merge in inference timer and historic predictions handling improvements.

freqtrade/freqai/data_drawer.py

@@ -85,6 +85,7 @@ class FreqaiDataDrawer:
         self.training_queue: Dict[str, int] = {}
         self.history_lock = threading.Lock()
         self.save_lock = threading.Lock()
+        self.pair_dict_lock = threading.Lock()
         self.old_DBSCAN_eps: Dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
@@ -228,6 +229,7 @@ class FreqaiDataDrawer:
 
     def pair_to_end_of_training_queue(self, pair: str) -> None:
         # march all pairs up in the queue
+        with self.pair_dict_lock:
             for p in self.pair_dict:
                 self.pair_dict[p]["priority"] -= 1
             # send pair to end of queue
@@ -261,13 +263,14 @@ class FreqaiDataDrawer:
         the strategy originally. Doing this allows FreqUI to always display the correct
         historic predictions.
         """
-        df = self.historic_predictions[pair]
 
-        # here are some pandas hula hoops to accommodate the possibility of a series
+        index = self.historic_predictions[pair].index[-1:]
-        # or dataframe depending number of labels requested by user
+        columns = self.historic_predictions[pair].columns
-        nan_df = pd.DataFrame(np.nan, index=df.index[-2:] + 2, columns=df.columns)
+
-        df = pd.concat([df, nan_df], ignore_index=True, axis=0)
+        nan_df = pd.DataFrame(np.nan, index=index, columns=columns)
-        df = self.historic_predictions[pair] = df[:-1]
+        self.historic_predictions[pair] = pd.concat(
+            [self.historic_predictions[pair], nan_df], ignore_index=True, axis=0)
+        df = self.historic_predictions[pair]
 
         # model outputs and associated statistics
         for label in predictions.columns:
@@ -523,7 +526,7 @@ class FreqaiDataDrawer:
                     history_data[pair][tf] = pd.concat(
                         [
                             history_data[pair][tf],
-                            strategy.dp.get_pair_dataframe(pair, tf).iloc[index:],
+                            df_dp.iloc[index:],
                         ],
                         ignore_index=True,
                         axis=0,

freqtrade/freqai/freqai_interface.py

@@ -7,11 +7,11 @@ import time
 from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Any, Dict, Tuple
-
 import numpy as np
 import pandas as pd
 from numpy.typing import NDArray
 from pandas import DataFrame
+from freqtrade.exchange import timeframe_to_seconds
 from threading import Lock
 from freqtrade.configuration import TimeRange
 from freqtrade.enums import RunMode
@@ -82,6 +82,9 @@ class IFreqaiModel(ABC):
         self.last_trade_database_summary: DataFrame = {}
         self.current_trade_database_summary: DataFrame = {}
         self.analysis_lock = Lock()
+        self.inference_time: float = 0
+        self.begin_time: float = 0
+        self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
 
     def assert_config(self, config: Dict[str, Any]) -> None:
 
@@ -104,6 +107,7 @@ class IFreqaiModel(ABC):
         self.dd.set_pair_dict_info(metadata)
 
         if self.live:
+            self.inference_timer('start')
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
             dk = self.start_live(dataframe, metadata, strategy, self.dk)
 
@@ -123,6 +127,8 @@ class IFreqaiModel(ABC):
 
         dataframe = dk.remove_features_from_df(dk.return_dataframe)
         del dk
+        if self.live:
+            self.inference_timer('stop')
         return dataframe
 
     @threaded
@@ -155,6 +161,8 @@ class IFreqaiModel(ABC):
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
 
+            self.dd.save_historic_predictions_to_disk()
+
     def start_backtesting(
         self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
     ) -> FreqaiDataKitchen:
@@ -340,7 +348,6 @@ class IFreqaiModel(ABC):
             # historical accuracy reasons.
             pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
 
-        self.dd.save_historic_predictions_to_disk()
         if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
             self.fit_live_predictions(dk, pair)
         self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
@@ -503,7 +510,6 @@ class IFreqaiModel(ABC):
         dk.set_new_model_names(pair, new_trained_timerange)
         self.dd.pair_dict[pair]["first"] = False
         if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
-            with self.analysis_lock:
             self.dd.pair_to_end_of_training_queue(pair)
         self.dd.save_data(model, pair, dk)
 
@@ -582,6 +588,28 @@ class IFreqaiModel(ABC):
 
         return
 
+    def inference_timer(self, do='start'):
+        """
+        Timer designed to track the cumulative time spent in FreqAI for one pass through
+        the whitelist. This will check if the time spent is more than 1/4 the time
+        of a single candle, and if so, it will warn the user of degraded performance
+        """
+        if do == 'start':
+            self.pair_it += 1
+            self.begin_time = time.time()
+        elif do == 'stop':
+            end = time.time()
+            self.inference_time += (end - self.begin_time)
+            if self.pair_it == self.total_pairs:
+                logger.info(
+                    f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
+                if self.inference_time > 0.25 * self.base_tf_seconds:
+                    logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to'
+                                   ' avoid blinding open trades and degrading performance.')
+                self.pair_it = 0
+                self.inference_time = 0
+        return
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.