give beta testers more information in the doc

docs/freqai.md

@@ -41,6 +41,23 @@ in the model.
 intermediate performance of the model during training. This data does not
 directly influence nodal weights within the model.
 
+## Install prerequisites
+
+Use `pip` to install the prerequisities with:
+
+`pip install -r requirements-freqai.txt`
+
+## Running from the example files
+
+An example strategy, example prediction model, and example config can all be found in 
+`freqtrade/templates/ExampleFreqaiStrategy.py`, `freqtrade/templates/ExamplePredictionModel.py`, 
+`config_examples/config_freqai.example.json`, respectively. Assuming the user has downloaded
+the necessary data, Freqai can be executed from these templates with:
+
+`freqtrade backtesting --config config_examples/config_freqai.example.json--strategy 
+ExampleFreqaiStrategy --freqaimodel ExamplePredictionModel
+--freqaimodel-path freqtrade/templates --strategy-path freqtrade/templates`
+
 ## Configuring the bot
 ### Example config file
 The user interface is isolated to the typical config file. A typical Freqai

freqtrade/freqai/data_kitchen.py

@@ -113,8 +113,6 @@ class FreqaiDataKitchen:
         with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp:
             self.data = json.load(fp)
             self.training_features_list = self.data["training_features_list"]
-            # if self.data.get("training_features_list"):
-            #     self.training_features_list = [*self.data.get("training_features_list")]
 
         self.data_dictionary["train_features"] = pd.read_pickle(
             self.model_path / str(self.model_filename + "_trained_df.pkl")

freqtrade/templates/ExamplePredictionModel.py

@@ -42,8 +42,8 @@ class ExamplePredictionModel(IFreqaiModel):
 
     def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
         """
-        Filter the training data and train a model to it. Train makes heavy use of the datahandler
-        for storing, saving, loading, and managed.
+        Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
+        for storing, saving, loading, and analyzing the data.
         :params:
         :unfiltered_dataframe: Full dataframe for the current training period
         :metadata: pair metadata from strategy.