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Biwavelet

Biwavelet is an indicator for harmonic forecast of the market based on one-dimensional wavelet transform functions. Wavelet transforms are one of the main tools of digital algorithmic trading. This is due to their ability to analyze and predict processes occurring in two independent basic dimensions - price and time. Wavelets have significant advantages over the Fourier transform, because wavelet transform allows evaluating not only the frequency spectrum of the price signal, but also the time at which a certain price harmonic appeared. The indicator includes the calculation of Morlet wavelets of complex type that allow evaluating the information about the frequency and time of a price signal, to interpret and reveal complex processes and patterns in the behavior of prices.


Adjustable Parameters

  • wavelet_type - type of the wavelet function.
  • wavelet_length - length of the price series for calculating the wavelet.
  • wavelet_value - type of indicator (0 - wavelet waves, 1 - force, 2 - phase).
  • wave_first - number of the first wavelet wave.
  • wave_second - number of the second wavelet wave (wave range).
  • signal - display signals on the chart.
  • signal_level - offset of the signals on the chart.
  • info_mode - window with additional information.
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Denis Moisei
168
Denis Moisei 2019.10.09 09:09 
 

Repainting!

Useless indicator. No support from seller, no info about how to use.

Waste of money.

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Version 3.56 2019.10.14
Исправлен и улучшен код индикатора.
Version 3.55 2019.10.14
Введен параметр start_bar - это бар для которого рассчитывается индикатор. Этот параметр позволит тестировать индикатор на исторических данных и находить оптимальные параметры.
Version 3.5 2019.10.10
Добавлены новые типы таймсерий для расчета индикатора: цена, разность цен, регрессия цены.
Version 3.0 2018.05.18
1. Fixed and improved the algorithm.
2. Added ability to work with any lengths of price series, including lengths not multiple of an integer power of 2.
Version 2.0 2017.11.23
Added new methods for calculating the wavelet functions: Morlet, Paul, Dog.