Statistics of VIX

The CBOE volatility index VIX  measures the market’s expectation of future volatility. It is the gauge of S&P500 equity market volatility.

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 panic 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.

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. The return with vix below 15 (red) is above 0% and with a fat tail to positive returns. Data from 2004-2018

 

The above chart suggests that going short on volatility, if VIX is above 25, seems to be a good idea, the next chart shows what will most probably go wrong during the next 25 days. The distribution diagram gives the maximum adverse movement of the VIX.

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

 

On the other side, the distribution of the maximum loss of the VIX during a 25 day period shows a median of below -20%.

 

Best to test for yourself, Tradesignal Equilla Code on request

 

Machine Learning – KNN using Tradesignal Equilla

I always thought that inspiration and experience is a key factor in trading. But everytime my chess computer beats me without any inspiration, just by brute force, I start to reconsider this assumption. This article will be about a brute force approach in trading.

Rule based trading

I have never been a great believer in classical technical analysis. If you ask 2 analysts about the current trend in the market, you get at least 3 answers. So I turned to algorithmic trading, using the tools of technical analysis in a new way, doing if..then conditions, backtesting them, refining the rules and parameters until the desired result was shown. These if..then based conditions, like if the market is above it`s 200 day line then go long, are mostly found by experience and inspiration. Isn’t my brain just a neural network which can be trained with historic data (experience), enhanced with a glass of wine for the inspiration?

Today I would like to take a voyage into machine learning. I would like to let my computer find the rules by itself, and just decide if I like the results or not. I’ll have the glass of wine with some friends and let the machine do the job; This sounds tempting to me, but can life really be as easy?

Unsupervised machine learning – kNN algorithm

The knn algorithm is one of the most simple machine learning algorithms. Learning might be the wrong label, in reality it is more of a classification algorithm. But first let’s see how it works.

The scatter chart above is a visualization of a two dimensional kNN data set. For this article I used a long term and a short term RSI. The dots represent the historic RSI values. Have a look at the fat circled point. It just means, that todays RSI1 has a value of 63, and RSI2 got a value of 70. Additionally the dots have got colours. A green dot means the market moved up on the following day, a red dot shows a falling market on the day after.

kNN – k nearest neighbours

To do a prediction of tomorrow’s market move, the kNN algorithm has a look at the historic data (shown on the scatter plot) 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 2 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. Call it classification or prediction, the kNN algorithm just has a look on what has happened in the past when the RSI indicators had a similar level. Have a look at this video, it is a great explanation on how the algorithm works.

kNN as Tradesignal Equilla Code

Computer kiddies would implement this algorithm in Python or R, but I would like to show you an implementation with the Tradesignal programming language Equilla. It is way faster than Python, and has got the advantage that I can directly see all the advantages and disadvantages on the chart. It is not just number crunching.

To implement the algorithm in Tradesignal we first have to do the shown scatter plot, but not graphically but store the 2 rsi values and the next days market move(colour of dots) into an array.

In line 8&9 the rsi values are calculated, line 12&13 calculates the next day`s market move. Line 15 to 20 then stores the data into the training data array. This is done for the first half of the data set, for my example I will use the data from bar 50 to 1000 on my chart of 2000 data points.

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.

Line 23 to 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.

We are 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 just have to trade it:

kNN algorithm performance

Lets have a look if this simple machine learning algorithm works. Using 2000 days of backward adjusted brent data, I used a 14 and 28 day RSI to predict the next day move. The training was done on bar 50 to 1000, and I used the 5 nearest neighbours for the classification.

Knn algorithm – conclusion

Judging on the shown graph it seems to work. It seems to be possible to use these 2 RSI indicators to predict tomorrow’s brent move.

kNN algorithm gives me a framework to test all kind of indicators or even different data sets easily and see if they have got any predictive value.

This is definitely an addition to classical algorithmic trading, using if..then conditions build from experience and intuition.

But you might still need some intuition to find the right data sets, indicators and parameters to get a useful prediction. Not everything can be done by machine learning…

 

 

 

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.

 

Position sizing – the easy way to great performance

Working on your position sizing algorithm is an easy way to pimp an existing trading strategy. Today we have a look at an energy trading strategy and how the position sizing can influence the performance of the strategy.

The screenshot shows you the returns of the same trading strategy, trading the same markets, the same time frames and using the same parameters. The returns on the left side look nice, making money every year. The returns on the right side are somehow shaky, and you would have to love volatility of returns if you would think about trading this basket. The only difference between the basket on the right and on the left side is the position sizing.

The energy basket:

The basket trades German power, base and peak (yearly, quarterly, monthly), coal, gas, emissions. All instruments are traded on a daily and weekly time frame chart, using the same parameters. If the daily trading uses a 10-period parameter, the weekly trading would use a 10-week parameter. This limits the degrees of freedom I have when doing the strategy-time frame-parameter merge, thus minimizing the curve fitting trap.

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Opening Range Breakout

Ein Opening Range Breakout System von Perry Kaufmann.

Es wurde im Magazin “Technical Analysis of Stocks&Commodities” im Juli 1994 besprochen, und wie es scheint, funktioniert es noch immer.

Das System wartet die erste Handelsstunde ab und geht dann bei Ereichen eines neuen Hochs oder Tiefs  long oder short.  Die Einstiegs Order (Stop Buy / Stop Sell) wird nicht exakt auf das Hoch / Tief gelegt, sondern ein paar Punkte darüber /darunter. (hier 20 Ticks)

Prinzipiell ist die Strategie der Afternoon Trader Strategie sehr ähnlich.

TSM(S) 1st hour breakout detail

Da die hier vorgestellte Systemversion  ursprünglich für dieTradestation 2000i in Easy Language geschrieben wurde, ist das Laden von 3 Zeitreihen ein wenig kompliziert gelöst. Aber es funktioniert.

Backtest mit adjustiertem DAX Future:

TSM(S) 1st hour breakout backtest

zum Tradesignal Equilla Code Passwort “code”

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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Reality vs. Robert W. Colby, CMT

Dont`t believe!

Papier ist geduldig, darum ist es oft besser wenn man selbst testet bevor man ein veröffentlichtes Handelssystem mit seinem Geld ausprobiert. Heute geht es hier um einen Handelssystem out of sample Test

Ein schönes Beispiel dafür ist eine Strategie aus Robert Colbys Buch “The Encyclopedia of Technical Market Indicators“, 2nd edition, 2003, page 791ff.

Darin wird ein einfaches moving average crossover Systeme vorgestellt, welches anscheinend seit beinahe 100 Jahren phänomenale Gewinne verspricht.

Hier eine Kopie aus dem Buch:

colby wma strategy

colby2

Tradesignal Backtest

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