46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
from typing import NamedTuple, List
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import arrow
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from pandas import DataFrame
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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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ticker_interval_in_minute = 60
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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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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[float]
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stop_loss: float
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roi: float
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trades: List[BTrade]
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profit_perc: float
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def _get_frame_time_from_offset(offset):
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return ticker_start_time.shift(
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minutes=(offset * ticker_interval_in_minute)).datetime
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def _build_backtest_dataframe(ticker_with_signals):
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columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
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frame = DataFrame.from_records(ticker_with_signals, 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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return frame
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