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Strategy Migration between V2 and V3

docs/strategy_migration.md

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Strategy Migration between V2 and V3

To support new markets and trade-types (namely short trades / trades with leverage), some things had to change in the interface. If you intend on using markets other than spot markets, please migrate your strategy to the new format.

We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in spot markets, there should be no changes necessary for now.

You can use the quick summary as checklist. Please refer to the detailed sections below for full migration details.

Quick summary / migration checklist

Note : forcesell, forcebuy, emergencysell are changed to force_exit, force_enter, emergency_exit respectively.

Extensive explanation

populate_buy_trend

In populate_buy_trend() - you will want to change the columns you assign from 'buy' to 'enter_long', as well as the method name from populate_buy_trend to populate_entry_trend.

python
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['buy', 'buy_tag']] = (1, 'rsi_cross')

    return dataframe

After:

python
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')

    return dataframe

Please refer to the Strategy documentation on how to enter and exit short trades.

populate_sell_trend

Similar to populate_buy_trend, populate_sell_trend() will be renamed to populate_exit_trend(). We'll also change the column from 'sell' to 'exit_long'.

python
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['sell', 'exit_tag']] = (1, 'some_exit_tag')
    return dataframe

After

python
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['exit_long', 'exit_tag']] = (1, 'some_exit_tag')
    return dataframe

Please refer to the Strategy documentation on how to enter and exit short trades.

custom_sell

custom_sell has been renamed to custom_exit. It's now also being called for every iteration, independent of current profit and exit_profit_only settings.

python
class AwesomeStrategy(IStrategy):
    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        # ...
python
class AwesomeStrategy(IStrategy):
    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        # ...

custom_entry_timeout

check_buy_timeout() has been renamed to check_entry_timeout(), and check_sell_timeout() has been renamed to check_exit_timeout().

python
class AwesomeStrategy(IStrategy):
    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, 
                            current_time: datetime, **kwargs) -> bool:
        return False

    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, 
                            current_time: datetime, **kwargs) -> bool:
        return False
python
class AwesomeStrategy(IStrategy):
    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
                            current_time: datetime, **kwargs) -> bool:
        return False

    def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
                            current_time: datetime, **kwargs) -> bool:
        return False

custom_stake_amount

New string argument side - which can be either "long" or "short".

python
class AwesomeStrategy(IStrategy):
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
                            entry_tag: Optional[str], **kwargs) -> float:
        # ... 
        return proposed_stake
python
class AwesomeStrategy(IStrategy):
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float | None, max_stake: float,
                            entry_tag: str | None, side: str, **kwargs) -> float:
        # ... 
        return proposed_stake

confirm_trade_entry

New string argument side - which can be either "long" or "short".

python
class AwesomeStrategy(IStrategy):
    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
                            **kwargs) -> bool:
      return True

After:

python
class AwesomeStrategy(IStrategy):
    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                            time_in_force: str, current_time: datetime, entry_tag: str | None, 
                            side: str, **kwargs) -> bool:
      return True

confirm_trade_exit

Changed argument sell_reason to exit_reason. For compatibility, sell_reason will still be provided for a limited time.

python
class AwesomeStrategy(IStrategy):
    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str,
                           current_time: datetime, **kwargs) -> bool:
    return True

After:

python
class AwesomeStrategy(IStrategy):
    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, exit_reason: str,
                           current_time: datetime, **kwargs) -> bool:
    return True

custom_entry_price

New string argument side - which can be either "long" or "short".

python
class AwesomeStrategy(IStrategy):
    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
                           entry_tag: Optional[str], **kwargs) -> float:
      return proposed_rate

After:

python
class AwesomeStrategy(IStrategy):
    def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
                           entry_tag: str | None, side: str, **kwargs) -> float:
      return proposed_rate

Adjust trade position changes

While adjust-trade-position itself did not change, you should no longer use trade.nr_of_successful_buys - and instead use trade.nr_of_successful_entries, which will also include short entries.

Helper methods

Added argument "is_short" to stoploss_from_open and stoploss_from_absolute. This should be given the value of trade.is_short.

python
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
        if current_profit > 0.10:
            return stoploss_from_open(0.07, current_profit)

        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)

        return 1

After:

python
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, after_fill: bool, 
                        **kwargs) -> float | None:
        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
        if current_profit > 0.10:
            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)

        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short, leverage=trade.leverage)

Strategy/Configuration settings

order_time_in_force

order_time_in_force attributes changed from "buy" to "entry" and "sell" to "exit".

python
    order_time_in_force: dict = {
        "buy": "gtc",
        "sell": "gtc",
    }

After:

python
    order_time_in_force: dict = {
        "entry": "GTC",
        "exit": "GTC",
    }

order_types

order_types have changed all wordings from buy to entry - and sell to exit. And two words are joined with _.

python
    order_types = {
        "buy": "limit",
        "sell": "limit",
        "emergencysell": "market",
        "forcesell": "market",
        "forcebuy": "market",
        "stoploss": "market",
        "stoploss_on_exchange": false,
        "stoploss_on_exchange_interval": 60
    }

After:

python
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "emergency_exit": "market",
        "force_exit": "market",
        "force_entry": "market",
        "stoploss": "market",
        "stoploss_on_exchange": false,
        "stoploss_on_exchange_interval": 60
    }

Strategy level settings

  • use_sell_signal -> use_exit_signal
  • sell_profit_only -> exit_profit_only
  • sell_profit_offset -> exit_profit_offset
  • ignore_roi_if_buy_signal -> ignore_roi_if_entry_signal
python
    # These values can be overridden in the config.
    use_sell_signal = True
    sell_profit_only = True
    sell_profit_offset: 0.01
    ignore_roi_if_buy_signal = False

After:

python
    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset: 0.01
    ignore_roi_if_entry_signal = False

unfilledtimeout

unfilledtimeout have changed all wordings from buy to entry - and sell to exit.

python
unfilledtimeout = {
        "buy": 10,
        "sell": 10,
        "exit_timeout_count": 0,
        "unit": "minutes"
    }

After:

python
unfilledtimeout = {
        "entry": 10,
        "exit": 10,
        "exit_timeout_count": 0,
        "unit": "minutes"
    }

order pricing

Order pricing changed in 2 ways. bid_strategy was renamed to entry_pricing and ask_strategy was renamed to exit_pricing. The attributes ask_last_balance -> price_last_balance and bid_last_balance -> price_last_balance were renamed as well. Also, price-side can now be defined as ask, bid, same or other. Please refer to the pricing documentation for more information.

json
{
    "bid_strategy": {
        "price_side": "bid",
        "use_order_book": true,
        "order_book_top": 1,
        "ask_last_balance": 0.0,
        "check_depth_of_market": {
            "enabled": false,
            "bids_to_ask_delta": 1
        }
    },
    "ask_strategy":{
        "price_side": "ask",
        "use_order_book": true,
        "order_book_top": 1,
        "bid_last_balance": 0.0
        "ignore_buying_expired_candle_after": 120
    }
}

after:

json
{
    "entry_pricing": {
        "price_side": "same",
        "use_order_book": true,
        "order_book_top": 1,
        "price_last_balance": 0.0,
        "check_depth_of_market": {
            "enabled": false,
            "bids_to_ask_delta": 1
        }
    },
    "exit_pricing":{
        "price_side": "same",
        "use_order_book": true,
        "order_book_top": 1,
        "price_last_balance": 0.0
    },
    "ignore_buying_expired_candle_after": 120
}

FreqAI strategy

The populate_any_indicators() method has been split into feature_engineering_expand_all(), feature_engineering_expand_basic(), feature_engineering_standard() andset_freqai_targets().

For each new function, the pair (and timeframe where necessary) will be automatically added to the column. As such, the definition of features becomes much simpler with the new logic.

For a full explanation of each method, please go to the corresponding freqAI documentation page

python

def populate_any_indicators(
        self, pair, df, tf, informative=None, set_generalized_indicators=False
    ):

        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"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)

            bollinger = qtpylib.bollinger_bands(
                qtpylib.typical_price(informative), window=t, stds=2.2
            )
            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]

            informative[f"%-{pair}bb_width-period_{t}"] = (
                informative[f"{pair}bb_upperband-period_{t}"]
                - informative[f"{pair}bb_lowerband-period_{t}"]
            ) / informative[f"{pair}bb_middleband-period_{t}"]
            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
            )

            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)

            informative[f"%-{pair}relative_volume-period_{t}"] = (
                informative["volume"] / informative["volume"].rolling(t).mean()
            ) # (1)

        informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
        informative[f"%-{pair}raw_volume"] = informative["volume"]
        informative[f"%-{pair}raw_price"] = informative["close"]
        # (2)

        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)

        # Add generalized indicators here (because in live, it will call this
        # function to populate indicators during training). Notice how we ensure not to
        # add them multiple times
        if set_generalized_indicators:
            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
            # (3)

            # user adds targets here by prepending them with &- (see convention below)
            df["&-s_close"] = (
                df["close"]
                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
                .mean()
                / df["close"]
                - 1
            )  # (4)

        return df
  1. Features - Move to feature_engineering_expand_all
  2. Basic features, not expanded across indicator_periods_candles - move tofeature_engineering_expand_basic().
  3. Standard features which should not be expanded - move to feature_engineering_standard().
  4. Targets - Move this part to set_freqai_targets().

freqai - feature engineering expand all

Features will now expand automatically. As such, the expansion loops, as well as the {pair} / {timeframe} parts will need to be removed.

python
    def feature_engineering_expand_all(self, dataframe, period, **kwargs) -> DataFrame::
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
        `include_corr_pairs`. In other words, a single feature defined in this function
        will automatically expand to a total of
        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
        `include_corr_pairs` numbers of features added to the model.

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/stable/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/stable/freqai-feature-engineering/#defining-the-features

        :param df: strategy dataframe which will receive the features
        :param period: period of the indicator - usage example:
        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
        """

        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=period, stds=2.2
        )
        dataframe["bb_lowerband-period"] = bollinger["lower"]
        dataframe["bb_middleband-period"] = bollinger["mid"]
        dataframe["bb_upperband-period"] = bollinger["upper"]

        dataframe["%-bb_width-period"] = (
            dataframe["bb_upperband-period"]
            - dataframe["bb_lowerband-period"]
        ) / dataframe["bb_middleband-period"]
        dataframe["%-close-bb_lower-period"] = (
            dataframe["close"] / dataframe["bb_lowerband-period"]
        )

        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)

        dataframe["%-relative_volume-period"] = (
            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
        )

        return dataframe

Freqai - feature engineering basic

Basic features. Make sure to remove the {pair} part from your features.

python
    def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs) -> DataFrame::
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
        In other words, a single feature defined in this function
        will automatically expand to a total of
        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
        numbers of features added to the model.

        Features defined here will *not* be automatically duplicated on user defined
        `indicator_periods_candles`

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/stable/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/stable/freqai-feature-engineering/#defining-the-features

        :param df: strategy dataframe which will receive the features
        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
        """
        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-raw_volume"] = dataframe["volume"]
        dataframe["%-raw_price"] = dataframe["close"]
        return dataframe

FreqAI - feature engineering standard

python
    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        This optional function will be called once with the dataframe of the base timeframe.
        This is the final function to be called, which means that the dataframe entering this
        function will contain all the features and columns created by all other
        freqai_feature_engineering_* functions.

        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
        This function is a good place for any feature that should not be auto-expanded upon
        (e.g. day of the week).

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details about feature engineering available:

        https://www.freqtrade.io/en/stable/freqai-feature-engineering

        :param df: strategy dataframe which will receive the features
        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
        """
        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
        return dataframe

FreqAI - set Targets

Targets now get their own, dedicated method.

python
    def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        Required function to set the targets for the model.
        All targets must be prepended with `&` to be recognized by the FreqAI internals.

        More details about feature engineering available:

        https://www.freqtrade.io/en/stable/freqai-feature-engineering

        :param df: strategy dataframe which will receive the targets
        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
        """
        dataframe["&-s_close"] = (
            dataframe["close"]
            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
            .mean()
            / dataframe["close"]
            - 1
            )

        return dataframe

FreqAI - New data Pipeline

If you have created your own custom IFreqaiModel with a custom train()/predict() function, and you still rely on data_cleaning_train/predict(), then you will need to migrate to the new pipeline. If your model does not rely on data_cleaning_train/predict(), then you do not need to worry about this migration. That means that this migration guide is relevant for a very small percentage of power-users. If you stumbled upon this guide by mistake, feel free to inquire in depth about your problem in the Freqtrade discord server.

The conversion involves first removing data_cleaning_train/predict() and replacing them with a define_data_pipeline() and define_label_pipeline() function to your IFreqaiModel class:

python
class MyCoolFreqaiModel(BaseRegressionModel):
    """
    Some cool custom IFreqaiModel you made before Freqtrade version 2023.6
    """
    def train(
        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
    ) -> Any:

        # ... your custom stuff

        # Remove these lines
        # data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
        # self.data_cleaning_train(dk)
        # data_dictionary = dk.normalize_data(data_dictionary)
        # (1)

        # Add these lines. Now we control the pipeline fit/transform ourselves
        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)

        (dd["train_features"],
         dd["train_labels"],
         dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
                                                                  dd["train_labels"],
                                                                  dd["train_weights"])

        (dd["test_features"],
         dd["test_labels"],
         dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
                                                             dd["test_labels"],
                                                             dd["test_weights"])

        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
        dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])

        # ... your custom code

        return model

    def predict(
        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:

        # ... your custom stuff

        # Remove these lines:
        # self.data_cleaning_predict(dk)
        # (2)

        # Add these lines:
        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
            dk.data_dictionary["prediction_features"], outlier_check=True)

        # Remove this line
        # pred_df = dk.denormalize_labels_from_metadata(pred_df)
        # (3)

        # Replace with these lines
        pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
        if self.freqai_info.get("DI_threshold", 0) > 0:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers

        # ... your custom code
        return (pred_df, dk.do_predict)
  1. Data normalization and cleaning is now homogenized with the new pipeline definition. This is created in the new define_data_pipeline() and define_label_pipeline() functions. The data_cleaning_train() and data_cleaning_predict() functions are no longer used. You can override define_data_pipeline() to create your own custom pipeline if you wish.
  2. Data normalization and cleaning is now homogenized with the new pipeline definition. This is created in the new define_data_pipeline() and define_label_pipeline() functions. The data_cleaning_train() and data_cleaning_predict() functions are no longer used. You can override define_data_pipeline() to create your own custom pipeline if you wish.
  3. Data denormalization is done with the new pipeline. Replace this with the lines below.