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 →
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 →
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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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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I published a bitcoin swing trading strategy in 2015 over here (German only). Time to review the methodology of swing trading and have a look on the performance. Can a rational strategy get an edge in an irrational market? Have a look and be surprised! Continue reading →
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.
- the quality of the signal
- the number of occurrences
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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 →
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 →
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 →
Monte Carlo Simulation uses the historic returns of your trading strategy to generate scenarios for future strategy returns. It provides a visual approach to volatility and can overcome limitations of other statistical methods.
Monte Carlo Simulation
Monte Carlo is the synonymous for a random process like the numbers picked by a roulette wheel. Continue reading →
This article is about the dollar cost averaging investment strategy and the influence of luck in it.
The Dollar Cost Averaging Investment Strategy
To invest parts of your income into financial markets has been a profitable approach, especially in times when bond yields are low. One approach to do so is the dollar cost averaging investment strategy. Continue reading →
Since S&P500 has lost 20% from its top in 2018 and everybody is talking about bear markets. See what has happened in history.
We all have been spoiled by artificially low volatility over the last years.
Now people blame the gone-wild president or algorithmic trading for the market correction, but let us have a look into history to see how common market corrections have been over the last century. Continue reading →
Analysing the market performance of the day session vs. the overnight movement reveals some interesting facts.
Daytime vs. Overnight Performance
The chart below gives a visual impression on where the performance of the SPY ETF is coming from.
The grey line represents a simple buy and hold approach. The green line shows the performance if you would have held SPY only during daytime, closing out in the evening and re-opening the position in the morning. Continue reading →
Gentrification has got a new victim; After 33 years my favourite pub in Berlin, Syndikat, has been kicked out by unethical investment company Pears Global Real Estate, run by the family patriarch Mark Pears. Continue reading →
So you are bullish on a specific stock, but you also have realised that timing is major problem? So what would be the best strategy to implement your bullish opinion but avoid the problems of any timing strategy?
Selling a put option might be the answer.
For discussing this question let’s use the current Apple chart as an example. The question is, if you are bullish on apple, should you buy 100 Apple stocks right away or should you sell an at-the-money put option. To find the pros and cons of these two possibilities let’s have a look at some charts. Continue reading →
“The stock market is never obvious. It is designed to fool most of the people, most of the time” Jesse Livermore
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 →
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 →
Markets have a high degree of randomness (and madness), but there are some things which hardly change over time. One is the width of an average market move before a counter-move can be observed. Continue reading →
Over the last days and weeks some traders have been worried if the currently ongoing correction in the markets will evolve into a crash, or if it is just a normal correction.
Crash or correction
The main difference between a correction and a crash is the panic level. But it is not the absolute level of .VIX, the CBOE implied volatility meter. It is the difference between realized and implied volatility that defines a crash which defines real panic. Continue reading →