A NEW ADAPTIVE MOVING AVERAGE (VAMA) TECHNICAL INDICATOR FOR FINANCIAL DATA SMOOTHING

Alex Pierrefeu ()
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Alex Pierrefeu: UNATI - Unité Analyse et Traitement de l'Information - NEUROSPIN - Service NEUROSPIN - Université Paris-Saclay - JOLIOT - Institut des Sciences du Vivant Frédéric JOLIOT - CEA - Commissariat à l'énergie atomique et aux énergies alternatives

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Abstract: The separation of the trend from random fluctuations (noise) is a major objective in technical analysis and for a long time two commons filters, the simple moving average and the exponential moving average have been used to achieve this goal, those two filters use one parameter to control this degree of separation, higher degree of separation involve smoother results but also more lag. Lag is defined as the effect of a moving average to show past trends instead of new ones, this effect his unavoidable with causal filters and is a major drawback in decision timing. In this article I will introduce a new adaptive moving average technical indicator (VAMA) who aim to provide smooth results as well as providing fast decision timing. This new method will be used for the construction of a simple MA crossover strategy in EURUSD, the results of this strategy will then be compared to the results of the same strategy using other adaptive moving averages to provide a comparison of the profitability of this indicator.

Date: 2019-06-01
Note: View the original document on HAL open archive server: https://hal.science/hal-02145175
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