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Merge pull request #1 from froggleston/frog-custom-sell-doc

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Update strategy-advanced.md
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Robert Davey 2021-05-10 19:48:09 +01:00 committed by GitHub
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@ -48,7 +48,7 @@ It is possible to define custom sell signals, indicating that specified position
For example you could implement a 1:2 risk-reward ROI with `custom_sell()`. For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
You should abstain from using custom_sell() signals in place of stoplosses though. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange. Using custom_sell() signals in place of stoplosses though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
!!! Note !!! Note
Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters. Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
@ -62,17 +62,19 @@ class AwesomeStrategy(IStrategy):
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs): current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# Get the row at trade open
trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
trade_row = dataframe.loc[dataframe['date'] == trade_open_date].squeeze() trade_open_row = dataframe.loc[dataframe['date'] == trade_open_date].squeeze()
# Above 20% profit, sell when rsi < 80 # Above 20% profit, sell when rsi < 80
if current_profit > 0.2: if current_profit > 0.2:
if trade_row['rsi'] < 80: if trade_open_row['rsi'] < 80:
return 'rsi_below_80' return 'rsi_below_80'
# Between 2% and 10%, sell if EMA-long above EMA-short # Between 2% and 10%, sell if EMA-long above EMA-short
if 0.02 < current_profit < 0.1: if 0.02 < current_profit < 0.1:
if trade_row['emalong'] > trade_row['emashort']: if trade_open_row['emalong'] > trade_open_row['emashort']:
return 'ema_long_below_80' return 'ema_long_below_80'
# Sell any positions at a loss if they are held for more than one day. # Sell any positions at a loss if they are held for more than one day.