199 lines
7.8 KiB
Python
199 lines
7.8 KiB
Python
import logging
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import re
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from pathlib import Path
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from typing import List, Optional
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import numpy as np
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from pandas import DataFrame, read_json, to_datetime
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from freqtrade import misc
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes, TradeList
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from freqtrade.data.converter import trades_dict_to_list
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from freqtrade.enums import CandleType, TradingMode
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from .idatahandler import IDataHandler
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logger = logging.getLogger(__name__)
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class JsonDataHandler(IDataHandler):
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_use_zip = False
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_columns = DEFAULT_DATAFRAME_COLUMNS
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@classmethod
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def ohlcv_get_available_data(
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cls, datadir: Path, trading_mode: TradingMode) -> 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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:param trading_mode: trading-mode to be used
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:return: List of Tuples of (pair, timeframe)
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"""
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if trading_mode == 'futures':
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datadir = datadir.joinpath('futures')
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_tmp = [
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re.search(
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cls._OHLCV_REGEX, p.name
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) for p in datadir.glob(f"*.{cls._get_file_extension()}")]
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return [
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(
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cls.rebuild_pair_from_filename(match[1]),
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match[2],
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CandleType.from_string(match[3])
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) for match in _tmp if match and len(match.groups()) > 1]
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@classmethod
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def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> 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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:param candle_type: Any of the enum CandleType (must match trading mode!)
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:return: List of Pairs
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"""
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candle = ""
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if candle_type != CandleType.SPOT:
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datadir = datadir.joinpath('futures')
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candle = f"-{candle_type}"
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_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.json)', p.name)
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for p in datadir.glob(f"*{timeframe}{candle}.{cls._get_file_extension()}")]
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# Check if regex found something and only return these results
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return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
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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 = data.copy()
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# Convert date to int
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_data['date'] = _data['date'].view(np.int64) // 1000 // 1000
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# Reset index, select only appropriate columns and save as json
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_data.reset_index(drop=True).loc[:, self._columns].to_json(
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filename, orient="values",
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compression='gzip' if self._use_zip else None)
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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(self._datadir, pair, timeframe, candle_type=candle_type)
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if not filename.exists():
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return DataFrame(columns=self._columns)
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try:
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pairdata = read_json(filename, orient='values')
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pairdata.columns = self._columns
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except ValueError:
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logger.error(f"Could not load data for {pair}.")
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return DataFrame(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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@classmethod
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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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_tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
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for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
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# Check if regex found something and only return these results to avoid exceptions.
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return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
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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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misc.file_dump_json(filename, data, is_zip=self._use_zip)
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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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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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if isinstance(tradesdata[0], dict):
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# Convert trades dict to list
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logger.info("Old trades format detected - converting")
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tradesdata = trades_dict_to_list(tradesdata)
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pass
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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 "json.gz" if cls._use_zip else "json"
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class JsonGzDataHandler(JsonDataHandler):
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_use_zip = True
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