2019-12-23 13:56:48 +00:00
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import re
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from pathlib import Path
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from typing import Dict, List, Optional
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2019-12-25 14:05:01 +00:00
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import numpy as np
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from pandas import DataFrame, read_json, to_datetime
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2019-12-23 13:56:48 +00:00
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from freqtrade import misc
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from freqtrade.configuration import TimeRange
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2019-12-25 14:05:01 +00:00
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from freqtrade.data.converter import clean_ohlcv_dataframe
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2019-12-23 13:56:48 +00:00
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from .idatahandler import IDataHandler
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class JsonDataHandler(IDataHandler):
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_use_zip = False
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2019-12-25 14:05:01 +00:00
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_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
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2019-12-23 13:56:48 +00:00
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@classmethod
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def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
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"""
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Returns a list of all pairs available in this datadir
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"""
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2019-12-25 09:21:30 +00:00
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_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.json)', p.name)
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2019-12-23 13:56:48 +00:00
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for p in datadir.glob(f"*{timeframe}.{cls._get_file_extension()}")]
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2019-12-25 09:21:30 +00:00
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# Check if regex found something and only return these results
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2019-12-25 09:25:30 +00:00
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return [match[0].replace('_', '/') for match in _tmp if match]
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2019-12-23 13:56:48 +00:00
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2019-12-25 10:08:49 +00:00
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def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
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2019-12-23 13:56:48 +00:00
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"""
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2019-12-25 14:05:01 +00:00
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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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:timeframe: Timeframe - used to generate filename
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:data: Dataframe containing OHLCV data
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:return: None
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2019-12-23 13:56:48 +00:00
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"""
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2019-12-25 14:05:01 +00:00
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filename = self._pair_data_filename(self._datadir, pair, timeframe)
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_data = data.copy()
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# Convert date to int
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_data['date'] = _data['date'].astype(np.int64) // 1000 // 1000
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2019-12-23 13:56:48 +00:00
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2019-12-25 14:05:01 +00:00
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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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2019-12-23 13:56:48 +00:00
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2019-12-25 10:08:49 +00:00
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def _ohlcv_load(self, pair: str, timeframe: str,
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timerange: Optional[TimeRange] = None,
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2019-12-25 14:05:01 +00:00
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fill_missing: bool = True,
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2019-12-25 10:08:49 +00:00
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drop_incomplete: bool = True,
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) -> DataFrame:
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2019-12-23 13:56:48 +00:00
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"""
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2019-12-25 10:08:49 +00:00
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Load data for one pair from disk.
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Implements the loading and conversation to a Pandas dataframe.
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2019-12-23 13:56:48 +00:00
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:return: Dataframe
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"""
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2019-12-25 14:05:01 +00:00
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filename = self._pair_data_filename(self._datadir, pair, timeframe)
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pairdata = read_json(filename, orient='values')
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pairdata.columns = self._columns
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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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2019-12-23 13:56:48 +00:00
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if timerange:
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pairdata = IDataHandler.trim_tickerlist(pairdata, timerange)
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2019-12-25 14:05:01 +00:00
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return clean_ohlcv_dataframe(pairdata, timeframe,
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pair=pair,
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fill_missing=fill_missing,
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drop_incomplete=drop_incomplete)
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def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> 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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"""
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raise NotImplementedError()
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2019-12-23 13:56:48 +00:00
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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 available in this datadir
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"""
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2019-12-25 09:21:30 +00:00
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_tmp = [re.search(r'^(\S+)(?=\-trades.json)', p.name)
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2019-12-23 13:56:48 +00:00
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for p in datadir.glob(f"*trades.{cls._get_file_extension()}")]
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2019-12-25 09:21:30 +00:00
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# Check if regex found something and only return these results to avoid exceptions.
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2019-12-25 09:25:30 +00:00
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return [match[0].replace('_', '/') for match in _tmp if match]
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2019-12-23 13:56:48 +00:00
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2019-12-25 10:08:49 +00:00
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def trades_store(self, pair: str, data: List[Dict]):
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2019-12-23 13:56:48 +00:00
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"""
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Store data
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"""
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2019-12-25 10:08:49 +00:00
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filename = self._pair_trades_filename(self._datadir, pair)
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2019-12-23 13:56:48 +00:00
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misc.file_dump_json(filename, data, is_zip=self._use_zip)
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2019-12-25 10:08:49 +00:00
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def trades_append(self, pair: str, data: DataFrame):
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2019-12-23 13:56:48 +00:00
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"""
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Append data to existing files
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"""
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raise NotImplementedError()
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2019-12-25 10:08:49 +00:00
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def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
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2019-12-23 13:56:48 +00:00
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"""
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Load a pair from file, either .json.gz or .json
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# TODO: validate timerange ...
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:return: List of trades
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"""
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2019-12-25 10:08:49 +00:00
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filename = self._pair_trades_filename(self._datadir, pair)
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2019-12-23 13:56:48 +00:00
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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 _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
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pair_s = pair.replace("/", "_")
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filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
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return filename
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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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@classmethod
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def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
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pair_s = pair.replace("/", "_")
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filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
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return filename
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class JsonGzDataHandler(JsonDataHandler):
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_use_zip = True
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