stable/freqtrade/data/dataprovider.py

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"""
Dataprovider
Responsible to provide data to the bot
including ticker and orderbook data, live and historical candle (OHLCV) data
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Common Interface for bot and strategy to access data.
"""
import logging
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional, Tuple
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from arrow import Arrow
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from pandas import DataFrame
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from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
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from freqtrade.data.history import load_pair_history
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from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.state import RunMode
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logger = logging.getLogger(__name__)
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class DataProvider:
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def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
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self._config = config
self._exchange = exchange
self._pairlists = pairlists
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self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
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def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
"""
Store cached Dataframe.
Using private method as this should never be used by a user
(but the class is exposed via `self.dp` to the strategy)
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param dataframe: analyzed dataframe
"""
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self.__cached_pairs[(pair, timeframe)] = (dataframe, Arrow.utcnow().datetime)
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def add_pairlisthandler(self, pairlists) -> None:
"""
Allow adding pairlisthandler after initialization
"""
self._pairlists = pairlists
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def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
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"""
Refresh data, called with each cycle
"""
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if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
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@property
def available_pairs(self) -> ListPairsWithTimeframes:
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"""
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
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Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
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"""
Get candle (OHLCV) data for the given pair as DataFrame
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Please use the `available_pairs` method to verify which pairs are currently cached.
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:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
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:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
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"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, timeframe or self._config['timeframe']),
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copy=copy)
else:
return DataFrame()
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def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
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"""
Get stored historical candle (OHLCV) data
:param pair: pair to get the data for
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:param timeframe: timeframe to get data for
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"""
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return load_pair_history(pair=pair,
timeframe=timeframe or self._config['timeframe'],
datadir=self._config['datadir'],
data_format=self._config.get('dataformat_ohlcv', 'json')
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)
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def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
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"""
Return pair candle (OHLCV) data, either live or cached historical -- depending
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on the runmode.
:param pair: pair to get the data for
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:param timeframe: timeframe to get data for
:return: Dataframe for this pair
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"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
# Get live OHLCV data.
data = self.ohlcv(pair=pair, timeframe=timeframe)
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else:
# Get historical OHLCV data (cached on disk).
data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
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if len(data) == 0:
logger.warning(f"No data found for ({pair}, {timeframe}).")
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return data
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def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
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"""
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
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:return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
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combination.
Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
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"""
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if (pair, timeframe) in self.__cached_pairs:
return self.__cached_pairs[(pair, timeframe)]
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else:
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return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
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def market(self, pair: str) -> Optional[Dict[str, Any]]:
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"""
Return market data for the pair
:param pair: Pair to get the data for
:return: Market data dict from ccxt or None if market info is not available for the pair
"""
return self._exchange.markets.get(pair)
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def ticker(self, pair: str):
"""
Return last ticker data from exchange
:param pair: Pair to get the data for
:return: Ticker dict from exchange or empty dict if ticker is not available for the pair
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"""
try:
return self._exchange.fetch_ticker(pair)
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except ExchangeError:
return {}
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def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
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"""
Fetch latest l2 orderbook data
Warning: Does a network request - so use with common sense.
:param pair: pair to get the data for
:param maximum: Maximum number of orderbook entries to query
:return: dict including bids/asks with a total of `maximum` entries.
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"""
return self._exchange.fetch_l2_order_book(pair, maximum)
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@property
def runmode(self) -> RunMode:
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"""
Get runmode of the bot
can be "live", "dry-run", "backtest", "edgecli", "hyperopt" or "other".
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"""
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return RunMode(self._config.get('runmode', RunMode.OTHER))
def current_whitelist(self) -> List[str]:
"""
fetch latest available whitelist.
Useful when you have a large whitelist and need to call each pair as an informative pair.
As available pairs does not show whitelist until after informative pairs have been cached.
:return: list of pairs in whitelist
"""
if self._pairlists:
return self._pairlists.whitelist
else:
raise OperationalException("Dataprovider was not initialized with a pairlist provider.")