mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-16 05:03:55 +00:00
131 lines
5.0 KiB
Python
131 lines
5.0 KiB
Python
import logging
|
|
|
|
from pandas import DataFrame, read_feather, to_datetime
|
|
|
|
from freqtrade.configuration import TimeRange
|
|
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
|
|
from freqtrade.enums import CandleType, TradingMode
|
|
|
|
from .idatahandler import IDataHandler
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class FeatherDataHandler(IDataHandler):
|
|
_columns = DEFAULT_DATAFRAME_COLUMNS
|
|
|
|
def ohlcv_store(
|
|
self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
|
|
) -> None:
|
|
"""
|
|
Store data in json format "values".
|
|
format looks as follows:
|
|
[[<date>,<open>,<high>,<low>,<close>]]
|
|
:param pair: Pair - used to generate filename
|
|
:param timeframe: Timeframe - used to generate filename
|
|
:param data: Dataframe containing OHLCV data
|
|
:param candle_type: Any of the enum CandleType (must match trading mode!)
|
|
:return: None
|
|
"""
|
|
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
|
|
self.create_dir_if_needed(filename)
|
|
|
|
data.reset_index(drop=True).loc[:, self._columns].to_feather(
|
|
filename, compression_level=9, compression="lz4"
|
|
)
|
|
|
|
def _ohlcv_load(
|
|
self, pair: str, timeframe: str, timerange: TimeRange | None, candle_type: CandleType
|
|
) -> 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.
|
|
:param candle_type: Any of the enum CandleType (must match trading mode!)
|
|
:return: DataFrame with ohlcv data, or empty DataFrame
|
|
"""
|
|
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
|
|
if not filename.exists():
|
|
# Fallback mode for 1M files
|
|
filename = self._pair_data_filename(
|
|
self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True
|
|
)
|
|
if not filename.exists():
|
|
return DataFrame(columns=self._columns)
|
|
|
|
pairdata = read_feather(filename)
|
|
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)
|
|
return pairdata
|
|
|
|
def ohlcv_append(
|
|
self, pair: str, timeframe: str, data: DataFrame, 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.
|
|
:param candle_type: Any of the enum CandleType (must match trading mode!)
|
|
"""
|
|
raise NotImplementedError()
|
|
|
|
def _trades_store(self, pair: str, data: DataFrame, trading_mode: TradingMode) -> None:
|
|
"""
|
|
Store trades data (list of Dicts) to file
|
|
:param pair: Pair - used for filename
|
|
:param data: Dataframe containing trades
|
|
column sequence as in DEFAULT_TRADES_COLUMNS
|
|
:param trading_mode: Trading mode to use (used to determine the filename)
|
|
"""
|
|
filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
|
|
self.create_dir_if_needed(filename)
|
|
data.reset_index(drop=True).to_feather(filename, compression_level=9, compression="lz4")
|
|
|
|
def trades_append(self, pair: str, data: DataFrame):
|
|
"""
|
|
Append data to existing files
|
|
:param pair: Pair - used for filename
|
|
:param data: Dataframe containing trades
|
|
column sequence as in DEFAULT_TRADES_COLUMNS
|
|
"""
|
|
raise NotImplementedError()
|
|
|
|
def _trades_load(
|
|
self, pair: str, trading_mode: TradingMode, timerange: TimeRange | None = None
|
|
) -> DataFrame:
|
|
"""
|
|
Load a pair from file, either .json.gz or .json
|
|
# TODO: respect timerange ...
|
|
:param pair: Load trades for this pair
|
|
:param trading_mode: Trading mode to use (used to determine the filename)
|
|
:param timerange: Timerange to load trades for - currently not implemented
|
|
:return: Dataframe containing trades
|
|
"""
|
|
filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
|
|
if not filename.exists():
|
|
return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
|
|
|
|
tradesdata = read_feather(filename)
|
|
|
|
return tradesdata
|
|
|
|
@classmethod
|
|
def _get_file_extension(cls):
|
|
return "feather"
|