Informative decorator updates for futures
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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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