58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
from typing import Dict, List, NamedTuple, Optional
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import arrow
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from pandas import DataFrame
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from freqtrade.enums import SellType
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from freqtrade.exchange import timeframe_to_minutes
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tests_start_time = arrow.get(2018, 10, 3)
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tests_timeframe = '1h'
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class BTrade(NamedTuple):
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"""
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Minimalistic Trade result used for functional backtesting
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"""
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sell_reason: SellType
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open_tick: int
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close_tick: int
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buy_tag: Optional[str] = None
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class BTContainer(NamedTuple):
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"""
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Minimal BacktestContainer defining Backtest inputs and results.
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"""
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data: List[List[float]]
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stop_loss: float
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roi: Dict[str, float]
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trades: List[BTrade]
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profit_perc: float
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trailing_stop: bool = False
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trailing_only_offset_is_reached: bool = False
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trailing_stop_positive: Optional[float] = None
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trailing_stop_positive_offset: float = 0.0
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use_sell_signal: bool = False
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use_custom_stoploss: bool = False
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def _get_frame_time_from_offset(offset):
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minutes = offset * timeframe_to_minutes(tests_timeframe)
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return tests_start_time.shift(minutes=minutes).datetime
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def _build_backtest_dataframe(data):
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columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
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columns = columns + ['buy_tag'] if len(data[0]) == 9 else columns
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frame = DataFrame.from_records(data, columns=columns)
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frame['date'] = frame['date'].apply(_get_frame_time_from_offset)
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# Ensure floats are in place
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for column in ['open', 'high', 'low', 'close', 'volume']:
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frame[column] = frame[column].astype('float64')
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if 'buy_tag' not in columns:
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frame['buy_tag'] = None
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return frame
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