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.

The first and second deviation of a curve

If you remember your high school math class you have heard about the first and second deviation of a curve and how to use it to find the turning points and extremes of a curve,

The deviation of a curve is nothing else than the momentum of a curve. It defines the slope of the curve. If the slope of a curve is zero, you have found a local extreme. The curve will change from rising to falling or the other way round.

The second deviation, or the momentum of momentum, shows you the turning points of the first deviation. This is important, as at this point the original curve will switch from acceleration to break mode.

But don’t worry about the theory behind the indicator, see the chart below to get the idea.

First the market data is smoothed.

Second the momentum of the market is calculated. Third the momentum of the momentum is calculated.

If the second momentum crosses above zero, the first momentum will have local low and the market will be in a state when the trend comes to an end.

Support and Resistance explanation

Algorithmic turning points

Using the logic laid out above the support&resistance indicator will show the turning points of the market in a timely manner.

Depending on level of market-smoothing it will show the long term or short term turning points.

support and resistance

If you are a trend follower you might only be interested in support levels. Therefore I added the possibility to use the Wells-Wilder Directional Movement Index as a trend filter.

support and resistance DMI filter

Tradesignal indicator code

I used the Tradesignal software suite to program this indicator. The code used is shown below.

It first calculates the first and second deviation of the smoothed market, and in a second step runs the turning point detection.

Tradesignal indicator code

Analysis done using the Tradesignal software suite and data from Refinitiv. Thanks!

Overnight Risk Premium in Equity and Commodity Markets

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

As an investor you can expect to get paid for taking risk. If someone sell its stock to you he gets the risk free return for holding cash,  but you will have to finance the risk of the stock moving against you. This risk is quite low when the market is open and liquid, as you could always sell the stock in case of an adverse movement. But when markets are closed you have to bear a higher risk as you will be bound to your position until the markets opens on the following day.

According to this theory the market returns over night should be positive, to compensate you for the higher risk of holding a position you can’t liquidate immediately. Let’s see if there is some truth in this theory and how big the overnight risk premium might be.

Test code and data

To test the theory I took daily market data from Refinitiv and used the Tradesignal code below to sum up the percent values of daily and overnight moves.

Tradesignal overnight performance test code

Tradesignal overnight performance test code

ETFs overnight risk premium

The chart below shows the outcome of the calculation for the QQQ ETF.  As it can be seen, the good performance over the last years has happened mostly when markets where closed. Even during the financial crisis the overnight returns have mostly been positive.

QQQ overnight risk premium

QQQ overnight risk premium

 

Having a look at the distribution of returns you see quite a different behaviour on e.g. a rolling sum of 10 day or night moves. The daily open to close returns show a higher tendency for big moves than the overnight move. So from a risk perspective the day session bears more risk than the night session.

QQQ overnight returns distribution

QQQ overnight returns distribution

 

QQQ is not the only ETF showing this excess overnight performance. The overview below gives you the data. The numbers show the sum of percent moves since 2001. (starting at 100)

SPY ETFs overnight performance analysis

SPY ETFs overnight performance analysis

A look at commodities

With futures and commodities this overnight effect is not as prolonged as with equities. Sometimes it even is non existent at all.

The chart below shows the overnight and daytime performance of the German Bund future. Beside the phase from 2015 to 2017 the overnight movement did not add to the total performance.

Bund Future FGBL overnight risk premium

Bund Future FGBL overnight risk premium

 

The december future on emission certificates (chart below) shows no significant overnight movement at all.

CFI2 Emissions overnight performance

CFI2 Emissions overnight performance

German power, yearly contract, shows a strong negative overnight performance.

F1BY overnight performance

F1BY overnight performance

Implications for trading

As the overnight move has got a significant impact on the total performance of equity markets, it will also have implications on the design of a trading strategy. A first implication of this overnight effect might be that you should not be short over night in equity markets, and you might not want to open your long position at the beginning of the day.  But keep in mind, if everyone knows the trick, this overnight movement will have implications for the first and last hour of trading. This will be a topic for another article…

 

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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