XeL Volatility BANDS
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WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED AS DEBUGING CODE The models included in the function have been taken from openly sources on the web so they could have some errors as in the calculation scheme and/or in it's programatic scheme. Debugging are welcome.
WHAT'S THIS...?
Work derived by previous own research in favour of this main fields of study:
- BUY TO SELL PRESSURES: Given current limitations and rise of prices for Market-Data fees, trying to count "real" market moves tick-by-tick is a real pain. I was very focused to measure this by estimation of Buyers against Sellers (or viceversa) just by playing around with the Range given of time-compression (OHLCv) data and trying to use accepted statistical methods like maximum-minimum count.
- VOLATILITY ESTIMATION: From the previous point, my concern was trying to found an Historical Volatiilty Estimator that can individually count Bullish moves (Minimum from previous close to current price) and Bearish ones (Maximum from previous close to current price) and estimate them by filtering deviations selectable from Square of Returns or Absolute Returns with an Elastic Volatility-Weighted Average Method. (First Order Impulse Response Filter against PriceVolume cumulatives over time.)
- BANDS: Two zones are mainly shadowed from derivations of Deviations: A BULL area and a BEAR area. This areas usually define cyclical regimes of asset price.
NOTES:
- This version DO NOT INCLUDE ALERTS.
- This version DO NOT INCLUDE STRATEGY: Feedback to write are welcome
CHEERS!
xel_arjona 2018.
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Penafian
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Hanya pengguna yang diberikan kebenaran oleh penulis mempunyai akses kepada skrip ini dan ini selalunya memerlukan pembayaran. Anda boleh menambahkan skrip kepada kegemaran anda tetapi anda hanya boleh menggunakannya selepas meminta kebenaran dan mendapatkannya daripada penulis — ketarhui lebih lanjut di sini. Untuk lebih butiran, ikuti arahan penulis di bawah atau hubungi xel_arjona secara terus.
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