Kaufman adaptive moving average

Kaufman adaptive moving averageKaufman Adaptive Moving Average | Trading Strategy (Setup & Filter)

I. Trading Strategy

Developer: Perry Kaufman (Kaufman Adaptive Moving Average KAMA). Source: Kaufman, P. J. (1995). Smarter Trading. Improving Performance in Changing Markets. New York: McGraw-Hill, Inc. Concept: Trading strategy based on an adaptive noise filter. Research Goal: Performance verification of the setup and filter. Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Trades: The Adaptive Moving Average (AMA) turns up. Short Trades: The Adaptive Moving Average turns down. Note: The AMA trendline appears to stop when markets have no direction. When markets trend, the AMA trendline catches up. Trade Entry: Long Trades: A buy at the close is placed after a bullish setup. Short Trades: A sell at the close is placed after a bearish setup. Trade Exit: Table 1. Portfolio: 42 futures markets from four major market sectors (commodities, currencies, interest rates, and equity indexes). Data: 32 years since 1980. Testing Platform: MATLAB®.

II. Sensitivity Test

ER_Length = [2, 100], Step = 2;

Kaufman Adaptive Moving Average | Trading Strategy (Setup)

I. Trading Strategy

Developer: Perry Kaufman (Kaufman Adaptive Moving Average KAMA). Source: Kaufman, P. J. (1995). Smarter Trading. Improving Performance in Changing Markets. New York: McGraw-Hill, Inc. Concept: Trading strategy based on an adaptive noise filter. Research Goal: Performance verification of the setup. Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Trades: The Adaptive Moving Average (AMA) turns up. Short Trades: The Adaptive Moving Average turns down. Note: The AMA trendline appears to stop when markets have no direction. When markets trend, the AMA trendline catches up. Trade Entry: Long Trades: A buy at the close is placed after a bullish setup. Short Trades: A sell at the close is placed after a bearish setup. Trade Exit: Table 1. Portfolio: 42 futures markets from four major market sectors (commodities, currencies, interest rates, and equity indexes). Data: 32 years since 1980. Testing Platform: MATLAB®.

II. Sensitivity Test

ER_Length = [2, 100], Step = 2;

FastMA_Length = [2, 28], Step = 1;

Online trading concepts

Online trading conceptsTuesday, March 2, 2010

Adaptive Moving Average

Adaptive Moving Averages changes its sensitivity to price fluctuations. The Adaptive Moving Average becomes more sensitive during periods when price is moving in a certain direction and becomes less sensitive to price movement when price is volatile .

The chart below of the E-mini Nasdaq 100 Futures contract shows the difference between an Exponential Moving Average which weights current prices more heavily than past prices and the Adaptive Moving Average which changes sensitivity based on price volatility:

The advantage of the Adaptive Moving Average is show above in the e-mini chart in the center where price became directionless and choppy. During that period the Adaptive Moving Average maintained a straight line appearance; whereas, the Exponential Moving Average moved with the choppiness of prices. However, when price trended, like on the far right of the e-mini chart above, the Adaptive Moving Average kept up with the Exponential Moving Average.

The Adaptive Moving Average is definitely an unique technical indicator that is worth further investigation.

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Aaron, Texas, USA

I have taken both the emini & forex courses offered by Trading Concepts.

I believe Todd Mitchell to be sincere in his enthusiasm for teaching trading. I absolutely do NOT believe this to be a scam.

I never had much luck trading the emini. It always resulted in losses for me. Perhaps the course is a bit too complicated, involving many extraneous (aside from the price action & the market) factors like NYSE tick and time of day. At times, I placed too much emphasis on these & wound up in some bad trades as a result.

The forex stuff was much simpler, however, and I found by limiting my practice to a portion of what was taught, I traded euro futures quite profitably. I generally limited things to momentum setups and price action. I never traded based on trend alone, though he counselled doing that some times.

I absolutely disagree with those who state the teachings involve ridiculous risk-reward. Yes, that may be true when you enter a trade, but if you religiously follow the risk reduction he teaches, if you aren't knocked out of a trade immediately, the trade rapidly will become low or no risk almost without fail.

In summary, whether you trade eminis or prefer forex/currency futures as I do, there are concepts in the course you will find invaluable in your trading and I highly recommend the course.

Adaptive portfolio trading strategy implementation

Adaptive portfolio trading strategy implementationadaptive portfolio trading strategy implementation

New Member

Hi everyone, I'm new here and getting my first post in with a problem.

This is mainly a learning exercise for myself. Basicly the algo applies an eigenvalue filter to identify if the market is good to trade on. I have implemented this part, and am up to 2c) in the algo as identified in the paper.

Im struggling to get my head around the state vectors and there uses. If i write out how I interpret the steps, I would be beyond grateful if somebody with a bit more experience could point out if I am wrong! I understand that this would probably not be the exact implementation (you could calculate some things at different times to make things more seamless im sure).

2c) find the various moments of the vector of returns for each asset. the return vector is the form r(1) = return at t-tau+1, r(2) = return at t-tau+2 etc. so this step would give a single mean value for each moment and asset. where t is the most recent time, and the return is that given by the return matrix.

2d) average each moment for all assetts. so this gives four values, the mean return, variance, scew and kurt over the past tau days.

2e) calculate again, but rather than t as the latest time, repeat this for all values of t from tau+2 to latest-1. use all previous values to find the percentile each moment sits in (high, mid, low). For simplicity, state low would 1, mid 2 etc.

then save the average of each moment in a state vector, so a high mean, low var, med scew, med kurt would be added to the vector denoted by 3,1,2,2.

2f) for the most recent stats calculated in d), find the percentile of each one using all values calculated in e), and calculate the state vector for the most recent readings. for example, the latest one may be 1,2,3,2.

2g) go into the vector 1,2,3,2 which was created by calculating 2e). If this is blank invest in risk free, and add the current readings. if this has several values, eg. the mean returns are 0.5 1 0.5 2, and variances 2,2,2,2, average each moment, and then calculate mean over variance and apply the rules to make a decision of investment. so in this example it would be 1/2, meaning invest in risk free.

Im not 100% sure where the logaritihmic return kv is used, if anybody knows I would be most grateful!

I hope that was clear, please ask if anything isnt!

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Forex tsd elite indicators free downloadForex TSD Elite Indicators Free Download

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This moving average strategy beats buy and hold by nearly3-to-1

This moving average strategy beats buy and hold by nearly3-to-1This Moving Average Strategy Beats Buy and Hold by Nearly 3-to-1

Moving averages (MAs) are one of the most popular trading tools. Their popularity may be due to their simplicity. Before there were calculators or computers, a 10-day simple moving average could be found by adding up the last 10 closing prices and moving the decimal point one space to the left. Ive talked to old floor traders who told me that was the reason the 10-day moving average become popular.

Now, MAs of any length are easy to calculate and widely used. We also have variations of the simple calculation. Rather than just adding up numbers and dividing by the total number, there are at least four other possible ways to find a moving average:

1. Exponential Moving Average: Assigns a greater weight to the more recent market action in an effort to be more responsive to changes in the trend.

2. Weighted Moving Average: Allows users to decide which data should be overweighted and allows for the weighting values to be changed.

3. Triangular Moving Average: Weights the middle of the data more heavily.

4. Adaptive Moving Average: Uses smoothing factors to adjust the number of days used in the calculations to current market conditions.

Each method has its proponents and each of the four methods adds a level of complexity to what was originally a simple indicator. Complexity, at least in my mind, is only OK if it adds value. Visually, it looks like the different moving averages move in the same general direction.

The chart below is a weekly chart of the SPDR SP 500 ETF (NYSE: SPY ) with the prices hidden so all we see are the moving averages. This eliminates the clutter on the chart and makes it possible to see that the moving averages rise and fall at the same time.

The adaptive moving average, the thin red line, stands out as consistently lagging the simple MA, shown as the thick blue line. At the bottom in 2009, the exponential MA, the brown line, was the last to signal a buy. That signal came after SPY had gained more than 35%. The other MAs signaled a buy after a gain of 25%. Large delays at bottoms are one of the most significant drawbacks of trading with a moving average. The other significant drawback is that there are a large number of small trades in a sideways market.

Based on the visual comparison, we can say that the averages are all close to each other. More detailed quantitative testing of the various MAs is required to develop a stronger opinion as to which one is best. The results are summarized in the table below. All results are for a 26-week MA and the system is always in the market, long when the price is above the MA and short when the price is below the MA.

Each MA delivered a low number of winning trades and none beat the market. Digging deeper, we learn that the performance problems are due to large losses on the short side.

Looking at the results for a long-only MA system, moving to cash when the price falls below the MA, we see much better performance.

Although the number of winning trades is still low, the adaptive MA beats buy and hold by a significant amount, nearly 3-to-1. This indicator will not call the top of the market. In fact, because it is calculated with historical data, it is impossible for any MA to signal at the exact top or bottom.

At the time of this writing, SPY is well above its adaptive MA, and based on this indicator alone, a bull market would be intact as long as SPY remains above $141.36. Of course, the precise value of the MA changes daily, and will likely be higher when the next bear market does begin.

There is no way to fully eliminate the problems associated with moving averages. But in my experience, the best way to use them is to apply an adaptive MA as a long-only signal. No matter what type of MA is used, when the prices are below the MA, the chances of profitable buys are low. Personally, Id consider selling any stock or ETF when the price moves below the 26-week moving average.

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Boosting trading strategies performance using vix indicator together with adual-objective evolutionBoosting Trading Strategies performance using VIX indicator together with a dual-objective Evolutionary Computation optimizer

Highlights

This work proposes a new EC approach to boost Trading Strategies (TS) performance.

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The approach is able to evolve a set of TSs for the diverse profiles of investors.

Table 6. Fig. 5.

Table 8. Fig. 6.

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Table 14. Fig. 9. Fig. 10. Fig. 11. Fig. 12.

Category archives ideas

Category archives ideasCategory Archives: Ideas

Four Problems with the Sharpe Ratio

The Sharpe Ratio, named after William Forsyth Sharpe, measures the excess return per unit of deviation in an investment asset or a trading strategy. There are the four potential problems in using the Sharpe Ratio to measure trading performance. The first two problems are … Continue

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Volume scalper indicator reviewVolume Scalper Indicator Review

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Laguerre breakout forex trading strategyLaguerre Breakout Forex Trading Strategy

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Laguerre Breakout Forex Trading Strategy Buy Signal

This strategy combines the power of trend lines and Adaptive Laguerre Filter. ALF is a variation on the Laguerre filter using a variable gamma factor. It is based on how well the filter is tracking a past look-back bars prices. ALF functions as a continuously self-adjusting overbought/oversold line. In the picture above you can see that the pair breaks out the green trend line while it is above the ALF (blue) line. This is your buy (long) signal.

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Forex kagi-an incredible manual forex trading systemForex Kagi-An Incredible Manual Forex Trading System

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>> It’s JAPANESE ADAPTIVE TECHNOLOGY, risk management and strong money management strategies work towards generating you fabulous profits while keeping losses at minimal levels.

>> Fights with your biggest enemy - Emotion

>> 24/7 Life time customer support and Live Chat: You can resolve all your queries within 12 hours.

Forex Kagi is for one and all. No matter if you are a newbie with zero Forex experience or if you are a full time Forex trader. Forex Kagi will scale your profits to staggering highs! Christopher Jackson is very serious about allowing LIMITED people in his Elite Inner Circle. So, hurry and book your copy now!

->> +545 Pips Profit AUD/JPY (ONE Long Trade)

->> +595 Pip Profit CAD/JPY (ONE Long Trade)

->> +600 Pip Profit USD/CAD (ONE Long Trade)

->> +645 Pip Profit NZD/USD (ONE Short Trade)

->> Trades on multiple currencies, stocks and bonds

If you are not backed with the right strategy, Forex trading can be very, very risky, and you may lose your hair! If you believe in playing safe and earning phenomenal profits, then, Forex Kagi is your answer…