Statistics of VIX

The CBOE volatility index VIX  measures the market’s expectation of future volatility. This article will show you some key statistics of VIX and help you to decide if it is better to buy or to sell volatility.

Statistics of VIX

The spikes to the top and the long phases of relatively low volatility are reflected in a left-leaning distribution diagram and a long tail towards the higher levels. The median value is 17%, meaning 50% of the prices are above (below) this level.

The next chart shows the distribution of returns over 25 trading days. The median price movement being slightly shifted to the negative area shows the mean reverting characteristics of volatility.

Buy or sell volatility?

Analysing the level of VIX and the returns afterwards yields an even more interesting picture:

The green line gives the 25 bar percentage returns of VIX, with VIX noting above 25, the red line gives the returns with VIX below 15. Observe the median of the two lines:

The median 25 bar return with VIX above 25 (green) is around -15%, only 20% of the returns are positive when VIX is currently above 25. Sell volatility.

The median returns with VIX currently below 15 (red) is above 0% and with a fat tail to positive returns. Buy volatility. (data from 2004-2018)

Adverse movement of VIX

The above chart suggests that going short on volatility, if VIX is above 25, seems to be a good idea. But it is not without risk. The chart below shows what can go wrong during the next 25 days. The distribution diagram gives the maximum adverse movement of the VIX, with VIX currently trading above 25.

The green line, VIX currently above 25, shows a +10% median maximum up movement over the next 25 days. So do not expect a short vola position to be without risk. Some adverse movement has to be expected.

On the other side, the distribution of the maximum loss of the VIX during a 25 day period shows a median of below -20%. This represents the profit potential of a short volatility position.

Conclusion of VIX statistics:

If you plan to short volatility wait until VIX is trading above 25. If you want to buy volatility, do so if VIX is trading below 15.

The analysis has been done using the tradesignal software suite.

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Kahler’s fair bet volatility

Volatility is a measure of risk. It describes how far a commodity will most probably move within a given period of time. The most common measure for volatility is historical volatility. But I do not like the complicated formula for standard deviation.

There has to be a better way to explain and calculate volatility….

Implied Volatility

The options market has got a perfect measure for volatility. Done without formulas, just by demand and supply. And as I believe in efficient markets, the option markets fair price for volatility will be my starting point.

To get the price for volatility at the option market you just have to place  a bet. Assume you want to know the (expected) volatility for the next 30 days, then you would just add the price for an at the money put and call with 30 days to expiry. Option traders call this bet a straddle, and you would win if the market moves more than the price you have payed for the (european style) put and call.

The fair price for a volatility bet

Implied volatility and this Straddle bet is the starting point to calculate my own volatility measure.

The fair price for this bet is, when neither the buyer nor the seller of the bet has got an advantage. In the long run it should be a zero sum game game for both of them. Calculating the fair bet price for a straddle is the idea behind my volatility measure.

Think about a simple coin flip game. If you bet on head you can either win 1€ if head is up or nothing if tail comes on top. What would be the fair price for such a bet?

As head and tail got the same probability, the expected return of a bet on head’s up would be 0.5€. If I would sell you a bet on the next coin flip, I would charge you this 0.5€ to make it a fair bet. So you would either lose the 0.5€ if tail’s up, or win 1€ -0.5€ if head’s up. In the long run this would be a zero sum game for both of us. Do the same thing for the tail is up bet. It also got a value of 0.5€.

Historical Volatility vs. Kahler’s Volatility:

Historical volatility uses standard deviation of daily log returns to describe the volatility of the market. The standard deviation of this +1 -1 coin flip experiment would be 1€. The same would be true if you would buy a head’s up and a tail’s up bet; it would also cost you 1€. So for this simple example the fair bet based volatility is the same as the historical volatility.

But the market is not a coin flip. There will be some differences between historical volatility and KVOL fair bet based volatility.

KVOL vs. historical volatility:

The chart shows you a comparison between KVOL (blue) and historical volatility (standard deviation). On the chart shown above both calculate the volatility for 10 day returns, using the previous 30 bars as data sample.

As you can see historical volatility and KVOL are highly correlated.

But there are some major differences:

As an example in the end of 2017/beginning of 2018 KVOL starts to rise as the market is exploding to the upside. This is due to the virtual call used to calculate KVOL gains value. At the same time historical volatility stays low, as the market has got one direction and no setbacks.

Another advantage of KVOL is it`s response to singular events. As you can see on Sept. 3rd on the chart above the singular big red candle leads to a spike in historical volatility. It also raises KVOL, but not as much. As both indicators are calculated over the same period of bars they both got the same speed of change, but when you have a look at the scale you will see the advantage of KVOL: Historical volatility jumps from 0.2 to over 0.5 – it more than doubles just because of a single event. KVOL also raises,but only from 0.2 to 0.3.

For me this mild response to to singular events is the main advantage. Imagine a portfolio based on value at risk – would it really be useful to half the exposure just because historical volatility jumps after a single red candle?

KVOL  – Tradesignal Equilla Code:

The code to calculate KVOL is simple and straightforward.

The inputs:

multi: just a multiplier, like you can display 1 or 2 standard deviations..

datapoints: The number of bars used to calculate KVOL

returnperiod: calculate the volatility for 1,2,3… bars

showresult: show the result as a percentage of the underlying or as an absolute number

show: show either kvol or the rank of  kvol within the last 100 bars. This gives an idea if volatility is high or low

Meta: subchart(true);
Inputs: multi(1.0), Datapoints(30), returnperiod(5), showresult(percent, absolute), show(result,rank);
Variables:Kvol, i, rp,rc, rpsum, rcsum, call, put, hh,ll;

rpsum=0;
rcsum=0;

for i =0 to datapoints-1 begin // loop over last bars
  rc=maxlist((close[i]-close[i+returnperiod])/close[i+returnperiod],0); // % return of call
  rp=maxlist((close[i+returnperiod]-close[i])/close[i+returnperiod],0); // % return of put
  rcsum=rcsum+rc; // sum of all %returns over time
  rpsum=rpsum+rp;
end;
      
call=rcsum/datapoints;
put=rpsum/datapoints;

Kvol=call+put;	
if show=result then drawline(multi*iff(showresult=percent,100*Kvol,Kvol*close),"KVOL");

hh=highest(kvol,100);
ll=lowest(kvol,100);
if show=rank and (hh-ll)>0 then drawline(100-100*(hh-kvol)/(hh-ll),"rank");

 

keep researching…

 

 

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A graphical approach to indicator testing

A graphical approach to indicator testing

The first step in algorithmic strategy design usually is to find some indicators which give you an edge and tell you something about tomorrow’s market behaviour. You could use a lot of statistics to describe this edge, but I like to take a graphical approach in indicator testing first, and only later on worry about the maths and statistics.

Scatter Charts

A scatter chart is a simple to read chart style to see the correlation between two input values. A regression line on the scatter chart gives you a visual idea if the two securities are positively or negatively correlated, the “cloud structure” of the scatter points tell you if this correlation is tight or loose.

This sample scatter shows the correlation between the DAX and DOW levels, and it can be easily seen that these two markets are tightly correlated in a positively way.

The horizontal scale is used for the second security (DAX), the vertical scale is used for the first security (DOW). This chart type is predefined in Tradesignal, just drag&drop it onto the securities on the chart and select the right amount of data to get the analysis you want to see. (eg. 2000-now). If you see a tight and positive correlation like on the chart above, It might be used to select the instrument you want to trade. If market A is easier to predict than market B, select A.

Scatter on Indicators

Although a scatter chart is usually used to show the correlation between two markets, it can also be used to show the correlation between two indicators.

The chart above shows the correlation between digital stochastic and momentum. Have a look at the clustering of points in on the right side of the scatter, a high level in digital stochastic usually goes with a high momentum. This insight enables you to get rid of momentum, as digital stochastic is easier to read than the shaky momentum. Less indicators = less parameters = less curve fitting.

Scatter prognosis

Doing this analysis and getting rid of parameters is great if you want to minimise the dangers of curve fitting, but it does not tell you if your indicator is of any use at all, when it come to describing tomorrows move of the market. Surely it is valuable insight that a high level of stochastics corresponds to a high momentum, but does a high momentum today also mean that the market will move up tomorrow? And this question about tomorrow is the key question I ask myself when searching for some edge.

To get a glimpse on the prognosis quality of an indicator we will have to add some colour to our scatter chart. This colour tells me what the market has done after a specific indicator level has been reached. Green for an up move, red for a down move, black for not decided by now.

This chart shows the prognosis quality of the stochastic indicator. The left chart shows the 1 day prognosis of a 5 day stochastic, the right chart gives you the 5 day prognosis of a 21 day stochastic. Observe the clustering of the red and green dots. (black for not decided by now) As you can see on the left chart, the one day prognosis using a 5 day stochastic is not the thing to do. Regardless if stochastic is high or low, you get a nice mixture of red and green dots. This means the market, at a given stochastic level, sometimes moved up, sometimes moved down. Not this behaviour is not very useful for trading. Only in the extreme, near 0 and 100, this indicator seems to implicate a bearish next day movement.

The right chart, showing the longer term prognosis of a long term stochastic seems to be more useful. High levels of the indicator also show positive returns on the 5 days after, unfortunately you can not reverse the logic, as low indicator levels give a rater mixed prognosis. This visual analysis can give you an idea which areas of the indicator might be useful for further analysis.

A one dimensional analysis like on the chart above could also be done without this scatter chart. Going from one dimension to two dimensions is more useful, as it directly can be translated to do a kNN machine learning trading strategy. Have a look at the following chart. It shows the scatter of two indicators and the implication on the next days market move.

Lets start with he right chart. As you can see the red and green dots are evenly distributed, meaning there is no useful correlation between the used indicators and the movement of the market on the day after. If you would use a kNN algorithm with these two indicators, I would bet it would not return great results. Even if you would get a positive return, it might just be a lucky hit or curve fitting.

The opposite is true for the chart on the left. Here you can see some nice clustering of the red and green dots. Low indicator levels seem to predict a bearish move, high indicator levels result in a bullish move on the next day. A distribution like this is the perfect starting point for investing some time in a kNN machine learning  trading strategy. The kNN algorithm would give you a strong prognosis with high or low indicator levels, and most probably only a weak or no prognosis when the indicators are around 50. The returns will be stable, no curve fitting problems should be expected.

Conclusion

Using a scatter chart can give you a nice visual indication if your indicator might be useful for a prognosis of the next days market move. This is valuable insight, as you can see the whole data universe with one glimpse, even before you do a thoroughly statistical analysis. Numbers can deceive you, pictures usually tell the complete story.

Tradesignal Equilla code:

 

 

 

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Machine learning: kNN algorithm explained

I always thought that inspiration and experience are key factors in trading. But every time my chess computer beats me without any inspiration, just by brute force, I get my doubts. This article will be about a brute force approach in trading. The kNN algorithm.

Rule based trading

Rule based trading – algorithmic trading, is just a name for a set of if..then rules which will define the machines trading decisions. e.g. if the market crosses below the 200 day line, then short 100 contracts. If the market rises by 2% then exit the position.  Easy stuff like this… (for the beginning)

This article will be a short introduction to machine learning. I will use a classic algorithm of machine learning to let my computer find a prediction for tomorrows market move. In the meantime I’ll have a glass of wine with some friends and let the machine do the job; At least that’s the idea, but can it be that simple in real life trading?

Unsupervised machine learning – kNN algorithm

The kNN algorithm is one of the most simple machine learning algorithms. Learning, in this case, is only a nice sounding label, in reality kNN is more of a classification algorithm.

This is how it woks:

The scatter chart above is a visualisation of a two dimensional kNN data set. For this article I used a classical indicators of technical analysis to do the prediction: a long-term and a short-term RSI indicator. The dots on the two dimensional scatter chart represent the historic RSI values at a given point of time.

Now have a look at the fat circled point. This point represents today’s value. It means, that today’s RSI1 has a value of 63, and RSI2 got a value of 70.

Additionally to the position on the chart the dots have got colours. A green dot means that the market moved up on the following day, a red dot shows a falling market on the day after.

We already know what has happened in history, so it is easy to colour the historic dots. But we do not know the colour of today’s dot, as it is not known where tomorrow’s market will end.

Based on the chart above, will it be a red or green dot? Will tomorrow be up or down?  Should I go long or should I go short?

kNN – k nearest neighbours

To do a prediction of tomorrow’s market move, the kNN algorithm uses the historic data shown on the scatter plot above and finds the k-nearest neighbours of today’s RSI values. As you can see, our current fat point is surrounded by red dots. This means, that every time the two RSI values have been in this area, the market fell on the day after. That’s why today’s data point is classified as red. Wish it would be that easy all the times…

Call it classification or prediction, the two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours, sees their state and thus classifies today point.

kNN as Tradesignal Equilla Code

In this article I would like to show you an implementation with the Tradesignal programming language Equilla.

To implement the algorithm in Tradesignal we first have to do the shown scatter plot. The algorithm stores the values in an array.

8/9 calculates the value of the fast and slow RSI indicators

12/13 looks what will happen on the day after (for the training data set)

16/17/18 stores everything in an array.

The next task to complete is to calculate the distances of today’s RSI point to all the historic points in the training data set.

23/27 calculates the euclidean distance of today’s point to all historic points, line 29 then creates a sorted list of all these distances to find the k nearest historic data points in the training data set.

Nearly done. The next step is just to find out what classification (colour) the nearest points have got and use this information to create a prediction for tomorrow. This is done in lines 33 to 35

Have a look at the scatter chart at the beginning. If this would be the data stored in our training data set, the prediction, using the 5 nearest neighbours, would be -5. All the 5 nearest neighbours of our current data point are red.

Now that we got a prediction for tomorrow, we need to make use of this prediction and trade it. The returns then will show if everything works as predicted.

Over here I just do a simple long/short interpretation of the prediction, but of course you could also use the quality of the prediction (+5 or +1?) in some sort of way. Position sizing…?

kNN algorithm performance

The next chart shows 2000 bars of daily Brent data. It uses a 14 and 28 day RSI to predict the next day’s move in the Brent oil market. The training was on the first half of the data set, and the 5 nearest neighbours did the classification.

Underneath the chart the returns of this test are shown. (strategy equity). On the bottom of the char you see the two RSI indicators used for the generation of the prediction / buy-sell command.

kNN algorithm – conclusion

The kNN algorithm offers a framework to test all kind of indicators easily to see if they have got any predictive value. Judging on the shown graph it seems to work. It seems to be possible to use these two RSI indicators to predict tomorrow’s Brent move.

But unfortunately this also could be just completely useless curve fitting. It is you who has to select the indicators and their periods and you will have to define if you like the outcome of a selected parameter set. To many degrees of freedom to be sure. The kNN algorithm is useful, but its application in finance has to be treated carefully. Otherwise bad surprises are guaranteed

Not everything can be done by brute force, inspiration and experience are key factors in finance…

The analysis has been done using the tradesignal software suite.

 

 

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Using Autocorrelation for phase detection

Autocorrelation is the correlation of the market with a delayed copy of itself. Usually calculated for a one day time-shift, it is a valuable indicator of the trendiness of the market.

If today is up and tomorrow is also up this would constitute a positive autocorrelation. If tomorrows market move is always in the opposite of today’s direction, the autocorrelation would be negative.

Autocorrelation and trendiness of markets

If autocorrelation is high it just means that yesterdays market direction is basically today’s market direction. And if the market has got the same direction every day we can call it a trend. The opposite would be true in a sideway market. Without an existing trend today’s direction will most probably not be tomorrows direction, thus we can speak about a sideway market.

Autocorrelation in German Power

But best to have a look at a chart. It shows a backward adjusted daily time series of German Power.

The indicator shows the close to close autocorrelation coefficient, calculated over 250 days. You will notice that it is always fluctuating around the zero line, never reaching +1 or -1, but let`s see if we can design a profitable trading strategy even with this little bit of autocorrelation.

The direction of autocorrelation

Waiting for an autocorrelation of +1 would be useless. There will never be the perfect trend in real world data. My working hypothesis is, that a rising autocorrelation means that the market is getting trendy, thus a rising autocorrelation would be the perfect environment for a trend following strategy. But first we have to define the direction of the autocorrelation:

To define the direction of the autocorrelation I am using my digital stochastic indicator, calculated over half of the period I calculated the autocorrelation. Digital stochastic has the big advantage that it is a quite smooth indicator without a lot of lag, thus making it easy to define its direction. The definition of a trending environment would just be: Trending market if digital stochastic is above it`s yesterdays value.

Putting autocorrelation phase detection to a test

The most simple trend following strategy I can think about is a moving average crossover strategy. It never works in reality, simply as markets are not trending all the time. But combined with the autocorrelation phase detection, it might have an edge.

Wooha! That`s pretty cool for such a simple strategy. It is trading (long/short) if the market is trending, but does nothing if the market is in a sideway phase. Exactly what I like when using a trend following strategy.

To compare it with the original moving average crossover strategy, the one without the autocorrelation phase detection, you will see the advantage of the autocorrelation phase filter immediately: The equity line is way more volatile than the filtered one and you got lots of drawdowns when the market is sideways.

Stability of parameters

German power has been a quite trendy market over the last years, that`s why even the unfiltered version of this simple trend following strategy shows a positive result, but let`s have a test on the period of the moving average.

Therefore I calculated the return on account of both strategies, the unfiltered and the autocorrelation filtered, for moving average lengths from 3 to 75 days.

Return on account (ROA) =100 if your max drawdown is as big as your return.

The left chart shows the autocorrelation filtered ROA, the right side the straight ahead moving average crossover strategy. You don`t have to be a genius to see the advantage of the autocorrelation filter. Whatever length of moving average you select, you will get a positive result. This stability of parameters can not be seen with the unfiltered strategy.

Autocorrelation conclusion:

Trend following strategies are easy to trade, but only make sense when the market is trending. As shown with the tests above, autocorrelation seems to be a nice way to find out if the market is in the right phase to apply a trend following strategy.

 

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Measuring your EDGE in algorithmic trading

There are a lot of statistics which can be used to describe algorithmic trading strategies returns. Risk reward ratio, profit factor, Sharpe ratio, standard deviation of returns… These are great statistics, but they miss an important factor: Are your returns statistically significant or just a collection of lucky noise. The EDGE statistic might me the answer to this question.

 

Statistics in trading:

If the returns of your trading strategy are positive with in-sample and out-of-sample data this is a first sign that you are on the right path. The next step would be to have a look at the risk-reward ratio of your trading to get an impression if the strategy might be useful in a real world environment.

Assuming that your average yearly returns are about twice as big as the worst case historic draw down you can even be more confident that your strategy is useful. But there is still one thing to check before you can be sure that you are not just seeing a curve fit bullshit strategy. The standard deviation of the daily returns vs. your average daily return.

Defining EDGE in algorithmic trading

Assume your strategy made 250$ over the last year. This averages to about 1$ per day. This 1$ is a good or bad return, depending on the standard deviation of your equity line. If the standard deviation of your equity is 2$, then the 1$ average return strategy would be a bad strategy, as your average returns are way too small in respect to the volatility of your equity. If your volatility of your return curve would just be 50ct and you still make 1$ per day on average, your strategy would be ingenious.

Edge is the ratio of your average returns vs the volatility of your equity line. To be on the safe side,  your average return should be about 5% above the 90% confidence interval of your equity line volatility.

The left chart is a strategy trading an one month RBOB time spread, the right chart shows the same strategy trading German power. Rbob has got an edge of 3%, German power has got an edge of 5%.

If I would have to select which market I want to trade with this sample strategy, I surely would select German power over the rbob time spread. Both curves have their up and downs, but rbob is heavily relying on a lucky trade in September. This lead to a high standard deviation of the equity line , giving you a low edge reading.

Conclusion

Observing the ration between your average daily returns vs. the volatility of your equity curve can give you some valuable insights in the quality of your strategy. If it just called a few lucky trades in history, it will also show a high volatility in returns. And this you most probably want to avoid when turning to algorithmic trading. It`s not just the absolute profit at the end of the year, it is also the path you took to get to this number. The smoother, the better!

Tradesignal Equilla Code for the edge indicator:

ask.

 

 

 

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Ranking: percent performance and volatility

When ranking a market analysts usually pick the percent performance since a given date as their key figure. If a stock has been at 100 last year and trades at 150 today, percent performance would show you a 50% gain (A). If another stock would only give a 30% gain (B), most people now would draw the conclusion that stock A would have been the better investment. But does this reflect reality?

Percent Performance and Volatility

In reality and as a trader I would never just buy and hold my position, I would always adjust my position size somehow related to the risk in it. I like instruments that rise smoothly, not the roller coaster ones which will only ruin my nerves. So ranking a market solely by percent performance is an useless statistic for me.

Lets continue with our example from above: if stock A, the one who made 50% has had a 10% volatility, and stock B, the 30% gainer, only had a 5% volatility, I surely would like to see stock B on top of my ranking list, and not the high vola but also high gain stock A.

Risking the same amount of money would have given me a bigger win with stock B.

Combining Performance and Volatility

To get stock B up in my ranking list I will have to combine the absolute gain with the market volatility in between. This can be done quite simple. Just add up the daily changes of the stock, normalized by market volatility.Have a look at the formula of this new indicator:

index(today)=index(yesterday)+(price(today)-price(yesterday))/(1.95*stdev(price(yesterday)-price(2 days ago),21))

In plain English: Today’s Vola Return Index equals yesterdays Vola Return Index plus the daily gain normalized by volatility

So if the index has been at 100, the volatility (as a 95% confidence interval over 21 days) is 1 and the stock made 2 points since yesterday, then today’s index would be 100 + 2/1 = 3

Vola Return Index vs. Percent Return Index

Lets have a look at a sample chart to compare the 2 ranking methods. I therefore picked the J.P.Morgan stock.

The upper indicator shows you a percent gain index. It sums up the daily percent gains of the stock movement, basically giving you an impression what you would have won when you would have kept your invested money constant.

The indicator on the bottom is the Vola Return Index. It represents your wins if you would have kept the risk invested into the stock constant. (=e.g. always invest 100$ on the 21 day 95%confidence interval of the daily returns)

Have a closer look at the differences of these two indicators up to October 2016. JPM is slightly up, and that`s why the percent change index is also in the positive area. During the same time the Vola Return Index just fluctuates around the zero line, as the volatility of JPM picked up during this period of time. To keep your risk invested constant over this period of time you would have downsized your position when JPMs volatility picked up, usually during a draw down. No good.

The same can be observed on the upper chart, showing the last months movements of the index. Right now, after the recent correction the percent change index is, like the JPM stock, up again. On the other side the Vola Return Index is still down, due to the rising volatility in JPM.

Vola Return Index – Ranking

Lets put this to a test and rank the 30 Dow Jones industrial stocks according to the percent return index and using my Vola Return Index as a comparison, calculated since 01/01/2015.

The first three stocks are the same, they got the highest vola and highest percent return. But JPM and Visa would get a different sorting. Just see how low the JPM Vola Index is, it would not be the 4th best stock.

Percent returns says JPM and Visa are abou the same, only the Vola Return Index shows that VISA would have been the better investment vehicle compared to JPM. But see for yourself on the chart…

Conclusion

Make sure your indicators show what you actually can do on the market. There is no use in just showing the percent gains of a stock if you trade some kind of VAR adjusted trading style.

Keeping you risk under control is one of the most important things in trading, and using the Vola Return Index instead of just plotting the percent performance can give you some key insights and keep you away from bad investment vehicles. Also have a look at this stock picking portfolio based on similar ideas.

 

Tradesignal Equilla Code for Vola Return Index:

 

 

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NASDAQ 100 long term candlestick scanner

A short update on the long term Candlestick Scanner.

The Candlestick Scanner scans the Nasdaq 100 stocks for long term bullish or bearish reversal patterns.

The basic idea is to search for hammer and hanging man candlestick patterns. Usually these patterns work nicely on daily charts. My Candlestick Scanner searches for these two patterns on every time frame, from a 1 day per bar compression up to a  250 days per bar compression. This enables me to use a simple, well defined and documented pattern as a description of short to long term reversal setups.

But see for yourself which Nasdaq stocks seem to change the direction according to the long term Candlestick Scan. The list gives you the duration of the reversal formation (expect about the same time to either reach the target or get stopped out) The detected pattern becomes a valid entry signal if a new high (hammer) or low (hanging man) is established.

Bullish reversals on the left side, bearish reversals on the right side.

 

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Monthly Seasonal Performance of Stocks

Seasonality changes over time!

First have a look at a screenshot of one of my favorite website investopedia.com They have some nice articles about the seasonal performance of stocks and the effects in trading. But unfortunately the information is not precise, and therefore misleading.

The chart shown suggests that the average return for the S&P500 (index or stocks?) has been positive, except for September. Further down they speak about the January effect, suggesting an average positive performance of stocks in January.

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The rhythm of the market

Usually we chart the market at it’s absolute level. But what, if we would just chart the net daily, weekly, monthly movement? Would this be an advantage? Would this show us new trading opportunities?

The short answer is: Yes! The trend is not everything, and it seems to be of some significance for further movements, if the market has moved more than x % from the beginning of the day, week or month.

But let’s have a look at some charts – and you will see how well it works:

The first chat is an intraday chart of EuroDollar, 8am-5pm CET. It shows you the daily net movement.

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Swing Trading Indikator

Lokale Hoch- und Tiefpunkte sind die Basis aller technischer Analyse Methoden. Durch die Abfolge dieser Punkte und deren Lage zueinander wird sowohl ein Trend als auch eine Seitwärtsphase definiert. Einzig die Bestimmung der Lokalen Hoch- und Tiefpunkte macht Probleme.

Lokale Hochs und Tiefs am Chart

Um die Umkehrpunkte am Chart zu bestimmen können Sie z.B. den Zig-Zag Indikator einsetzten. Er ist in jeder besseren Chartsoftware enthalten.  Auch könnten Sie die hier bereits mehrfach erwähnte Swing Punkt Definition verwenden.

Beide Vorgehensweisen haben jedoch auch Nachteile: Der Zig-Zag Indikator verfügt eine fixe % Einstellung für die Marktvolatilität. Deshalb muss der Indikator für jeden Markt und jede Zeitebene extra angepasst werden. Die Swing Punkt Definition verwendet zwar keine Parameter, dadurch dass sich das Swing Muster jedoch nur über 3 Bars erstreckt, eignet sich dieses Kursmuster eher zur Definition von sehr kurzfristigen Hoch- und Tiefpunkten.

Swing Punkte – auto adjust

Um den angesprochenen Schwachstellen vorhandener Indikatoren abzuhelfen habe ich einen Indikator entwickelt, der diese Schwachstellen beseitigt. Er passt sich automatisch an die Marktvolatilität an. So wird es möglich den Indikator in verschiedenen Märkten und Zeitebenen ohne Anpassungen zu verwenden.

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DAX Ichimoku Scanner Update

Die vergangene Woche brachte, dank der EZB, auch im DAX Veränderungen mit sich. Die Ichimoku Scanner Bewertung hat sich von +5 auf +2 (3*bull, 1*bear) geändert.

Hier die Sicht auf den DAX Index, Tageschart mit Ichimoku Indikator und der automatischen

DAX Ichimoku Scanner Analyse:

DAX Ichimoku

Zur Erinnerung: Der Indikator zeigt die in einer Zahl zusammengefasste Bewertung des Ichimoku Indikators. Um auf die +2 zu kommen werden folgende Punkte Vergeben:

  •  -1 Kurs unter Kijun
  •  0  Chikou in seiner Kerze und über der Wolke
  •  +1 Tenkan über Kijun
  •  +1  Senkou 1 über Senkou 2
  •  +1 Kurs über Wolke

Die geglättete Version dieses Indikators hat ins negative gedreht:

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Digitale Stochastic

Die slow stochastic ist ein alter Begleiter für alle Trader. Zuverlässig zeigt dieser Indikator die Überkauft und Überverkauft Bereiche an. Doch leider hat der Indikator einen entscheidenden Nachteil – er hat einen sehr unruhigen Kurvenverlauf.

Stochastic Digital – mehr als nur Überkauft / Überverkauft

 

Die Digitale Stochastic glättet die normale Stochastic nicht nur, sie sorgt mit einer digitalisierung auch für einen ansprechenden Kurvenverlauf.

Hier ein Beispiel für den DAX Future im Stundenchart.

Digitale Stochastic DAX Stunde

Unter der digitalen Stochastic sehen Sie die altbekannte slow Stochastic mit der selben Parametereinstellung. Der Chart ist grün gefärbt wenn die digitale Stochastic steigt, ansonsten ist der Chart rot hinterlegt.

Digitale Stochastic Trading

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Ichimoku Scanner

Der Ichimoku Kinko Hyo ist in Japan einer der beliebtesten Indikatoren. Es ist ein Indikator der Informationen über Trendrichtung und Trendstärke kombiniert.

Zudem gibt er schon heute seinen zukünftigen Wert an. Damit ist er ein Unikum unter den Chart Indikatoren.

 

Karin Roller Ichimoku Analyse

Wie dieser großartige Indikator interpretiert wird, habe ich hier aus der gestrigen DAX Tagesanalyse von Karin Rollers Webseite fit4trading.de kopiert. Sie definiert im Wesentlichen 5 Kriterien nach denen der Ichimoku Indikator zu bewerten ist.

Ichimoku Scanner

Anhand dieser 5 Kriterien habe ich einen Oszillator entwickelt, der ihnen auf einen Blick zeigt, wie der Ichimoku Indikator aktuell zu interpretieren ist.

Dreht man die Logik dieser 5 Ichimoku Kriterien um, erhält man einen Oszillator der immer zwischen -5 und +5 pendelt. (Den Tradesignal Code dazu finden Sie am Ende des Artikels)

Neben dem absoluten Wert des Indikators ist natürlich auch seine Richtung von Interesse.  Sehen Sie am nächsten Bild, wie selbsterklärend die Interpretation dieses Ichimoku Oszillator ist.

DAX Ichimoku Chart

Aktuell steht der Oszillator, wie auch Karin Rollers Ichimoku Analyse, auf plus 5 – alles bullish.

Ichimoku DAX Scanner

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DAX Marktbreite und 200 Tage Linie

Bald ist es wieder so weit und der DAX steigt über seine 200 Tage Linie. Das wäre an und für sich nichts Besonderes, steigt oder fällt der DAX doch jeden Tag über irgendeinen gleitenden Durchschnitt, doch ist die 200 Tage Linie etwas besonders an sich: Sie ist ein selbst erfüllender Indikator!

 Bankberater, Bild, Hausfrauen

Wenn jemand eigentlich nichts über technische Analyse weiß, die 200 Tage Linie kennt er. Jeder Bankberater versucht damit seine Aktien zu verkaufen; Wenn der Markt über der 200 Tage Linie liegt soll das  ein bullishes Signal sein.

DAX Above 200 Day Average

 

Und in der Tat, da jeder diesen Indikator kennt, ist es tatsächlich ein bullishes Signal wenn der Markt über seiner 200 Tage Linie liegt, schlussendlich trauen sich dann alle die fast nix von technischer Analyse wissen in den Markt.

DAX Marktbreite

Um zu sehen ob so ein Schnitt des DAX über die 200 Tage Linie auch signifikant oder nur ein Strohfeuer ist, kann man die Marktbreite des Index untersuchen.

Marktbreite=Wie viel % der Aktien eines Index liegen über der 200 Tage Linie

Diesen Indikator sehen Sie unter dem DAX Chart abgebildet.

DAX Above 200 Day Average history

Allgemein gilt für die Interpretation, dass wenn der DAX über der 200 Tage Linie liegt und mehr als 50% der Aktien des DAX ebenfalls über der 200 Tage Linie liegen, man trendfolgend vorgehen kann. Wenn dann einmal 100% der Aktien über der 200 Tage Linie liegen ist die Blase meist zu Ende und man sollte die Position mit einem engen Stop absichern.

zum Tradesignal Equilla Code Passwort “code”

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Selbstlernende Handelssysteme

Ein jeder kennt die klassischen Indikatoren wie RSI oder Stochastic. Und ein jeder kennt die dazugehörigen Handelsanweisungen: Long, wenn überverkauft, Short wenn überkauft. Und zumindest im Lehrbuch funktioniert das auch. Aber wie sieht das ganze am realen Chart aus? Würden Sie dem Lehrbuch vertrauen und Ihren Kunden auch einen baldigen Kauf empfehlen wenn der RSI unter 20 liegt?

Testen anstatt zu studieren

Schön, wenn ein Indikator im Lehrbuch funktioniert, doch will ich hier ein Verfahren darstellen, bei dem der Indikator selbst angibt ob, wann und wie gut er funktioniert! Dazu habe ich mir für diesen Beitrag den RSI Indikator vorgenommen.

Zunächst wird der Wert des Indikators betrachtet, sowie, ob er steigt oder fällt. Mit diesen beiden Kriterien lässt sich der RSI einfach klassifizieren.

RSI Prognose

Dann erfolgt der eigentliche Backtest: Innerhalb der letzten 1000 Bars wird nun geschaut, wie sich der Markt bei einem gleichen Indikatorstand (zwischen 90 und 100) und Richtung (über Triggerlinie) verhalten hat.

 

Am Bild kam dies innerhalb der letzten 100 bars 36 mal vor. Dabei war die durchschnittliche Bewegung innerhalb der darauffolgenden 5 min DAX Futures Kerze -0.03%. Der RSI hat beim aktuellen Stand also eine negative Kurs Prognose.

Dass es auch nach dem nächsten bar statistisch nach unten geht, sieht man an den 5 Prognose Punkten am Chart. Sie zeigen, wie sich der Markt statistisch innerhalb der nächsten 5 Bars verhalten hat, unter der Bedingung, dass der RSI den aktuellen Stand und Richtung hatte.

Markt Performance als Indikator

Der obige Screenshot zeigt den Indikator und die Prognose für den kommenden bar (sowie die 4 darauf folgenden). Er zeigt jedoch nicht, wie sich diese Prognosen in der Vergangenheit verhalten haben, in welchen Bereichen der Indikator in der Vergangenheit seine höchste Aussagekraft hatte. Dies ist am nächsten Chart dargestellt.

RSI Prognose

Am Bild ist unter dem eigentlichen RSI seine aktuelle prognose für den nächsten Bar dargestellt. Um diese prognose ist ein Bollingerband gelegt, um so die Bereiche zu definieren, an welchen der RSI seine höchste Aussagekraft hat (= die stärkste Bewegung vorhersagt)

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RSI als intraday Candlestick

Der RSI – Relative Strength Indikator – ist ein Klassiker, Zeit für ein wenig Erneuerung.

RSI Classic: Intraday Bewegung geht verloren

Der RSI ist ein Linienindikator, basierend auf dem Schlusskurs der Kerze. So wird z.B. der 14 Tage RSI auf Basis der letzten 14 Tages Schlusskurse berechnet. Dies hat den großen Nachteil, dass der RSI im Lauf des Tages seinen Wert verändert. Für den aktuellen Wert am live Chart wird anstatt des (noch nicht bekannten) Schlusskurses des Tages der aktuelle Wert des Marktes verwendet.

RSI Intraday: Tagesverlauf darstellen

Mein RSI Candlestick Indikator bildet diesen Intradayverlauf des Tages ab. Dazu verwende ich einen Kerzenchart. Die Eröffnung der Kerze ist der Stand des RSI am Anfang der Kerze (des Tages…)

Hoch und Tief der Kerze geben die Höchst und Tiefstwerte des RSI im Verlauf der aktuellen Kerze an. Der Close der Kerze entspricht dem klassischen RSI auf Schlusskursbasis.

RSI Candlesick Indikator

Interpretation: Candlesick und RSI

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Multiple Timeframe Candlestick Scanner

Längerfristige Umkehrformationen erkennen

Candlesticks eignen sich sehr gut um eine Top oder Boden Formation allgemein und einfach zu beschreiben. Hier stelle ich einen Indikator vor, der die Hammer / Hanging Man Formation nutzt, um damit auf einfache Weise Umkehrformationen am Chart zu erkennen.

Hanging Man Formation

Die Hanging Man Formation ist eines der einfachsten Candlestick Muster. Es zeigt das Ende einer bullishen Bewegung an.

Definition der Hanging Man Formation:

  • nach einer Aufwärtsbewegung
  • ein neues Hoch wird ausgebildet
  • kleiner, fallender Kerzenkörper
  • langer Docht (oben)
  • kurze Lunte (unten)

Ein Trade wird eingegangen, sobald das Tief der Formation unterschritten wird.

 

 Multiple Timeframe Candlestick Formation

Streng nach der Literatur ist eine Hammer / Hanging Man eine auf einen Bar beschränkte Formation. Das kann ein Stunden, Tages oder Wochenbar sein. Ich habe das Konzept für diesen multiple timeframe Indikator ein wenig erweitert.

Der Indikator versucht eine Hammer / Hanging Man Formation am Chart zu erkennen, und das unabhängig davon, wie viele Bars zur Ausbildung derselben verwendet wurden.

multiple Timeframe Hammer Analyse

In diesem Beispiel wurde vor 4 Tagen eine bullishe Hammer Formation gefunden, welche sich über 40 Tage erstreckt.

Einsatz des multiple Timeframe Candlestick Indikators

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Saisonale Muster: sell in may and go away…?

“Sell in may and go away” Wer kennt nicht diese alte Börsenregel, aber ist sie auch wirklich sinnvoll? Mit dem hier vorgestellten Indikator sind saisonale Muster leicht aufzuspüren und auf ihre Profitabilität zu überprüfen.

 

Saisonale Muster

Warum saisonale Muster erscheinen, dafür gibt es viele Erklärungen. Weizen ist zur Erntezeit einfach billiger zu haben als in den Monaten davor, der Gasverbrauch ist im Sommer niedriger als im Winter, gegen Ende des Jahres wird noch Geld veranlagt, am Anfang des Jahres abgezogen, all dies hat Auswirkungen auf den durchschnittlichen Preis in diesen Monaten.

Am ersten Chart sehen Sie die meinen seasonal Indikator. Er zeigt die durchschnittliche Monats Performance des DAX (seit 1987)  in Prozenten. Jeder Balken stellt einen Monat dar. Die beiden negativen Balken sind der August und September, der hohe rote Balken ist der April.

 

dax seasonal indikator

Sell in may and go away scheint im Dax also nicht so falsch zu sein, nach der guten Performance im April, ist der Mai durchschnittlich deutlich schlechter.

Backtest Seasonal Strategie

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Zyklus Analyse im Seitwärtsmarkt

In aktuellen Märkten, ohne dominanten Trend, ist der kurzfristige dominante Marktzyklus oft der einzige Anhaltspunkt für gutes Timing. Und um die Länge des aktuellen Zyklus zu bestimmen hat man mit dem “corona cycle length” Indikator von John Ehlers das richtige Werkzeug.

Ich will  nicht den Originalartikl von John Ehlers zu diesem Indikator replizieren, sie finden das pdf unter diesem link zum download: http://goo.gl/PR96Nf

Hier finden sie den  Tradesignal Indikator Code zum copy&paste download.

Zyklus Analyse EuroDollar

Der EuroDollar, nachdem der Abwärtstrend gebrochen war und der Aufwärtstrend noch auf sich warten lässt, wurde zur idealen Spielwiese für die Zyklus Analyse.

euro dollar cycle analysis

Der John Ehlers Zyklus Indikator zeigt aktuell eine Zykluslänge von 28 bars an (daily chart). Dies hat vom Tief Mitte April bis zum Tief Mitte Mai perfekt funktioniert, das nächste Tief ist dann für den kommenden Montag indiziert.

 

zum Tradesignal Equilla Code Passwort “code”

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