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The RaposaDivergenceV1 strategy is designed to backtest trading strategies by incorporating various technical analysis (TA) indicators. Here is a breakdown of its key components:
populate_indicators function:
This function calculates and adds multiple TA indicators to the provided DataFrame. It is recommended to enable only the required indicators to optimize performance.
In the given code, the Relative Strength Index (RSI) indicator is calculated and added to the DataFrame.
populate_buy_trend function: This function generates the buy signals based on the TA indicators and other conditions.
The DataFrame, already populated with indicators, is used to determine the buy signal. The buy signal is triggered when certain conditions are met, such as the occurrence of consecutive higher lows in the price pattern, RSI being below a specified threshold (rsi_buy), and positive volume. The buy signal is denoted by setting the 'buy' column to 1 for the corresponding data points. populate_sell_trend function: This function generates the sell signals based on the TA indicators and other conditions. Similar to populate_buy_trend, the sell signal is determined using the DataFrame with indicators. The sell signal is triggered when conditions such as consecutive higher highs, RSI being above a specified threshold (rsi_sell), and positive volume are met. The sell signal is denoted by setting the 'sell' column to 1 for the corresponding data points. Custom functions for finding extrema: The provided code includes four additional functions (getPeaks) that are not directly part of the strategy class. These functions are used to identify consecutive higher or lower lows/highs in the price pattern. They utilize the argrelextrema function from NumPy to find the indices of extrema (peaks) in the data. Depending on the specific function, they check for consecutive higher highs, lower highs, higher lows, or lower lows within a given number of periods (order and K parameters). When the required number of consecutive extrema is found, they are stored in a list and returned. Overall, the RaposaDivergenceV1 strategy combines the calculation of TA indicators, such as RSI, with the identification of price pattern extrema to generate buy and sell signals based on predefined conditions.
In the given code, the Relative Strength Index (RSI) indicator is calculated and added to the DataFrame.
populate_buy_trend function: This function generates the buy signals based on the TA indicators and other conditions.
The DataFrame, already populated with indicators, is used to determine the buy signal. The buy signal is triggered when certain conditions are met, such as the occurrence of consecutive higher lows in the price pattern, RSI being below a specified threshold (rsi_buy), and positive volume. The buy signal is denoted by setting the 'buy' column to 1 for the corresponding data points. populate_sell_trend function: This function generates the sell signals based on the TA indicators and other conditions. Similar to populate_buy_trend, the sell signal is determined using the DataFrame with indicators. The sell signal is triggered when conditions such as consecutive higher highs, RSI being above a specified threshold (rsi_sell), and positive volume are met. The sell signal is denoted by setting the 'sell' column to 1 for the corresponding data points. Custom functions for finding extrema: The provided code includes four additional functions (getPeaks) that are not directly part of the strategy class. These functions are used to identify consecutive higher or lower lows/highs in the price pattern. They utilize the argrelextrema function from NumPy to find the indices of extrema (peaks) in the data. Depending on the specific function, they check for consecutive higher highs, lower highs, higher lows, or lower lows within a given number of periods (order and K parameters). When the required number of consecutive extrema is found, they are stored in a list and returned. Overall, the RaposaDivergenceV1 strategy combines the calculation of TA indicators, such as RSI, with the identification of price pattern extrema to generate buy and sell signals based on predefined conditions.
startup_candle_count : 40 rsi: 11.230%
stoploss: -0.3 timeframe: 5m hash(sha256): 81c9f77961080158616bfda0f51063cb920cefd0ef7fe7c40cc5c68ae4485bff indicators: order rsi_buy volume rsi close_lows close_highs rsi_highs rsi_sell rsi_lows k_value
No similar strategies found. (based on used indicators)
last change: 2023-07-03 04:53:13