stable/tests/optimize/__init__.py
2020-02-11 04:17:10 +03:00

52 lines
1.5 KiB
Python

from typing import Dict, List, NamedTuple, Optional
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[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: Optional[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