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Merge pull request #7458 from xmatthias/dataformat/feather

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new Dataformats feather and Parquet
This commit is contained in:
Matthias
2022-09-25 09:00:22 +02:00
committed by GitHub
parent 96336cb552 2fffe7c5dd
commit f4fac53a13
11 changed files with 433 additions and 38 deletions
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2
freqtrade/commands/cli_options.py
View File

@@ -440,7 +440,7 @@ AVAILABLE_CLI_OPTIONS = {
"dataformat_trades": Arg(
'--data-format-trades',
help='Storage format for downloaded trades data. (default: `jsongz`).',
choices=constants.AVAILABLE_DATAHANDLERS,
choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
),
"show_timerange": Arg(
'--show-timerange',

5
freqtrade/constants.py
View File

@@ -36,7 +36,8 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5']
AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['feather', 'parquet']
BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
BACKTEST_CACHE_DEFAULT = 'day'
@@ -434,7 +435,7 @@ CONF_SCHEMA = {
},
'dataformat_trades': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'enum': AVAILABLE_DATAHANDLERS_TRADES,
'default': 'jsongz'
},
'position_adjustment_enable': {'type': 'boolean'},

130
freqtrade/data/history/featherdatahandler.py Normal file
View File

@@ -0,0 +1,130 @@
import logging
from typing import Optional
from pandas import DataFrame, read_feather, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
from freqtrade.enums import CandleType
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: Optional[TimeRange], 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,
infer_datetime_format=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: 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
"""
# filename = self._pair_trades_filename(self._datadir, pair)
raise NotImplementedError()
# array = pa.array(data)
# array
# feather.write_feather(data, filename)
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:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
raise NotImplementedError()
# filename = self._pair_trades_filename(self._datadir, pair)
# tradesdata = misc.file_load_json(filename)
# if not tradesdata:
# return []
# return tradesdata
@classmethod
def _get_file_extension(cls):
return "feather"

1
freqtrade/data/history/hdf5datahandler.py
View File

@@ -81,6 +81,7 @@ class HDF5DataHandler(IDataHandler):
raise ValueError("Wrong dataframe format")
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata = pairdata.reset_index(drop=True)
return pairdata
def ohlcv_append(

6
freqtrade/data/history/idatahandler.py
View File

@@ -375,6 +375,12 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
elif datatype == 'hdf5':
from .hdf5datahandler import HDF5DataHandler
return HDF5DataHandler
elif datatype == 'feather':
from .featherdatahandler import FeatherDataHandler
return FeatherDataHandler
elif datatype == 'parquet':
from .parquetdatahandler import ParquetDataHandler
return ParquetDataHandler
else:
raise ValueError(f"No datahandler for datatype {datatype} available.")

129
freqtrade/data/history/parquetdatahandler.py Normal file
View File

@@ -0,0 +1,129 @@
import logging
from typing import Optional
from pandas import DataFrame, read_parquet, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
from freqtrade.enums import CandleType
from .idatahandler import IDataHandler
logger = logging.getLogger(__name__)
class ParquetDataHandler(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_parquet(filename)
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange], 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_parquet(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,
infer_datetime_format=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: 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
"""
# filename = self._pair_trades_filename(self._datadir, pair)
raise NotImplementedError()
# array = pa.array(data)
# array
# feather.write_feather(data, filename)
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:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
raise NotImplementedError()
# filename = self._pair_trades_filename(self._datadir, pair)
# tradesdata = misc.file_load_json(filename)
# if not tradesdata:
# return []
# return tradesdata
@classmethod
def _get_file_extension(cls):
return "parquet"