236 lines
10 KiB
Python
236 lines
10 KiB
Python
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.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
|
|
|
|
|
|
# TODO-lev: @pytest.mark.parametrize('candle_type', ['mark', ''])
|
|
def test_informative_decorator(mocker, default_conf):
|
|
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', CandleType.SPOT): test_data_5m,
|
|
('XRP/USDT', '30m', CandleType.SPOT): test_data_30m,
|
|
('XRP/USDT', '1h', CandleType.SPOT): test_data_1h,
|
|
('LTC/USDT', '5m', CandleType.SPOT): test_data_5m,
|
|
('LTC/USDT', '30m', CandleType.SPOT): test_data_30m,
|
|
('LTC/USDT', '1h', CandleType.SPOT): test_data_1h,
|
|
('NEO/USDT', '30m', CandleType.SPOT): test_data_30m,
|
|
('NEO/USDT', '5m', CandleType.SPOT): test_data_5m,
|
|
('NEO/USDT', '1h', CandleType.SPOT): test_data_1h,
|
|
('ETH/USDT', '1h', CandleType.SPOT): test_data_1h,
|
|
('ETH/USDT', '30m', CandleType.SPOT): test_data_30m,
|
|
('ETH/BTC', '1h', CandleType.SPOT): test_data_1h,
|
|
}
|
|
from .strats.informative_decorator_strategy import InformativeDecoratorTest
|
|
default_conf['stake_currency'] = 'USDT'
|
|
strategy = InformativeDecoratorTest(config=default_conf)
|
|
exchange = get_patched_exchange(mocker, default_conf)
|
|
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', CandleType.SPOT),
|
|
('LTC/USDT', '1h', CandleType.SPOT),
|
|
('XRP/USDT', '30m', CandleType.SPOT),
|
|
('LTC/USDT', '30m', CandleType.SPOT),
|
|
('NEO/USDT', '1h', CandleType.SPOT),
|
|
('NEO/USDT', '30m', CandleType.SPOT),
|
|
('NEO/USDT', '5m', CandleType.SPOT),
|
|
('ETH/BTC', '1h', CandleType.SPOT),
|
|
('ETH/USDT', '30m', CandleType.SPOT)]
|
|
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, CandleType.SPOT)] 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
|