OPEN-SOURCE SCRIPT
TASC 2023.05 Cong Adaptive Moving Average
█ OVERVIEW
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.
█ CONCEPTS
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.
█ CALCULATIONS
Following Perry Kaufman's approach, Scott Cong's AMA is calculated progressively as:
AMA = α * Close + (1 − α) * AMA(1),
where:
The smoothing factor determines the performance of AMA. In Cong's approach, it is calculated as:
α = Result / Effort,
where:
As the price range is always no greater than the total journey, α is ensured to be between 0 and 1.
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.
█ CONCEPTS
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.
█ CALCULATIONS
Following Perry Kaufman's approach, Scott Cong's AMA is calculated progressively as:
AMA = α * Close + (1 − α) * AMA(1),
where:
- 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,
where:
- 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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