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The Dimond strategy is a backtesting strategy implemented as a class that inherits from the IStrategy interface. It consists of three main functions: populate_indicators, populate_buy_trend, and populate_sell_trend. The populate_indicators function takes a dataframe and metadata as input and returns the same dataframe.
In this strategy, it doesn't modify the dataframe or add any additional indicators.
The populate_buy_trend function is responsible for populating the buy signals in the dataframe.
It calculates two exponential moving averages (EMAs) based on the specified buy_fast and buy_slow values. It then checks for a bullish crossover condition where the buy_ema_fast crosses above the buy_ema_slow. If the condition is met, it assigns a value of 1 to the 'buy' column in the dataframe. The populate_sell_trend function is similar to populate_buy_trend but handles the sell signals. It uses different parameters (sell_push, sell_shift, sell_ema_fast, and sell_ema_slow) when DUALFIT is not True. It calculates the EMAs based on the buy_fast and buy_slow values. It checks for a bearish crossover condition where the sell_ema_fast crosses below the sell_ema_slow multiplied by the specified sell_push value. If the condition is met, it assigns a value of 1 to the 'sell' column in the dataframe. Overall, the strategy identifies buy signals when the fast EMA crosses above the slow EMA and sell signals when the fast EMA crosses below the slow EMA (with additional conditions for sell signals when DUALFIT is not True).
In this strategy, it doesn't modify the dataframe or add any additional indicators.
The populate_buy_trend function is responsible for populating the buy signals in the dataframe.
It calculates two exponential moving averages (EMAs) based on the specified buy_fast and buy_slow values. It then checks for a bullish crossover condition where the buy_ema_fast crosses above the buy_ema_slow. If the condition is met, it assigns a value of 1 to the 'buy' column in the dataframe. The populate_sell_trend function is similar to populate_buy_trend but handles the sell signals. It uses different parameters (sell_push, sell_shift, sell_ema_fast, and sell_ema_slow) when DUALFIT is not True. It calculates the EMAs based on the buy_fast and buy_slow values. It checks for a bearish crossover condition where the sell_ema_fast crosses below the sell_ema_slow multiplied by the specified sell_push value. If the condition is met, it assigns a value of 1 to the 'sell' column in the dataframe. Overall, the strategy identifies buy signals when the fast EMA crosses above the slow EMA and sell signals when the fast EMA crosses below the slow EMA (with additional conditions for sell signals when DUALFIT is not True).
stoploss: -0.241 timeframe: 5m hash(sha256): 36d476b4e3e23a40cb7952a1c193214cf457c1bfcad8dafa905bc7c508cecc8f indicators: sell_ema_slow buy_ema_slow sell_ema_fast buy_ema_fast
No similar strategies found. (based on used indicators)
last change: 2024-04-29 19:41:47