Remove abstract interface for now

This commit is contained in:
Matthias 2019-12-25 11:09:29 +01:00
parent 48728e2d66
commit d923bab828

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@ -3,71 +3,77 @@ Abstract datahandler interface.
It's subclasses handle and storing data from disk. It's subclasses handle and storing data from disk.
""" """
import logging
from abc import ABC, abstractmethod, abstractclassmethod from abc import ABC, abstractmethod, abstractclassmethod
from pathlib import Path from pathlib import Path
from typing import Dict, List, Optional from typing import Dict, List, Optional
from copy import deepcopy
from pandas import DataFrame from pandas import DataFrame
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.data.converter import parse_ticker_dataframe
logger = logging.getLogger(__name__)
class IDataHandler(ABC): class IDataHandler(ABC):
def __init__(self, datadir: Path, pair: str) -> None: def __init__(self, datadir: Path) -> None:
self._datadir = datadir self._datadir = datadir
self._pair = pair
@abstractclassmethod # TODO: create abstract interface
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
def ohlcv_load(self, pair, timeframe: str,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
) -> DataFrame:
""" """
Returns a list of all pairs available in this datadir Load cached ticker history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange
:param fill_up_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
:param startup_candles: Additional candles to load at the start of the period
:return: DataFrame with ohlcv data, or empty DataFrame
"""
# Fix startup period
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
pairdf = self._ohlcv_load(pair, timeframe,
timerange=timerange_startup,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
if pairdf.empty():
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return pairdf
else:
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
return pairdf
def _validate_pairdata(pair, pairdata: DataFrame, timerange: TimeRange):
"""
Validates pairdata for missing data at start end end and logs warnings.
:param pairdata: Dataframe to validate
:param timerange: Timerange specified for start and end dates
""" """
@abstractmethod if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
def ohlcv_store(self, timeframe: str, data: DataFrame): logger.warning('Missing data at start for pair %s, data starts at %s',
""" pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
Store data if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
""" logger.warning('Missing data at end for pair %s, data ends at %s',
pair, arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
@abstractmethod
def ohlcv_append(self, timeframe: str, data: DataFrame):
"""
Append data to existing files
"""
@abstractmethod
def ohlcv_load(self, timeframe: str, timerange: Optional[TimeRange] = None) -> DataFrame:
"""
Load data for one pair
:return: Dataframe
"""
@abstractclassmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs available in this datadir
"""
@abstractmethod
def trades_store(self, data: DataFrame):
"""
Store data
"""
@abstractmethod
def trades_append(self, data: DataFrame):
"""
Append data to existing files
"""
@abstractmethod
def trades_load(self, timerange: Optional[TimeRange] = None):
"""
Load data for one pair
:return: Dataframe
"""
@staticmethod @staticmethod
def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]: def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]: