Machine Learning – KNN using Tradesignal

I always thought that inspiration and experience is a key factor in trading. But everytime my chess computer beats me without any inspiration, just by brute force, I start to reconsider this assumption. This article will be about a brute force approach in trading.

Rule based trading

I have never been a great believer in classical technical analysis. If you ask 2 analysts about the current trend in the market, you get at least 3 answers. So I turned to algorithmic trading, using the tools of technical analysis in a new way, doing if..then conditions, backtesting them, refining the rules and parameters until the desired result was shown. These if..then based conditions, like if the market is above it`s 200 day line then go long, are mostly found by experience and inspiration. Isn’t my brain just a neural network which can be trained with historic data (experience), enhanced with a glass of wine for the inspiration?

Today I would like to take a voyage into machine learning. I would like to let my computer find the rules by itself, and just decide if I like the results or not. I’ll have the glass of wine with some friends and let the machine do the job; This sounds tempting to me, but can life really be as easy?

Unsupervised machine learning – kNN algorithm

The knn algorithm is one of the most simple machine learning algorithms. Learning might be the wrong label, in reality it is more of a classification algorithm. But first let’s see how it works.

The scatter chart above is a visualization of a two dimensional kNN data set. For this article I used a long term and a short term RSI. The dots represent the historic RSI values. Have a look at the fat circled point. It just means, that todays RSI1 has a value of 63, and RSI2 got a value of 70. Additionally the dots have got colours. A green dot means the market moved up on the following day, a red dot shows a falling market on the day after.

kNN – k nearest neighbours

To do a prediction of tomorrow’s market move, the kNN algorithm has a look at the historic data (shown on the scatter plot) and finds the k nearest neighbours of today’s RSI values. As you can see, our current fat point is surrounded by red dots. This means, that every time the 2 RSI values have been in this area, the market fell on the day after. That’s why today’s data point is classified as red. Call it classification or prediction, the kNN algorithm just has a look on what has happened in the past when the RSI indicators had a similar level. Have a look at this video, it is a great explanation on how the algorithm works.

kNN in Tradesignal

Computer kiddies would implement this algorithm in Python or R, but I would like to show you an implementation with the Tradesignal programming language Equilla. It is way faster than Python, and has got the advantage that I can directly see all the advantages and disadvantages on the chart. It is not just number crunching.

To implement the algorithm in Tradesignal we first have to do the shown scatter plot, but not graphically but store the 2 rsi values and the next days market move(colour of dots) into an array.

In line 8&9 the rsi values are calculated, line 12&13 calculates the next day`s market move. Line 15 to 20 then stores the data into the training data array. This is done for the first half of the data set, for my example I will use the data from bar 50 to 1000 on my chart of 2000 data points.

The next task to complete is to calculate the distances of today’s rsi point to all the historic points in the training data set.

Line 23 to 27 calculates the euclidean distance of today’s point to all historic points, line 29 then creates a sorted list of all these distances to find the k nearest historic data points in the training data set.

We are nearly done. The next step is just to find out what classification (colour) the nearest points have got and use this information to create a prediction for tomorrow. This is done in lines 33 to 35

Have a look at the scatter chart at the beginning. If this would be the data stored in our training data set, the prediction, using the 5 nearest neighbours, would be -5. All the 5 nearest neighbours of our current data point are red.

Now that we got a prediction for tomorrow, we just have to trade it:

kNN algorithm performance

Lets have a look if this simple machine learning algorithm works. Using 2000 days of backward adjusted brent data, I used a 14 and 28 day RSI to predict the next day move. The training was done on bar 50 to 1000, and I used the 5 nearest neighbours for the classification.

Knn algorithm – conclusion

Judging on the shown graph it seems to work. It seems to be possible to use these 2 RSI indicators to predict tomorrow’s brent move.

kNN algorithm gives me a framework to test all kind of indicators or even different data sets easily and see if they have got any predictive value.

This is definitely an addition to classical algorithmic trading, using if..then conditions build from experience and intuition.

But you might still need some intuition to find the right data sets, indicators and parameters to get a useful prediction. Not everything can be done by machine learning…




Using Autocorrelation for phase detection

Autocorrelation is the correlation of the market with a delayed copy of itself. Usually calculated for a one day time-shift, it is a valuable indicator of the trendiness of the market.

If today is up and tomorrow is also up this would constitute a positive autocorrelation. If tomorrows market move is always in the opposite of today’s direction, the autocorrelation would be negative.

Autocorrelation and trendiness of markets

If autocorrelation is high it just means that yesterdays market direction is basically today’s market direction. And if the market has got the same direction every day we can call it a trend. The opposite would be true in a sideway market. Without an existing trend today’s direction will most probably not be tomorrows direction, thus we can speak about a sideway market.

Autocorrelation in German Power

But best to have a look at a chart. It shows a backward adjusted daily time series of German Power.

The indicator shows the close to close autocorrelation coefficient, calculated over 250 days. You will notice that it is always fluctuating around the zero line, never reaching +1 or -1, but let`s see if we can design a profitable trading strategy even with this little bit of autocorrelation.

The direction of autocorrelation

Waiting for an autocorrelation of +1 would be useless. There will never be the perfect trend in real world data. My working hypothesis is, that a rising autocorrelation means that the market is getting trendy, thus a rising autocorrelation would be the perfect environment for a trend following strategy. But first we have to define the direction of the autocorrelation:

To define the direction of the autocorrelation I am using my digital stochastic indicator, calculated over half of the period I calculated the autocorrelation. Digital stochastic has the big advantage that it is a quite smooth indicator without a lot of lag, thus making it easy to define its direction. The definition of a trending environment would just be: Trending market if digital stochastic is above it`s yesterdays value.

Putting autocorrelation phase detection to a test

The most simple trend following strategy I can think about is a moving average crossover strategy. It never works in reality, simply as markets are not trending all the time. But combined with the autocorrelation phase detection, it might have an edge.

Wooha! That`s pretty cool for such a simple strategy. It is trading (long/short) if the market is trending, but does nothing if the market is in a sideway phase. Exactly what I like when using a trend following strategy.

To compare it with the original moving average crossover strategy, the one without the autocorrelation phase detection, you will see the advantage of the autocorrelation phase filter immediately: The equity line is way more volatile than the filtered one and you got lots of drawdowns when the market is sideways.

Stability of parameters

German power has been a quite trendy market over the last years, that`s why even the unfiltered version of this simple trend following strategy shows a positive result, but let`s have a test on the period of the moving average.

Therefore I calculated the return on account of both strategies, the unfiltered and the autocorrelation filtered, for moving average lengths from 3 to 75 days.

Return on account (ROA) =100 if your max drawdown is as big as your return.

The left chart shows the autocorrelation filtered ROA, the right side the straight ahead moving average crossover strategy. You don`t have to be a genius to see the advantage of the autocorrelation filter. Whatever length of moving average you select, you will get a positive result. This stability of parameters can not be seen with the unfiltered strategy.

Autocorrelation conclusion:

Trend following strategies are easy to trade, but only make sense when the market is trending. As shown with the tests above, autocorrelation seems to be a nice way to find out if the market is in the right phase to apply a trend following strategy.


Measuring your EDGE in algorithmic trading

There are a lot of statistics which can be used to describe algorithmic trading strategies returns. Risk reward ratio, profit factor, Sharpe ratio, standard deviation of returns… These are great statistics, but they miss an important factor: Are your returns statistically significant or just a collection of lucky noise. The EDGE statistic might me the answer to this question.


Statistics in trading:

If the returns of your trading strategy are positive with in-sample and out-of-sample data this is a first sign that you are on the right path. The next step would be to have a look at the risk-reward ratio of your trading to get an impression if the strategy might be useful in a real world environment.

Assuming that your average yearly returns are about twice as big as the worst case historic draw down you can even be more confident that your strategy is useful. But there is still one thing to check before you can be sure that you are not just seeing a curve fit bullshit strategy. The standard deviation of the daily returns vs. your average daily return.

Defining EDGE

Assume your strategy made 250$ over the last year. This averages to about 1$ per day. This 1$ is a good or bad return, depending on the standard deviation of your equity line. If the standard deviation of your equity is 2$, then the 1$ average return strategy would be a bad strategy, as your average returns are way too small in respect to the volatility of your equity. If your volatility of your return curve would just be 50ct and you still make 1$ per day on average, your strategy would be ingenious.

Edge is the ratio of your average returns vs the volatility of your equity line. To be on the safe side,  your average return should be about 5% above the 90% confidence interval of your equity line volatility.

The left chart is a strategy trading an one month RBOB time spread, the right chart shows the same strategy trading German power. Rbob has got an edge of 3%, German power has got an edge of 5%.

If I would have to select which market I want to trade with this sample strategy, I surely would select German power over the rbob time spread. Both curves have their up and downs, but rbob is heavily relying on a lucky trade in September. This lead to a high standard deviation of the equity line , giving you a low edge reading.


Observing the ration between your average daily returns vs. the volatility of your equity curve can give you some valuable insights in the quality of your strategy. If it just called a few lucky trades in history, it will also show a high volatility in returns. And this you most probably want to avoid when turning to algorithmic trading. It`s not just the absolute profit at the end of the year, it is also the path you took to get to this number. The smoother, the better!

[Equilla / Easy Language code for EDGE indicator]


Position sizing – the easy way to great performance

Working on your position sizing algorithm is an easy way to pimp an existing trading strategy. Today we have a look at an energy trading strategy and how the position sizing can influence the performance of the strategy.

The screenshot shows you the returns of the same trading strategy, trading the same markets, the same time frames and using the same parameters. The returns on the left side look nice, making money every year. The returns on the right side are somehow shaky, and you would have to love volatility of returns if you would think about trading this basket. The only difference between the basket on the right and on the left side is the position sizing.

The energy basket:

The basket trades German power, base and peak (yearly, quarterly, monthly), coal, gas, emissions. All instruments are traded on a daily and weekly time frame chart, using the same parameters. If the daily trading uses a 10-period parameter, the weekly trading would use a 10-week parameter. This limits the degrees of freedom I have when doing the strategy-time frame-parameter merge, thus minimizing the curve fitting trap.

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143th Hedgework – Interview


“Automate the search for promising assets”

The use of trading systems means, among other things, that more markets and smaller time levels can be considered with the same team. The higher trading frequency and diversification into more markets and time levels will in turn lead to better performance and reduced risk for the investor. Philipp Kahler von Tradesignal chatted at the 143rd Hedgework from the sewing box of a technical analyst. He answers the most important questions here.

HEDGEWORK: Mr. Kahler, you represent the quantitative side of portfolio management. Has fundamental analysis become obsolete?
Philipp Kahler: Quantitative analysis deals with the creation of investment rules, which are so clearly defined that you can even teach them to a computer. Whether these rules are based on price or fundamental data is the same. The only important thing is that you can test the rules and regulations in a meaningful way and that the result is convincing.

HEDGEWORK: What are the advantages of technical analysis?
Kahler: Compared to fundamental data, technical indicators have the advantage that they are available in real time. It is not the distributions in the last quarter that are decisive, but rather what the market is doing today. My market-to-market result is then also evaluated on the basis of current prices. Whether the technical indicators are better than the fundamental analysis indicators is unclear, but as a trader, the most important thing for me as a trader is to have my risk under control NOW – and that’s easier with technical analysis.

HEDGEWORK: In your presentation, you talk about the transition from technical to evidence-based technical analysis. What’s behind it?
Kahler: I mean quantitative models that make use of classical technical analysis in the toolbox. Indicators and simple price patterns are ideal for searching markets for opportunities. However, even the best technical analysis indicator alone will hardly justify a successful trading approach. The combination of several indicators, perhaps even combined with pre-screening by fundamental analysis – this is the best basis for creating a stable, quantitatively controlled portfolio.

HEDGEWORK: In which areas are quantitative systems particularly useful?
Kahler: If the holding period of a position is somewhere between five minutes and two weeks, then there is no way around technical analysis. If your holding period is between two weeks and several months, the technical analysis will at least provide you with valuable services for timing your decisions. What you do is not decisive.

HEDGEWORK: Quantitative systems can, in a first step, bring structure to investment decisions?
Kahler: Yes, you can automate the search for promising values, improve the timing of trading activities, and you will also see an improvement in the delivered performance by avoiding emotional decisions.

HEDGEWORK: What role do FinTechs and Robo Advisor play? Has their increased market presence already made a significant difference?
Kahler: Yes and no. Of course, flash-crashs are caused by the increased use of machines, but if you look at the Dow Jones for more than 100 years, you will soon find that the market hasn’t changed. The daily/weekly/annual volatility has been almost constant for more than 100 years. These new technologies have the strongest influence not on market behaviour, but on the business model of traditional asset managers.

HEDGEWORK: What added value can an investor expect from a quantitatively controlled portfolio?
Kahler: One advantage is that the use of trading systems means that more markets and smaller time levels can be viewed with the same team. The higher trading frequency and diversification into more markets and time levels will then lead to better performance and reduced risk for the investor.

HEDGEWORK: Could you please describe how you proceed with a new product offering or a new investment strategy?
Kahler: This can be done in two ways. Either I have a trading system that I am convinced of. I then look for all the markets in which it functions and combine them into a quantitatively managed portfolio. Or I get a market given. Then I try to develop systems that work without adapting the parameters, e. g. in the hour/day and week range. Several such approaches are then combined to form a portfolio.

HEDGEWORK: Backtesting is an essential part of a product launch. These are sometimes not very reliable in retrospect. What is important to note here? What are the pitfalls?
Kahler: The baking test is not the problem. The adaptation of the strategy to the market – the parameterization and weighting of the individual components of the trading system – is critical. Since only a few adjustment screws lead to a high degree of adaptability of the trading system, there is a high risk that one adapts too much to past data, called curve fitting, without the system’s set of rules really saying anything decisive about the market.

HEDGEWORK: What method do you propose for testing the robustness of a strategy?
Kahler: On the one hand, you can first test the stability of the parameters. If a system includes the 200-day line, then it should work roughly as well with the 150-day and 250-day line. In a further step, the system must then be tested with unknown data. If a trend-following model works in Germany, for example, then it should not fail in the USA either. And finally, you should let the system disappear in the drawer for half a year and then check again to see if the real-time results were as expected.

HEDGEWORK: Maybe we can take a little more look at your sewing box. What practical tips can you give prospective quants?
Kahler: It’s easy: Learn to trade! Without a computer, with real money, so losses really hurt. Even though studying science is an advantage, I do have the experience that traders who are not blinded by numbers because of their own experience will eventually develop the more stable systems.

HEDGEWORK: Finally, perhaps a glimpse into the future?
Kahler: Computers will take on more and more tasks, the worldwide availability and comparability of brand names will in many cases make brand names unimportant. Investors no longer ask for a certain fund, but want to invest their money with manageable risk and limited correlation to other markets. Whether an algorithm or an analyst does this is information that will not reach the investor at some point. Since the computer often delivers better performance and is paid less than the classic fund manager, it doesn’t take much imagination to estimate future developments.

The interview was conducted by Ronny Kohl, automatic translation by DeepL


Philipp Kahler is Senior Quantitative Analyst at Tradesignal Ltd. and advises financial institutions and energy trading companies on the development of quantitative-based trading strategies. He gained professional experience as a trading system developer in proprietary trading at Berliner Landesbank. There, he managed a wide range of very successful, systematic trading systems for several years.

120th Hedgework – Interview


“I believe in self-fulfilling prophecy”

Algorithmic trading is often surrounded by the nimbus of mysterious and impenetrable. But with the right tools and methods, the opportunities and risks of automated trading strategies can be exploited. Philipp Kahler, Senior Quantitative Analyst at Intalus Group, showed the way to systematic investment at the 120th Hedgework in Frankfurt.

Philipp Kahler,
Intalus Group
Hedgework: Mr. Kahler, in short: What is Algorithmic Trading?
Philipp Kahler: Algorithmic Trading is trading with pre-defined and tested rules. The rules can come from higher mathematics, where game theory has its roots or is based on technical analysis.

Hedgework: Why should institutional investors focus on technical analysis and algorithmic trading?
Kahler: The technical analysis displays information quickly and clearly. Contrary to this are the many investment recommendations in the subjunctive; that the stock XY next week should be bullish for the reasons and could reach a new high. Then a purchase might be advisable. The rule-based technical analysis does not dare to speculate. The software simply pings when a situation arises that is in line with the rules. In real time, and not maybe and next week.

Hedgework: How does it work?
Kahler: I like to program rule-based strategies with indicators and chart patterns. One or two classical indicators define the market phase, a chart pattern or an oscillator signal then gives the final GO for entry. The position is then hedged by a sales and time stop. If nothing happens or if it goes wrong, the position is closed. Any accumulated profits and any counter-signals then lead to new exit instructions for the following day at the end of the day.
So I’m not trying to predict what might happen tomorrow, but my algorithm contains a number of rules for scenarios that stand for and against my position. Just as an emotionless and deliberate professional would do.

Hedgework: What indicators do you look for?
Kahler: I like to use classics such as moving averages, Directional Movement Index, Parabolic or ADX for trend determination. For entry or exit, significant high and low points, candlestick formations, oscillators. Generally speaking, I use indicators that other traders use, but I don’t always use them in the classic way. And I believe in self-fulfilling prophecy (laughs).

Hedgework: Why do you use technical analysis to develop algorithms?
Kahler: Technical analysis shows me how the others see the market. One can now argue whether the markets are efficient and whether every available information is reflected in the price, but this is a theoretical discussion. In practice, I see that other traders use technical analysis to determine entry and exit. And I don’t want to ignore that information.

Hedgework: You are thus betting very heavily on the right time to buy and sell. The latest research is more in the direction that market timing is not possible or does not bring anything.
Kahler: Let’s assume this hypothesis is correct. Timing is not possible. Then the movements of the markets are a purely random walk. Yesterday’s events have no influence on today’s events. And this is clearly contradicted by every insight into one’s own experience and behavioral finance. Yesterday influences how we think and act today. And that gives me the foundation of technical analysis.
If the market then falls by 20 percent in one day and they don’t stop because timing doesn’t work, they can do so tomorrow at -30 percent. Or write an article about why this was the right thing to do at that time, as the market is now back to baseline again.

Hedgework: How can the market universe be searched for promising values using algorithms?
Kahler: Define promising! Values that have been in trend for a long time and for which you hope that they will rise for a few more days? Or values that are at least 75 percent below their high and now trade at twice as much volume as a year ago? Values where a reversal pattern has occurred today that every retailer knows?
The beauty of scanning the markets is that you can find out from thousands of values exactly those that meet your own rules.

Hedgework: You compare the values at different time levels. What is the reason for or statement can the user draw from the comparison?
Kahler: I think that mass psychology works best where there are masses. So, if only the people who see a candlestick pattern on the 60-minute chart come into this market, then the following movement will probably not be so great. However, if the same candlestick pattern appears on the weekly, daily and 60-minute charts, there is likely to be more movement as more traders are involved. Since this is not easy to find, I have to rely on automated scans of my software. Manually looking for these scenarios would degenerate into work.

Hedgework: Be long when the chart is green and short when it is red. That sounds trivial. Is that really it?
Kahler: When the pedestrian lights switch to green, do you walk blindly across the street? Or maybe you want to check again if there is no car coming?
Same with one of my red-green models. The colour is comparable to the traffic light. The second step is to confirm the trend with a new high or low. Then there is also the question of positionsizing and risk management. All these are simple building blocks that combine to form a rather complex trading model.

Hedgework: Is this approach also suitable for institutional investors who do not have a large investment team?
Kahler: Technical Analysis is a useful tool for this type of analysis. It allows me to automate a lot of things, such as scanning markets and baking strategies. Automatically generated trading signals and alarms are then the mechanical assistants that enable the Portfolio Manager to monitor a universe of markets and strategies in a short period of time. Fortunately, the software industry has reached the point where there is hardly any need for dedicated programmers, but at least the first prototype can be created by the dealer himself.

Hedgework: In volatile markets in particular, this sounds like a lot of reallocations and transactions and transaction costs. This is at the expense of performance. What is your experience?
Kahler: But in volatile markets, however, there are also the best opportunities. If the markets move, money can be earned well.
However, the cost of reallocations and the associated work involved are clearly an important criterion in system development. The number of transactions can be adjusted by selecting the time levels and trading approaches in such a way that it can be done with the given trading team.

Hedgework: How long is the average holding period?
Kahler: If it goes wrong, it only takes a few seconds. In my strategies, however, I usually work several days to weeks.

Hedgework: Does the approach also work at portfolio level?
Kahler: It works particularly well at portfolio level thanks to diversification. It is almost impossible to achieve a steady performance with only one traded value. However, if I am dealing with a universe of non-correlated values, then continuous performance is possible with relatively simple strategies.

Hedgework: How do you determine the optimal position size in a portfolio?
Kahler: I risk the same amount per trade, depending on the planned trading frequency and within certain limits for the invested capital. The various systems are also weighted according to the volatility of the results.

Hedgework: What happens to the money that does not flow into the stock market due to negative signals?
Kahler: No absolute return approach will always be invested. That is why it makes sense – irrespective of the investment issue – to be active in several markets and time levels. Cleverly chosen, with little correlation to each other, the investment ratio then remains fairly constant and the problem is eliminated.

Hedgework: What is the equity/bond ratio in a portfolio? Does the stock market always have priority?
Kahler: No, no, not just stocks or pensions. Absolute return means that at the end of the year you want to see a certain return at a given risk. It is not known which market this will bring me in the future. Surely the Bund Future has been good at trend-following strategies in recent years, but what about next year? Perhaps the return on investment then comes from oil, gold or other sources. That is why the idea is to search markets for certain rules and then act where the traffic lights turn green.

Hedgework: In which market environment does this approach work best and where not?
Kahler: I like working with trend-following strategies. They are easy to develop and implement on the market. In order to ensure that the yield is right, the trends should not be too small, both in terms of time and volatility. And this is where the scanning method comes into play again. I have to look for the markets and time levels in which the markets meet my criteria. Or I need to have a switch for another strategy, one that works well in sideways markets.

Hedgework: How do you deal with black swans and chance?
Kahler: There’s not much you can do about a real Black Swan within the trading system world. After all, by definition, it is a risk that has not been known to date or has been completely misinterpreted. Only a strategy outside the stock market can help.
But all the market shocks that have occurred in history must be looked at very carefully. Not only on the chart, but best in conversation with participants. It doesn’t look so wild on the chart, but when you see how everything else goes wrong on such days, you don’t automatically invest everything in a market-system combination.

Hedgework: What is the advantage of your strategy over other trading strategies based on technical analysis?
Kahler: It’s my strategy. I developed it according to my convictions and it does what I would do by hand. This doesn’t have to be any better than all the other strategies on the market, but the trader’s psychology also comes into play in system trading. If you don’t believe in your strategy, you leave it at the first setback and then you’re not in the good phase. A good system only makes losses – simply because it is used in the wrong environment or by the wrong dealer. My advice: always be honest with yourself. The rule-based technical analysis supports this, as it leaves no room for interpretation.

The interview was conducted by Alexander Heintze, translated by Deepl Übersetzer

EEX Phelix Base Yearly – Buy Wednesday, short Thursday?

When it comes to simple trading strategies, the day of the week is surely one of the best things to start with. That’s nothing new when it comes to equity markets. Everybody knows about the calendar effects, based on when the big funds get and invest their money. I do not know about any fundamental reason for the day-of-week effect in German power trading, but is seems to be a fruitful approach.

First of all I have to point out that it is not only the day of the week which is important. A strategy that just buys on Wednesdays and sells 1 or 2 days later would be doomed. But if you add a little filter which confirms the original idea, you will end up with a profitable trading strategy.

This filter will just be a confirmation of the expected move: If you suspect that Wednesday ignites a bullish movement, then wait until Thursday and only buy if the market exceeds Wednesdays high. Same for the short side, wait for a new low before you enter!

Have a look at the chart. The strategy shown buys on Thursdays if Wednesdays high is exceeded. The position is closed 2 days after the entry.

If you run a simple test which day of the week is the best to get ready for a long trade the day after then the next chart shows the return on account of the strategy using data from 2012 up to now: (exit one day after entry)

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Bitcoin Trading Strategy – review of returns

Bitcoin is not as bullish as it used to be. May it be due to fundamental reasons like transaction cost and slow speed, or maybe the herd found a new playground, whatever it might be, it is a good time to have a look how my bitcoin trading strategy performed.

The bitcoin trading strategy uses two moving averages for the trend detection, and, when the averages say bullish, the strategy will buy if the market moves above it`s old swing high.

The position is protected with an exit at the last swing low and a 3% trailing stop.

But have a look how this simple strategy performed over the last two years:

Trading on a daily timeframe and investing 10000€ with each entry, the strategy managed to get more than a 100% return over the last 2 years.

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The rhythm of the market

Usually we chart the market at it’s absolute level. But what, if we would just chart the net daily, weekly, monthly movement? Would this be an advantage? Would this show us new trading opportunities?

The short answer is: Yes! The trend is not everything, and it seems to be of some significance for further movements, if the market has moved more than x % from the beginning of the day, week or month.

But let’s have a look at some charts – and you will see how well it works:

The first chat is an intraday chart of EuroDollar, 8am-5pm CET. It shows you the daily net movement.

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Bitcoin Handelsstrategie

Die Cryprowährung Bitcoin ist zurück!

Sie erlebte ihren Hype vor 2014, doch ging es seitdem fast nur noch bergab. Nach den Hochs um 1000$ für einen Bitcoin Ende 2013 verfiel der Preis bis auf 150$. Doch diese Zeiten scheinen vorbei zu sein, bitcoin is back!

Bitcoin Chart Analyse

Der Chart zeigt den Bitcoin / USD Verlauf der vergangenen 3 Jahre. Es spring sofort ins Auge, dass die lanfristige  fallende Trendlinie im Juni 2015 gebrochen wurde. Seitdem ist neben den Kursen auch das gehandelte Volumen stark am Steigen. (Kurs- und Volumsdaten von

Bitcoin Chartanalyse

Noch immer ist die Volatilität des Marktes extrem hoch, Bitcoin ist ein reines Spekulationsobjekt, beliebt bei Leuten mit Hang zur Weltverschwörung.  Dies sind die besten Voraussetzungen dafür, dass eine automatisches Bitcoin Handelssystem funktionieren kann. Die hohe Volatilität ermöglicht zudem mit geringem Kapitaleinsatz ansprechende Gewinne.

Bitcoin Handelsstrategie

ich bin nicht an die Börse gegangen um mir den ganzen Tag Gedanken über Trendlinien zu machen, ein automatischen Handelssystem für Bitcoin ist da schon eher mein Ding.

Meine Bitcoin Handelsstrategie basiert auf klassischem Swing Trading. Die Strategie selbst wurde auf von mir auf der IFTA Konferenz in Tokyo vorgestellt, IFTA Mitglieder können den vollständigen Systemcode auf der Webseite laden.

Die Basis des System sind die Swing Punkte.

Bitcoin Swing Points

Eine programmierte Definition dieser Punkte finden Sie im Swing Point Stop

Kombiniert man diese Swing Punkte mit einer einfachen Trenderkennung, ergibt sich ein hoch profitables Bitcoin Handelssystem.

Bitcoin Trading Strategy

Bitcoin Handels Regeln:

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Opening Range Breakout

Ein Opening Range Breakout System von Perry Kaufmann.

Es wurde im Magazin “Technical Analysis of Stocks&Commodities” im Juli 1994 besprochen, und wie es scheint, funktioniert es noch immer.

Das System wartet die erste Handelsstunde ab und geht dann bei Ereichen eines neuen Hochs oder Tiefs  long oder short.  Die Einstiegs Order (Stop Buy / Stop Sell) wird nicht exakt auf das Hoch / Tief gelegt, sondern ein paar Punkte darüber /darunter. (hier 20 Ticks)

Prinzipiell ist die Strategie der Afternoon Trader Strategie sehr ähnlich.

TSM(S) 1st hour breakout detail

Da die hier vorgestellte Systemversion  ursprünglich für dieTradestation 2000i in Easy Language geschrieben wurde, ist das Laden von 3 Zeitreihen ein wenig kompliziert gelöst. Aber es funktioniert.

Backtest mit adjustiertem DAX Future:

TSM(S) 1st hour breakout backtest

zum Tradesignal Equilla Code Passwort “code”