I am happy to announce that I will be hosting a free webinar series on quantitative analysis and algorithmic trading. Dates and times for the first shows can be found over here: Tradesignal Webinar Series
Over the last 20 years equity markets and ETFs did a significant part of their total performance over night. This article will examine the relationship of in-session moves vs. the out-of-session moves of ETFs and commodities.
The overnight risk premium
Whenever the market shows an exceptional day ranges it is time to take bite. See how you can profit from large daily market moves.
When looking at any chart, you will surely notice that the large candles tend to close near the high or low. This is due to herding. Once the market is moving significantly, everyone hops on and the large move becomes even larger. This is true for daily, weekly and intraday candles.
The chart shows an indicator which plots the daily move. Every opening is set to zero and the absolute move of the day is drawn. Around these normalised candles a long term 2 standard deviation volatility band is drawn. Right now the 2 standard deviation volatility for SPX is about +/- 46 points.
Take a bite before the market closes
As you can see this +/-46 point barrier above/below the opening of the day is a wonderful entry point. If you enter long 46 points above the opening and go short 46 points below the opening nearly all entries would have lead to a profitable trade. To get an even higher probability of success you can volume as a confirmation. Large moves must also show high volume. The exit is done at the end of the session. This analysis does not give any indication for the next days move. So be fast, take your bite and go home with a small profit and no overnight position.
No free lunch
On the chart it looks easy, but be careful. As an example the last bar shown on the chart first crossed the band to the downside, reversed and crossed above the upper band. So you will need to use a trailing stop to lock in profits and avoid to take the full -46 to +46 points trade as a loss!
German Power prices can be explained by supply and demand, but also by causal correlations to underlying energy future prices. A properly weighted basket of gas, coal and emissions should therefore be able to resemble the moves of the power price. This article will introduce multivariate regression analysis to calculate the influence of the underlying markets on a given benchmark. It is an example of a machine learning algorithm used in analysis and trading.
Multivariate regression analysis
VIX futures are usually in contango, meaning that the next month future is quoting at a higher price than the current month VIX future. But this spread in not constant, and at the end of the expiry cycle an interesting VIX future spread trading idea comes to my mind…
End of cycle VIX futures spread trading
Having a look at the chart below you hopefully see the spread trading idea by yourself: Continue reading
Whenever you develop an algorithmic trading strategy, unwanted curve fitting is one of the most dangerous hazards. It will lead to substantial losses in real time trading. This article will show you some ways to detect if the performance of your algorithmic trading strategy is based on curve fitting.
Curve fitting – what is it?
Every algorithmic trading strategy will have some parameters. There is no way around it. You will have to decide what length your indicators have, you will have to specify a specific amount for your stop loss or profit target. Beside the actual rules of your strategy the chosen parameters will usually significantly influence the back-test performance of your strategy. And with any parameter you add the danger of curve fitting rises significantly. Continue reading
When developing a new trading strategy you are usually confronted with multiple tasks: Design the entry, design the exit and design position sizing and overall risk control. This article is about how you can test the edge of your entry signal before thinking about your exit strategy. The results of these tests will guide you to the perfect exit for the tested entry signal (entry-exit combination)
Quality of an Entry Signal
When you develop a new idea for an entry signal there are two things you would like to see after the entry: no risk and fast profits. This would be the perfect entry with the highest possible edge. In reality the market response to your entry will be risk and chance. With a good entry the upside would outnumber the downside. Continue reading
When selling implied volatility you want the market to stay within the expected range. But what is the historic probability that markets behave as expected? And what other analysis could be done to enhance your chances and find the periods when it is wise to sell an at the money straddle? This article will try to give some answers to this question.
The normal distribution cone
Classical technical indicators like RSI and Stochastic are commonly used to build algorithmic trading strategies. But do these indicators really give you an edge in your market? Are they able to define the times when you want to be invested? This article will show you a way to quantify and compare the edge of technical indicators. Knowing the edge of the indicator makes it an easy task to select the right indicator for your market.
The edge of an indicator
Any technical indicator, let it be RSI, moving averages or jobless claims, has got a primary goal. It should signal if it is a wise idea to be invested or not. If this indicator signal has any value, on the next day the market should have a higher return than it has on average. Otherwise the usage of no indicator and a buy and hold investing approach would be the best solution.
The edge of an indicator in investing consists of two legs.
- the quality of the signal
- the number of occurrences
Analyzing at which time daily market extremes are established shows the significance of the first and last hours of market action. See how different markets show different behavior and see what can be learned from this analysis.
Probability of Extremes
A day of trading usually starts with a lot of fantasies for the future, then we try to survive the day and end it with a lot of hope for tomorrow. This psychological pattern can also be shown when analyzing intraday market data. A high level of fantasies usually leads to a strong market movement, and thus market extremes can often be seen near the beginning or the end of the trading session. Continue reading
The stock market shows some astonishingly stable date based patterns. Using a performance heat map of the S&P500 index, these patterns are easily found.
Date based performance
The chart below shows the profit factor of a long only strategy investing in the S&P500. Green is good, red is bad. The strategy is strictly date based. It always buys and sells on specific days of the month. Continue reading
Adding some random noise to historic market data can be a great way to test the stability of your trading strategy. A stable strategy will show similar profits with noisy and original data. If the noise has a great impact on your results, the strategy might be over fitted to the actual historic data.
Synthetic market data?
Generating completely synthetic market data to test algorithmic trading strategies is a dangerous endeavour. You easily lose significant properties like classic chart patterns or the trend properties of your market. Continue reading
Monte Carlo Simulation uses the historic returns of your trading strategy to generate scenarios for future strategy returns. It provides a visual approach to volatility and can overcome limitations of other statistical methods.
Monte Carlo Simulation
Monte Carlo is the synonymous for a random process like the numbers picked by a roulette wheel. Continue reading
Factor investing has been around in portfolio management for some years. Based on algorithmic rules it became the big thing in trading and the ETF industry. But is there still some money to be made? Is small beta still smart or just beta? This article will give you a Tradesignal framework to test the factor investing ideas by your own.
Buy and hold has been a profitable approach in investing. But customers ask for more. So technical analysis came around and held up the promise that market timing is possible. As the returns did not match this promise, algorithmic trading was invented. Clearly defined rules made it possible to backtest any given strategy, and if done properly, the returns equal the ones promised during the backtest. But this requires a lot of intellectual power and relies on cheap execution, so these returns are usually not available to the public. Continue reading
This article is about the dollar cost averaging investment strategy and the influence of luck in it.
The Dollar Cost Averaging Investment Strategy
To invest parts of your income into financial markets has been a profitable approach, especially in times when bond yields are low. One approach to do so is the dollar cost averaging investment strategy. Continue reading
So you are bullish on a specific stock, but you also have realised that timing is major problem? So what would be the best strategy to implement your bullish opinion but avoid the problems of any timing strategy?
Selling a put option might be the answer.
For discussing this question let’s use the current Apple chart as an example. The question is, if you are bullish on apple, should you buy 100 Apple stocks right away or should you sell an at-the-money put option. To find the pros and cons of these two possibilities let’s have a look at some charts. Continue reading
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
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