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 from freqtrade.data.converter import trades_dict_to_list from .idatahandler import IDataHandler, TradeList logger = logging.getLogger(__name__) class JsonDataHandler(IDataHandler): _use_zip = False _columns = DEFAULT_DATAFRAME_COLUMNS @classmethod def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> 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 :return: List of Pairs """ _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.json)', p.name) for p in datadir.glob(f"*{timeframe}.{cls._get_file_extension()}")] # Check if regex found something and only return these results return [match[0].replace('_', '/') for match in _tmp if match] def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None: """ Store data in json format "values". format looks as follows: [[,,,,]] :param pair: Pair - used to generate filename :timeframe: Timeframe - used to generate filename :data: Dataframe containing OHLCV data :return: None """ filename = self._pair_data_filename(self._datadir, pair, timeframe) _data = data.copy() # Convert date to int _data['date'] = _data['date'].astype(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] = None, ) -> 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. :return: DataFrame with ohlcv data, or empty DataFrame """ filename = self._pair_data_filename(self._datadir, pair, timeframe) if not filename.exists(): return DataFrame(columns=self._columns) pairdata = read_json(filename, orient='values') pairdata.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_purge(self, pair: str, timeframe: str) -> bool: """ Remove data for this pair :param pair: Delete data for this pair. :param timeframe: Timeframe (e.g. "5m") :return: True when deleted, false if file did not exist. """ filename = self._pair_data_filename(self._datadir, pair, timeframe) if filename.exists(): filename.unlink() return True return False def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None: """ Append data to existing data structures :param pair: Pair :param timeframe: Timeframe this ohlcv data is for :param data: Data to append. """ 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 [match[0].replace('_', '/') 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 def trades_purge(self, pair: str) -> bool: """ Remove data for this pair :param pair: Delete data for this pair. :return: True when deleted, false if file did not exist. """ filename = self._pair_trades_filename(self._datadir, pair) if filename.exists(): filename.unlink() return True return False @classmethod def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path: pair_s = misc.pair_to_filename(pair) filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}') return filename @classmethod def _get_file_extension(cls): return "json.gz" if cls._use_zip else "json" @classmethod def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path: pair_s = misc.pair_to_filename(pair) filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}') return filename class JsonGzDataHandler(JsonDataHandler): _use_zip = True