MENU
EDU Articles

Learn about investing, trading, retirement, banking, personal finance and more.

Ad is loading...
Help CenterFree ProductsPremium Products
IntroductionMarket AbbreviationsStock Market StatisticsThinking about Your Financial FutureSearch for AdvisorsFinancial CalculatorsFinancial MediaFederal Agencies and Programs
Investment PortfoliosModern Portfolio TheoriesInvestment StrategyPractical Portfolio Management InfoDiversificationRatingsActivities AbroadTrading Markets
Investment Terminology and InstrumentsBasicsInvestment TerminologyTradingBondsMutual FundsExchange Traded Funds (ETF)StocksAnnuities
Technical Analysis and TradingAnalysis BasicsTechnical IndicatorsTrading ModelsPatternsTrading OptionsTrading ForexTrading CommoditiesSpeculative Investments
Cryptocurrencies and BlockchainBlockchainBitcoinEthereumLitecoinRippleTaxes and Regulation
RetirementSocial Security BenefitsLong-Term Care InsuranceGeneral Retirement InfoHealth InsuranceMedicare and MedicaidLife InsuranceWills and Trusts
Retirement Accounts401(k) and 403(b) PlansIndividual Retirement Accounts (IRA)SEP and SIMPLE IRAsKeogh PlansMoney Purchase/Profit Sharing PlansSelf-Employed 401(k)s and 457sPension Plan RulesCash-Balance PlansThrift Savings Plans and 529 Plans and ESA
Personal FinancePersonal BankingPersonal DebtHome RelatedTax FormsSmall BusinessIncomeInvestmentsIRS Rules and PublicationsPersonal LifeMortgage
Corporate BasicsBasicsCorporate StructureCorporate FundamentalsCorporate DebtRisksEconomicsCorporate AccountingDividendsEarnings

What is the KAMA (adaptive moving average)?

The Kaufman’s Adaptive Moving Average (KAMA) was developed by analyst Perry Kaufman in an attempt to cancel out the noise of market volatility and inefficiency by using an efficiency ratio multiple.

Kaufman’s algorithm is a bid to cancel out “noise” in the data used to create a moving average line. The Exponential Moving Average (EMA) is imperfect in part because of its reliance on historical data – if the data is not current, it tells traders nothing about how an asset may trend in the future. Some traders also believe that EMAs are biased by virtue of weighting recent data more heavily, which can lead to false signals and potential losing trades.

Kaufman’s solution was to use an Efficiency Ratio, which aims to minimize the amount of short term volatility which is not part of an actual trend, and a Smoothing Constant, which uses short term alpha (Fast Alpha) and long term alpha (Slow Alpha) to balance out the AMA when the volatility conditions change.

The effect is that the AMA will follow low volatility price conditions closely but will “take a step back” when prices become more volatile, in an attempt to disregard the temporary noise and keep an eye on the overall trend.

Traders of all skill and experience levels can use moving averages, but additional confirmation of trading decisions – free of emotion and inherent bias – is a useful option to have in any trader’s tool kit. Augmenting a AMA with quality artificial intelligence tools from Tickeron can help traders identify trade ideas, confirm trends, and make better, more profitable trades more often.

Ad is loading...