Trading hand signals - tradingpithistory.com
It’s nearly the end of the Summer and I hope you enjoyed a nice time. The blog will go on a short break until the end of August, while I (hopefully) enjoy some good weather in the French Cevennes and the English Lake District.
In the mean time, I’ll leave you with a link to a pretty cool website I found (Trading Pit history – sample pics above), which documents the various hand signals used on the futures trading floors. A typical part of the trading folklore, and a language of its own, probably in decline due to the advent of electronic trading.
New readers: Welcome to the blog! You might be interested in catching up on the posts from the main 2 topics of the Summer: roll yield and back-testing results statistical significance – or older posts (the “Popular Posts” section on the left-hand side is a good place to start).
You might also want to subscribe to the blog to make sure you are not missing on any of the action…
Roll yield posts:
Back-testing results statistical significance posts:
Tune back in at the beginning of September…
Tags:
August 18th, 2010 · Backtest
The second method to evaluate the statistical significance of a back-test result presented by Aronson (in EBTA) is the Monte Carlo Permutation. This is an extension of the classic Monte Carlo method, applied to rule testing.
The concept behind the Monte Carlo Permutation is similar to the Bootstrap method:
- Generate multiple random outputs based on the single sample data from the back-test.
- compare the random Monte Carlo outputs to the back-test output to evaluate its statistical significance.
The difference lies in how the multiple random outputs are generated. Whereas the bootstrap generates a sampling distribution for the back-tested rule return, the Monte Carlo Permutation focuses on the pairing of the rule positions with the instrument daily return. Its resampling randomly associates the rule positions with the market returns, without replacement.
The H0 hypothesis in the Monte Carlo Permutation test asserts that the returns of the rule evaluated are a sample from a non-profitable population, or, in other words, that rule positions are randomly correlated to market returns.
Monte Carlo Illustration
Imagine the following back-test result, presented day by day: [Read more →]
Tags: aronson·monte-carlo
In part 1 of this bootstrap post, we looked at how to apply the method to establish the statistical significance of a single trading rule. In Part 2, we’ll look at how to deal with the data mining bias, the impact of geometric vs. arithmetic mean return. The code implementing the bootstrap test is available for download at the bottom of this post.
Dealing with Data Mining Bias
The approach described in the single rule test is not valid when performing data mining (whether testing different rules or different parameter values of the same rule). As per the data mining bias (explained previously), the (best) rule selected from the data mining process will invariably owe a large part of its over-performance to random (good) luck.
The way the bootstrap test deals with the data mining bias is by implementing a concept introduced in White’s Reality Check. The Reality Check derives the [Read more →]
Tags: aronson bootstrap data mining
As mentioned in the Evidence-based Technical Analysis review post, the main value of the book lies in the presentation of the two methods allowing for computing the statistical significance of trading strategy results, despite having a single sample of data:
Both methods solve the problem of estimating the degree of random variation in a test statistic when there is only a single sample of data and, therefore, only a single value of the test statistic.
Today, let’s look at the bootstrap test, with a practical application of it. [Read more →]
Tags: aronson·bootstrap·data mining
August 9th, 2010 · Futures
Or a case for going short VIX despite high bullish consensus?
I have been going on about roll yield and term structure for a few posts, and through two very concrete examples we’ll see how it can affect your trading and system development
A reader recently mentioned a paper (pdf by Sloyer and Tolkin) presenting a theoretical trading strategy which improves the risk-return profile of standard equity-bond portfolio by adding allocation to equity volatility represented by the VIX index. The idea sounds good on paper (no pun intended), but a “small” assumption might render the strategy impossible to implement practically:
VIX futures can realistically be included as an asset in a passively managed portfolio as the futures can be rolled relatively cheaply from one contract to the next as each contract expires.
The Current VIX Situation
Taking a look at the current VIX futures curve clearly invalidates the assumption above:
VIX futures curve - click to zoom in
At the current levels, the contango rate is over 100% annualized – definitely no [Read more →]
Tags: peso·roll yield·VIX
Welcome to my online repository of research and insights on automated trading system development
