The "GuruSkippyasurmuni" strategy is a backtesting strategy implemented as a class that inherits from the "IStrategy" interface. Here's a brief description of what the strategy does:
The method "populate_indicators" is responsible for populating indicators in the trading dataframe. It returns the updated dataframe.
The method "populate_buy_trend" generates buy conditions based on multiple indicators and their values.
It creates a list of conditions and applies them to the dataframe, marking the "buy" column as 1 for the corresponding rows that meet the conditions.
The updated dataframe is returned. The method "populate_sell_trend" is similar to "populate_buy_trend" but generates sell conditions instead. It creates a list of conditions and applies them to the dataframe, marking the "sell" column as 1 for the corresponding rows that meet the conditions. The updated dataframe is returned. The method "should_cancel(self, trade: Trade, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs)" determines whether a trade should be canceled. If the sell reason is 'sell_signal' and the calculated profit ratio is less than 0.055, it returns False to cancel the trade; otherwise, it returns True. The method "get_custom_stake(self, proposed_stake: float, min_stake: float, max_stake: float, **kwargs)" calculates the custom stake amount for a trade based on the current total stake amount, maximum number of open trades, and a position adjustment factor. If the custom stake is greater than or equal to the minimum stake, it returns the custom stake; if it's less than the minimum stake, it returns the minimum stake; otherwise, it returns the proposed stake. The method "get_stop_loss_stake(self, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs)" is used to determine the stop-loss stake for a trade. It retrieves the analyzed dataframe for the trade pair and timeframe, checks if it's valid, and updates the last candle date in the custom_info dictionary. If the current profit is greater than -0.105, it returns None. If the last candle's buy value is greater than 0, it calculates the stake amount based on the number of successful buys and a multiplier, ensuring it's greater than or equal to the minimum stake. If there's an exception or the stake amount is less than the minimum stake, it returns None; otherwise, it returns the calculated stake amount. Overall, the strategy utilizes indicators, generates buy and sell conditions, determines whether to cancel a trade, calculates stake amounts, and applies these rules to the trading dataframe for backtesting.
The method "populate_buy_trend" generates buy conditions based on multiple indicators and their values.
It creates a list of conditions and applies them to the dataframe, marking the "buy" column as 1 for the corresponding rows that meet the conditions.
The updated dataframe is returned. The method "populate_sell_trend" is similar to "populate_buy_trend" but generates sell conditions instead. It creates a list of conditions and applies them to the dataframe, marking the "sell" column as 1 for the corresponding rows that meet the conditions. The updated dataframe is returned. The method "should_cancel(self, trade: Trade, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs)" determines whether a trade should be canceled. If the sell reason is 'sell_signal' and the calculated profit ratio is less than 0.055, it returns False to cancel the trade; otherwise, it returns True. The method "get_custom_stake(self, proposed_stake: float, min_stake: float, max_stake: float, **kwargs)" calculates the custom stake amount for a trade based on the current total stake amount, maximum number of open trades, and a position adjustment factor. If the custom stake is greater than or equal to the minimum stake, it returns the custom stake; if it's less than the minimum stake, it returns the minimum stake; otherwise, it returns the proposed stake. The method "get_stop_loss_stake(self, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs)" is used to determine the stop-loss stake for a trade. It retrieves the analyzed dataframe for the trade pair and timeframe, checks if it's valid, and updates the last candle date in the custom_info dictionary. If the current profit is greater than -0.105, it returns None. If the last candle's buy value is greater than 0, it calculates the stake amount based on the number of successful buys and a multiplier, ensuring it's greater than or equal to the minimum stake. If there's an exception or the stake amount is less than the minimum stake, it returns None; otherwise, it returns the calculated stake amount. Overall, the strategy utilizes indicators, generates buy and sell conditions, determines whether to cancel a trade, calculates stake amounts, and applies these rules to the trading dataframe for backtesting.
stoploss: -1 timeframe: 5m hash(sha256): f86ffa6384b4e3a1a71b51814c34d9e2e472e151619eaf4008b8d13d44c266dc indicators: volume indicator_trend_sma crossed_indicator max_open_trades date indicator buy sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-29 02:44:37