Z-Score Factor Portfolio Weighting

Technical Indicators can be used for timing and weighting strategies. Using momentum as an example, you could go long if the momentum turns positive, or you could dimension the weight of your position depending on the level of momentum. Applied on a portfolio of assets, this would be called factor investing. This article will show you a way to weight your portfolio using factors. Continue reading

The coastline paradox and the fractal dimension of markets

Coastlines are fractal curves. When you zoom in, you will see similar shaped curves on every scale. The same is true for market data. On a naked chart you can hardly tell if it is a daily or hourly chart. This article will explore this feature of crinkly curves and show how much markets and coastlines have in common.

The coastline paradox

When trying to measure the length of the British coastline you will quickly notice, that the length measured depends on the length of the ruler you use. The shorter the ruler, the longer the measured length of the coastline.

When measuring a straight line, the length of the ruler has no influence. You can measure 1 meter with a 1cm ruler applied 100 times or with a 50cm ruler applied 2 times. Both methods will give you the same result. Not so when measuring a crinkly line like a coast.

British coastline length paradox

British coastline length paradox (c) wikipedia

In 1967 Benoit Mandelbrot wrote a famous article in Science magazine about this problem. This was the birth of fractal geometry. The basic assumption was, that if a curve is self similar, this self similarity can be described by the fractal dimension of a curve. Self similarity means, that if you zoom into a curve, it looks similar on all zoom levels.

Coastline paradox in financial markets

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Detecting Support and Resistance Levels

Support & Resistance levels are essential for every trader. The define the decision points of the markets. If you are long and the market falls below the previous support level, you most probably have got the wrong position and better exit.

The detection of support and resistance levels is usually highly subjective and based on the analysts experience. In this article I will use a simple algorithm to detect the levels and show them on the chart. Continue reading

Profit from large daily moves

Whenever the market shows an exceptional day ranges it is time to take bite. See how you can profit from large daily market moves.

Open-Close Range

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!

 

 

 

 

Python Regression Analysis: Drivers of German Power Prices

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

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How to detect unwanted curve fitting during backtest

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

The Edge of an Entry Signal

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

The Edge of Technical Indicators

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.

  1. the quality of the signal
  2. the number of occurrences

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Daily Extremes – Significance of time

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

Noisy Data strategy testing

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