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
from tests.conftest import generate_test_data, get_patched_exchange
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']
informative = generate_test_data('1h', 40)
result = merge_informative_pair(data, informative, '15m', '1h', ffill=False)
# 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'] is pd.NaT
assert result.iloc[5]['date_1h'] is pd.NaT
assert result.iloc[6]['date_1h'] is pd.NaT
# 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'] is pd.NaT
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_merge_informative_pair_suffix():
data = generate_test_data('15m', 20)
informative = generate_test_data('1h', 20)
result = merge_informative_pair(data, informative, '15m', '1h',
append_timeframe=False, suffix="suf")
assert 'date' in result.columns
assert result['date'].equals(data['date'])
assert 'date_suf' in result.columns
assert 'open_suf' in result.columns
assert 'open_1h' not in result.columns
def test_merge_informative_pair_suffix_append_timeframe():
data = generate_test_data('15m', 20)
informative = generate_test_data('1h', 20)
with pytest.raises(ValueError, match=r"You can not specify `append_timeframe` .*"):
merge_informative_pair(data, informative, '15m', '1h', suffix="suf")
@pytest.mark.parametrize("side,profitrange", [
# profit range for long is [-1, inf] while for shorts is [-inf, 1]
("long", [-0.99, 2, 30]),
("short", [-2.0, 0.99, 30]),
])
def test_stoploss_from_open(side, profitrange):
open_price_ranges = [
[0.01, 1.00, 30],
[1, 100, 30],
[100, 10000, 30],
]
for open_range in open_price_ranges:
for open_price in np.linspace(*open_range):
for desired_stop in np.linspace(-0.50, 0.50, 30):
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(*profitrange):
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 pytest.approx(stop_price) == expected_stop_price
@pytest.mark.parametrize("side,rel_stop,curr_profit,leverage,expected", [
# profit range for long is [-1, inf] while for shorts is [-inf, 1]
("long", 0, -1, 1, 1),
("long", 0, 0.1, 1, 0.09090909),
("long", -0.1, 0.1, 1, 0.18181818),
("long", 0.1, 0.2, 1, 0.08333333),
("long", 0.1, 0.5, 1, 0.266666666),
("long", 0.1, 5, 1, 0.816666666), # 500% profit, set stoploss to 10% above open price
("long", 0, 5, 10, 3.3333333), # 500% profit, set stoploss break even
("long", 0.1, 5, 10, 3.26666666), # 500% profit, set stoploss to 10% above open price
("long", -0.1, 5, 10, 3.3999999), # 500% profit, set stoploss to 10% belowopen price
("short", 0, 0.1, 1, 0.1111111),
("short", -0.1, 0.1, 1, 0.2222222),
("short", 0.1, 0.2, 1, 0.125),
("short", 0.1, 1, 1, 1),
("short", -0.01, 5, 10, 10.01999999), # 500% profit at 10x
])
def test_stoploss_from_open_leverage(side, rel_stop, curr_profit, leverage, expected):
stoploss = stoploss_from_open(rel_stop, curr_profit, side == 'short', leverage)
assert pytest.approx(stoploss) == expected
open_rate = 100
if stoploss != 1:
if side == 'long':
current_rate = open_rate * (1 + curr_profit / leverage)
stop = current_rate * (1 - stoploss / leverage)
assert pytest.approx(stop) == open_rate * (1 + rel_stop / leverage)
else:
current_rate = open_rate * (1 - curr_profit / leverage)
stop = current_rate * (1 + stoploss / leverage)
assert pytest.approx(stop) == open_rate * (1 - rel_stop / leverage)
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