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The Kamaflage strategy is a trading strategy implemented in Python for backtesting purposes. It consists of three main functions: populate_indicators, populate_buy_trend, and populate_sell_trend. In the populate_indicators function, several technical indicators are calculated and added to the input dataframe.
These indicators include the SAR (Stop and Reverse), RMI (Relative Momentum Index), KAMA (Kaufman's Adaptive Moving Average) with different time periods, MACD (Moving Average Convergence Divergence) and its components, and a volume moving average.
The populate_buy_trend function defines the conditions for generating a buy signal.
It checks various conditions, including the relationship between different KAMA time periods, MACD values, RMI values, and the volume compared to the volume moving average. If these conditions are met, the buy column in the dataframe is set to 1. The populate_sell_trend function determines the conditions for generating a sell signal. It considers whether there is an active trade, the RMI value, the current profit of the trade, and the volume. If the conditions are satisfied, the sell column in the dataframe is set to 1. There are also some additional code snippets that check price conditions for buying or selling orders based on the orderbook information. These snippets are not directly related to the strategy itself. Finally, there is a commented code block that seems to be related to determining whether to continue a trade based on the achieved return on investment (ROI). It checks the ROI value and compares it with the maximum rate, the current rate, and the open rate of the trade. However, this code block is currently commented out and not used in the strategy.
These indicators include the SAR (Stop and Reverse), RMI (Relative Momentum Index), KAMA (Kaufman's Adaptive Moving Average) with different time periods, MACD (Moving Average Convergence Divergence) and its components, and a volume moving average.
The populate_buy_trend function defines the conditions for generating a buy signal.
It checks various conditions, including the relationship between different KAMA time periods, MACD values, RMI values, and the volume compared to the volume moving average. If these conditions are met, the buy column in the dataframe is set to 1. The populate_sell_trend function determines the conditions for generating a sell signal. It considers whether there is an active trade, the RMI value, the current profit of the trade, and the volume. If the conditions are satisfied, the sell column in the dataframe is set to 1. There are also some additional code snippets that check price conditions for buying or selling orders based on the orderbook information. These snippets are not directly related to the strategy itself. Finally, there is a commented code block that seems to be related to determining whether to continue a trade based on the achieved return on investment (ROI). It checks the ROI value and compares it with the maximum rate, the current rate, and the open rate of the trade. However, this code block is currently commented out and not used in the strategy.
startup_candle_count : 20 sar: -0.277%
stoploss: -1 timeframe: 5m hash(sha256): 7bbb3db17a366343e89aac29cc3a879d6a7fc3e585555bd9f5c70e663e46be7b indicators: bids macdsignal sell kama3 rmi volume_ma volume asks kama21 close price macd runmode buy macdhist sar
No similar strategies found. (based on used indicators)
last change: 2023-06-30 13:15:20