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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.
오픈 소스 스크립트
진정한 트레이딩뷰 정신에 따라 이 스크립트 작성자는 트레이더가 기능을 검토하고 검증할 수 있도록 오픈소스로 공개했습니다. 작성자에게 찬사를 보냅니다! 무료로 사용할 수 있지만 코드를 다시 게시할 경우 하우스 룰이 적용된다는 점을 기억하세요.
Tools and ideas for all Pine coders: tradingview.com/u/PineCoders/
TASC: traders.com/
There won't be a publication in September; we'll be back in October.
TASC: traders.com/
There won't be a publication in September; we'll be back in October.
면책사항
이 정보와 게시물은 TradingView에서 제공하거나 보증하는 금융, 투자, 거래 또는 기타 유형의 조언이나 권고 사항을 의미하거나 구성하지 않습니다. 자세한 내용은 이용 약관을 참고하세요.
오픈 소스 스크립트
진정한 트레이딩뷰 정신에 따라 이 스크립트 작성자는 트레이더가 기능을 검토하고 검증할 수 있도록 오픈소스로 공개했습니다. 작성자에게 찬사를 보냅니다! 무료로 사용할 수 있지만 코드를 다시 게시할 경우 하우스 룰이 적용된다는 점을 기억하세요.
Tools and ideas for all Pine coders: tradingview.com/u/PineCoders/
TASC: traders.com/
There won't be a publication in September; we'll be back in October.
TASC: traders.com/
There won't be a publication in September; we'll be back in October.
면책사항
이 정보와 게시물은 TradingView에서 제공하거나 보증하는 금융, 투자, 거래 또는 기타 유형의 조언이나 권고 사항을 의미하거나 구성하지 않습니다. 자세한 내용은 이용 약관을 참고하세요.