start tracking the current candle in FreqAI, add robustness to corr_df caching and inference timer, add test for cache corr_df
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@@ -98,6 +98,7 @@ class FreqaiDataDrawer:
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"model_filename": "", "trained_timestamp": 0,
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"data_path": "", "extras": {}}
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self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
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self.current_candle: datetime = datetime.fromtimestamp(637887600)
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def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
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"""
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@@ -636,6 +637,8 @@ class FreqaiDataDrawer:
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axis=0,
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)
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self.current_candle = history_data[dk.pair][self.config['timeframe']].iloc[-1]['date']
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def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
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"""
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Load pair histories for all whitelist and corr_pairlist pairs.
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@@ -75,7 +75,7 @@ class IFreqaiModel(ABC):
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if self.keras and self.ft_params.get("DI_threshold", 0):
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self.ft_params["DI_threshold"] = 0
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logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
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self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
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self.CONV_WIDTH = self.freqai_info.get("conv_width", 1)
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if self.ft_params.get("inlier_metric_window", 0):
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self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
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self.pair_it = 0
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@@ -93,7 +93,7 @@ class IFreqaiModel(ABC):
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# get_corr_dataframes is controlling the caching of corr_dataframes
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# for improved performance. Careful with this boolean.
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self.get_corr_dataframes: bool = True
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self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
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self._threads: List[threading.Thread] = []
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self._stop_event = threading.Event()
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@@ -339,6 +339,7 @@ class IFreqaiModel(ABC):
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if self.dd.historic_data:
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self.dd.update_historic_data(strategy, dk)
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logger.debug(f'Updating historic data on pair {metadata["pair"]}')
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self.track_current_candle()
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if not self.follow_mode:
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@@ -683,8 +684,8 @@ class IFreqaiModel(ABC):
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" avoid blinding open trades and degrading performance.")
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self.pair_it = 0
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self.inference_time = 0
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if self.corr_pairlist:
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self.get_corr_dataframes = True
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# if self.corr_pairlist:
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# self.get_corr_dataframes = True
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return
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def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
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@@ -766,6 +767,18 @@ class IFreqaiModel(ABC):
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return dataframe
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def track_current_candle(self):
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"""
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Checks if the latest candle appended by the datadrawer is
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equivalent to the latest candle seen by FreqAI. If not, it
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asks to refresh the cached corr_dfs, and resets the pair
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counter.
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"""
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if self.dd.current_candle > self.current_candle:
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self.get_corr_dataframes = True
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self.pair_it = 0
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self.current_candle = self.dd.current_candle
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# Following methods which are overridden by user made prediction models.
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# See freqai/prediction_models/CatboostPredictionModel.py for an example.
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