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https://github.com/freqtrade/freqtrade.git
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396 lines
17 KiB
Python
396 lines
17 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.enums import CandleType
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from freqtrade.resolvers.strategy_resolver import StrategyResolver
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from freqtrade.strategy import merge_informative_pair, stoploss_from_absolute, stoploss_from_open
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from tests.conftest import generate_test_data, get_patched_exchange
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def test_merge_informative_pair():
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data = generate_test_data('15m', 40)
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informative = generate_test_data('1h', 40)
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cols_inf = list(informative.columns)
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result = merge_informative_pair(data, informative, '15m', '1h', ffill=True)
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assert isinstance(result, pd.DataFrame)
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assert list(informative.columns) == cols_inf
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assert len(result) == len(data)
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assert 'date' in result.columns
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assert result['date'].equals(data['date'])
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assert 'date_1h' in result.columns
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assert 'open' in result.columns
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assert 'open_1h' in result.columns
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assert result['open'].equals(data['open'])
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assert 'close' in result.columns
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assert 'close_1h' in result.columns
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assert result['close'].equals(data['close'])
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assert 'volume' in result.columns
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assert 'volume_1h' in result.columns
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assert result['volume'].equals(data['volume'])
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# First 3 rows are empty
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assert result.iloc[0]['date_1h'] is pd.NaT
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assert result.iloc[1]['date_1h'] is pd.NaT
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assert result.iloc[2]['date_1h'] is pd.NaT
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# Next 4 rows contain the starting date (0:00)
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assert result.iloc[3]['date_1h'] == result.iloc[0]['date']
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assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
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assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
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assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
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# Next 4 rows contain the next Hourly date original date row 4
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assert result.iloc[7]['date_1h'] == result.iloc[4]['date']
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assert result.iloc[8]['date_1h'] == result.iloc[4]['date']
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informative = generate_test_data('1h', 40)
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result = merge_informative_pair(data, informative, '15m', '1h', ffill=False)
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# First 3 rows are empty
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assert result.iloc[0]['date_1h'] is pd.NaT
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assert result.iloc[1]['date_1h'] is pd.NaT
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assert result.iloc[2]['date_1h'] is pd.NaT
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# Next 4 rows contain the starting date (0:00)
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assert result.iloc[3]['date_1h'] == result.iloc[0]['date']
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assert result.iloc[4]['date_1h'] is pd.NaT
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assert result.iloc[5]['date_1h'] is pd.NaT
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assert result.iloc[6]['date_1h'] is pd.NaT
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# Next 4 rows contain the next Hourly date original date row 4
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assert result.iloc[7]['date_1h'] == result.iloc[4]['date']
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assert result.iloc[8]['date_1h'] is pd.NaT
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def test_merge_informative_pair_weekly():
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# Covers roughly 2 months - until 2023-01-10
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data = generate_test_data('1h', 1040, '2022-11-28')
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informative = generate_test_data('1w', 40, '2022-11-01')
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informative['day'] = informative['date'].dt.day_name()
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result = merge_informative_pair(data, informative, '1h', '1w', ffill=True)
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assert isinstance(result, pd.DataFrame)
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# 2022-12-24 is a Saturday
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candle1 = result.loc[(result['date'] == '2022-12-24T22:00:00.000Z')]
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assert candle1.iloc[0]['date'] == pd.Timestamp('2022-12-24T22:00:00.000Z')
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assert candle1.iloc[0]['date_1w'] == pd.Timestamp('2022-12-12T00:00:00.000Z')
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candle2 = result.loc[(result['date'] == '2022-12-24T23:00:00.000Z')]
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assert candle2.iloc[0]['date'] == pd.Timestamp('2022-12-24T23:00:00.000Z')
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assert candle2.iloc[0]['date_1w'] == pd.Timestamp('2022-12-12T00:00:00.000Z')
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# 2022-12-25 is a Sunday
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candle3 = result.loc[(result['date'] == '2022-12-25T22:00:00.000Z')]
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assert candle3.iloc[0]['date'] == pd.Timestamp('2022-12-25T22:00:00.000Z')
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# Still old candle
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assert candle3.iloc[0]['date_1w'] == pd.Timestamp('2022-12-12T00:00:00.000Z')
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candle4 = result.loc[(result['date'] == '2022-12-25T23:00:00.000Z')]
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assert candle4.iloc[0]['date'] == pd.Timestamp('2022-12-25T23:00:00.000Z')
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assert candle4.iloc[0]['date_1w'] == pd.Timestamp('2022-12-19T00:00:00.000Z')
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def test_merge_informative_pair_monthly():
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# Covers roughly 2 months - until 2023-01-10
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data = generate_test_data('1h', 1040, '2022-11-28')
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informative = generate_test_data('1M', 40, '2022-01-01')
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result = merge_informative_pair(data, informative, '1h', '1M', ffill=True)
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assert isinstance(result, pd.DataFrame)
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candle1 = result.loc[(result['date'] == '2022-12-31T22:00:00.000Z')]
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assert candle1.iloc[0]['date'] == pd.Timestamp('2022-12-31T22:00:00.000Z')
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assert candle1.iloc[0]['date_1M'] == pd.Timestamp('2022-11-01T00:00:00.000Z')
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candle2 = result.loc[(result['date'] == '2022-12-31T23:00:00.000Z')]
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assert candle2.iloc[0]['date'] == pd.Timestamp('2022-12-31T23:00:00.000Z')
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assert candle2.iloc[0]['date_1M'] == pd.Timestamp('2022-12-01T00:00:00.000Z')
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# Candle is empty, as the start-date did fail.
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candle3 = result.loc[(result['date'] == '2022-11-30T22:00:00.000Z')]
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assert candle3.iloc[0]['date'] == pd.Timestamp('2022-11-30T22:00:00.000Z')
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assert candle3.iloc[0]['date_1M'] is pd.NaT
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# First candle with 1M data merged.
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candle4 = result.loc[(result['date'] == '2022-11-30T23:00:00.000Z')]
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assert candle4.iloc[0]['date'] == pd.Timestamp('2022-11-30T23:00:00.000Z')
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assert candle4.iloc[0]['date_1M'] == pd.Timestamp('2022-11-01T00:00:00.000Z')
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def test_merge_informative_pair_same():
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data = generate_test_data('15m', 40)
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informative = generate_test_data('15m', 40)
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result = merge_informative_pair(data, informative, '15m', '15m', ffill=True)
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assert isinstance(result, pd.DataFrame)
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assert len(result) == len(data)
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assert 'date' in result.columns
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assert result['date'].equals(data['date'])
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assert 'date_15m' in result.columns
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assert 'open' in result.columns
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assert 'open_15m' in result.columns
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assert result['open'].equals(data['open'])
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assert 'close' in result.columns
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assert 'close_15m' in result.columns
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assert result['close'].equals(data['close'])
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assert 'volume' in result.columns
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assert 'volume_15m' in result.columns
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assert result['volume'].equals(data['volume'])
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# Dates match 1:1
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assert result['date_15m'].equals(result['date'])
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def test_merge_informative_pair_lower():
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data = generate_test_data('1h', 40)
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informative = generate_test_data('15m', 40)
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with pytest.raises(ValueError, match=r"Tried to merge a faster timeframe .*"):
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merge_informative_pair(data, informative, '1h', '15m', ffill=True)
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def test_merge_informative_pair_empty():
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data = generate_test_data('1h', 40)
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informative = pd.DataFrame(columns=data.columns)
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result = merge_informative_pair(data, informative, '1h', '2h', ffill=True)
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assert result['date'].equals(data['date'])
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assert list(result.columns) == [
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'date',
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'open',
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'high',
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'low',
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'close',
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'volume',
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'date_2h',
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'open_2h',
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'high_2h',
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'low_2h',
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'close_2h',
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'volume_2h'
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]
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# We merge an empty dataframe, so all values should be NaN
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for col in ['date_2h', 'open_2h', 'high_2h', 'low_2h', 'close_2h', 'volume_2h']:
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assert result[col].isnull().all()
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def test_merge_informative_pair_suffix():
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data = generate_test_data('15m', 20)
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informative = generate_test_data('1h', 20)
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result = merge_informative_pair(data, informative, '15m', '1h',
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append_timeframe=False, suffix="suf")
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assert 'date' in result.columns
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assert result['date'].equals(data['date'])
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assert 'date_suf' in result.columns
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assert 'open_suf' in result.columns
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assert 'open_1h' not in result.columns
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assert list(result.columns) == [
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'date',
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'open',
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'high',
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'low',
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'close',
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'volume',
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'date_suf',
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'open_suf',
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'high_suf',
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'low_suf',
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'close_suf',
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'volume_suf'
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]
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def test_merge_informative_pair_suffix_append_timeframe():
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data = generate_test_data('15m', 20)
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informative = generate_test_data('1h', 20)
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with pytest.raises(ValueError, match=r"You can not specify `append_timeframe` .*"):
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merge_informative_pair(data, informative, '15m', '1h', suffix="suf")
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@pytest.mark.parametrize("side,profitrange", [
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# profit range for long is [-1, inf] while for shorts is [-inf, 1]
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("long", [-0.99, 2, 30]),
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("short", [-2.0, 0.99, 30]),
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])
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def test_stoploss_from_open(side, profitrange):
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open_price_ranges = [
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[0.01, 1.00, 30],
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[1, 100, 30],
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[100, 10000, 30],
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]
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for open_range in open_price_ranges:
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for open_price in np.linspace(*open_range):
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for desired_stop in np.linspace(-0.50, 0.50, 30):
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if side == 'long':
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# -1 is not a valid current_profit, should return 1
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assert stoploss_from_open(desired_stop, -1) == 1
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else:
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# 1 is not a valid current_profit for shorts, should return 1
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assert stoploss_from_open(desired_stop, 1, True) == 1
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for current_profit in np.linspace(*profitrange):
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if side == 'long':
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current_price = open_price * (1 + current_profit)
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expected_stop_price = open_price * (1 + desired_stop)
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stoploss = stoploss_from_open(desired_stop, current_profit)
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stop_price = current_price * (1 - stoploss)
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else:
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current_price = open_price * (1 - current_profit)
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expected_stop_price = open_price * (1 - desired_stop)
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stoploss = stoploss_from_open(desired_stop, current_profit, True)
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stop_price = current_price * (1 + stoploss)
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assert stoploss >= 0
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# Technically the formula can yield values greater than 1 for shorts
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# even though it doesn't make sense because the position would be liquidated
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if side == 'long':
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assert stoploss <= 1
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# there is no correct answer if the expected stop price is above
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# the current price
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if ((side == 'long' and expected_stop_price > current_price)
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or (side == 'short' and expected_stop_price < current_price)):
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assert stoploss == 0
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else:
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assert pytest.approx(stop_price) == expected_stop_price
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@pytest.mark.parametrize("side,rel_stop,curr_profit,leverage,expected", [
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# profit range for long is [-1, inf] while for shorts is [-inf, 1]
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("long", 0, -1, 1, 1),
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("long", 0, 0.1, 1, 0.09090909),
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("long", -0.1, 0.1, 1, 0.18181818),
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("long", 0.1, 0.2, 1, 0.08333333),
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("long", 0.1, 0.5, 1, 0.266666666),
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("long", 0.1, 5, 1, 0.816666666), # 500% profit, set stoploss to 10% above open price
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("long", 0, 5, 10, 3.3333333), # 500% profit, set stoploss break even
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("long", 0.1, 5, 10, 3.26666666), # 500% profit, set stoploss to 10% above open price
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("long", -0.1, 5, 10, 3.3999999), # 500% profit, set stoploss to 10% belowopen price
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("short", 0, 0.1, 1, 0.1111111),
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("short", -0.1, 0.1, 1, 0.2222222),
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("short", 0.1, 0.2, 1, 0.125),
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("short", 0.1, 1, 1, 1),
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("short", -0.01, 5, 10, 10.01999999), # 500% profit at 10x
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])
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def test_stoploss_from_open_leverage(side, rel_stop, curr_profit, leverage, expected):
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stoploss = stoploss_from_open(rel_stop, curr_profit, side == 'short', leverage)
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assert pytest.approx(stoploss) == expected
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open_rate = 100
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if stoploss != 1:
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if side == 'long':
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current_rate = open_rate * (1 + curr_profit / leverage)
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stop = current_rate * (1 - stoploss / leverage)
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assert pytest.approx(stop) == open_rate * (1 + rel_stop / leverage)
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else:
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current_rate = open_rate * (1 - curr_profit / leverage)
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stop = current_rate * (1 + stoploss / leverage)
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assert pytest.approx(stop) == open_rate * (1 - rel_stop / leverage)
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def test_stoploss_from_absolute():
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assert pytest.approx(stoploss_from_absolute(90, 100)) == 1 - (90 / 100)
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assert pytest.approx(stoploss_from_absolute(90, 100)) == 0.1
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assert pytest.approx(stoploss_from_absolute(95, 100)) == 0.05
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assert pytest.approx(stoploss_from_absolute(100, 100)) == 0
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assert pytest.approx(stoploss_from_absolute(110, 100)) == 0
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assert pytest.approx(stoploss_from_absolute(100, 0)) == 1
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assert pytest.approx(stoploss_from_absolute(0, 100)) == 1
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assert pytest.approx(stoploss_from_absolute(0, 100, False, leverage=5)) == 5
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assert pytest.approx(stoploss_from_absolute(90, 100, True)) == 0
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assert pytest.approx(stoploss_from_absolute(100, 100, True)) == 0
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assert pytest.approx(stoploss_from_absolute(110, 100, True)) == -(1 - (110 / 100))
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assert pytest.approx(stoploss_from_absolute(110, 100, True)) == 0.1
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assert pytest.approx(stoploss_from_absolute(105, 100, True)) == 0.05
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assert pytest.approx(stoploss_from_absolute(105, 100, True, 5)) == 0.05 * 5
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assert pytest.approx(stoploss_from_absolute(100, 0, True)) == 1
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assert pytest.approx(stoploss_from_absolute(0, 100, True)) == 0
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assert pytest.approx(stoploss_from_absolute(100, 1, is_short=True)) == 1
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assert pytest.approx(stoploss_from_absolute(100, 1, is_short=True, leverage=5)) == 5
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@pytest.mark.parametrize('trading_mode', ['futures', 'spot'])
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def test_informative_decorator(mocker, default_conf_usdt, trading_mode):
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candle_def = CandleType.get_default(trading_mode)
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default_conf_usdt['candle_type_def'] = candle_def
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test_data_5m = generate_test_data('5m', 40)
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test_data_30m = generate_test_data('30m', 40)
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test_data_1h = generate_test_data('1h', 40)
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data = {
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('XRP/USDT', '5m', candle_def): test_data_5m,
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('XRP/USDT', '30m', candle_def): test_data_30m,
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('XRP/USDT', '1h', candle_def): test_data_1h,
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('XRP/BTC', '1h', candle_def): test_data_1h, # from {base}/BTC
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('LTC/USDT', '5m', candle_def): test_data_5m,
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('LTC/USDT', '30m', candle_def): test_data_30m,
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('LTC/USDT', '1h', candle_def): test_data_1h,
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('LTC/BTC', '1h', candle_def): test_data_1h, # from {base}/BTC
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('NEO/USDT', '30m', candle_def): test_data_30m,
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('NEO/USDT', '5m', CandleType.SPOT): test_data_5m, # Explicit request with '' as candletype
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('NEO/USDT', '15m', candle_def): test_data_5m, # Explicit request with '' as candletype
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('NEO/USDT', '1h', candle_def): test_data_1h,
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('ETH/USDT', '1h', candle_def): test_data_1h,
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('ETH/USDT', '30m', candle_def): test_data_30m,
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('ETH/BTC', '1h', CandleType.SPOT): test_data_1h, # Explicitly selected as spot
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}
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default_conf_usdt['strategy'] = 'InformativeDecoratorTest'
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strategy = StrategyResolver.load_strategy(default_conf_usdt)
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exchange = get_patched_exchange(mocker, default_conf_usdt)
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strategy.dp = DataProvider({}, exchange, None)
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mocker.patch.object(strategy.dp, 'current_whitelist', return_value=[
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'XRP/USDT', 'LTC/USDT', 'NEO/USDT'
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])
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assert len(strategy._ft_informative) == 7 # Equal to number of decorators used
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informative_pairs = [
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('XRP/USDT', '1h', candle_def),
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('XRP/BTC', '1h', candle_def),
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('LTC/USDT', '1h', candle_def),
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('LTC/BTC', '1h', candle_def),
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('XRP/USDT', '30m', candle_def),
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('LTC/USDT', '30m', candle_def),
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('NEO/USDT', '1h', candle_def),
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('NEO/USDT', '30m', candle_def),
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('NEO/USDT', '5m', candle_def),
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('NEO/USDT', '15m', candle_def),
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('NEO/USDT', '2h', CandleType.FUTURES),
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('ETH/BTC', '1h', CandleType.SPOT), # One candle remains as spot
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('ETH/USDT', '30m', candle_def)]
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for inf_pair in informative_pairs:
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assert inf_pair in strategy.gather_informative_pairs()
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def test_historic_ohlcv(pair, timeframe, candle_type):
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return data[
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(pair, timeframe or strategy.timeframe, CandleType.from_string(candle_type))].copy()
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mocker.patch('freqtrade.data.dataprovider.DataProvider.historic_ohlcv',
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side_effect=test_historic_ohlcv)
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analyzed = strategy.advise_all_indicators(
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{p: data[(p, strategy.timeframe, candle_def)] for p in ('XRP/USDT', 'LTC/USDT')})
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expected_columns = [
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'rsi_1h', 'rsi_30m', # Stacked informative decorators
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'neo_usdt_rsi_1h', # NEO 1h informative
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|
'rsi_NEO_USDT_neo_usdt_NEO/USDT_30m', # Column formatting
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|
'rsi_from_callable', # Custom column formatter
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|
'eth_btc_rsi_1h', # Quote currency not matching stake currency
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|
'rsi', 'rsi_less', # Non-informative columns
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|
'rsi_5m', # Manual informative dataframe
|
|
]
|
|
for _, dataframe in analyzed.items():
|
|
for col in expected_columns:
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|
assert col in dataframe.columns
|