258 lines
9.9 KiB
Python
258 lines
9.9 KiB
Python
"""
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Abstract datahandler interface.
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It's subclasses handle and storing data from disk.
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"""
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import logging
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from abc import ABC, abstractclassmethod, abstractmethod
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from copy import deepcopy
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import List, Optional, Type
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import ListPairsWithTimeframes
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from freqtrade.data.converter import (clean_ohlcv_dataframe,
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trades_remove_duplicates, trim_dataframe)
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from freqtrade.exchange import timeframe_to_seconds
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logger = logging.getLogger(__name__)
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# Type for trades list
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TradeList = List[List]
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class IDataHandler(ABC):
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def __init__(self, datadir: Path) -> None:
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self._datadir = datadir
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@abstractclassmethod
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def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
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"""
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Returns a list of all pairs with ohlcv data available in this datadir
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:param datadir: Directory to search for ohlcv files
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:return: List of Tuples of (pair, timeframe)
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"""
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@abstractclassmethod
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def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
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"""
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Returns a list of all pairs with ohlcv data available in this datadir
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for the specified timeframe
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:param datadir: Directory to search for ohlcv files
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:param timeframe: Timeframe to search pairs for
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:return: List of Pairs
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"""
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@abstractmethod
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def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
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"""
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Store data in json format "values".
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format looks as follows:
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[[<date>,<open>,<high>,<low>,<close>]]
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:param pair: Pair - used to generate filename
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:timeframe: Timeframe - used to generate filename
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:data: Dataframe containing OHLCV data
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:return: None
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"""
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@abstractmethod
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def _ohlcv_load(self, pair: str, timeframe: str,
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timerange: Optional[TimeRange] = None,
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) -> DataFrame:
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"""
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Internal method used to load data for one pair from disk.
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Implements the loading and conversion to a Pandas dataframe.
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Timerange trimming and dataframe validation happens outside of this method.
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:param pair: Pair to load data
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:param timeframe: Timeframe (e.g. "5m")
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:param timerange: Limit data to be loaded to this timerange.
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Optionally implemented by subclasses to avoid loading
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all data where possible.
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:return: DataFrame with ohlcv data, or empty DataFrame
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"""
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@abstractmethod
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def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
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"""
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Remove data for this pair
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:param pair: Delete data for this pair.
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:param timeframe: Timeframe (e.g. "5m")
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:return: True when deleted, false if file did not exist.
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"""
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@abstractmethod
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def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
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"""
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Append data to existing data structures
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:param pair: Pair
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:param timeframe: Timeframe this ohlcv data is for
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:param data: Data to append.
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"""
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@abstractclassmethod
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def trades_get_pairs(cls, datadir: Path) -> List[str]:
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"""
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Returns a list of all pairs for which trade data is available in this
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:param datadir: Directory to search for ohlcv files
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:return: List of Pairs
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"""
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@abstractmethod
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def trades_store(self, pair: str, data: TradeList) -> None:
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"""
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Store trades data (list of Dicts) to file
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:param pair: Pair - used for filename
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:param data: List of Lists containing trade data,
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column sequence as in DEFAULT_TRADES_COLUMNS
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"""
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@abstractmethod
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def trades_append(self, pair: str, data: TradeList):
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"""
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Append data to existing files
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:param pair: Pair - used for filename
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:param data: List of Lists containing trade data,
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column sequence as in DEFAULT_TRADES_COLUMNS
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"""
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@abstractmethod
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def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
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"""
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Load a pair from file, either .json.gz or .json
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:param pair: Load trades for this pair
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:param timerange: Timerange to load trades for - currently not implemented
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:return: List of trades
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"""
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@abstractmethod
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def trades_purge(self, pair: str) -> bool:
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"""
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Remove data for this pair
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:param pair: Delete data for this pair.
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:return: True when deleted, false if file did not exist.
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"""
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def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
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"""
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Load a pair from file, either .json.gz or .json
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Removes duplicates in the process.
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:param pair: Load trades for this pair
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:param timerange: Timerange to load trades for - currently not implemented
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:return: List of trades
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"""
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return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
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def ohlcv_load(self, pair, timeframe: str,
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timerange: Optional[TimeRange] = None,
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fill_missing: bool = True,
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drop_incomplete: bool = True,
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startup_candles: int = 0,
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warn_no_data: bool = True
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) -> DataFrame:
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"""
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Load cached candle (OHLCV) data for the given pair.
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:param pair: Pair to load data for
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:param timeframe: Timeframe (e.g. "5m")
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:param timerange: Limit data to be loaded to this timerange
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:param fill_missing: Fill missing values with "No action"-candles
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:param drop_incomplete: Drop last candle assuming it may be incomplete.
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:param startup_candles: Additional candles to load at the start of the period
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:param warn_no_data: Log a warning message when no data is found
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:return: DataFrame with ohlcv data, or empty DataFrame
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"""
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# Fix startup period
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timerange_startup = deepcopy(timerange)
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if startup_candles > 0 and timerange_startup:
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timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
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pairdf = self._ohlcv_load(pair, timeframe,
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timerange=timerange_startup)
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if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
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return pairdf
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else:
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enddate = pairdf.iloc[-1]['date']
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if timerange_startup:
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self._validate_pairdata(pair, pairdf, timerange_startup)
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pairdf = trim_dataframe(pairdf, timerange_startup)
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if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
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return pairdf
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# incomplete candles should only be dropped if we didn't trim the end beforehand.
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pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
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pair=pair,
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fill_missing=fill_missing,
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drop_incomplete=(drop_incomplete and
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enddate == pairdf.iloc[-1]['date']))
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self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
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return pairdf
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def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
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"""
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Warn on empty dataframe
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"""
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if pairdf.empty:
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if warn_no_data:
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logger.warning(
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f'No history data for pair: "{pair}", timeframe: {timeframe}. '
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'Use `freqtrade download-data` to download the data'
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)
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return True
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return False
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def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
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"""
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Validates pairdata for missing data at start end end and logs warnings.
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:param pairdata: Dataframe to validate
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:param timerange: Timerange specified for start and end dates
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"""
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if timerange.starttype == 'date':
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start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
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if pairdata.iloc[0]['date'] > start:
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logger.warning(f"Missing data at start for pair {pair}, "
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f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
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if timerange.stoptype == 'date':
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stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
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if pairdata.iloc[-1]['date'] < stop:
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logger.warning(f"Missing data at end for pair {pair}, "
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f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
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def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
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"""
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Get datahandler class.
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Could be done using Resolvers, but since this may be called often and resolvers
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are rather expensive, doing this directly should improve performance.
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:param datatype: datatype to use.
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:return: Datahandler class
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"""
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if datatype == 'json':
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from .jsondatahandler import JsonDataHandler
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return JsonDataHandler
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elif datatype == 'jsongz':
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from .jsondatahandler import JsonGzDataHandler
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return JsonGzDataHandler
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else:
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raise ValueError(f"No datahandler for datatype {datatype} available.")
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def get_datahandler(datadir: Path, data_format: str = None,
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data_handler: IDataHandler = None) -> IDataHandler:
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"""
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:param datadir: Folder to save data
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:data_format: dataformat to use
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:data_handler: returns this datahandler if it exists or initializes a new one
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"""
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if not data_handler:
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HandlerClass = get_datahandlerclass(data_format or 'json')
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data_handler = HandlerClass(datadir)
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return data_handler
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