diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md
index 5969f43c6..e45ada2b1 100644
--- a/docs/freqai-parameter-table.md
+++ b/docs/freqai-parameter-table.md
@@ -48,5 +48,5 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `learning_rate` | Boosting learning rate during regression. <br> **Datatype:** Float.
 | `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
 |  |  **Extraneous parameters**
-| `keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `model_save_type` | Indicate to FreqAI if the model save type needs to be something besides typical `joblib` files. Available (`keras` (hd5) and `stable_baselines` (zip).  <br> **Datatype:** str. <br> Default: `joblib`.
 | `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: 2.
diff --git a/freqtrade/freqai/base_models/BaseTensorFlowModel.py b/freqtrade/freqai/base_models/BaseTensorFlowModel.py
index 00f9d6cba..01a076b8f 100644
--- a/freqtrade/freqai/base_models/BaseTensorFlowModel.py
+++ b/freqtrade/freqai/base_models/BaseTensorFlowModel.py
@@ -3,10 +3,10 @@ from time import time
 from typing import Any
 
 from pandas import DataFrame
-
+import numpy as np
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
-
+import tensorflow as tf
 
 logger = logging.getLogger(__name__)
 
@@ -17,6 +17,13 @@ class BaseTensorFlowModel(IFreqaiModel):
     User *must* inherit from this class and set fit() and predict().
     """
 
+    def __init__(self, **kwargs):
+        super().__init__(config=kwargs['config'])
+        self.keras = True
+        if self.ft_params.get("DI_threshold", 0):
+            self.ft_params["DI_threshold"] = 0
+            logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
+
     def train(
         self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
     ) -> Any:
@@ -68,3 +75,76 @@ class BaseTensorFlowModel(IFreqaiModel):
                     f"({end_time - start_time:.2f} secs) --------------------")
 
         return model
+
+
+class WindowGenerator:
+    def __init__(
+        self,
+        input_width,
+        label_width,
+        shift,
+        train_df=None,
+        val_df=None,
+        test_df=None,
+        train_labels=None,
+        val_labels=None,
+        test_labels=None,
+        batch_size=None,
+    ):
+        # Store the raw data.
+        self.train_df = train_df
+        self.val_df = val_df
+        self.test_df = test_df
+        self.train_labels = train_labels
+        self.val_labels = val_labels
+        self.test_labels = test_labels
+        self.batch_size = batch_size
+        self.input_width = input_width
+        self.label_width = label_width
+        self.shift = shift
+        self.total_window_size = input_width + shift
+        self.input_slice = slice(0, input_width)
+        self.input_indices = np.arange(self.total_window_size)[self.input_slice]
+
+    def make_dataset(self, data, labels=None):
+        data = np.array(data, dtype=np.float32)
+        if labels is not None:
+            labels = np.array(labels, dtype=np.float32)
+        ds = tf.keras.preprocessing.timeseries_dataset_from_array(
+            data=data,
+            targets=labels,
+            sequence_length=self.total_window_size,
+            sequence_stride=1,
+            sampling_rate=1,
+            shuffle=False,
+            batch_size=self.batch_size,
+        )
+
+        return ds
+
+    @property
+    def train(self):
+        return self.make_dataset(self.train_df, self.train_labels)
+
+    @property
+    def val(self):
+        return self.make_dataset(self.val_df, self.val_labels)
+
+    @property
+    def test(self):
+        return self.make_dataset(self.test_df, self.test_labels)
+
+    @property
+    def inference(self):
+        return self.make_dataset(self.test_df)
+
+    @property
+    def example(self):
+        """Get and cache an example batch of `inputs, labels` for plotting."""
+        result = getattr(self, "_example", None)
+        if result is None:
+            # No example batch was found, so get one from the `.train` dataset
+            result = next(iter(self.train))
+            # And cache it for next time
+            self._example = result
+        return result
diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py
index 1839724f8..cc081e046 100644
--- a/freqtrade/freqai/data_drawer.py
+++ b/freqtrade/freqai/data_drawer.py
@@ -413,10 +413,13 @@ class FreqaiDataDrawer:
         save_path = Path(dk.data_path)
 
         # Save the trained model
-        if not dk.keras:
+        model_type = self.freqai_info.get('model_save_type', 'joblib')
+        if model_type == 'joblib':
             dump(model, save_path / f"{dk.model_filename}_model.joblib")
-        else:
+        elif model_type == 'keras':
             model.save(save_path / f"{dk.model_filename}_model.h5")
+        elif 'stable_baselines' in model_type:
+            model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
             dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
@@ -492,15 +495,20 @@ class FreqaiDataDrawer:
             dk.data_path / f"{dk.model_filename}_trained_df.pkl"
         )
 
+        model_type = self.freqai_info.get('model_save_type', 'joblib')
         # try to access model in memory instead of loading object from disk to save time
         if dk.live and coin in self.model_dictionary:
             model = self.model_dictionary[coin]
-        elif not dk.keras:
+        elif model_type == 'joblib':
             model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
-        else:
+        elif model_type == 'keras':
             from tensorflow import keras
-
             model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
+        elif model_type == 'stable_baselines':
+            mod = __import__('stable_baselines3', fromlist=[
+                             self.freqai_info['rl_config']['model_type']])
+            MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
+            model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
 
         if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
             dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index f4fa4e5fd..a1386beb7 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -76,9 +76,10 @@ class FreqaiDataKitchen:
         self.backtest_predictions_folder: str = "backtesting_predictions"
         self.live = live
         self.pair = pair
+        self.model_save_type = self.freqai_config.get('model_save_type', 'joblib')
 
         self.svm_model: linear_model.SGDOneClassSVM = None
-        self.keras: bool = self.freqai_config.get("keras", False)
+        # self.model_save_type: bool = self.freqai_config.get("keras", False)
         self.set_all_pairs()
         if not self.live:
             if not self.config["timerange"]:
@@ -571,7 +572,7 @@ class FreqaiDataKitchen:
         predict: bool = If true, inference an existing SVM model, else construct one
         """
 
-        if self.keras:
+        if self.model_save_type == 'keras':
             logger.warning(
                 "SVM outlier removal not currently supported for Keras based models. "
                 "Skipping user requested function."
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index d9f917338..bba7672ef 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -72,10 +72,10 @@ class IFreqaiModel(ABC):
         self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
         self.scanning = False
         self.ft_params = self.freqai_info["feature_parameters"]
-        self.keras: bool = self.freqai_info.get("keras", False)
-        if self.keras and self.ft_params.get("DI_threshold", 0):
-            self.ft_params["DI_threshold"] = 0
-            logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
+        # self.keras: bool = self.freqai_info.get("keras", False)
+        # if self.keras and self.ft_params.get("DI_threshold", 0):
+        #     self.ft_params["DI_threshold"] = 0
+        #     logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
         self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
         if self.ft_params.get("inlier_metric_window", 0):
             self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
@@ -627,7 +627,8 @@ class IFreqaiModel(ABC):
 
         # # for keras type models, the conv_window needs to be prepended so
         # # viewing is correct in frequi
-        if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
+        if (not self.freqai_info.get('model_save_type', 'joblib') or
+                self.ft_params.get('inlier_metric_window', 0)):
             n_lost_points = self.freqai_info.get('conv_width', 2)
             zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
                                  columns=hist_preds_df.columns)
diff --git a/freqtrade/freqai/prediction_models/CNNPredictionModel.py b/freqtrade/freqai/prediction_models/CNNPredictionModel.py
new file mode 100644
index 000000000..badc65626
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/CNNPredictionModel.py
@@ -0,0 +1,144 @@
+import logging
+from typing import Any, Dict, Tuple
+
+from pandas import DataFrame
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+import tensorflow as tf
+from freqtrade.freqai.base_models.BaseTensorFlowModel import BaseTensorFlowModel, WindowGenerator
+from tensorflow.keras.layers import Input, Conv1D, Dense
+from tensorflow.keras.models import Model
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# tf.config.run_functions_eagerly(True)
+# tf.data.experimental.enable_debug_mode()
+
+MAX_EPOCHS = 10
+
+
+class CNNPredictionModel(BaseTensorFlowModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), fit().
+    """
+
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        """
+        train_df = data_dictionary["train_features"]
+        train_labels = data_dictionary["train_labels"]
+        test_df = data_dictionary["test_features"]
+        test_labels = data_dictionary["test_labels"]
+        n_labels = len(train_labels.columns)
+        if n_labels > 1:
+            raise OperationalException(
+                "Neural Net not yet configured for multi-targets. Please "
+                " reduce number of targets to 1 in strategy."
+            )
+
+        n_features = len(data_dictionary["train_features"].columns)
+        BATCH_SIZE = self.freqai_info.get("batch_size", 64)
+        input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
+
+        w1 = WindowGenerator(
+            input_width=self.CONV_WIDTH,
+            label_width=1,
+            shift=1,
+            train_df=train_df,
+            val_df=test_df,
+            train_labels=train_labels,
+            val_labels=test_labels,
+            batch_size=BATCH_SIZE,
+        )
+
+        model = self.create_model(input_dims, n_labels)
+
+        steps_per_epoch = np.ceil(len(test_df) / BATCH_SIZE)
+        lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(
+            0.001, decay_steps=steps_per_epoch * 1000, decay_rate=1, staircase=False
+        )
+
+        early_stopping = tf.keras.callbacks.EarlyStopping(
+            monitor="loss", patience=3, mode="min", min_delta=0.0001
+        )
+
+        model.compile(
+            loss=tf.losses.MeanSquaredError(),
+            optimizer=tf.optimizers.Adam(lr_schedule),
+            metrics=[tf.metrics.MeanAbsoluteError()],
+        )
+
+        model.fit(
+            w1.train,
+            epochs=MAX_EPOCHS,
+            shuffle=False,
+            validation_data=w1.val,
+            callbacks=[early_stopping],
+            verbose=1,
+        )
+
+        return model
+
+    def predict(
+        self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first=True
+    ) -> Tuple[DataFrame, DataFrame]:
+        """
+        Filter the prediction features data and predict with it.
+        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
+        :return:
+        :predictions: np.array of predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_dataframe)
+        filtered_dataframe, _ = dk.filter_features(
+            unfiltered_dataframe, dk.training_features_list, training_filter=False
+        )
+        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
+        dk.data_dictionary["prediction_features"] = filtered_dataframe
+
+        # optional additional data cleaning/analysis
+        self.data_cleaning_predict(dk, filtered_dataframe)
+
+        if first:
+            full_df = dk.data_dictionary["prediction_features"]
+
+            w1 = WindowGenerator(
+                input_width=self.CONV_WIDTH,
+                label_width=1,
+                shift=1,
+                test_df=full_df,
+                batch_size=len(full_df),
+            )
+
+            predictions = self.model.predict(w1.inference)
+            len_diff = len(dk.do_predict) - len(predictions)
+            if len_diff > 0:
+                dk.do_predict = dk.do_predict[len_diff:]
+
+        else:
+            data = dk.data_dictionary["prediction_features"]
+            data = tf.expand_dims(data, axis=0)
+            predictions = self.model(data, training=False)
+
+        predictions = predictions[:, 0, 0]
+        pred_df = DataFrame(predictions, columns=dk.label_list)
+
+        pred_df = dk.denormalize_labels_from_metadata(pred_df)
+
+        return (pred_df, np.ones(len(pred_df)))
+
+    def create_model(self, input_dims, n_labels) -> Any:
+
+        input_layer = Input(shape=(input_dims[1], input_dims[2]))
+        Layer_1 = Conv1D(filters=32, kernel_size=(self.CONV_WIDTH,), activation="relu")(input_layer)
+        Layer_3 = Dense(units=32, activation="relu")(Layer_1)
+        output_layer = Dense(units=n_labels)(Layer_3)
+        return Model(inputs=input_layer, outputs=output_layer)