S&P adjusted for M1 and CPI

The seemingly unlimited printing of money by central banks has driven markets to unimagined heights. But what does market performance really look like after adjusting for M1 money supply and inflation CPI?

Money supply M1 and inflation CPI

Let’s first have a look at the absolute numbers for consumer price index (for urban consumers) and the US M1 money supply. Luckily there is no direct correlation between those two measures. On this chart an expansion of M1 did not lead to a rising inflation. But there are surely other examples in history.

CPI and M1 money supply

CPI and M1 money supply  (CPI left scale, M1 right scale)

CPI adjusted stock market

Adjusting the stock market (S&P500) for CPI shows its real growth rate from the point of an average consumer. It is not 45 times higher than 1970, but only 5 times higher. If your investment in 1970 bought you one average consumer basket, it will buy you 5 baskets right now, but not 45, as the nominal value might suggest.

SPX CPI adjusted 1970

SPX CPI adjusted 1970, % gains since 1970

House price index adjusted S&P 500

Some rich investors cynically call the basket of goods the CPI calculation is based on the “poor peoples basket”. So let’s adjust the market for the “middle class CPI” – the S&P Case-Shiller home price index. It is based on residential house prices.

SPX house price adjusted 1987

SPX house price adjusted 1987, % gains since 1987

The nominal value of the market should buy you 12 houses now, when you invested the equivalent of one house in 1987. But unfortunately the house prices went up too, so the adjusted calculation shows, that you only can afford 2 and a half houses with the money invested in 1987.

If you invested at the beginning of 2000, the money made on the market would hardly buy you a new swimming pool. 11% up for the adjusted value…

SPX house price adjusted 2000

SPX house price adjusted 2000, % gains

Money printing – Inflation Angst

The impact of inflation has been quite dramatic, it ate most of the returns which made you feel rich. Inflation Angst is mostly based on the incredible expansion of money supply. So let’s adjust the market with M1.

SPX M1 adjusted 1975

SPX M1 adjusted 1975

The next chart clearly shows how we printed the market up since the financial crisis in 2008. M1 adjustment does not leave any gains in financial markets. You just have more paper dollar as there have been more paper dollars printed. (which went to stock owners, but not to working class people)

SPX M1 adjusted 2008

SPX M1 adjusted 2008

Bitcoin adjusted S&P 500

Given the incredible money printing and the expected high inflation, a lot of people think about alternative assets.

Big Elon is one of the most prominent advocates of the idea to put your money not into inflationary dollars but Bitcoin. The Bitcoin algorithm makes this asset a deflationary one. The maximum number of coins is limited, and as people lose their wallets the available number of coins is actually decreasing.  So the more money is put into this market, the more Bitcoin goes up. You need more and more paper dollars to buy a Bitcoin.

Noting the S&P 500 in Bitcoin instead of dollars shows the effects of this:

S&P 500 bitcoin adjusted

S&P 500 bitcoin adjusted

Since the corona lows in march 2020 S&P 500 gained 70% in dollar value, but it lost 80% from the point of a bitcoin investor.

So chose wisely in which market/currency you put your money, and don’t base your expectations on the nominal gains of any market.  Always adjust for CPI or the measure of the product/asset basket your luck depends on.

 

thanks to FRED and Refinitiv for the data, thanks to Tradesignal for the charting software.

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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RSI Hellfire Heatmap Indicator

Chart analysis is all about visualizing data. The RSI hellfire indicator uses a heat-map to visualizes how overbought or oversold the market is on a broad scale. This helps to get a broad picture of the current market setup.

Multiple Time-frame Relative Strength Index

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VIX Futures spread trading

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

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

A simple algorithm to detect complex chart patterns

Finding complex chart patterns has never been an easy task. This article will give you a simple algorithm and a ready to use indicator for complex chart pattern recognition. You will have the freedom to detect any pattern with any pattern length. It has been described as Fréchet distance in literature. This article shows a simple adaptation for chart pattern analysis.

Defining a chart pattern

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The Probability of Normality

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

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