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The "GodCard" strategy is a backtesting strategy implemented using the Freqtrade framework. Here is a short description of what the strategy does:
The strategy is designed for a 5-minute timeframe. It includes various parameters that can be optimized during backtesting, such as cooldown_lookback, stop_duration, and use_stop_protection.
The strategy uses protection mechanisms like cooldown periods and stop-loss guards to mitigate risks.
It employs technical indicators such as RSI (Relative Strength Index), SAR Parabol, and Bollinger Bands to make buy and sell decisions.
The buy conditions include criteria like RSI thresholds and the position of the closing price relative to Bollinger Bands. The sell conditions consider RSI thresholds and the position of the SAR Parabol indicator relative to the closing price and Bollinger Bands. The strategy includes a minimal ROI (Return on Investment) table that defines the desired profits at different holding periods. It incorporates a stop-loss mechanism to limit potential losses. The strategy supports trailing stop functionality to secure profits as the price moves in a favorable direction. The informative_pairs function returns an empty list, indicating that there are no specific pairs to prioritize for trading. The populate_indicators function calculates and adds various indicators to the dataframe, including RSI, SAR Parabol, and multiple Bollinger Bands. The populate_buy_trend function applies the defined buy conditions and marks the corresponding buy signals in the dataframe. The populate_sell_trend function applies the defined sell conditions and marks the corresponding sell signals in the dataframe. Overall, the GodCard strategy combines multiple indicators and conditions to generate buy and sell signals for backtesting trading strategies.
The strategy uses protection mechanisms like cooldown periods and stop-loss guards to mitigate risks.
It employs technical indicators such as RSI (Relative Strength Index), SAR Parabol, and Bollinger Bands to make buy and sell decisions.
The buy conditions include criteria like RSI thresholds and the position of the closing price relative to Bollinger Bands. The sell conditions consider RSI thresholds and the position of the SAR Parabol indicator relative to the closing price and Bollinger Bands. The strategy includes a minimal ROI (Return on Investment) table that defines the desired profits at different holding periods. It incorporates a stop-loss mechanism to limit potential losses. The strategy supports trailing stop functionality to secure profits as the price moves in a favorable direction. The informative_pairs function returns an empty list, indicating that there are no specific pairs to prioritize for trading. The populate_indicators function calculates and adds various indicators to the dataframe, including RSI, SAR Parabol, and multiple Bollinger Bands. The populate_buy_trend function applies the defined buy conditions and marks the corresponding buy signals in the dataframe. The populate_sell_trend function applies the defined sell conditions and marks the corresponding sell signals in the dataframe. Overall, the GodCard strategy combines multiple indicators and conditions to generate buy and sell signals for backtesting trading strategies.
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stoploss: -0.087 timeframe: 5m hash(sha256): e06d7d46985c24a1965691ad4e755f96f257d5395087cc9639e4bcbd279fc350 indicators: upper mid lower bb_middleband2 bb_middleband3 bb_upperband3 volume bb_middleband rsi close bb_upperband bb_lowerband bb_lowerband3 bb_lowerband2 sar bb_upperband2
No similar strategies found. (based on used indicators)
last change: 2024-08-02 10:35:13