Improve generate_test_data to make it easier to use
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@ -39,17 +39,25 @@ def hyperopt(hyperopt_conf, mocker):
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def hyperopt_results():
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return pd.DataFrame(
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{
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'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
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'pair': ['ETH/USDT', 'ETH/USDT', 'ETH/USDT', 'ETH/USDT'],
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'profit_ratio': [-0.1, 0.2, -0.1, 0.3],
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'profit_abs': [-0.2, 0.4, -0.2, 0.6],
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'trade_duration': [10, 30, 10, 10],
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'amount': [0.1, 0.1, 0.1, 0.1],
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'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.STOP_LOSS, SellType.ROI],
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'open_date':
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[
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datetime(2019, 1, 1, 9, 15, 0),
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datetime(2019, 2, 1, 8, 55, 0),
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datetime(2019, 3, 1, 9, 15, 0),
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datetime(2019, 4, 1, 9, 15, 0),
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],
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'close_date':
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[
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datetime(2019, 1, 1, 9, 26, 3, 478039),
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datetime(2019, 2, 1, 9, 26, 3, 478039),
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datetime(2019, 3, 1, 9, 26, 3, 478039),
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datetime(2019, 4, 1, 9, 26, 3, 478039),
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]
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datetime(2019, 1, 1, 9, 25, 0),
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datetime(2019, 2, 1, 9, 25, 0),
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datetime(2019, 3, 1, 9, 25, 0),
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datetime(2019, 4, 1, 9, 25, 0),
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],
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}
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)
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@ -9,13 +9,13 @@ from freqtrade.strategy import (merge_informative_pair, stoploss_from_absolute,
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timeframe_to_minutes)
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def generate_test_data(timeframe: str, size: int):
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def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
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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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date = pd.date_range(start, periods=size, freq=f'{tf_mins}min', tz='UTC')
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df = pd.DataFrame({
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'date': date,
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'open': base,
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