131 lines
4.9 KiB
Python
131 lines
4.9 KiB
Python
import logging
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from typing import Optional
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from pandas import DataFrame, read_feather, to_datetime
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
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from freqtrade.enums import CandleType
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from .idatahandler import IDataHandler
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logger = logging.getLogger(__name__)
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class FeatherDataHandler(IDataHandler):
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_columns = DEFAULT_DATAFRAME_COLUMNS
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def ohlcv_store(
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self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> 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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:param timeframe: Timeframe - used to generate filename
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:param data: Dataframe containing OHLCV data
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:param candle_type: Any of the enum CandleType (must match trading mode!)
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:return: None
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"""
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filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
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self.create_dir_if_needed(filename)
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data.reset_index(drop=True).loc[:, self._columns].to_feather(
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filename, compression_level=9, compression='lz4')
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def _ohlcv_load(self, pair: str, timeframe: str,
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timerange: Optional[TimeRange], candle_type: CandleType
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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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:param candle_type: Any of the enum CandleType (must match trading mode!)
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:return: DataFrame with ohlcv data, or empty DataFrame
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"""
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filename = self._pair_data_filename(
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self._datadir, pair, timeframe, candle_type=candle_type)
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if not filename.exists():
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# Fallback mode for 1M files
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filename = self._pair_data_filename(
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self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
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if not filename.exists():
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return DataFrame(columns=self._columns)
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pairdata = read_feather(filename)
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pairdata.columns = self._columns
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pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
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'low': 'float', 'close': 'float', 'volume': 'float'})
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pairdata['date'] = to_datetime(pairdata['date'],
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unit='ms',
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utc=True,
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infer_datetime_format=True)
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return pairdata
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def ohlcv_append(
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self,
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pair: str,
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timeframe: str,
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data: DataFrame,
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candle_type: CandleType
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) -> 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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:param candle_type: Any of the enum CandleType (must match trading mode!)
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"""
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raise NotImplementedError()
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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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# filename = self._pair_trades_filename(self._datadir, pair)
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raise NotImplementedError()
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# array = pa.array(data)
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# array
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# feather.write_feather(data, filename)
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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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raise NotImplementedError()
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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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# TODO: respect timerange ...
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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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raise NotImplementedError()
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# filename = self._pair_trades_filename(self._datadir, pair)
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# tradesdata = misc.file_load_json(filename)
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# if not tradesdata:
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# return []
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# return tradesdata
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@classmethod
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def _get_file_extension(cls):
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return "feather"
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