def startup_candle_count(self):
return int(self.freqai_info.get("fit_live_predictions_candles", 100) / 2)
- def __init__(self, config: dict) -> None:
- super().__init__(config)
+ def bot_start(self, **kwargs) -> None:
self.pairs = self.config.get("exchange", {}).get("pair_whitelist")
if not self.pairs:
raise ValueError(
self.models_full_path = Path(
self.config["user_data_dir"]
/ "models"
- / f"{self.config.get('freqai', {}).get('identifier', 'no_id_provided')}"
+ / f"{self.freqai_info.get('identifier', 'no_id_provided')}"
)
self.__period_params: dict[str, dict] = {}
for pair in self.pairs:
self.__period_params[pair] = self.load_period_best_params(pair) or {}
- logger.info(
- f"Loaded period best params for {pair}: {self.__period_params[pair]}"
- )
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
"label_period_candles",
self.freqai_info["feature_parameters"]["label_period_candles"],
)
- logger.info(
- f"label_period_candles: {label_period_candles} for {pair}"
- )
min_peaks = argrelmin(
dataframe["low"].values,
order=label_period_candles,
1,
)
- # pair = str(metadata.get("pair"))
- # self.__period_params[pair]["label_period_candles"] = dataframe[
- # "label_period_candles"
- # ]
- # logger.info(
- # f"label_period_candles: {self.__period_params[pair]['label_period_candles']}"
- # )
- # logger.info(
- # f"label_period_candles extra returns: {dataframe['label_period_candles']}"
- # )
+ if "label_period_candles" in dataframe.columns:
+ pair = str(metadata.get("pair"))
+ self.__period_params[pair]["label_period_candles"] = dataframe[
+ "label_period_candles"
+ ].iloc[-1]
dataframe["minima_threshold"] = dataframe[MINIMA_THRESHOLD_COLUMN]
dataframe["maxima_threshold"] = dataframe[MAXIMA_THRESHOLD_COLUMN]