TASC 2023.05 Cong Adaptive Moving Average


TASC's May 2023 edition of Traders' Tips features an article titled "An Adaptive Moving Average For Swing Trading" by Scott Cong. The article presents a new adaptive moving average (​AMA) that adjusts its parameters automatically based on market volatility. The ​AMA tracks price closely during trending movements and remains flat during congestion areas.


Conventional moving averages (MAs) use a fixed lookback period, which may lead to limited performance in constantly changing market conditions. Perry ​Kaufman's adaptive moving average, first described in his 1995 book Smarter Trading, is a great example of how an ​AMA can self-adjust to adapt to changing environments. Scott Cong draws inspiration from ​Kaufman's approach and proposes a new way to calculate the ​AMA smoothing factor.


Following Perry Kaufman's approach, Scott Cong's ​AMA is calculated progressively as:
AMA = α * Close + (1 − α) * AMA(1),
  • Close = Close of the current bar
  • AMA(1) = ​AMA value of the previous bar
  • α = Smoothing factor between 0 and 1, defined by the lookback period
The smoothing factor determines the performance of ​AMA. In Cong's approach, it is calculated as:
α = Result / Effort,
  • Result = Highest price of the n period − Lowest price of the n period
  • Effort = Sum(​TR, n), where ​TR stands for Wilder’s true range values of individual bars of the n period
  • n = Lookback period
As the price range is always no greater than the total journey, α is ensured to be between 0 and 1.

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