stable/freqtrade/data/history/hdf5datahandler.py

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import logging
import re
from pathlib import Path
from typing import List, Optional
import numpy as np
import pandas as pd
from freqtrade.configuration import TimeRange
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from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
ListPairsWithTimeframes, TradeList)
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from freqtrade.enums import CandleType
from .idatahandler import IDataHandler
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logger = logging.getLogger(__name__)
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class HDF5DataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
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@classmethod
def ohlcv_get_available_data(cls, datadir: Path, trading_mode: str) -> ListPairsWithTimeframes:
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"""
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
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:return: List of Tuples of (pair, timeframe)
"""
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if trading_mode == 'futures':
datadir = datadir.joinpath('futures')
_tmp = [
re.search(
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cls._OHLCV_REGEX, p.name
) for p in datadir.glob("*.h5")
]
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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]
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@classmethod
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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
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:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: List of Pairs
"""
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candle = ""
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if candle_type != CandleType.SPOT:
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)
for p in datadir.glob(f"*{timeframe}{candle}.h5")]
# 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(
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self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType) -> None:
"""
Store data in hdf5 file.
:param pair: Pair - used to generate filename
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:param timeframe: Timeframe - used to generate filename
:param data: Dataframe containing OHLCV data
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:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: None
"""
key = self._pair_ohlcv_key(pair, timeframe)
_data = data.copy()
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
self.create_dir_if_needed(filename)
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_data.loc[:, self._columns].to_hdf(
filename, key, mode='a', complevel=9, complib='blosc',
format='table', data_columns=['date']
)
def _ohlcv_load(self, pair: str, timeframe: str,
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timerange: Optional[TimeRange], candle_type: CandleType
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) -> pd.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.
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:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: DataFrame with ohlcv data, or empty DataFrame
"""
key = self._pair_ohlcv_key(pair, timeframe)
filename = self._pair_data_filename(
self._datadir,
pair,
timeframe,
candle_type=candle_type
)
if not filename.exists():
return pd.DataFrame(columns=self._columns)
where = []
if timerange:
if timerange.starttype == 'date':
where.append(f"date >= Timestamp({timerange.startts * 1e9})")
if timerange.stoptype == 'date':
where.append(f"date <= Timestamp({timerange.stopts * 1e9})")
pairdata = pd.read_hdf(filename, key=key, mode="r", where=where)
if list(pairdata.columns) != self._columns:
raise ValueError("Wrong dataframe format")
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
return pairdata
def ohlcv_append(
self,
pair: str,
timeframe: str,
data: pd.DataFrame,
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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.
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: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.h5)', p.name)
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for p in datadir.glob("*trades.h5")]
# 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
"""
key = self._pair_trades_key(pair)
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pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS).to_hdf(
self._pair_trades_filename(self._datadir, pair), key,
mode='a', complevel=9, complib='blosc',
format='table', data_columns=['timestamp']
)
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:
"""
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Load a pair from h5 file.
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
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key = self._pair_trades_key(pair)
filename = self._pair_trades_filename(self._datadir, pair)
if not filename.exists():
return []
where = []
if timerange:
if timerange.starttype == 'date':
where.append(f"timestamp >= {timerange.startts * 1e3}")
if timerange.stoptype == 'date':
where.append(f"timestamp < {timerange.stopts * 1e3}")
trades: pd.DataFrame = pd.read_hdf(filename, key=key, mode="r", where=where)
trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
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return trades.values.tolist()
@classmethod
def _get_file_extension(cls):
return "h5"
@classmethod
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def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
return f"{pair}/ohlcv/tf_{timeframe}"
@classmethod
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def _pair_trades_key(cls, pair: str) -> str:
return f"{pair}/trades"