2022-01-29 18:59:54 +00:00
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from dataclasses import dataclass
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from typing import Any, Callable, Optional, Union
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from pandas import DataFrame
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2021-12-03 13:11:24 +00:00
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from freqtrade.enums import CandleType
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from freqtrade.exceptions import OperationalException
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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PopulateIndicators = Callable[[Any, DataFrame, dict], DataFrame]
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2022-01-29 18:59:54 +00:00
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@dataclass
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class InformativeData:
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asset: Optional[str]
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timeframe: str
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fmt: Union[str, Callable[[Any], str], None]
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ffill: bool
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candle_type: Optional[CandleType]
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def informative(timeframe: str, asset: str = '',
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fmt: Optional[Union[str, Callable[[Any], str]]] = None,
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*,
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candle_type: Optional[CandleType] = None,
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ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
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"""
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A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
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define informative indicators.
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Example usage:
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@informative('1h')
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def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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return dataframe
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:param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
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:param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
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current pair.
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:param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
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specified, defaults to:
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* {base}_{quote}_{column}_{timeframe} if asset is specified.
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* {column}_{timeframe} if asset is not specified.
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Format string supports these format variables:
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* {asset} - full name of the asset, for example 'BTC/USDT'.
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* {base} - base currency in lower case, for example 'eth'.
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* {BASE} - same as {base}, except in upper case.
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* {quote} - quote currency in lower case, for example 'usdt'.
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* {QUOTE} - same as {quote}, except in upper case.
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* {column} - name of dataframe column.
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* {timeframe} - timeframe of informative dataframe.
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:param ffill: ffill dataframe after merging informative pair.
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:param candle_type: '', mark, index, premiumIndex, or funding_rate
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"""
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_asset = asset
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_timeframe = timeframe
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_fmt = fmt
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_ffill = ffill
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_candle_type = CandleType.from_string(candle_type) if candle_type else None
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def decorator(fn: PopulateIndicators):
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informative_pairs = getattr(fn, '_ft_informative', [])
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informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill, _candle_type))
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setattr(fn, '_ft_informative', informative_pairs)
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return fn
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return decorator
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def _format_pair_name(config, pair: str) -> str:
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return pair.format(stake_currency=config['stake_currency'],
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stake=config['stake_currency']).upper()
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def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata: dict,
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inf_data: InformativeData,
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populate_indicators: PopulateIndicators):
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asset = inf_data.asset or ''
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timeframe = inf_data.timeframe
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fmt = inf_data.fmt
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candle_type = inf_data.candle_type
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config = strategy.config
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if asset:
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# Insert stake currency if needed.
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asset = _format_pair_name(config, asset)
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else:
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# Not specifying an asset will define informative dataframe for current pair.
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asset = metadata['pair']
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market = strategy.dp.market(asset)
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if market is None:
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raise OperationalException(f'Market {asset} is not available.')
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base = market['base']
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quote = market['quote']
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# Default format. This optimizes for the common case: informative pairs using same stake
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# currency. When quote currency matches stake currency, column name will omit base currency.
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# This allows easily reconfiguring strategy to use different base currency. In a rare case
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# where it is desired to keep quote currency in column name at all times user should specify
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# fmt='{base}_{quote}_{column}_{timeframe}' format or similar.
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if not fmt:
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fmt = '{column}_{timeframe}' # Informatives of current pair
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if inf_data.asset:
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fmt = '{base}_{quote}_' + fmt # Informatives of other pairs
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inf_metadata = {'pair': asset, 'timeframe': timeframe}
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inf_dataframe = strategy.dp.get_pair_dataframe(asset, timeframe, candle_type)
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inf_dataframe = populate_indicators(strategy, inf_dataframe, inf_metadata)
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formatter: Any = None
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if callable(fmt):
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formatter = fmt # A custom user-specified formatter function.
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else:
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formatter = fmt.format # A default string formatter.
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fmt_args = {
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'BASE': base.upper(),
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'QUOTE': quote.upper(),
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'base': base.lower(),
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'quote': quote.lower(),
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'asset': asset,
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'timeframe': timeframe,
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}
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inf_dataframe.rename(columns=lambda column: formatter(column=column, **fmt_args),
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inplace=True)
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date_column = formatter(column='date', **fmt_args)
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if date_column in dataframe.columns:
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raise OperationalException(f'Duplicate column name {date_column} exists in '
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f'dataframe! Ensure column names are unique!')
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dataframe = merge_informative_pair(dataframe, inf_dataframe, strategy.timeframe, timeframe,
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ffill=inf_data.ffill, append_timeframe=False,
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date_column=date_column)
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return dataframe
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