Over the last days and weeks some traders have been worried if the currently ongoing correction in the markets will evolve into a crash, or if it is just a normal correction.
The main difference between a correction and a crash is the panic level. But it is not the absolute level of .VIX, the CBOE implied volatility meter. It is the difference between realized and implied volatility that defines a crash which defines real panic. Continue reading
Usually it makes no sense to fight against normal distribution. But there are setups which have got a high probability of unexpected behaviour. Volatility can be the key to future market movements.
Bollinger Bands are a great tool to describe market volatility. And my favourite tool to measure the width of Bollinger Bands is Bollinger percentile.
Like the IV percentile indicator my Bollinger percentile indicator is a probabilistic indicator. It gives the probability of Bollinger Bands having a narrower upper band – lower band range than currently given. Continue reading
Volatility trading: when to buy and when to sell volatility
You got to know when to hold ’em,
Know when to fold ’em,
Know when to walk away,
And know when to run.
(Kenny Rogers)
Volatility is a nicely reverting time series. If it is high chances are good that it will come down again. The only problem is to find out when volatility is high, and when it is low. Unfortunately there are no absolute levels, you can’t say that 50% implied volatility is high, as this specific stock might have an implied volatility of 80% most of the time. So you can only compare the current volatility level to historic levels and so define if volatility is currently high or low. Continue reading
The Hindenburg Omen is an indicator which is believed to forecast market crashes. Unfortunately it does not work, but the idea behind this indicator is worth to be discussed.
The Hindenburg Omen is a market breadth indicator. It describes how correlated stocks are within a market behave.
I already had a market breadth indicator in this blog a long time ago, the percentage of stocks within a market above the 200 day average. The Hindenburg Omen does not use moving averages, it is based on the number of new highs and lows in the market.
To get a warning signal for an upcoming stock market crash the Hindenburg Omen indicator observes the number of stocks making new 54 week highs and the number of stocks making 54 week lows. In a strong bull market you will usually see a lot of new highs but hardly any new lows, in a bear market you will see new lows, but no new highs. Continue reading
(1) You shall only trade when the chances are on your side
Selling volatility can be a profitable game, but only if you sold a higher volatility than the market realises later on. Comparing realised and current implied volatility gives you an idea if the chances are on your side.
We already had a look at realised volatility and what the fair price for a straddle might be. Have a look at the kvol–fair bet articles. These articles present a way to calculate the historically correct price for a straddle. Whenever you sell a straddle (to sell volatility), implied volatility should be higher than the fair bet price. Only then you will win on a statistical basis. Also have a look at the statistics of VIX, to get a clue when a downturn in volatility can be expected. Continue reading
“Tomorrow never happens. It’s all the same fucking day, man. ” Janis Joplin
Analysing history and hoping it will somehow repeat itself is the big hope of all quantitative traders. This article is about the distribution of market returns, but not about normal distribution, Gauss and standard deviation. This article is about the visualisation of market returns and what can be learned from it.
Probability distribution diagrams show the probability of a specific outcome. How likely is it that the market will be at a specific price sometimes in the future? How does a specific bullish or bearish indicator signal affect the future market behaviour on a statistical basis? An approaching visualisation of the statistical probabilities are the best way to understand market behaviour and find your chances in trading. Continue reading
Ever since John Bollinger introduced his Bollinger Bands in the early 1980s the bands have been a favourite indicator to all technical trades. This article is about the prediction capabilities of Bollinger bands.It researches the Bollinger breakout probability.
How good are the chances to be outside or inside of the bands in the future? How do these probabilities relate to the current position the market has got relative to today’s Bollinger band? What impact has overall volatility on these statistics? These questions will be answered below.
By definition of the indicator most of of the times the market will trade inside the Bollinger band. But this is only of minor interest to me. As a trader I am more interested on what will happen in a few days from now. Where will the future market be? Shall I bet on a breakout or sell a straddle?
So I did some tests on the forward prediction qualities of the Bollinger band indicator.
For all tests I used the 20 day, 2 standard deviations setting, which is the standard setting for most charting packages. Then I analysed the positioning of the market in 20 days form now to see if Bollinger bands can be of any help with these questions. Continue reading
I have been in search for a signal I could use for a short vertical spread or naked short option strategy. So my main concern has been to find a level, which will most probably not be penetrated over the next few bars.
This is what I came up with.
We are all familiar with oscillators like the RSI indicator. It gives an idea if the market is oversold or overbought. Continue reading
The markets will go up and down, and usually it’s not my business why they do it, I am just interested in making my luck with a position on the right side of the trade. Continue reading
If you want to trade volatility, you can place a bet on the option market. Just buy an at the money put and call, and at expiry day you will either win or lose, depending on the actual market move since you bought the straddle and the price you paid for the straddle. To put it simple, if the market moves more than you paid for the two options you will win, otherwise you will lose. This article is about a back test of volatility.
When I look at the S&P500 I could buy or short a straddle with 16 business days until expiry right now for around 70$. That’s the implied volatility.
When I look at the standard deviation of 16 day returns, using the last 30 days to calculate it, it shows me a volatility of around 30$. That’s historical volatility.
When I use my own fair bet KVOL Volatility, it gives me a volatility of about 50$
Now I got three measures for volatility, but which one is the best prediction for future market volatility? And how big will the error (=wins and losses) be if we place this bet over and over again?
Placing an perpetual bet on future volatility using the payback profile of a short straddle will give me an idea on how good historical volatility and Kahler’s volatility was able to predict future volatility. In a perfect world this virtual test strategy should be zero sum game; if not, future volatility is either over or underestimated by these 2 indicators. Continue reading
The 200 day average is considered as a key indicator in everyday technical analysis. It tells us if markets are bullish or bearish. But can this claim be proved statistically, or is it just an urban legend handed down from one generation of technical analysts to the next? Let’s find out and demystify the 200 day moving average.
Sometimes my data provider has not got the data I am looking for. Searching for downloadable csv data I recently came across google spreadsheets. It provides an easy way to get historical stock price data. Save it as csv and use it with your Tradesignal.
The only thing you will have to do is to open a google spreadsheet in your browser, add a formula as shown in one cell and the data will be pulled. Copy&Paste the data to another spreadsheet and save it as csv. Continue reading
We already had a post regarding the mean reverting tendency of Volatility, now it`s time to make some money using this information.
If a bitchy prime minister and a crazy president weren’t enough, for the upcoming months the seasonal chart is also indicating further price setbacks.
Analyzing the average monthly performance of the German DAX index a distinct pattern of seasonality can be observed. On average June has been down 0.6%, but the big trouble is yet to come.
How to calculate volatility based on the expected return of a straddle strategy has been shown in part 1 of fair bet volatility KVOL.
KVOL uses the given amount of historic returns to calculate an expected value of an at the money put and call option. The sum of these prices are the historic fair value for implied volatility. It can be used to compare current market implied volatility to historic fair values.
Beside calculating KVOL for a specific return period it can also be used to show it as a projection indicator on the chart.
The example on the chart gives such an expectation channel for the s&P500 at the beginning of each month. The 250 days before are used to calculate KVOL. The line underneath the chart is running KVOL for 13 trading days. Continue reading
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. Continue reading
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…. Continue reading
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.
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.
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.
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.
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.
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.
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.
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.
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?
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.
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
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.
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…
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.
