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考夫曼自适应移动平均线(KAMA)

Kaufman Adaptive Correlation OscillatorIntroduction The correlation oscillator is a technical indicator that measure the linear relationship between the market closing price and a simple increasing line, the indicator is in a (-1,1) range and rise when price is up-trending and fall when price is down-trending. Another characteristic of the indicator is its inherent smoothing which provide a noise free (to some extent) oscillator. Such indicator use simple moving averages as well as estimates of the standard deviation for its calculation, but we can easily make it adaptive, this is why i propose this new technical indicator that create an adaptive correlation oscillator based on the Kaufman adaptive moving average. The Indicator The length parameter control the period window of the moving average, larger periods return smoother results while having a low kurtosis, which mean that values will remain around 1 or -1 a longer period of time. Pre-filtering apply a Kaufman adaptive moving average to the input, which allow for a smoother output. No pre-filtering in orange, pre-filtering in yellow, period = 100 for both oscillators. If you are not aware of the Kaufman adaptive moving average, such moving average return more reactive results when price is trending and smoother results when price is ranging, this also apply for the proposed indicator. Conclusion Classical correlation coefficients could use this approach, therefore the linear relationships between any variables could be measured. The fact that the indicator is adaptive add a certain potential, however such combination make the indicator have the drawback of kama + the correlation oscillator, which might appear at certain points. Thanks for reading !
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由alexgrover提供
16
Powered Kaufman Adaptive Moving AverageIntroduction The ability the Kaufman adaptive moving average (KAMA) has to be flat during ranging markets and close to the price during trending markets is what make this moving average one of the most useful in technical analysis. KAMA is calculated by using exponential averaging using the efficiency ratio (ER) as smoothing variable where 1 > ER > 0 . An increasing efficiency ratio indicate a trending market. Based on one of my latest indicator (see Kaufman Adaptive Bands) i propose this modified KAMA that allow to emphasis the abilities of KAMA by powering the efficiency ratio. I also added a new option that allow for even more adaptivity. The Indicator The indicator is a simple KAMA of period length that use a powered ER with exponent factor . When factor = 1 the indicator is a simple KAMA, however when factor > 1 there can be more emphasis on the flattening effect of KAMA. You can also restrain this effect by using 1 > factor > 0 Note that when the exponent is lower than 1 and greater than 0 you are basically applying a nth square root to the value, for example pow(2,0.5) = sqrt(2) because 1/0.5 = 2, in our case : pow(ER,factor > 1) < ER and pow(ER,1 > factor > 0) > ER Self Powered P-KAMA When the self powered option is checked you are basically powering ER with the reciprocal of ER as exponent, however factor does no longer change anything. This can give interesting results since the exponent depend on the market trend strength. In orange the self powered KAMA of period length = 50 and in blue a basic powered KAMA with a factor of 3 and a period of length = 50. Conclusion Applying basic math to indicators is always fun and easy to do, if you have adaptive moving averages using exponential averaging try powering your smoothing variable in order to see interesting results. I hope you like this indicator. Thanks for reading !
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由alexgrover提供
31
Kaufman Adaptive BandsIntroduction Bands are quite efficient in technical analysis, they can provide support and resistance levels, provide breakouts points, trailing stop loss/take profits positions and can show the current market volatility to the user. Most of the time bands are made from a central tendency estimator like a moving average plus/minus a volatility indicator. Therefore bands can be made out of pretty much everything thus allowing for any kind of flavors. So i propose a band indicator made from a Kaufman adaptive moving average using an estimate of the standard deviation. Construction The Kaufman moving average is an exponential averager using the efficiency ratio as smoothing variable, length control the period of kama and in order to provide more smoothness a power parameter has been introduced, higher values of power will return smoother results. The volatility indicator is made from a biased estimation of the standard deviation by using the square root of the mean of the square minus the square of the mean method, except that we use kama instead of a mean. The bands are made by adding/subtracting this volatility indicator with kama. How To Use The ability of the indicator to adapt to the current market state is what makes him a great tool for avoiding major exposition during ranging market, therefore the indicator will have a greater motion during trending market, or more simply the bands will move during trending markets while staying "flat" during ranging ones. Therefore the indicator might be more suited to breakouts, even if some cases will return what where turning points, this is particularly true during ranging markets. Of course the efficiency ratio is not an "unbiased" trend metric indicator, it can consider high volatility markets as trending markets. Its one of his downsides. High values of power will create smoother bands. When using a low power parameter use an higher mult. In general using a low power value will make the bands move more freely as well as making them closer to each others. Conclusion At least the indicator is really nice to the eyes when using high power values, its ability to adapt to the market is a great addition to other more classical bands indicators, i also introduced a volatility estimator based on kama, some might have used the following estimation : kama(abs(price - kama)) which would have created a slower result. A trailing stop might be made from it if i see request about such addition. If you are curious here are some more images of the indicator performing on different markets. Thanks for reading !
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由alexgrover提供
17
Bryant Adaptive Moving Average@ChartArt got my attention to this idea. This type of moving average was originally developed by Michael R. Bryant (Adaptrade Software newsletter, April 2014). Mr. Bryant suggested a new approach, so called Variable Efficiency Ratio (VER), to obtain adaptive behaviour for the moving average. This approach is based on Perry Kaufman' idea with Efficiency Ratio (ER) which was used by Mr. Kaufman to create KAMA. As result Mr. Bryant got a moving average with adaptive lookback period. This moving average has 3 parameters: Initial lookback Trend Parameter Maximum lookback The 2nd parameter, Trend Parameter can take any positive or negative value and determines whether the lookback length will increase or decrease with increasing ER. Changing Trend Parameter we can obtain KAMA' behaviour To learn more see www.adaptrade.com
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由everget提供
1
MA Study: Different Types and More [NeoButane]A study of moving averages that utilizes different tricks I've learned to optimize them. Included is Bollinger Bands, Guppy (GMMA) and Super Guppy. The method used to make it MtF should be more precise and smoother than regular MtF methods that use the security function. For intraday timeframes, each number represents each hour, with 24 equal to 1 day. For daily, 3 is 3 day, for weekly, 4 is the 4 weekly, etc. If you're on a higher timeframe than the one selected, the length will not change. Log-space is used to make calculations work on many cryptos. The rules for color changing Guppy is changed to make it not as choppy on MAs other than EMA. Note that length does not affect SWMA and VWAP and source does not affect VWAP. A short summary of each moving average can be found here: medium.com List of included MAs: ALMA: Arnaud Legoux Double EMA EMA: Exponential Hull MA KAMA: Kaufman Adaptive Linear Regression Curve LSMA: Least Squares SMA: Simple SMMA/RMA: Smoothed/Running SWMA: Symm. Weighted TMA: Triangular Triple EMA VWMA: Volume Weighted WMA: Weighted ZLEMA: Zero Lag VWAP: Vol Weighted Average Welles Wilder MA
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由NeoButane提供
11
KAMA: Kaufman Adaptive Moving Average x2/LogCalculation begins at the beginning of the bar, eliminating incorrect moving average weighting at the very beginning of the ticker you're watching. This is important for new stocks, futures, altcoins, etc. The inputs for the fast/slow alphas are now normal integers, with the calculation (2 / (y + 1)) for alpha added after input. Comes with two moving averages and a setting for geometric mean/log. Source is adjustable but using the close works best, especially with how this particular MA is calculated in the first place. Besides that, this is mostly the same as other KAMAs on TradingView, but I'd like to say I put a bit more care into this one. It is important to know that the acceptable length for KAMA is within the boundaries of the alpha lengths. For example, the default lengths are 2 and 30 for alpha, so the acceptable length for KAMA is within 2-30. stockcharts.com www.technicalindicators.net
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由NeoButane提供
2
Kaufman Moving Average Adaptive (KAMA) StrategyEveryone wants a short-term, fast trading trend that works without large losses. That combination does not exist. But it is possible to have fast trading trends in which one must get in or out of the market quickly, but these have the distinct disadvantage of being whipsawed by market noise when the market is volatile in a sideways trending market. During these periods, the trader is jumping in and out of positions with no profit-making trend in sight. In an attempt to overcome the problem of noise and still be able to get closer to the actual change of the trend, Kaufman developed an indicator that adapts to market movement. This indicator, an adaptive moving average (AMA), moves very slowly when markets are moving sideways but moves swiftly when the markets also move swiftly, change directions or break out of a trading range.
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由HPotter提供
1
Kaufman Adaptive Moving AverageFrom Stockcharts.com: "Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements." This is different from other users' KAMA's because it allows the user to adjust more parameters that can adjust the indicator in more precise ways without needing to change the source code.
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由jacobnie2008提供
1