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The "min5v5" strategy is designed for backtesting trading strategies. It is implemented as a class that inherits from the "IStrategy" class. Here's a brief description of what the strategy does:
"populate_indicators" method:
This method populates the indicators for the strategy using the input dataframe and metadata.
It calls the "informative_15m_indicators" function to generate informative 15-minute indicators.
The informative indicators are merged with the main dataframe using the "merge_informative_pair" function.
Finally, the normal timeframe indicators are calculated using the "normal_tf_indicators" function. The updated dataframe with indicators is returned. "populate_buy_trend" method: This method populates the buy signals for the strategy using the input dataframe and metadata. It defines a list called "dont_buy_conditions" to store conditions for not buying. Several conditions are appended to the "dont_buy_conditions" list, based on various criteria. The dataframe is updated with buy signals when specific conditions are met, setting the "buy" and "buy_tag" columns accordingly. If any of the "dont_buy_conditions" are met, the buy signals are set to 0. The updated dataframe with buy signals is returned. "populate_sell_trend" method: This method populates the sell signals for the strategy using the input dataframe and metadata. Conditions for selling are defined and stored in the "conditions" list. The dataframe is updated with sell signals when any of the defined conditions are met, setting the "sell" column to 1. The updated dataframe with sell signals is returned. Overall, the strategy calculates indicators, determines buy signals based on specific conditions, and generates sell signals based on different criteria. It allows for backtesting and evaluating the performance of the trading strategy.
It calls the "informative_15m_indicators" function to generate informative 15-minute indicators.
The informative indicators are merged with the main dataframe using the "merge_informative_pair" function.
Finally, the normal timeframe indicators are calculated using the "normal_tf_indicators" function. The updated dataframe with indicators is returned. "populate_buy_trend" method: This method populates the buy signals for the strategy using the input dataframe and metadata. It defines a list called "dont_buy_conditions" to store conditions for not buying. Several conditions are appended to the "dont_buy_conditions" list, based on various criteria. The dataframe is updated with buy signals when specific conditions are met, setting the "buy" and "buy_tag" columns accordingly. If any of the "dont_buy_conditions" are met, the buy signals are set to 0. The updated dataframe with buy signals is returned. "populate_sell_trend" method: This method populates the sell signals for the strategy using the input dataframe and metadata. Conditions for selling are defined and stored in the "conditions" list. The dataframe is updated with sell signals when any of the defined conditions are met, setting the "sell" column to 1. The updated dataframe with sell signals is returned. Overall, the strategy calculates indicators, determines buy signals based on specific conditions, and generates sell signals based on different criteria. It allows for backtesting and evaluating the performance of the trading strategy.
startup_candle_count : 200 hma_50: 0.001% ema_100: -0.026% rsi_slow: 0.002%
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.318 timeframe: 5m hash(sha256): 20781deb6797ac6f7b722489add6d84f6013cf4bf2dc0f13d9f0de01093e435d indicators: rsi_buy EWO ewo_high ewo_low high_offset_2 close ewo_high_2 sell_signal profit_threshold rsi_fast ma_sell_val ma_buy_val volume low_offset retries high_offset low_offset_2 base_nb_candles_buy rsi_fast_buy ___retries base_nb_candles_sell hma_50 buy buy_tag sma_9 ema_100 close_15m lookback_candles max_slippage rsi_slow rsi low
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last change: 2024-09-29 01:20:44