212 lines
7.9 KiB
Python
212 lines
7.9 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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import pandas as pd
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
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ListPairsWithTimeframes, TradeList)
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from freqtrade.enums import CandleType
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from .idatahandler import IDataHandler
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logger = logging.getLogger(__name__)
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class HDF5DataHandler(IDataHandler):
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_columns = DEFAULT_DATAFRAME_COLUMNS
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@classmethod
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def ohlcv_get_available_data(cls, datadir: Path, trading_mode: str) -> 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("*.h5")
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]
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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 + '.h5)', p.name)
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for p in datadir.glob(f"*{timeframe}{candle}.h5")]
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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: pd.DataFrame, candle_type: CandleType) -> None:
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"""
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Store data in hdf5 file.
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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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key = self._pair_ohlcv_key(pair, timeframe)
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_data = data.copy()
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filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
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_data.loc[:, self._columns].to_hdf(
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filename, key, mode='a', complevel=9, complib='blosc',
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format='table', data_columns=['date']
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)
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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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) -> pd.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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key = self._pair_ohlcv_key(pair, timeframe)
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filename = self._pair_data_filename(
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self._datadir,
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pair,
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timeframe,
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candle_type=candle_type
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)
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if not filename.exists():
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return pd.DataFrame(columns=self._columns)
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where = []
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if timerange:
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if timerange.starttype == 'date':
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where.append(f"date >= Timestamp({timerange.startts * 1e9})")
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if timerange.stoptype == 'date':
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where.append(f"date <= Timestamp({timerange.stopts * 1e9})")
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pairdata = pd.read_hdf(filename, key=key, mode="r", where=where)
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if list(pairdata.columns) != self._columns:
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raise ValueError("Wrong dataframe format")
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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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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: pd.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.h5)', p.name)
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for p in datadir.glob("*trades.h5")]
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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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key = self._pair_trades_key(pair)
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pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS).to_hdf(
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self._pair_trades_filename(self._datadir, pair), key,
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mode='a', complevel=9, complib='blosc',
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format='table', data_columns=['timestamp']
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)
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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 h5 file.
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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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key = self._pair_trades_key(pair)
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filename = self._pair_trades_filename(self._datadir, pair)
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if not filename.exists():
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return []
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where = []
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if timerange:
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if timerange.starttype == 'date':
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where.append(f"timestamp >= {timerange.startts * 1e3}")
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if timerange.stoptype == 'date':
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where.append(f"timestamp < {timerange.stopts * 1e3}")
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trades: pd.DataFrame = pd.read_hdf(filename, key=key, mode="r", where=where)
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trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
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return trades.values.tolist()
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@classmethod
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def _get_file_extension(cls):
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return "h5"
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@classmethod
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def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
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return f"{pair}/ohlcv/tf_{timeframe}"
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@classmethod
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def _pair_trades_key(cls, pair: str) -> str:
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return f"{pair}/trades"
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