fix lock bug, update docstring

freqtrade/freqai/data_drawer.py

@@ -38,8 +38,7 @@ class FreqaiDataDrawer:
     """
     Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
     /loading to/from disk.
-    This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is
+    This object remains persistent throughout live/dry.
-    reinstantiated for each coin.
 
     Record of contribution:
     FreqAI was developed by a group of individuals who all contributed specific skillsets to the
@@ -56,7 +55,7 @@ class FreqaiDataDrawer:
 
     Beta testing and bug reporting:
     @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
-    Juha Nykänen @suikula, Wagner Costa @wagnercosta
+    Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
     def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
@@ -85,6 +84,7 @@ class FreqaiDataDrawer:
         self.load_historic_predictions_from_disk()
         self.training_queue: Dict[str, int] = {}
         self.history_lock = threading.Lock()
+        self.save_lock = threading.Lock()
         self.old_DBSCAN_eps: Dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
@@ -145,9 +145,10 @@ class FreqaiDataDrawer:
         """
         Save data drawer full of all pair model metadata in present model folder.
         """
-        with open(self.pair_dictionary_path, 'w') as fp:
+        with self.save_lock:
-            rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
+            with open(self.pair_dictionary_path, 'w') as fp:
-                           number_mode=rapidjson.NM_NATIVE)
+                rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
+                               number_mode=rapidjson.NM_NATIVE)
 
     def save_follower_dict_to_disk(self):
         """

freqtrade/freqai/data_kitchen.py

@@ -3,7 +3,6 @@ import datetime
 import logging
 import shutil
 from pathlib import Path
-from threading import Lock
 from typing import Any, Dict, List, Tuple
 
 import numpy as np
@@ -35,6 +34,9 @@ class FreqaiDataKitchen:
     Class designed to analyze data for a single pair. Employed by the IFreqaiModel class.
     Functionalities include holding, saving, loading, and analyzing the data.
 
+    This object is not persistent, it is reinstantiated for each coin, each time the coin
+    model needs to be inferenced or trained.
+
     Record of contribution:
     FreqAI was developed by a group of individuals who all contributed specific skillsets to the
     project.
@@ -50,7 +52,7 @@ class FreqaiDataKitchen:
 
     Beta testing and bug reporting:
     @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
-    Juha Nykänen @suikula, Wagner Costa @wagnercosta
+    Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
     def __init__(
@@ -71,7 +73,6 @@ class FreqaiDataKitchen:
         self.model_filename: str = ""
         self.live = live
         self.pair = pair
-        self.analysis_lock = Lock()
 
         self.svm_model: linear_model.SGDOneClassSVM = None
         self.keras: bool = self.freqai_config.get("keras", False)
@@ -964,7 +965,6 @@ class FreqaiDataKitchen:
         for tf in tfs:
             if tf == tfs[-1]:
                 sgi = True  # doing this last allows user to use all tf raw prices in labels
-            with self.analysis_lock:
                 dataframe = strategy.populate_any_indicators(
                     pair,
                     dataframe.copy(),
@@ -972,7 +972,6 @@ class FreqaiDataKitchen:
                     informative=base_dataframes[tf],
                     set_generalized_indicators=sgi
                 )
-            with self.analysis_lock:
                 if pairs:
                     for i in pairs:
                         if pair in i:

freqtrade/freqai/freqai_interface.py

@@ -12,7 +12,7 @@ import numpy as np
 import pandas as pd
 from numpy.typing import NDArray
 from pandas import DataFrame
-
+from threading import Lock
 from freqtrade.configuration import TimeRange
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
@@ -52,7 +52,7 @@ class IFreqaiModel(ABC):
 
     Beta testing and bug reporting:
     @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
-    Juha Nykänen @suikula, Wagner Costa @wagnercosta
+    Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
     def __init__(self, config: Dict[str, Any]) -> None:
@@ -81,6 +81,7 @@ class IFreqaiModel(ABC):
         self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
         self.last_trade_database_summary: DataFrame = {}
         self.current_trade_database_summary: DataFrame = {}
+        self.analysis_lock = Lock()
 
     def assert_config(self, config: Dict[str, Any]) -> None:
 
@@ -114,10 +115,10 @@ class IFreqaiModel(ABC):
         elif not self.follow_mode:
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
             logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
-
+            with self.analysis_lock:
-            dataframe = self.dk.use_strategy_to_populate_indicators(
+                dataframe = self.dk.use_strategy_to_populate_indicators(
-                strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+                    strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
-            )
+                )
             dk = self.start_backtesting(dataframe, metadata, self.dk)
 
         dataframe = dk.remove_features_from_df(dk.return_dataframe)
@@ -289,9 +290,10 @@ class IFreqaiModel(ABC):
         # load the model and associated data into the data kitchen
         self.model = self.dd.load_data(metadata["pair"], dk)
 
-        dataframe = self.dk.use_strategy_to_populate_indicators(
+        with self.analysis_lock:
-            strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+            dataframe = self.dk.use_strategy_to_populate_indicators(
-        )
+                strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+            )
 
         if not self.model:
             logger.warning(
@@ -485,9 +487,10 @@ class IFreqaiModel(ABC):
             data_load_timerange, pair, dk
         )
 
-        unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
+        with self.analysis_lock:
-            strategy, corr_dataframes, base_dataframes, pair
+            unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
-        )
+                strategy, corr_dataframes, base_dataframes, pair
+            )
 
         unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
 
@@ -500,7 +503,7 @@ 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 dk.analysis_lock:
+            with self.analysis_lock:
                 self.dd.pair_to_end_of_training_queue(pair)
         self.dd.save_data(model, pair, dk)