freqtrade_origin/tests/optimize/__init__.py

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from typing import Dict, List, NamedTuple, Optional
import arrow
from pandas import DataFrame
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from freqtrade.enums import SellType
from freqtrade.exchange import timeframe_to_minutes
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tests_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
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buy_tag: Optional[str] = None
class BTContainer(NamedTuple):
"""
Minimal BacktestContainer defining Backtest inputs and results.
"""
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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
use_custom_stoploss: bool = False
def _get_frame_time_from_offset(offset):
minutes = offset * timeframe_to_minutes(tests_timeframe)
return tests_start_time.shift(minutes=minutes).datetime
def _build_backtest_dataframe(data):
columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'enter_long', 'exit_long',
'enter_short', 'exit_short']
if len(data[0]) == 8:
# No short columns
data = [d + [0, 0] for d in data]
columns = columns + ['long_tag'] if len(data[0]) == 11 else columns
frame = DataFrame.from_records(data, 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')
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if 'long_tag' not in columns:
frame['long_tag'] = None
if 'short_tag' not in columns:
frame['short_tag'] = None
return frame