from math import isclose import numpy as np import pandas as pd import pytest from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import CandleType from freqtrade.resolvers.strategy_resolver import StrategyResolver from freqtrade.strategy import (merge_informative_pair, stoploss_from_absolute, stoploss_from_open, timeframe_to_minutes) from tests.conftest import get_patched_exchange def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'): np.random.seed(42) tf_mins = timeframe_to_minutes(timeframe) base = np.random.normal(20, 2, size=size) date = pd.date_range(start, periods=size, freq=f'{tf_mins}min', tz='UTC') df = pd.DataFrame({ 'date': date, 'open': base, 'high': base + np.random.normal(2, 1, size=size), 'low': base - np.random.normal(2, 1, size=size), 'close': base + np.random.normal(0, 1, size=size), 'volume': np.random.normal(200, size=size) } ) df = df.dropna() return df def test_merge_informative_pair(): data = generate_test_data('15m', 40) informative = generate_test_data('1h', 40) result = merge_informative_pair(data, informative, '15m', '1h', ffill=True) assert isinstance(result, pd.DataFrame) assert len(result) == len(data) assert 'date' in result.columns assert result['date'].equals(data['date']) assert 'date_1h' in result.columns assert 'open' in result.columns assert 'open_1h' in result.columns assert result['open'].equals(data['open']) assert 'close' in result.columns assert 'close_1h' in result.columns assert result['close'].equals(data['close']) assert 'volume' in result.columns assert 'volume_1h' in result.columns assert result['volume'].equals(data['volume']) # First 3 rows are empty assert result.iloc[0]['date_1h'] is pd.NaT assert result.iloc[1]['date_1h'] is pd.NaT assert result.iloc[2]['date_1h'] is pd.NaT # Next 4 rows contain the starting date (0:00) assert result.iloc[3]['date_1h'] == result.iloc[0]['date'] assert result.iloc[4]['date_1h'] == result.iloc[0]['date'] assert result.iloc[5]['date_1h'] == result.iloc[0]['date'] assert result.iloc[6]['date_1h'] == result.iloc[0]['date'] # Next 4 rows contain the next Hourly date original date row 4 assert result.iloc[7]['date_1h'] == result.iloc[4]['date'] assert result.iloc[8]['date_1h'] == result.iloc[4]['date'] def test_merge_informative_pair_same(): data = generate_test_data('15m', 40) informative = generate_test_data('15m', 40) result = merge_informative_pair(data, informative, '15m', '15m', ffill=True) assert isinstance(result, pd.DataFrame) assert len(result) == len(data) assert 'date' in result.columns assert result['date'].equals(data['date']) assert 'date_15m' in result.columns assert 'open' in result.columns assert 'open_15m' in result.columns assert result['open'].equals(data['open']) assert 'close' in result.columns assert 'close_15m' in result.columns assert result['close'].equals(data['close']) assert 'volume' in result.columns assert 'volume_15m' in result.columns assert result['volume'].equals(data['volume']) # Dates match 1:1 assert result['date_15m'].equals(result['date']) def test_merge_informative_pair_lower(): data = generate_test_data('1h', 40) informative = generate_test_data('15m', 40) with pytest.raises(ValueError, match=r"Tried to merge a faster timeframe .*"): merge_informative_pair(data, informative, '1h', '15m', ffill=True) def test_stoploss_from_open(): open_price_ranges = [ [0.01, 1.00, 30], [1, 100, 30], [100, 10000, 30], ] # profit range for long is [-1, inf] while for shorts is [-inf, 1] current_profit_range_dict = {'long': [-0.99, 2, 30], 'short': [-2.0, 0.99, 30]} desired_stop_range = [-0.50, 0.50, 30] for side, current_profit_range in current_profit_range_dict.items(): for open_range in open_price_ranges: for open_price in np.linspace(*open_range): for desired_stop in np.linspace(*desired_stop_range): if side == 'long': # -1 is not a valid current_profit, should return 1 assert stoploss_from_open(desired_stop, -1) == 1 else: # 1 is not a valid current_profit for shorts, should return 1 assert stoploss_from_open(desired_stop, 1, True) == 1 for current_profit in np.linspace(*current_profit_range): if side == 'long': current_price = open_price * (1 + current_profit) expected_stop_price = open_price * (1 + desired_stop) stoploss = stoploss_from_open(desired_stop, current_profit) stop_price = current_price * (1 - stoploss) else: current_price = open_price * (1 - current_profit) expected_stop_price = open_price * (1 - desired_stop) stoploss = stoploss_from_open(desired_stop, current_profit, True) stop_price = current_price * (1 + stoploss) assert stoploss >= 0 # Technically the formula can yield values greater than 1 for shorts # eventhough it doesn't make sense because the position would be liquidated if side == 'long': assert stoploss <= 1 # there is no correct answer if the expected stop price is above # the current price if ((side == 'long' and expected_stop_price > current_price) or (side == 'short' and expected_stop_price < current_price)): assert stoploss == 0 else: assert isclose(stop_price, expected_stop_price, rel_tol=0.00001) def test_stoploss_from_absolute(): assert pytest.approx(stoploss_from_absolute(90, 100)) == 1 - (90 / 100) assert pytest.approx(stoploss_from_absolute(90, 100)) == 0.1 assert pytest.approx(stoploss_from_absolute(95, 100)) == 0.05 assert pytest.approx(stoploss_from_absolute(100, 100)) == 0 assert pytest.approx(stoploss_from_absolute(110, 100)) == 0 assert pytest.approx(stoploss_from_absolute(100, 0)) == 1 assert pytest.approx(stoploss_from_absolute(0, 100)) == 1 assert pytest.approx(stoploss_from_absolute(90, 100, True)) == 0 assert pytest.approx(stoploss_from_absolute(100, 100, True)) == 0 assert pytest.approx(stoploss_from_absolute(110, 100, True)) == -(1 - (110 / 100)) assert pytest.approx(stoploss_from_absolute(110, 100, True)) == 0.1 assert pytest.approx(stoploss_from_absolute(105, 100, True)) == 0.05 assert pytest.approx(stoploss_from_absolute(100, 0, True)) == 1 assert pytest.approx(stoploss_from_absolute(0, 100, True)) == 0 assert pytest.approx(stoploss_from_absolute(100, 1, True)) == 1 @pytest.mark.parametrize('trading_mode', ['futures', 'spot']) def test_informative_decorator(mocker, default_conf_usdt, trading_mode): candle_def = CandleType.get_default(trading_mode) default_conf_usdt['candle_type_def'] = candle_def test_data_5m = generate_test_data('5m', 40) test_data_30m = generate_test_data('30m', 40) test_data_1h = generate_test_data('1h', 40) data = { ('XRP/USDT', '5m', candle_def): test_data_5m, ('XRP/USDT', '30m', candle_def): test_data_30m, ('XRP/USDT', '1h', candle_def): test_data_1h, ('LTC/USDT', '5m', candle_def): test_data_5m, ('LTC/USDT', '30m', candle_def): test_data_30m, ('LTC/USDT', '1h', candle_def): test_data_1h, ('NEO/USDT', '30m', candle_def): test_data_30m, ('NEO/USDT', '5m', CandleType.SPOT): test_data_5m, # Explicit request with '' as candletype ('NEO/USDT', '15m', candle_def): test_data_5m, # Explicit request with '' as candletype ('NEO/USDT', '1h', candle_def): test_data_1h, ('ETH/USDT', '1h', candle_def): test_data_1h, ('ETH/USDT', '30m', candle_def): test_data_30m, ('ETH/BTC', '1h', CandleType.SPOT): test_data_1h, # Explicitly selected as spot } default_conf_usdt['strategy'] = 'InformativeDecoratorTest' strategy = StrategyResolver.load_strategy(default_conf_usdt) exchange = get_patched_exchange(mocker, default_conf_usdt) strategy.dp = DataProvider({}, exchange, None) mocker.patch.object(strategy.dp, 'current_whitelist', return_value=[ 'XRP/USDT', 'LTC/USDT', 'NEO/USDT' ]) assert len(strategy._ft_informative) == 6 # Equal to number of decorators used informative_pairs = [ ('XRP/USDT', '1h', candle_def), ('LTC/USDT', '1h', candle_def), ('XRP/USDT', '30m', candle_def), ('LTC/USDT', '30m', candle_def), ('NEO/USDT', '1h', candle_def), ('NEO/USDT', '30m', candle_def), ('NEO/USDT', '5m', candle_def), ('NEO/USDT', '15m', candle_def), ('NEO/USDT', '2h', CandleType.FUTURES), ('ETH/BTC', '1h', CandleType.SPOT), # One candle remains as spot ('ETH/USDT', '30m', candle_def)] for inf_pair in informative_pairs: assert inf_pair in strategy.gather_informative_pairs() def test_historic_ohlcv(pair, timeframe, candle_type): return data[ (pair, timeframe or strategy.timeframe, CandleType.from_string(candle_type))].copy() mocker.patch('freqtrade.data.dataprovider.DataProvider.historic_ohlcv', side_effect=test_historic_ohlcv) analyzed = strategy.advise_all_indicators( {p: data[(p, strategy.timeframe, candle_def)] for p in ('XRP/USDT', 'LTC/USDT')}) expected_columns = [ 'rsi_1h', 'rsi_30m', # Stacked informative decorators 'neo_usdt_rsi_1h', # NEO 1h informative 'rsi_NEO_USDT_neo_usdt_NEO/USDT_30m', # Column formatting 'rsi_from_callable', # Custom column formatter 'eth_btc_rsi_1h', # Quote currency not matching stake currency 'rsi', 'rsi_less', # Non-informative columns 'rsi_5m', # Manual informative dataframe ] for _, dataframe in analyzed.items(): for col in expected_columns: assert col in dataframe.columns