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The M5FftRsi strategy is designed to backtest trading using a combination of RSI (Relative Strength Index) and Fourier Transform techniques. Here is a breakdown of what the strategy does:
The strategy defines several parameters such as the time period for RSI (Nrsi), the sample size for FFT (N), the number of harmonics for the fast curve (QtF), the number of harmonics for the slow curve (QtS), phase offsets for the fast and slow curves (PhF and PhS), amplitude values for the DC component of the fast and slow curves (AmpArm0F and AmpArm0S), delta timeframe for signal (deltaT), and buy/sell signal ratios (deltaVb and deltaVs). The populate_indicators function calculates the RSI for the given input data and stores it in the input column of the dataframe.

The function performs Fourier Transform calculations to generate the fast and slow curves.

It iterates over a range of values k and calculates the sum of cosine and sine components based on the input data and the parameters defined earlier.

The results are stored in the fourierf and fouriers columns of the dataframe. The strategy determines buy and sell signals based on the calculated curves and the buy/sell signal ratios. It compares the ratios of the fast and slow curves at the current time and a specified delta timeframe with the predefined threshold values (deltaVb and deltaVs). If the conditions are met, it sets the compra (buy) or vendi (sell) columns to True. The populate_buy_trend function identifies the points where the compra column transitions from False to True, indicating a buy signal. It sets the buy column to 1 at those points. The populate_sell_trend function identifies the points where the vendi column transitions from False to True, indicating a sell signal. It sets the sell column to 1 at those points. Overall, this strategy combines RSI and Fourier Transform techniques to generate buy and sell signals based on the calculated curves and predefined thresholds.

The function performs Fourier Transform calculations to generate the fast and slow curves.

It iterates over a range of values k and calculates the sum of cosine and sine components based on the input data and the parameters defined earlier.

The results are stored in the fourierf and fouriers columns of the dataframe. The strategy determines buy and sell signals based on the calculated curves and the buy/sell signal ratios. It compares the ratios of the fast and slow curves at the current time and a specified delta timeframe with the predefined threshold values (deltaVb and deltaVs). If the conditions are met, it sets the compra (buy) or vendi (sell) columns to True. The populate_buy_trend function identifies the points where the compra column transitions from False to True, indicating a buy signal. It sets the buy column to 1 at those points. The populate_sell_trend function identifies the points where the vendi column transitions from False to True, indicating a sell signal. It sets the sell column to 1 at those points. Overall, this strategy combines RSI and Fourier Transform techniques to generate buy and sell signals based on the calculated curves and predefined thresholds.

stoploss:-0.075timeframe:1hhash(sha256):c60e84584adaee80f06a78e2c22bd5bfa8b8dfcef104ef1afed3d5dd034a45adindicators:fourierf input compra vendi close fourier fouriers

No similar strategies found. (based on used indicators)last change: 2023-07-01 22:35:11