Detecting Excess Market Moves

Sometimes markets move too fast and too far. This article is all about a new method to detect excess market moves. A new indicator will be presented, which overcomes many of the downsides of traditional ones like RSI or Bollinger Bands.

Overbought and Oversold Indicators

I am sure you are familiar with the traditional indicators RSI and Bollinger Bands. Legions of analysts have used them to detect excess market moves and market reversals. You might have used a RSI reading above 70 or below 30 or a market close outside of a 2 standard deviation Bollinger Band to do so. But these traditional indicators have a big downside: they are always calculated over a given period of time: 14-bar RSI, or 20-bar Bollinger Bands. You can surely adjust this setting to your market, but who tells you that e.g. a 17-bar calculation period is better than taking the last 14 or 20 bars into account? Going this way is a slippery road and you might fall down the cliffs of curve fitting.

classic excess indicators

classic excess indicators

A new algorithm for excess detection

Bollinger Bands already has the key ingredient for a useful reversal detector: it measures the market move in standard deviations. But it does the measure only over a fixed interval setting, thus missing a lot of shorter or longer excess market moves.

To overcome this restriction and find all excess moves, I did me an indicator which searches for excess moves over multiple intervals. This is how it works:

First the algorithm calculates a volatility measure. All data on the chart, prior to the testing bar, is used. The formula used is described over here. After the volatility of the market has been calculated, the algorithm measures all market moves for a given number of bars. A standard setting might be to calculate all moves between 5 and 200 bars. From this list of moves, normalised in volatility multiples, the algorithm picks the biggest moves. Then a trigger value is applied and the algorithm checks if the biggest move found is more than e.g. 3 times the expected volatility. To show the found move on the chart, the algorithm waits for another bar, and if this bar does not form a new low for bearish moves or a new high after a bull move, the move is shown on the chart. A reversal or at least an end of the exuberant market move can be expected.

market excess detection

market excess detection

On the chart above you see this new indicator in action. The right chart is Bitcoin on an hourly timeframe, the right one is German power on a daily timeframe. Both charts use the same settings and search excess moves with a length of 5 to 200 bars. The lines are fixed (confirmed) with a 2-bar counter move.

Some examples for excess detection indicator

To see the effect of different settings have a look at the chart below. All 3 charts detect moves between 10 and 200 (hourly) bars. A 1 bar confirmation delay is used. The difference between the charts is the minimum volatility multiple which is used to detect the excess. From left to right it uses a 1, 3 and 5 times the average fair bet volatility to define the minimum move.

Kahler excess detector - different settings

Kahler excess detector – different settings

Statistical test of excess indicator

If this indicator is any useful, the market should show a different behaviour on the bars after an excess move has been detected than on an average day. To see if this is true the signal efficiency can bet tested using the methodology described in an earlier post.

excess detector test

excess detector test

The chart above shows the excess detector applied on daily JPY Forex data. Only bullish reversals are detected. On the right side you see the average profit factor for the days after a reversal (2*vola, 1 bar confirmation) has been detected. Although it has been a falling market (magenta benchmark below 1) the signal generates showed an average profit factor of more than one – a strong indication that this indicator is able to predict a bullish move after a sell off has been detected. Even in an overall bearish market.

Usage and general thoughts

There is no indicator which will tell you what the future will bring, but a good indicator will flash a warning sign if the current state of the market is going to change. My excess indicator, like Bollinger Bands and RSI, will tell you when the markets have moved too far. It does this detection independently from a fixed period setting. You have to decide which timeframe you are interested in, e.g. short term = 3 to 10 bars, mid term=10 to 21 bars, long term=21 to 200 bars, and then use the signals of this indicator as a setup to your trading strategy.

If the indicator tells me that there has been an excess bearish move, I will not set up a new short position. I neither would set up a long position if this sell off happened in a bearish market. But I will think about using a tight trailing stop to lock in the profits of my short position.

If there has been an excess bearish move in an uptrend, I might want to start to scale into a long position, using a tight stop loss at the beginning and the let it run until my indicator flashes a warning sign in the other direction.

Never forget that money is made with position sizing and risk management, although a nice indicator can help… :)

Tradesignal Indicator Code

You can download the source code of this indicator as a txt file. Copy and paste the content of the text file into a new indicator in Tradesignal and start exploring.  You will need the latest version of TS (10.2) and have a chart with at least 1000 bars of data. By downloading the indicator you are accepting the smallprint.

Kahler’s Excess Detector

 

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!

 

 

 

 

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

S&P500 – when to be invested

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

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

Technical vs. Quantitative Analysis

“The stock market is never obvious. It is designed to fool most of the people, most of the time” Jesse Livermore

Technical Analysis

Technical analysis is a form of market analysis based on historic price patterns. The basic assumption of technical analysis is, that human behaviour does not change over time, and thus similar historic market behaviour will lead to similar future behaviour. Technical analysis is a predictive form of analysis, a technical analyst will try to estimate what the market might most probably do over the next period of time. Continue reading

An Algorithmic Stock Picking Portfolio

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

Bollingerband: The search for volatility

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

IV Percentile – when to sell volatility

Volatility trading: when to buy and when to sell volatility

You got to know when to hold ’em,
Know when to fold ’em,
Know when to walk away,
And know when to run.
(Kenny Rogers)

When to sell implied volatility

Volatility is a nicely reverting time series. If it is high chances are good that it will come down again. The only problem is to find out when volatility is high, and when it is low. Unfortunately there are no absolute levels, you can’t say that 50% implied volatility is high, as this specific stock might have an implied volatility of 80% most of the time. So you can only compare the current volatility level to historic levels and so define if volatility is currently high or low. Continue reading

Implied vs. Realized Volatility for NASDAQ100 stocks

(1) You shall only trade when the chances are on your side

Comparing implied and realised volatility

Selling volatility can be a profitable game, but only if you sold a higher volatility than the market realises later on. Comparing realised and current implied volatility gives you an idea if the chances are on your side.

We already had a look at realised volatility and what the fair price for a straddle might be. Have a look at the kvolfair bet articles. These articles present a way to calculate the historically correct price for a straddle. Whenever you sell a straddle (to sell volatility), implied volatility should be higher than the fair bet price. Only then you will win on a statistical basis. Also have a look at the statistics of VIX, to get a clue when a downturn in volatility can be expected. Continue reading

Scanning for Support and Resistance Probabilities

I have been in search for a signal I could use for a short vertical spread or naked short option strategy. So my main concern has been to find a level, which will most probably not be penetrated over the next few bars.

This is what I came up with.

Algorithmic RSI Support and Resistance Levels

We are all familiar with oscillators like the RSI indicator. It gives an idea if the market is oversold or overbought. Continue reading

NASDAQ 100 long term candlestick scanner

A short update on the long term Candlestick Scanner.

The Candlestick Scanner scans the Nasdaq 100 stocks for long term bullish or bearish reversal patterns.

The basic idea is to search for hammer and hanging man candlestick patterns. Usually these patterns work nicely on daily charts. My Candlestick Scanner searches for these two patterns on every time frame, from a 1 day per bar compression up to a  250 days per bar compression. This enables me to use a simple, well defined and documented pattern as a description of short to long term reversal setups.

But see for yourself which Nasdaq stocks seem to change the direction according to the long term Candlestick Scan. The list gives you the duration of the reversal formation (expect about the same time to either reach the target or get stopped out) The detected pattern becomes a valid entry signal if a new high (hammer) or low (hanging man) is established.

Bullish reversals on the left side, bearish reversals on the right side.

read more about how to detect your own chart pattern in this article

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.

Continue reading