def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
Loads backtest data and returns the data combined with the timerange
as tuple.
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=self.timerange,
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
min_date, max_date = history.get_timerange(data)
logger.info(
f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days).')
# Adjust startts forward if not enough data is available
self.timerange.adjust_start_if_necessary(
timeframe_to_seconds(self.timeframe), self.required_startup,
min_date)
self.progress.set_new_value(1)
return data, self.timerange
def historic_ohlcv(self,
pair: str,
timeframe: str = None,
candle_type: str = '') -> DataFrame:
"""
Get stored historical candle (OHLCV) data
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:param candle_type: '', mark, index, premiumIndex, or funding_rate
"""
_candle_type = CandleType.from_string(
candle_type
) if candle_type != '' else self._config['candle_type_def']
saved_pair = (pair, str(timeframe), _candle_type)
if saved_pair not in self.__cached_pairs_backtesting:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))
# Move informative start time respecting startup_candle_count
timerange.subtract_start(
timeframe_to_seconds(str(timeframe)) *
self._config.get('startup_candle_count', 0))
self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
pair=pair,
timeframe=timeframe or self._config['timeframe'],
datadir=self._config['datadir'],
timerange=timerange,
data_format=self._config.get('dataformat_ohlcv', 'json'),
candle_type=_candle_type,
)
return self.__cached_pairs_backtesting[saved_pair].copy()
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
min_date, max_date = history.get_timerange(data)
logger.info('Loading data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(
timeframe_to_seconds(self.timeframe), self.required_startup,
min_date)
return data, timerange
def ohlcv_load(
self,
pair,
timeframe: str,
candle_type: CandleType,
timerange: Optional[TimeRange] = None,
fill_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
warn_no_data: bool = True,
) -> DataFrame:
"""
Load cached candle (OHLCV) data for the given pair.
:param pair: Pair to load data for
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange
:param fill_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
:param warn_no_data: Log a warning message when no data is found
:param candle_type: Any of the enum CandleType (must match trading mode!)
: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,
candle_type=candle_type)
if self._check_empty_df(pairdf, pair, timeframe, candle_type,
warn_no_data):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
if timerange_startup:
self._validate_pairdata(pair, pairdf, timeframe, candle_type,
timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, candle_type,
warn_no_data):
return pairdf
# incomplete candles should only be dropped if we didn't trim the end beforehand.
pairdf = clean_ohlcv_dataframe(
pairdf,
timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete
and enddate == pairdf.iloc[-1]['date']))
self._check_empty_df(pairdf, pair, timeframe, candle_type,
warn_no_data)
return pairdf
def init_plotscript(config, markets: List, startup_candles: int = 0):
"""
Initialize objects needed for plotting
:return: Dict with candle (OHLCV) data, trades and pairs
"""
if "pairs" in config:
pairs = expand_pairlist(config['pairs'], markets)
else:
pairs = expand_pairlist(config['exchange']['pair_whitelist'], markets)
# Set timerange to use
timerange = TimeRange.parse_timerange(config.get('timerange'))
data = load_data(
datadir=config.get('datadir'),
pairs=pairs,
timeframe=config['timeframe'],
timerange=timerange,
startup_candles=startup_candles,
data_format=config.get('dataformat_ohlcv', 'json'),
)
if startup_candles and data:
min_date, max_date = get_timerange(data)
logger.info(f"Loading data from {min_date} to {max_date}")
timerange.adjust_start_if_necessary(
timeframe_to_seconds(config['timeframe']), startup_candles,
min_date)
no_trades = False
filename = config.get('exportfilename')
if config.get('no_trades', False):
no_trades = True
elif config['trade_source'] == 'file':
if not filename.is_dir() and not filename.is_file():
logger.warning("Backtest file is missing skipping trades.")
no_trades = True
try:
trades = load_trades(
config['trade_source'],
db_url=config.get('db_url'),
exportfilename=filename,
no_trades=no_trades,
strategy=config.get('strategy'),
)
except ValueError as e:
raise OperationalException(e) from e
if not trades.empty:
trades = trim_dataframe(trades, timerange, 'open_date')
return {
"ohlcv": data,
"trades": trades,
"pairs": pairs,
"timerange": timerange,
}
def load_pair_history(
pair: str,
timeframe: str,
datadir: Path,
timerange: Optional[TimeRange] = None,
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
) -> DataFrame:
"""
Loads cached ticker history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param timerange: Limit data to be loaded to this timerange
:param refresh_pairs: Refresh pairs from exchange.
(Note: Requires exchange to be passed as well.)
:param exchange: Exchange object (needed when using "refresh_pairs")
: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
"""
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(
timeframe_to_seconds(timeframe) * startup_candles)
# The user forced the refresh of pairs
if refresh_pairs:
download_pair_history(datadir=datadir,
exchange=exchange,
pair=pair,
timeframe=timeframe,
timerange=timerange)
pairdata = load_tickerdata_file(datadir,
pair,
timeframe,
timerange=timerange_startup)
if pairdata:
if timerange_startup:
_validate_pairdata(pair, pairdata, timerange_startup)
return parse_ticker_dataframe(pairdata,
timeframe,
pair=pair,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data')
return None
def get_signal(
self,
pair: str,
timeframe: str,
dataframe: DataFrame
) -> Tuple[bool, bool, Optional[str], Optional[str]]:
"""
Calculates current signal based based on the buy / sell columns of the dataframe.
Used by Bot to get the signal to buy or sell
:param pair: pair in format ANT/BTC
:param timeframe: timeframe to use
:param dataframe: Analyzed dataframe to get signal from.
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
return False, False, None, None
latest_date = dataframe['date'].max()
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
# Explicitly convert to arrow object to ensure the below comparison does not fail
latest_date = arrow.get(latest_date)
# Check if dataframe is out of date
timeframe_minutes = timeframe_to_minutes(timeframe)
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
)
return False, False, None, None
buy = latest[SignalType.BUY.value] == 1
sell = False
if SignalType.SELL.value in latest:
sell = latest[SignalType.SELL.value] == 1
buy_tag = latest.get(SignalTagType.BUY_TAG.value, None)
exit_tag = latest.get(SignalTagType.EXIT_TAG.value, None)
# Tags can be None, which does not resolve to False.
buy_tag = buy_tag if isinstance(buy_tag, str) else None
exit_tag = exit_tag if isinstance(exit_tag, str) else None
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'], pair, str(buy), str(sell))
timeframe_seconds = timeframe_to_seconds(timeframe)
if self.ignore_expired_candle(latest_date=latest_date,
current_time=datetime.now(timezone.utc),
timeframe_seconds=timeframe_seconds,
buy=buy):
return False, sell, buy_tag, exit_tag
return buy, sell, buy_tag, exit_tag
def ohlcv_load(self,
pair,
timeframe: str,
timerange: Optional[TimeRange] = None,
fill_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
warn_no_data: bool = True) -> DataFrame:
"""
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_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
:param warn_no_data: Log a warning message when no data is found
: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)
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data')
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
# incomplete candles should only be dropped if we didn't trim the end beforehand.
return clean_ohlcv_dataframe(
pairdf,
timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete
and enddate == pairdf.iloc[-1]['date']))
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historical candle (OHLCV) data
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
"""
saved_pair = (pair, str(timeframe))
if saved_pair not in self.__cached_pairs_backtesting:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))
# Move informative start time respecting startup_candle_count
timerange.subtract_start(
timeframe_to_seconds(str(timeframe)) *
self._config.get('startup_candle_count', 0))
self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
pair=pair,
timeframe=timeframe or self._config['timeframe'],
datadir=self._config['datadir'],
timerange=timerange,
data_format=self._config.get('dataformat_ohlcv', 'json'))
return self.__cached_pairs_backtesting[saved_pair].copy()
def load_pair_history(pair: str,
timeframe: str,
datadir: Path,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
) -> DataFrame:
"""
Load cached ticker history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
: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
"""
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
if pairdata:
if timerange_startup:
_validate_pairdata(pair, pairdata, timerange_startup)
return parse_ticker_dataframe(pairdata, timeframe, pair=pair,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return DataFrame()
def load_bt_data(self):
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=Path(self.config['datadir']),
pairs=self.config['exchange']['pair_whitelist'],
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
)
min_date, max_date = history.get_timeframe(data)
logger.info('Loading data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(
timeframe_to_seconds(self.timeframe), self.required_startup,
min_date)
return data, timerange
def define_index(min_date: int, max_date: int, ticker_interval: str) -> int:
"""
Return the index of a specific date
"""
interval_seconds = timeframe_to_seconds(ticker_interval)
return int((max_date - min_date) / interval_seconds)