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The "sniper" strategy is a backtesting trading strategy implemented in Python. It uses various technical indicators and signals to generate buy and sell signals for trading. Here is a brief description of the strategy:
Import necessary libraries: numpy, pandas, and technical indicators.
Define the "sniper" class as a subclass of the Freqtrade's IStrategy class.
Set the strategy's interface version and define the minimal ROI (return on investment) table.
Set the stop loss and trailing stop parameters for the strategy. Specify the timeframe for the strategy and set it to '15m' (15 minutes). Enable the processing of only new candles for efficiency. Define the order types for buy and sell signals. Set up the plot configuration for visualizing indicators. Implement the "populate_indicators" function to calculate various technical indicators such as Aroon, Ichimoku, Pivot Points, VWMACD, VIDYA, TD Sequential, and others. The calculated indicators are added as additional columns to the dataframe. Implement the "populate_buy_trend" function to define the conditions for generating buy signals based on crossed above signals of the close price and VIDYA indicator, TD Sequential count equal to 9, and Aroon Up crossing above 77. Implement the "populate_sell_trend" function to define the conditions for generating sell signals based on crossed below signals of Aroon Down below 68, Aroon Up below 24, Tenkan Sen crossing below Kijun Sen, and positive volume. The strategy aims to identify potential buying opportunities when certain conditions are met and selling opportunities when other conditions are met. The specific indicator values and parameters used in the strategy can be adjusted and optimized for different trading instruments and timeframes.
Define the "sniper" class as a subclass of the Freqtrade's IStrategy class.
Set the strategy's interface version and define the minimal ROI (return on investment) table.
Set the stop loss and trailing stop parameters for the strategy. Specify the timeframe for the strategy and set it to '15m' (15 minutes). Enable the processing of only new candles for efficiency. Define the order types for buy and sell signals. Set up the plot configuration for visualizing indicators. Implement the "populate_indicators" function to calculate various technical indicators such as Aroon, Ichimoku, Pivot Points, VWMACD, VIDYA, TD Sequential, and others. The calculated indicators are added as additional columns to the dataframe. Implement the "populate_buy_trend" function to define the conditions for generating buy signals based on crossed above signals of the close price and VIDYA indicator, TD Sequential count equal to 9, and Aroon Up crossing above 77. Implement the "populate_sell_trend" function to define the conditions for generating sell signals based on crossed below signals of Aroon Down below 68, Aroon Up below 24, Tenkan Sen crossing below Kijun Sen, and positive volume. The strategy aims to identify potential buying opportunities when certain conditions are met and selling opportunities when other conditions are met. The specific indicator values and parameters used in the strategy can be adjusted and optimized for different trading instruments and timeframes.
startup_candle_count : 30 VIDYA: -70.209%
stoploss: -0.5 timeframe: 15m hash(sha256): 20902b0badefd430af0a81c917ffdf890144953e94c171bfbab118f7b4008d09 indicators: kijun_sen aroonup volume vwmacds signal vwmacd tenkan close VIDYA td kijun aroondown pivot TD_count tenkan_sen
No similar strategies found. (based on used indicators)
last change: 2023-07-01 13:06:54