13 KiB
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
- Strategy methods:
populate_buy_trend()->populate_entry_trend()populate_sell_trend()->populate_exit_trend()custom_sell()->custom_exit()check_buy_timeout()->check_entry_timeout()check_sell_timeout()->check_exit_timeout()- New
sideargument to callbacks without trade object - Changed argument name in
confirm_trade_exit
- Dataframe columns:
- trade-object now has the following new properties:
is_short,enter_side,exit_sideandtrade_direction.sell_reason->exit_reason
- Renamed
trade.nr_of_successful_buystotrade.nr_of_successful_entries(mostly relevant foradjust_trade_position()) - Introduced new
leveragecallback. - Informative pairs can now pass a 3rd element in the Tuple, defining the candle type.
@informativedecorator now takes an optionalcandle_typeargument.- helper methods
stoploss_from_openandstoploss_from_absolutenow takeis_shortas additional argument. INTERFACE_VERSIONshould be set to 3.- Strategy/Configuration settings.
order_time_in_forcebuy -> entry, sell -> exit.order_typesbuy -> entry, sell -> exit.unfilledtimeoutbuy -> 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_entry_timeout
check_buy_timeout() has been renamed to check_entry_timeout(), and check_sell_timeout() has been renamed to check_exit_timeout().
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
class AwesomeStrategy(IStrategy):
def check_entry_timeout(self, pair: str, trade: 'Trade', order: dict,
current_time: datetime, **kwargs) -> bool:
return False
def check_exit_timeout(self, pair: str, trade: 'Trade', order: dict,
current_time: datetime, **kwargs) -> bool:
return False
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
confirm_trade_exit
Changed argument sell_reason to exit_reason.
For compatibility, sell_reason will still be provided for a limited time.
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:
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
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
After:
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",
}
After:
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
}
After:
order_types = {
"entry": "limit",
"exit": "limit",
"emergencyexit": "market",
"forceexit": "market",
"forceentry": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_on_exchange_interval": 60
}
unfilledtimeout
unfilledtimeout have changed all wordings from buy to entry - and sell to exit.
unfilledtimeout = {
"buy": 10,
"sell": 10,
"exit_timeout_count": 0,
"unit": "minutes"
}
After:
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.
{
"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
}
}
after:
{
"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
}
}