stable/tests/strategy/test_strategy_helpers.py
2022-01-25 06:44:20 +01:00

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