stable/docs/strategy_migration.md

Strategy Migration between V2 and V3

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.

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.

Quick summary / migration checklist

• Strategy methods:
  • populate_buy_trend() -> populate_entry_trend()
  • populate_sell_trend() -> populate_exit_trend()
  • custom_sell() -> custom_exit()
• Dataframe columns:
  • buy -> enter_long
  • sell -> exit_long
  • buy_tag -> enter_tag (used for both long and short trades)
  • New column enter_short and corresponding new column exit_short
• trade-object now has the following new properties: is_short, enter_side, exit_side and trade_direction.
• New side argument to callbacks without trade object
  • custom_stake_amount
  • confirm_trade_entry
• Renamed trade.nr_of_successful_buys to trade.nr_of_successful_entries (mostly relevant for adjust_trade_position()).
• Introduced new leverage callback.
• Informative pairs can now pass a 3rd element in the Tuple, defining the candle type.
• @informative decorator now takes an optional candle_type argument.
• helper methods stoploss_from_open and stoploss_from_absolute now take is_short as additional argument.
• INTERFACE_VERSION should be set to 3.
• Strategy/Configuration settings.
  • order_time_in_force buy -> entry, sell -> exit.
  • order_types buy -> entry, sell -> exit.

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.

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:

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".

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

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

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()
        # ...

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-stake-amount

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

class AwesomeStrategy(IStrategy):
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            entry_tag: Optional[str], **kwargs) -> float:
        # ... 
        return proposed_stake

class AwesomeStrategy(IStrategy):
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            entry_tag: Optional[str], side: str, **kwargs) -> float:
        # ... 
        return proposed_stake

confirm_trade_entry

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

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:

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], 
                            side: str, **kwargs) -> bool:
      return True

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.

    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

    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, is_short=trade.is_short)

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

Strategy/Configuration settings

order_time_in_force

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

    order_time_in_force: Dict = {
        "buy": "gtc",
        "sell": "gtc",
    }

    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.

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

    order_types = {
        "entry": "limit",
        "exit": "limit",
        "emergencyexit": "market",
        "forceexit": "market",
        "forceentry": "market",
        "stoploss": "market",
        "stoploss_on_exchange": false,
        "stoploss_on_exchange_interval": 60