df['Open time']
df['Open'] = df['Open'].astype(float)
df['High'] = df['High'].astype(float)
df['Low'] = df['Low'].astype(float)
df['Close'] = df['Close'].astype(float)
df['Volume'] = df['Volume'].astype(float)
df['Close time'] = pd.to_datetime(df['Close time'], unit='ms', utc=True)
df['Quote volume'] = df['Quote volume'].astype(float)
df['Number of trades'] = df['Number of trades'].astype(int)
df['Taker base volume'] = df['Taker base volume'].astype(float)
df['Taker quote volume'] = df['Taker quote volume'].astype(float)
del df['Ignore']
return df
