169 lines
6.6 KiB
Markdown
169 lines
6.6 KiB
Markdown
# Strategy Migration between V2 and V3
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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.
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To support new markets and trade-types (namely short trades / trades with leverage), some things had to change in the interface.
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If you intend on using markets other than spot markets, please migrate your strategy to the new format.
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## Quick summary / migration checklist
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* Dataframe columns:
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* `buy` -> `enter_long`
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* `sell` -> `exit_long`
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* `buy_tag` -> `enter_tag` (used for both long and short trades)
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* New column `enter_short` and corresponding new column `exit_short`
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* trade-object now has the following new properties: `is_short`, `enter_side`, `exit_side` and `trade_direction`.
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* New `side` argument to callbacks without trade object
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* `custom_stake_amount`
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* `confirm_trade_entry`
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* Renamed `trade.nr_of_successful_buys` to `trade.nr_of_successful_entries`.
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* Introduced new [`leverage` callback](strategy-callbacks.md#leverage-callback)
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* Informative pairs can now pass a 3rd element in the Tuple, defining the candle type.
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* `@informative` decorator now takes an optional `candle_type` argument
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* helper methods `stoploss_from_open` and `stoploss_from_absolute` now take `is_short` as additional argument.
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* `INTERFACE_VERSION` should be set to 3.
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## Extensive explanation
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### `populate_buy_trend`
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In `populate_buy_trend()` - you will want to change the columns you assign from `'buy`' to `'enter_long`
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```python hl_lines="9"
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
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(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard
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(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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['buy', 'buy_tag']] = (1, 'rsi_cross')
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return dataframe
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```
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After:
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```python hl_lines="9"
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
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(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard
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(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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['enter_long', 'enter_tag']] = (1, 'rsi_cross')
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return dataframe
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```
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### `populate_sell_trend`
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``` python hl_lines="9"
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
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(dataframe['tema'] > dataframe['bb_middleband']) & # Guard
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(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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['sell', 'exit_tag']] = (1, 'some_exit_tag')
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return dataframe
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```
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After
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``` python hl_lines="9"
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
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(dataframe['tema'] > dataframe['bb_middleband']) & # Guard
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(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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['exit_long', 'exit_tag']] = (1, 'some_exit_tag')
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return dataframe
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```
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### Custom-stake-amount
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New string argument `side` - which can be either `"long"` or `"short"`.
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``` python hl_lines="4"
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class AwesomeStrategy(IStrategy):
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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proposed_stake: float, min_stake: float, max_stake: float,
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entry_tag: Optional[str], **kwargs) -> float:
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# ...
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return proposed_stake
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```
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``` python hl_lines="4"
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class AwesomeStrategy(IStrategy):
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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proposed_stake: float, min_stake: float, max_stake: float,
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entry_tag: Optional[str], side: str, **kwargs) -> float:
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# ...
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return proposed_stake
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```
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### `confirm_trade_entry`
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New string argument `side` - which can be either `"long"` or `"short"`.
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``` python hl_lines="5"
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class AwesomeStrategy(IStrategy):
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: Optional[str],
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**kwargs) -> bool:
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return True
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```
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After:
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``` python hl_lines="5"
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class AwesomeStrategy(IStrategy):
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: Optional[str],
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side: str, **kwargs) -> bool:
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return True
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```
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### Adjust trade position changes
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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.
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### Helper methods
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Added argument "is_short" to `stoploss_from_open` and `stoploss_from_absolute`.
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This should be given the value of `trade.is_short`.
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``` python hl_lines="5 7"
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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# once the profit has risen above 10%, keep the stoploss at 7% above the open price
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if current_profit > 0.10:
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return stoploss_from_open(0.07, current_profit)
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return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
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return 1
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```
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``` python hl_lines="5 7"
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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# once the profit has risen above 10%, keep the stoploss at 7% above the open price
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if current_profit > 0.10:
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return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
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return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)
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```
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