General

An Algorithmic Stock Picking Portfolio

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In this article I will discuss a simple algorithmic stock picking approach based on momentum and volatility. The goal will be to generate excess returns versus a capital weighted stock basket.

Alpha and Beta

Investing in assets with low volatility and high return is on a lot of peoples wish list. Portfolios which archive this goal will have a high Sharpe ratio and in the end get the investors money. By reverse engineering this criteria, one can find promising stocks to invest in and out perform a given capital weighted index.

Alpha and beta are measures to describe an assets performance relative to its index. Both are used in the CAPM – capital asset pricing model.

Alpha is a measure for an assets excess return compared to an index. Continue reading →

Market crash or market correction?

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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.

Crash or 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 →

Bollingerband: The search for volatility

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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 width percentile

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 →

IV Percentile – when to sell volatility

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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)

When to sell implied volatility

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 – Stock Market Crash Ahead?

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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.

Market Breadth – Hindenburg Omen

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.

Hindenburg Omen: idea and calculation

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 →

Implied vs. Realized Volatility for NASDAQ100 stocks

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(1) You shall only trade when the chances are on your side

Comparing implied and realised volatility

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 →

Distribution of Returns

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“Tomorrow never happens. It’s all the same fucking day, man. ”  Janis Joplin

Distribution of Returns

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 →

Bet on Bollinger

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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.

Bollinger Bands Breakout Probability

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 →

Scanning for Support and Resistance Probabilities

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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.

Algorithmic RSI Support and Resistance Levels

We are all familiar with oscillators like the RSI indicator. It gives an idea if the market is oversold or overbought. Continue reading →

Backtesting Market Volatility

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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.

The fair price for 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?

Backtesting volatility

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 →

Demystifying the 200 day average

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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.

The 200 day moving average

Continue reading →

Google EOD csv stock price data download

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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 →

Seasonal trouble ahead

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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.

Seasonality of DAX

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.

Continue reading →

KVOL Volatility part 2

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How to calculate volatility based on the expected return of a straddle strategy has been shown in part 1 of fair bet volatility KVOL.

Using and Displaying K-Volatility:

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 →

A graphical approach to indicator testing

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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

Continue reading →