import logging from typing import 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, 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 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(): # Fallback mode for 1M files filename = self._pair_data_filename( self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True) 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() 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