optuna_default_config = {
"enabled": False,
"n_jobs": min(
- self.freqai_info.get("optuna_hyperopt", {}).get("n_jobs", 1),
+ self.config.get("freqai", {})
+ .get("optuna_hyperopt", {})
+ .get("n_jobs", 1),
max(int(self.max_system_threads / 4), 1),
),
"storage": "file",
}
return {
**optuna_default_config,
- **self.freqai_info.get("optuna_hyperopt", {}),
+ **self.config.get("freqai", {}).get("optuna_hyperopt", {}),
}
@property
)
n_pairs = len(self.pairs)
label_frequency_candles = max(
- 2, 2 * n_pairs, int(self.ft_params.get("label_frequency_candles", 12))
+ 2,
+ 2 * n_pairs,
+ int(
+ self.config.get("feature_parameters", {}).get(
+ "label_frequency_candles", 12
+ )
+ ),
)
cache_key = label_frequency_candles
if cache_key not in self._optuna_label_candle_pool_full_cache:
def minimal_roi(self) -> dict[str, Any]:
timeframe_minutes = timeframe_to_minutes(self.config.get("timeframe", "5m"))
fit_live_predictions_candles = int(
- self.freqai_info.get("fit_live_predictions_candles", 100)
+ self.config.get("freqai", {}).get("fit_live_predictions_candles", 100)
)
return {str(timeframe_minutes * fit_live_predictions_candles): -1}
@cached_property
def protections(self) -> list[dict[str, Any]]:
fit_live_predictions_candles = int(
- self.freqai_info.get("fit_live_predictions_candles", 100)
+ self.config.get("freqai", {}).get("fit_live_predictions_candles", 100)
)
estimated_trade_duration_candles = int(
self.config.get("estimated_trade_duration_candles", 48)
@cached_property
def startup_candle_count(self) -> int:
# Match the predictions warmup period
- return self.freqai_info.get("fit_live_predictions_candles", 100)
+ return self.config.get("freqai", {}).get("fit_live_predictions_candles", 100)
@cached_property
def max_open_trades_per_side(self) -> int: