Remove remaining CustomModel references

docs/freqai.md

@@ -166,7 +166,7 @@ config setup includes:
 Features are added by the user inside the `populate_any_indicators()` method of the strategy 
 by prepending indicators with `%` and labels are added by prepending `&`.  
 There are some important components/structures that the user *must* include when building their feature set.
-As shown below, `with self.model.bridge.lock:` must be used to ensure thread safety - especially when using third
+As shown below, `with self.freqai.lock:` must be used to ensure thread safety - especially when using third
 party libraries for indicator construction such as TA-lib.  
 Another structure to consider is the  location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
 This is where the user will add single features and labels to their feature set to avoid duplication from 
@@ -191,7 +191,7 @@ various configuration parameters which multiply the feature set such as `include
         :coin: the name of the coin which will modify the feature names.
         """
 
-        with self.model.bridge.lock:
+        with self.freqai.lock:
             if informative is None:
                 informative = self.dp.get_pair_dataframe(pair, tf)
 
@@ -370,7 +370,6 @@ for each pair, for each backtesting window within the bigger `--timerange`.
 The Freqai strategy requires the user to include the following lines of code in the strategy:
 
 ```python
-    from freqtrade.freqai.strategy_bridge import CustomModel
 
     def informative_pairs(self):
         whitelist_pairs = self.dp.current_whitelist()
@@ -385,9 +384,6 @@ The Freqai strategy requires the user to include the following lines of code in
                 informative_pairs.append((pair, tf))
         return informative_pairs
 
-    def bot_start(self):
-        self.model = CustomModel(self.config)
-
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
         self.freqai_info = self.config["freqai"]
@@ -400,7 +396,7 @@ The Freqai strategy requires the user to include the following lines of code in
         # the target mean/std values for each of the labels created by user in 
         # `populate_any_indicators()` for each training period.
 
-        dataframe = self.model.bridge.start(dataframe, metadata, self)
+        dataframe = self.freqai.start(dataframe, metadata, self)
 
         return dataframe
 ```
@@ -648,7 +644,7 @@ below this value. An example usage in the strategy may look something like:
             dataframe["do_predict"],
             dataframe["target_upper_quantile"],
             dataframe["target_lower_quantile"],
-        ) = self.model.bridge.start(dataframe, metadata, self)
+        ) = self.freqai.start(dataframe, metadata, self)
 
         return dataframe
 

freqtrade/templates/FreqaiExampleStrategy.py

@@ -19,7 +19,6 @@ class FreqaiExampleStrategy(IStrategy):
     """
     Example strategy showing how the user connects their own
     IFreqaiModel to the strategy. Namely, the user uses:
-    self.model = CustomModel(self.config)
     self.freqai.start(dataframe, metadata)
 
     to make predictions on their data. populate_any_indicators() automatically

tests/freqai/conftest.py

@@ -76,7 +76,7 @@ def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
     strategy = get_patched_freqai_strategy(mocker, freqaiconf)
     exchange = get_patched_exchange(mocker, freqaiconf)
     strategy.dp = DataProvider(freqaiconf, exchange)
-    freqai = strategy.model.bridge
+    freqai = strategy.freqai
     freqai.live = True
     freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
     timerange = TimeRange.parse_timerange("20180110-20180114")
@@ -91,7 +91,7 @@ def get_freqai_analyzed_dataframe(mocker, freqaiconf):
     exchange = get_patched_exchange(mocker, freqaiconf)
     strategy.dp = DataProvider(freqaiconf, exchange)
     strategy.freqai_info = freqaiconf.get("freqai", {})
-    freqai = strategy.model.bridge
+    freqai = strategy.freqai
     freqai.live = True
     freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
     timerange = TimeRange.parse_timerange("20180110-20180114")
@@ -107,7 +107,7 @@ def get_ready_to_train(mocker, freqaiconf):
     exchange = get_patched_exchange(mocker, freqaiconf)
     strategy.dp = DataProvider(freqaiconf, exchange)
     strategy.freqai_info = freqaiconf.get("freqai", {})
-    freqai = strategy.model.bridge
+    freqai = strategy.freqai
     freqai.live = True
     freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
     timerange = TimeRange.parse_timerange("20180110-20180114")

tests/strategy/strats/freqai_test_strat.py

@@ -16,7 +16,6 @@ class freqai_test_strat(IStrategy):
     """
     Example strategy showing how the user connects their own
     IFreqaiModel to the strategy. Namely, the user uses:
-    self.model = CustomModel(self.config)
     self.freqai.start(dataframe, metadata)
 
     to make predictions on their data. populate_any_indicators() automatically