Informative decorator updates for futures
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Matthias
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@ -15,11 +15,13 @@ class InformativeData(NamedTuple):
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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: CandleType
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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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@ -54,12 +56,11 @@ def informative(timeframe: str, asset: str = '',
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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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# TODO-lev: Add candle_type to InformativeData
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informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill,
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CandleType.SPOT))
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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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@ -76,6 +77,8 @@ def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata:
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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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@ -102,7 +105,7 @@ def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata:
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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)
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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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@ -146,7 +146,8 @@ class IStrategy(ABC, HyperStrategyMixin):
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cls_method = getattr(self.__class__, attr_name)
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if not callable(cls_method):
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continue
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informative_data_list = getattr(cls_method, '_ft_informative', None)
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informative_data_list = getattr(
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cls_method, '_ft_informative', None)
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if not isinstance(informative_data_list, list):
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# Type check is required because mocker would return a mock object that evaluates to
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# True, confusing this code.
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@ -156,6 +157,10 @@ class IStrategy(ABC, HyperStrategyMixin):
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if timeframe_to_minutes(informative_data.timeframe) < strategy_timeframe_minutes:
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raise OperationalException('Informative timeframe must be equal or higher than '
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'strategy timeframe!')
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if not informative_data.candle_type:
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informative_data = InformativeData(
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informative_data.asset, informative_data.timeframe, informative_data.fmt,
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informative_data.ffill, config['candle_type_def'])
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self._ft_informative.append((informative_data, cls_method))
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@abstractmethod
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@ -456,14 +461,17 @@ class IStrategy(ABC, HyperStrategyMixin):
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# Compatibility code for 2 tuple informative pairs
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informative_pairs = [
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(p[0], p[1], CandleType.from_string(p[2]) if len(
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p) > 2 else self.config.get('candle_type_def', CandleType.SPOT))
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p) > 2 and p[2] != '' else self.config.get('candle_type_def', CandleType.SPOT))
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for p in informative_pairs]
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for inf_data, _ in self._ft_informative:
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# Get default candle type if not provided explicitly.
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candle_type = (inf_data.candle_type if inf_data.candle_type
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else self.config.get('candle_type_def', CandleType.SPOT))
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if inf_data.asset:
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pair_tf = (
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_format_pair_name(self.config, inf_data.asset),
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inf_data.timeframe,
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inf_data.candle_type
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candle_type,
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)
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informative_pairs.append(pair_tf)
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else:
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@ -471,7 +479,7 @@ class IStrategy(ABC, HyperStrategyMixin):
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raise OperationalException('@informative decorator with unspecified asset '
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'requires DataProvider instance.')
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for pair in self.dp.current_whitelist():
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informative_pairs.append((pair, inf_data.timeframe, inf_data.candle_type))
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informative_pairs.append((pair, inf_data.timeframe, candle_type))
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return list(set(informative_pairs))
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def get_strategy_name(self) -> str:
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@ -20,7 +20,11 @@ class InformativeDecoratorTest(IStrategy):
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def informative_pairs(self):
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# Intentionally return 2 tuples, must be converted to 3 in compatibility code
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return [('NEO/USDT', '5m')]
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return [
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('NEO/USDT', '5m'),
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('NEO/USDT', '15m', ''),
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('NEO/USDT', '2h', 'futures'),
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]
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['buy'] = 0
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@ -44,7 +48,7 @@ class InformativeDecoratorTest(IStrategy):
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return dataframe
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# Quote currency different from stake currency test.
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@informative('1h', 'ETH/BTC')
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@informative('1h', 'ETH/BTC', candle_type='spot')
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def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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@ -171,24 +171,27 @@ def test_stoploss_from_absolute():
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assert pytest.approx(stoploss_from_absolute(100, 1, True)) == 1
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# TODO-lev: @pytest.mark.parametrize('candle_type', ['mark', ''])
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def test_informative_decorator(mocker, default_conf):
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@pytest.mark.parametrize('trading_mode', ['futures', 'spot'])
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def test_informative_decorator(mocker, default_conf, trading_mode):
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candle_def = CandleType.get_default(trading_mode)
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default_conf['candle_type_def'] = candle_def
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test_data_5m = generate_test_data('5m', 40)
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test_data_30m = generate_test_data('30m', 40)
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test_data_1h = generate_test_data('1h', 40)
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data = {
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('XRP/USDT', '5m', CandleType.SPOT): test_data_5m,
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('XRP/USDT', '30m', CandleType.SPOT): test_data_30m,
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('XRP/USDT', '1h', CandleType.SPOT): test_data_1h,
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('LTC/USDT', '5m', CandleType.SPOT): test_data_5m,
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('LTC/USDT', '30m', CandleType.SPOT): test_data_30m,
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('LTC/USDT', '1h', CandleType.SPOT): test_data_1h,
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('NEO/USDT', '30m', CandleType.SPOT): test_data_30m,
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('NEO/USDT', '5m', CandleType.SPOT): test_data_5m,
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('NEO/USDT', '1h', CandleType.SPOT): test_data_1h,
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('ETH/USDT', '1h', CandleType.SPOT): test_data_1h,
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('ETH/USDT', '30m', CandleType.SPOT): test_data_30m,
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('ETH/BTC', '1h', CandleType.SPOT): test_data_1h,
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('XRP/USDT', '5m', candle_def): test_data_5m,
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('XRP/USDT', '30m', candle_def): test_data_30m,
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('XRP/USDT', '1h', candle_def): test_data_1h,
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('LTC/USDT', '5m', candle_def): test_data_5m,
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('LTC/USDT', '30m', candle_def): test_data_30m,
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('LTC/USDT', '1h', candle_def): test_data_1h,
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('NEO/USDT', '30m', candle_def): test_data_30m,
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('NEO/USDT', '5m', CandleType.SPOT): test_data_5m, # Explicit request with '' as candletype
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('NEO/USDT', '15m', candle_def): test_data_5m, # Explicit request with '' as candletype
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('NEO/USDT', '1h', candle_def): test_data_1h,
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('ETH/USDT', '1h', candle_def): test_data_1h,
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('ETH/USDT', '30m', candle_def): test_data_30m,
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('ETH/BTC', '1h', CandleType.SPOT): test_data_1h, # Explicitly selected as spot
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}
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from .strats.informative_decorator_strategy import InformativeDecoratorTest
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default_conf['stake_currency'] = 'USDT'
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@ -201,26 +204,29 @@ def test_informative_decorator(mocker, default_conf):
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assert len(strategy._ft_informative) == 6 # Equal to number of decorators used
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informative_pairs = [
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('XRP/USDT', '1h', CandleType.SPOT),
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('LTC/USDT', '1h', CandleType.SPOT),
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('XRP/USDT', '30m', CandleType.SPOT),
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('LTC/USDT', '30m', CandleType.SPOT),
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('NEO/USDT', '1h', CandleType.SPOT),
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('NEO/USDT', '30m', CandleType.SPOT),
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('NEO/USDT', '5m', CandleType.SPOT),
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('ETH/BTC', '1h', CandleType.SPOT),
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('ETH/USDT', '30m', CandleType.SPOT)]
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('XRP/USDT', '1h', candle_def),
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('LTC/USDT', '1h', candle_def),
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('XRP/USDT', '30m', candle_def),
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('LTC/USDT', '30m', candle_def),
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('NEO/USDT', '1h', candle_def),
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('NEO/USDT', '30m', candle_def),
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('NEO/USDT', '5m', candle_def),
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('NEO/USDT', '15m', candle_def),
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('NEO/USDT', '2h', CandleType.FUTURES),
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('ETH/BTC', '1h', CandleType.SPOT), # One candle remains as spot
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('ETH/USDT', '30m', candle_def)]
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for inf_pair in informative_pairs:
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assert inf_pair in strategy.gather_informative_pairs()
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def test_historic_ohlcv(pair, timeframe, candle_type):
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return data[
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(pair, timeframe or strategy.timeframe, CandleType.from_string(candle_type))].copy()
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mocker.patch('freqtrade.data.dataprovider.DataProvider.historic_ohlcv',
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side_effect=test_historic_ohlcv)
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analyzed = strategy.advise_all_indicators(
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{p: data[(p, strategy.timeframe, CandleType.SPOT)] for p in ('XRP/USDT', 'LTC/USDT')})
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{p: data[(p, strategy.timeframe, candle_def)] for p in ('XRP/USDT', 'LTC/USDT')})
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expected_columns = [
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'rsi_1h', 'rsi_30m', # Stacked informative decorators
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'neo_usdt_rsi_1h', # NEO 1h informative
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