from typing import Dict, List, NamedTuple import arrow from pandas import DataFrame from freqtrade.exchange import timeframe_to_minutes from freqtrade.strategy.interface import SellType ticker_start_time = arrow.get(2018, 10, 3) tests_timeframe = '1h' class BTrade(NamedTuple): """ Minimalistic Trade result used for functional backtesting """ sell_reason: SellType open_tick: int close_tick: int class BTContainer(NamedTuple): """ Minimal BacktestContainer defining Backtest inputs and results. """ data: List[float] stop_loss: float roi: Dict[str, float] trades: List[BTrade] profit_perc: float trailing_stop: bool = False trailing_only_offset_is_reached: bool = False trailing_stop_positive: float = None trailing_stop_positive_offset: float = 0.0 use_sell_signal: bool = False def _get_frame_time_from_offset(offset): return ticker_start_time.shift(minutes=(offset * timeframe_to_minutes(tests_timeframe)) ).datetime def _build_backtest_dataframe(ticker_with_signals): columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell'] frame = DataFrame.from_records(ticker_with_signals, columns=columns) frame['date'] = frame['date'].apply(_get_frame_time_from_offset) # Ensure floats are in place for column in ['open', 'high', 'low', 'close', 'volume']: frame[column] = frame[column].astype('float64') return frame