@staticmethod
def get_trade_entry_date(trade: Trade) -> datetime:
- return timeframe_to_prev_date(QuickAdapterV3.timeframe, trade.open_date_utc)
+ return timeframe_to_prev_date(
+ QuickAdapterV3.config.get("timeframe"), trade.open_date_utc
+ )
@staticmethod
def get_trade_duration_candles(df: DataFrame, trade: Trade) -> Optional[int]:
return None
trade_duration_minutes = (current_date - entry_date).total_seconds() / 60.0
return int(
- trade_duration_minutes / timeframe_to_minutes(QuickAdapterV3.timeframe)
+ trade_duration_minutes
+ / timeframe_to_minutes(QuickAdapterV3.config.get("timeframe"))
)
@staticmethod
current_profit: float,
**kwargs,
) -> Optional[float]:
- df, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+ df, _ = self.dp.get_analyzed_dataframe(
+ pair=pair, timeframe=self.config.get("timeframe")
+ )
if df.empty:
return None
current_profit: float,
**kwargs,
) -> Optional[str]:
- df, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+ df, _ = self.dp.get_analyzed_dataframe(
+ pair=pair, timeframe=self.config.get("timeframe")
+ )
if df.empty:
return None
if trades_per_side >= max_open_trades_per_side:
return False
- df, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+ df, _ = self.dp.get_analyzed_dataframe(
+ pair=pair, timeframe=self.config.get("timeframe")
+ )
if df.empty:
return False
last_candle = df.iloc[-1]