Use market data to get base and quote currencies in @informative() decorator.
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@ -67,7 +67,7 @@ class Backtesting:
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self.all_results: Dict[str, Dict] = {}
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self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
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self.dataprovider = DataProvider(self.config, None)
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self.dataprovider = DataProvider(self.config, self.exchange)
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if self.config.get('strategy_list', None):
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for strat in list(self.config['strategy_list']):
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@ -80,12 +80,11 @@ def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata:
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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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if '/' in asset:
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base, quote = asset.split('/')
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else:
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# When futures are supported this may need reevaluation.
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# base, quote = asset, ''
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raise OperationalException('Not implemented.')
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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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@ -20,7 +20,7 @@ class InformativeDecoratorTest(IStrategy):
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startup_candle_count: int = 20
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def informative_pairs(self):
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return [('BTC/USDT', '5m')]
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return [('NEO/USDT', '5m')]
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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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@ -38,8 +38,8 @@ class InformativeDecoratorTest(IStrategy):
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return dataframe
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# Simple informative test.
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@informative('1h', 'BTC/{stake}')
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def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@informative('1h', 'NEO/{stake}')
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def populate_indicators_neo_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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@ -50,7 +50,7 @@ class InformativeDecoratorTest(IStrategy):
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return dataframe
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# Formatting test.
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@informative('30m', 'BTC/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}')
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@informative('30m', 'NEO/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}')
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def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['rsi'] = 14
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return dataframe
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@ -68,7 +68,7 @@ class InformativeDecoratorTest(IStrategy):
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dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
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# Mixing manual informative pairs with decorators.
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informative = self.dp.get_pair_dataframe('BTC/USDT', '5m')
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informative = self.dp.get_pair_dataframe('NEO/USDT', '5m')
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informative['rsi'] = 14
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '5m', ffill=True)
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@ -7,6 +7,7 @@ import pytest
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.strategy import (merge_informative_pair, stoploss_from_absolute, stoploss_from_open,
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timeframe_to_minutes)
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from tests.conftest import get_patched_exchange
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def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
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@ -155,9 +156,9 @@ def test_informative_decorator(mocker, default_conf):
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('LTC/USDT', '5m'): test_data_5m,
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('LTC/USDT', '30m'): test_data_30m,
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('LTC/USDT', '1h'): test_data_1h,
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('BTC/USDT', '30m'): test_data_30m,
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('BTC/USDT', '5m'): test_data_5m,
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('BTC/USDT', '1h'): test_data_1h,
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('NEO/USDT', '30m'): test_data_30m,
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('NEO/USDT', '5m'): test_data_5m,
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('NEO/USDT', '1h'): test_data_1h,
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('ETH/USDT', '1h'): test_data_1h,
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('ETH/USDT', '30m'): test_data_30m,
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('ETH/BTC', '1h'): test_data_1h,
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@ -165,15 +166,16 @@ def test_informative_decorator(mocker, default_conf):
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from .strats.informative_decorator_strategy import InformativeDecoratorTest
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default_conf['stake_currency'] = 'USDT'
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strategy = InformativeDecoratorTest(config=default_conf)
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strategy.dp = DataProvider({}, None, None)
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exchange = get_patched_exchange(mocker, default_conf)
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strategy.dp = DataProvider({}, exchange, None)
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mocker.patch.object(strategy.dp, 'current_whitelist', return_value=[
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'XRP/USDT', 'LTC/USDT', 'BTC/USDT'
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'XRP/USDT', 'LTC/USDT', 'NEO/USDT'
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])
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assert len(strategy._ft_informative) == 6 # Equal to number of decorators used
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informative_pairs = [('XRP/USDT', '1h'), ('LTC/USDT', '1h'), ('XRP/USDT', '30m'),
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('LTC/USDT', '30m'), ('BTC/USDT', '1h'), ('BTC/USDT', '30m'),
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('BTC/USDT', '5m'), ('ETH/BTC', '1h'), ('ETH/USDT', '30m')]
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('LTC/USDT', '30m'), ('NEO/USDT', '1h'), ('NEO/USDT', '30m'),
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('NEO/USDT', '5m'), ('ETH/BTC', '1h'), ('ETH/USDT', '30m')]
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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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@ -186,8 +188,8 @@ def test_informative_decorator(mocker, default_conf):
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{p: data[(p, strategy.timeframe)] 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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'btc_usdt_rsi_1h', # BTC 1h informative
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'rsi_BTC_USDT_btc_usdt_BTC/USDT_30m', # Column formatting
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'neo_usdt_rsi_1h', # NEO 1h informative
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'rsi_NEO_USDT_neo_usdt_NEO/USDT_30m', # Column formatting
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'rsi_from_callable', # Custom column formatter
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'eth_btc_rsi_1h', # Quote currency not matching stake currency
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'rsi', 'rsi_less', # Non-informative columns
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