import logging import re from pathlib import Path from typing import List, Optional import numpy as np from pandas import DataFrame, read_json, to_datetime from freqtrade import misc from freqtrade.configuration import TimeRange from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes, TradeList from freqtrade.data.converter import trades_dict_to_list from freqtrade.enums import CandleType from .idatahandler import IDataHandler logger = logging.getLogger(__name__) class JsonDataHandler(IDataHandler): _use_zip = False _columns = DEFAULT_DATAFRAME_COLUMNS @classmethod def ohlcv_get_available_data(cls, datadir: Path, trading_mode: str) -> ListPairsWithTimeframes: """ Returns a list of all pairs with ohlcv data available in this datadir :param datadir: Directory to search for ohlcv files :param trading_mode: trading-mode to be used :return: List of Tuples of (pair, timeframe) """ if trading_mode != 'spot': datadir = datadir.joinpath('futures') _tmp = [ re.search( cls._OHLCV_REGEX, p.name ) for p in datadir.glob(f"*.{cls._get_file_extension()}")] return [ ( cls.rebuild_pair_from_filename(match[1]), match[2], CandleType.from_string(match[3]) ) for match in _tmp if match and len(match.groups()) > 1] @classmethod def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> List[str]: """ Returns a list of all pairs with ohlcv data available in this datadir for the specified timeframe :param datadir: Directory to search for ohlcv files :param timeframe: Timeframe to search pairs for :param candle_type: Any of the enum CandleType (must match trading mode!) :return: List of Pairs """ candle = "" if candle_type != CandleType.SPOT: datadir = datadir.joinpath('futures') candle = f"-{candle_type}" _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + '.json)', p.name) for p in datadir.glob(f"*{timeframe}{candle}.{cls._get_file_extension()}")] # Check if regex found something and only return these results return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match] def ohlcv_store( self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None: """ Store data in json format "values". format looks as follows: [[,,,,]] :param pair: Pair - used to generate filename :param timeframe: Timeframe - used to generate filename :param data: Dataframe containing OHLCV data :param candle_type: Any of the enum CandleType (must match trading mode!) :return: None """ filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type) self.create_dir_if_needed(filename) _data = data.copy() # Convert date to int _data['date'] = _data['date'].view(np.int64) // 1000 // 1000 # Reset index, select only appropriate columns and save as json _data.reset_index(drop=True).loc[:, self._columns].to_json( filename, orient="values", compression='gzip' if self._use_zip else None) def _ohlcv_load(self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType ) -> DataFrame: """ Internal method used to load data for one pair from disk. Implements the loading and conversion to a Pandas dataframe. Timerange trimming and dataframe validation happens outside of this method. :param pair: Pair to load data :param timeframe: Timeframe (e.g. "5m") :param timerange: Limit data to be loaded to this timerange. Optionally implemented by subclasses to avoid loading all data where possible. :param candle_type: Any of the enum CandleType (must match trading mode!) :return: DataFrame with ohlcv data, or empty DataFrame """ filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type) if not filename.exists(): return DataFrame(columns=self._columns) try: pairdata = read_json(filename, orient='values') pairdata.columns = self._columns except ValueError: logger.error(f"Could not load data for {pair}.") return DataFrame(columns=self._columns) pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float', 'volume': 'float'}) pairdata['date'] = to_datetime(pairdata['date'], unit='ms', utc=True, infer_datetime_format=True) return pairdata def ohlcv_append( self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType ) -> None: """ Append data to existing data structures :param pair: Pair :param timeframe: Timeframe this ohlcv data is for :param data: Data to append. :param candle_type: Any of the enum CandleType (must match trading mode!) """ raise NotImplementedError() @classmethod def trades_get_pairs(cls, datadir: Path) -> List[str]: """ Returns a list of all pairs for which trade data is available in this :param datadir: Directory to search for ohlcv files :return: List of Pairs """ _tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name) for p in datadir.glob(f"*trades.{cls._get_file_extension()}")] # Check if regex found something and only return these results to avoid exceptions. return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match] def trades_store(self, pair: str, data: TradeList) -> None: """ Store trades data (list of Dicts) to file :param pair: Pair - used for filename :param data: List of Lists containing trade data, column sequence as in DEFAULT_TRADES_COLUMNS """ filename = self._pair_trades_filename(self._datadir, pair) misc.file_dump_json(filename, data, is_zip=self._use_zip) def trades_append(self, pair: str, data: TradeList): """ Append data to existing files :param pair: Pair - used for filename :param data: List of Lists containing trade data, column sequence as in DEFAULT_TRADES_COLUMNS """ raise NotImplementedError() def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList: """ Load a pair from file, either .json.gz or .json # TODO: respect timerange ... :param pair: Load trades for this pair :param timerange: Timerange to load trades for - currently not implemented :return: List of trades """ filename = self._pair_trades_filename(self._datadir, pair) tradesdata = misc.file_load_json(filename) if not tradesdata: return [] if isinstance(tradesdata[0], dict): # Convert trades dict to list logger.info("Old trades format detected - converting") tradesdata = trades_dict_to_list(tradesdata) pass return tradesdata @classmethod def _get_file_extension(cls): return "json.gz" if cls._use_zip else "json" class JsonGzDataHandler(JsonDataHandler): _use_zip = True