diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json
index aeb1cb13d..330bcd9f5 100644
--- a/config_examples/config_freqai.example.json
+++ b/config_examples/config_freqai.example.json
@@ -75,7 +75,6 @@
             "weight_factor": 0.9,
             "principal_component_analysis": false,
             "use_SVM_to_remove_outliers": true,
-            "stratify_training_data": 0,
             "indicator_max_period_candles": 20,
             "indicator_periods_candles": [10, 20]
         },
diff --git a/freqtrade/templates/FreqaiHybridExampleStrategy.py b/freqtrade/templates/FreqaiHybridExampleStrategy.py
new file mode 100644
index 000000000..0a91455f5
--- /dev/null
+++ b/freqtrade/templates/FreqaiHybridExampleStrategy.py
@@ -0,0 +1,259 @@
+import logging
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical import qtpylib
+
+from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair
+
+
+logger = logging.getLogger(__name__)
+
+
+class FreqaiExampleHybridStrategy(IStrategy):
+    """
+    Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
+    FreqAI to bolster a typical Freqtrade strategy.
+
+    Launching this strategy would be:
+
+    freqtrade trade --strategy FreqaiExampleHyridStrategy --strategy-path freqtrade/templates
+    --freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
+
+    or the user simply adds this to their config:
+
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": true,
+        "train_period_days": 15,
+        "identifier": "uniqe-id",
+        "feature_parameters": {
+            "include_timeframes": [
+                "3m",
+                "15m",
+                "1h"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT",
+                "ETH/USDT"
+            ],
+            "label_period_candles": 20,
+            "include_shifted_candles": 2,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "principal_component_analysis": false,
+            "use_SVM_to_remove_outliers": true,
+            "indicator_max_period_candles": 20,
+            "indicator_periods_candles": [10, 20]
+        },
+        "data_split_parameters": {
+            "test_size": 0,
+            "random_state": 1
+        },
+        "model_training_parameters": {
+            "n_estimators": 800
+        }
+    },
+
+    Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
+    """
+
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04
+    }
+
+    plot_config = {
+        'main_plot': {
+            'tema': {},
+        },
+        'subplots': {
+            "MACD": {
+                'macd': {'color': 'blue'},
+                'macdsignal': {'color': 'orange'},
+            },
+            "RSI": {
+                'rsi': {'color': 'red'},
+            },
+            "Up_or_down": {
+                '&s-up_or_down': {'color': 'green'},
+            }
+        }
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = True
+
+    # Hyperoptable parameters
+    buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
+    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
+    short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
+    exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
+
+    # FreqAI required function, leave as is or add additional informatives to existing structure.
+    def informative_pairs(self):
+        whitelist_pairs = self.dp.current_whitelist()
+        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+        informative_pairs = []
+        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+            for pair in whitelist_pairs:
+                informative_pairs.append((pair, tf))
+            for pair in corr_pairs:
+                if pair in whitelist_pairs:
+                    continue  # avoid duplication
+                informative_pairs.append((pair, tf))
+        return informative_pairs
+
+    # FreqAI required function, user can add or remove indicators, but general structure
+    # must stay the same.
+    def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+        """
+        User feeds these indicators to FreqAI to train a classifier to decide
+        if the market will go up or down.
+
+        :param pair: pair to be used as informative
+        :param df: strategy dataframe which will receive merges from informatives
+        :param tf: timeframe of the dataframe which will modify the feature names
+        :param informative: the dataframe associated with the informative pair
+        """
+
+        coin = pair.split('/')[0]
+
+        if informative is None:
+            informative = self.dp.get_pair_dataframe(pair, tf)
+
+        # first loop is automatically duplicating indicators for time periods
+        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+
+            t = int(t)
+            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+            informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
+            informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+            informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
+            informative[f"%-{coin}relative_volume-period_{t}"] = (
+                informative["volume"] / informative["volume"].rolling(t).mean()
+            )
+
+        # FreqAI needs the following lines in order to detect features and automatically
+        # expand upon them.
+        indicators = [col for col in informative if col.startswith("%")]
+        # This loop duplicates and shifts all indicators to add a sense of recency to data
+        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
+            if n == 0:
+                continue
+            informative_shift = informative[indicators].shift(n)
+            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
+            informative = pd.concat((informative, informative_shift), axis=1)
+
+        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
+        skip_columns = [
+            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        df = df.drop(columns=skip_columns)
+
+        # User can set the "target" here (in present case it is the
+        # "up" or "down")
+        if set_generalized_indicators:
+            # User "looks into the future" here to figure out if the future
+            # will be "up" or "down". This same column name is available to
+            # the user
+            df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
+                                           df["close"], 'up', 'down')
+
+        return df
+
+    # flake8: noqa: C901
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # User creates their own custom strat here. Present example is a supertrend
+        # based strategy.
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        # TA indicators to combine with the Freqai targets
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Bollinger Bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe["bb_percent"] = (
+            (dataframe["close"] - dataframe["bb_lowerband"]) /
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+        )
+        dataframe["bb_width"] = (
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+        )
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) &
+                (df['tema'] <= df['bb_middleband']) &  # Guard: tema below BB middle
+                (df['tema'] > df['tema'].shift(1)) &  # Guard: tema is raising
+                (df['volume'] > 0) &  # Make sure Volume is not 0
+                (df['do_predict'] == 1) &  # Make sure Freqai is confident in the prediction
+                # Only enter trade if Freqai thinks the trend is in this direction
+                (df['&s-up_or_down'] == 'up')
+            ),
+            'enter_long'] = 1
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) &
+                (df['tema'] > df['bb_middleband']) &  # Guard: tema above BB middle
+                (df['tema'] < df['tema'].shift(1)) &  # Guard: tema is falling
+                (df['volume'] > 0) &  # Make sure Volume is not 0
+                (df['do_predict'] == 1) &  # Make sure Freqai is confident in the prediction
+                # Only enter trade if Freqai thinks the trend is in this direction
+                (df['&s-up_or_down'] == 'down')
+            ),
+            'enter_short'] = 1
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) &
+                (df['tema'] > df['bb_middleband']) &  # Guard: tema above BB middle
+                (df['tema'] < df['tema'].shift(1)) &  # Guard: tema is falling
+                (df['volume'] > 0)  # Make sure Volume is not 0
+            ),
+
+            'exit_long'] = 1
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) &
+                # Guard: tema below BB middle
+                (df['tema'] <= df['bb_middleband']) &
+                (df['tema'] > df['tema'].shift(1)) &  # Guard: tema is raising
+                (df['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'exit_short'] = 1
+
+        return df