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Replace more occurances of ticker_interval
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1c57a4ac35
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@ -39,12 +39,12 @@ class TimeRange:
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if self.startts:
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self.startts = self.startts - seconds
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def adjust_start_if_necessary(self, ticker_interval_secs: int, startup_candles: int,
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def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
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min_date: arrow.Arrow) -> None:
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"""
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Adjust startts by <startup_candles> candles.
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Applies only if no startup-candles have been available.
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:param ticker_interval_secs: Ticker interval in seconds e.g. `timeframe_to_seconds('5m')`
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:param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
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:param startup_candles: Number of candles to move start-date forward
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:param min_date: Minimum data date loaded. Key kriterium to decide if start-time
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has to be moved
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@ -55,7 +55,7 @@ class TimeRange:
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# If no startts was defined, or backtest-data starts at the defined backtest-date
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logger.warning("Moving start-date by %s candles to account for startup time.",
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startup_candles)
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self.startts = (min_date.timestamp + ticker_interval_secs * startup_candles)
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self.startts = (min_date.timestamp + timeframe_secs * startup_candles)
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self.starttype = 'date'
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@staticmethod
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@ -106,10 +106,10 @@ class IHyperOpt(ABC):
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roi_t_alpha = 1.0
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roi_p_alpha = 1.0
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ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
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timeframe_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
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# We define here limits for the ROI space parameters automagically adapted to the
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# ticker_interval used by the bot:
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# timeframe used by the bot:
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#
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# * 'roi_t' (limits for the time intervals in the ROI tables) components
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# are scaled linearly.
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@ -117,8 +117,8 @@ class IHyperOpt(ABC):
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#
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# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
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# method for the 5m ticker interval.
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roi_t_scale = ticker_interval_mins / 5
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roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
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roi_t_scale = timeframe_mins / 5
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roi_p_scale = math.log1p(timeframe_mins) / math.log1p(5)
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roi_limits = {
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'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
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'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
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@ -7,7 +7,7 @@ from freqtrade.exchange import timeframe_to_minutes
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from freqtrade.strategy.interface import SellType
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ticker_start_time = arrow.get(2018, 10, 3)
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tests_ticker_interval = '1h'
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tests_timeframe = '1h'
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class BTrade(NamedTuple):
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@ -36,7 +36,7 @@ class BTContainer(NamedTuple):
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def _get_frame_time_from_offset(offset):
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return ticker_start_time.shift(minutes=(offset * timeframe_to_minutes(tests_ticker_interval))
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return ticker_start_time.shift(minutes=(offset * timeframe_to_minutes(tests_timeframe))
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).datetime
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@ -9,7 +9,7 @@ from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.strategy.interface import SellType
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from tests.conftest import patch_exchange
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from tests.optimize import (BTContainer, BTrade, _build_backtest_dataframe,
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_get_frame_time_from_offset, tests_ticker_interval)
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_get_frame_time_from_offset, tests_timeframe)
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# Test 0: Sell with signal sell in candle 3
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# Test with Stop-loss at 1%
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@ -293,7 +293,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
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"""
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default_conf["stoploss"] = data.stop_loss
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default_conf["minimal_roi"] = data.roi
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default_conf["ticker_interval"] = tests_ticker_interval
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default_conf["ticker_interval"] = tests_timeframe
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default_conf["trailing_stop"] = data.trailing_stop
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default_conf["trailing_only_offset_is_reached"] = data.trailing_only_offset_is_reached
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# Only add this to configuration If it's necessary
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@ -307,7 +307,7 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
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get_fee = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
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backtesting = Backtesting(default_conf)
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assert backtesting.config == default_conf
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assert backtesting.ticker_interval == '5m'
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assert backtesting.timeframe == '5m'
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assert callable(backtesting.strategy.tickerdata_to_dataframe)
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assert callable(backtesting.strategy.advise_buy)
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assert callable(backtesting.strategy.advise_sell)
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