mirror of
https://github.com/freqtrade/freqtrade.git
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135 lines
4.6 KiB
Python
135 lines
4.6 KiB
Python
from math import isclose
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import numpy as np
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import pandas as pd
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import pytest
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from freqtrade.strategy import merge_informative_pair, stoploss_from_open, timeframe_to_minutes
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def generate_test_data(timeframe: str, size: int):
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np.random.seed(42)
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tf_mins = timeframe_to_minutes(timeframe)
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base = np.random.normal(20, 2, size=size)
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date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
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df = pd.DataFrame({
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'date': date,
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'open': base,
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'high': base + np.random.normal(2, 1, size=size),
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'low': base - np.random.normal(2, 1, size=size),
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'close': base + np.random.normal(0, 1, size=size),
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'volume': np.random.normal(200, size=size)
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}
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)
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df = df.dropna()
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return df
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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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result = merge_informative_pair(data, informative, '15m', '1h', 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_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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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_stoploss_from_open():
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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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current_profit_range = [-0.99, 2, 30]
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desired_stop_range = [-0.50, 0.50, 30]
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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(*desired_stop_range):
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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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for current_profit in np.linspace(*current_profit_range):
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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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assert stoploss >= 0
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assert stoploss <= 1
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stop_price = current_price * (1 - stoploss)
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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 expected_stop_price > current_price:
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assert stoploss == 0
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else:
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assert isclose(stop_price, expected_stop_price, rel_tol=0.00001)
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