stable/tests/strategy/test_strategy_helpers.py

190 lines
7.2 KiB
Python

from math import isclose
import numpy as np
import pandas as pd
import pytest
from freqtrade.data.dataprovider import DataProvider
from freqtrade.strategy import merge_informative_pair, stoploss_from_open, timeframe_to_minutes
def generate_test_data(timeframe: str, size: int):
np.random.seed(42)
tf_mins = timeframe_to_minutes(timeframe)
base = np.random.normal(20, 2, size=size)
date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
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],
]
current_profit_range = [-0.99, 2, 30]
desired_stop_range = [-0.50, 0.50, 30]
for open_range in open_price_ranges:
for open_price in np.linspace(*open_range):
for desired_stop in np.linspace(*desired_stop_range):
# -1 is not a valid current_profit, should return 1
assert stoploss_from_open(desired_stop, -1) == 1
for current_profit in np.linspace(*current_profit_range):
current_price = open_price * (1 + current_profit)
expected_stop_price = open_price * (1 + desired_stop)
stoploss = stoploss_from_open(desired_stop, current_profit)
assert stoploss >= 0
assert stoploss <= 1
stop_price = current_price * (1 - stoploss)
# there is no correct answer if the expected stop price is above
# the current price
if expected_stop_price > current_price:
assert stoploss == 0
else:
assert isclose(stop_price, expected_stop_price, rel_tol=0.00001)
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'): test_data_5m,
('XRP/USDT', '30m'): test_data_30m,
('XRP/USDT', '1h'): test_data_1h,
('LTC/USDT', '5m'): test_data_5m,
('LTC/USDT', '30m'): test_data_30m,
('LTC/USDT', '1h'): test_data_1h,
('BTC/USDT', '30m'): test_data_30m,
('BTC/USDT', '5m'): test_data_5m,
('BTC/USDT', '1h'): test_data_1h,
('ETH/USDT', '1h'): test_data_1h,
('ETH/USDT', '30m'): test_data_30m,
('ETH/BTC', '1h'): test_data_1h,
}
from .strats.informative_decorator_strategy import InformativeDecoratorTest
default_conf['stake_currency'] = 'USDT'
strategy = InformativeDecoratorTest(config=default_conf)
strategy.dp = DataProvider({}, None, None)
mocker.patch.object(strategy.dp, 'current_whitelist', return_value=[
'XRP/USDT', 'LTC/USDT', 'BTC/USDT'
])
assert len(strategy._ft_informative) == 6 # Equal to number of decorators used
informative_pairs = [('XRP/USDT', '1h'), ('LTC/USDT', '1h'), ('XRP/USDT', '30m'),
('LTC/USDT', '30m'), ('BTC/USDT', '1h'), ('BTC/USDT', '30m'),
('BTC/USDT', '5m'), ('ETH/BTC', '1h'), ('ETH/USDT', '30m')]
for inf_pair in informative_pairs:
assert inf_pair in strategy.gather_informative_pairs()
def test_historic_ohlcv(pair, timeframe):
return data[(pair, timeframe or strategy.timeframe)].copy()
mocker.patch('freqtrade.data.dataprovider.DataProvider.historic_ohlcv',
side_effect=test_historic_ohlcv)
analyzed = strategy.advise_all_indicators(
{p: data[(p, strategy.timeframe)] for p in ('XRP/USDT', 'LTC/USDT')})
expected_columns = [
'rsi_1h', 'rsi_30m', # Stacked informative decorators
'btc_rsi_1h', # BTC 1h informative
'rsi_BTC_USDT_btc_usdt_BTC/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