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