The point I wish to highlight is the simple evolutionary fact that we have been born with a psychological makeup that by its very automatic programming is designed to blow up our trading accounts.

With this in mind we clearly need to be spending less time focusing on our trading strategies; doing endless back-tests, optimizing entry and exit points and then trying to calculate the best position size, and more on our state of mind at the critical points in our research process and then during the trade execution and open position monitoring phase.

There are many benefits to keeping a journal, both psychological and practical but for this short article I wish to focus on just two:
1. Discipline: In my experience I have found most traders lacking in discipline. By maintaining a journal you are forced to commit your thoughts to action; action requires effort, only winners are prepared to make the effort. Furthermore it is human nature to react with instinctive reflex when in stressful situations, by forcing oneself to first write down your plan of action it neutralizes the reflex and allows you to consider the prevailing circumstances. When writing the words or pasting images of charts you are giving the subconscious part of your mind (let’s call it your limbic system) a chance to be checked by your conscious thinking part of your brain.
2. Evaluation: When you are in the moment it is very difficult to objectively assess your actions; by keeping a detailed journal rich with information about your state of mind, the state of the markets, and other important trade statistics you provide yourself the opportunity to evaluate areas of weakness and mistakes in your process as well as your personality. The benefit of a journal is not limited to your own evaluation, as for greater benefit you can bring objective experts such as mentors, psychologists and colleagues into the review phase.

When designing the RAPA web portal we were very mindful of the fact that part of a journal is the work you do on a blog, Twitter account and other social media and we wanted to make it as easy as possible to centralize all posts, so we designed a Wall for each community member. The Wall contains a journal and all social media connections posted automatically to the wall; the level of privacy is determined by the member on a post by post basis with the default of every post set to private “Hide” by clicking on “Share” posts become part of a public Wall.

RAPA Wall – posts

RAPA Wall – statistics

To further enhance the usability of the journal we have seamlessly integrated the trade and PNL database with each members Wall profile. For active traders and large portfolios it can be very difficult and time consuming to maintain a detailed trade journal. By integrating the trade database members can now do a detailed review of their portfolio and there accompanying posts on any given historic date.

Michael Berman, Ph.D.
www.rapacapintro.com

So for instance SRS a USA ETF was consolidated at 1 for 3, so if the share price was 10 it is now 30. In this case for our existing options you cannot use the consolidated underlying. We will have a release out today or tomorrow that will cater for such an event.

If anyone comes across anything unusual and would like it incorporated into the dashboard, drop us an email or post a suggestion on the forum.

Mike

The CAPM model is the core of the modern portfolio theory and risk management. It was independently developed by Jack Treynor, William Sharpe and John Linter in the mid sixties. The main notion of the CAPM is the which is a measure of premium for accepting the risk (relative to a portfolio or a broad market). More formally the CAPM model assumes the relation of the form,

between the expected return of the asset and the expected return of the broad market. The value of beta measures the dependence between the returns of the asset and the returns of the market and the value of is usually attributed to the skill of the fund manager. The coefficients and are usually being found using the linear regression of historic returns.

It is very easy to understand the CAPM model. For example, if the beta of the stock is 1.5 this means that each time the market goes up by 1% we may expect our stock to go up by 1.5% percent. Therefore, it grants 50% more returns than the market. However, when the market goes down by one percent, our expected loss is -1.5% and investing in the stock bears more risk than investing into the market. We caution the reader that having zero does not mean that the stock is risk-free, this only means that the risk associated with the beta is not (cor)related with the risk associated with the market.

For the US stocks we usually describe the broad market by the Dow Jones index or the S&P 500.

I am now going to show how to compute the CAPM model in RAPA. For this, go on the Instrument Analysis tab and select the stock (in our example RES) and the broad  market index (S& P 500)

We may also select the date ranges for regression calculations and the size of the rolling window. When done, press RUN FROM YAHOO to see al the cells populated. As the output we get the values of alpha and beta as well as other useful statistics – the correlation between the stock and the index and R-squared which measures the goodness of fit of the CAPM model. We also get two nice visualizations of the CAPM model:

The first graph shows how the value of beta changes over time, while the second graph illustrates the goodness of fit of the model as well as the graph of the relation between the index returns (x-axis) and the stock returns (y-axis).

Vladimir

As you can see volatility came off a very low base of ~10% prior to the GFC only to see MASSIVE volatility spikes to historic highs of 80% in the midst of the panic of the GFC.
Since then we have witnessed a continuous downtrend in volatility save for 2 very nasty volatility spikes.

What is of further interest is to see how the 30-day rolling volatility of the volatility index has dropped further. Based on the latest oscillation to its recent low and its subsequent turnaround, I believe you could think based on probability that we are entering a period of increased volatility. What this would therefore imply based on a high R-squared of 0.57 with the S&P500 that we are likely to see a retracement in this broad market index.

In order to compare apples with apples we need to treat options as if we were investing the equivalent in the underlying stock. This way we can tell how long or how short we are and then we are able to do all the risk metrics we commonly report in RAPA.

The equation is really quite simple:
Stock Value equivalent = option delta * stock price * quantity of options * option multiplier

This is what a typical look will include.

In this note I just want to mention a few technical details. We have recommended setting up the XML file with the 365 day option. It is important to be aware that you may have more than 365 days in your account history. I will leave it to your discretion how to create a longer history file, you could simply customize the dates.

Assuming you do that for the initial setup and then revert to the suggested 365 day lookback period, I want to describe what in fact will take place during the import. Let us assume you created a 365 RAPA.xml file on 1 January 2012 which has in fact a full 365 days worth of data, and you imported this data into RAPA via the “update NAV xml file button on the dashboard”.

Let us now assume that the date is 1 February 2012 and you have a new RAPA.xml file to update. What our RAPA software does is look through the new xml file and see what the first date is. It will see that the first date is 1 February 2011 (365 days look back) and therefore it will leave all entries from 1 January 2011 to this date, it will then post data from the new xml file all dates thereafter. This way by updating an XML file any time in a 365 day loop will ensure that there are no gaps in the NAV series and that you continue to build on your existing performance time series.

I hope that I haven’t overly complicated this issue. If you are still having trouble contact us through support@rapadashboard.comand we will help you work it out.

RAPA has its own Bayesian/Probability Calculator with built in expectancy statistics on the Instrument analysis sheet. In this example we have placed 12 years of the SPY ETF into the database, and we have queried the database with the question what will happen next after 6 consecutive up or down days. To talk you through the example, there is a 50.85% probability of the 7th return being positive but the average positive is likely to be 0.35% this is based on 30=N samples meeting this criteria, but there is a 49.15% probability that the 7th return will be lower and the average is likely to be a -0.61% and here there are 29 in the sample. As a statistical Bayes probability player what would you do before the markets opened on the 7th day of the consecutive run?

Expectancy = (50.85% x 0.35%) – (49.15% x 0.61%) = -0.12%

A betting man would therefore bet that the market would be down and is likely to make 0.12% if he was short the 7th day. Interestingly if the market drops for 6 days in a row, the expectancy is very strong for a positive day. The only point worth making is that there are fewer 10 & 7 respectively in the down sequence.

What is very clear is that volatility and beta are constantly oscillating around a mean, so when working with beta as RAPA is programmed to do, it is possible that some times the beta being used is simply at an extreme point in its cycle and about to mean revert. At these times it is quite possible that beta adjusted positions on the dashboard are slightly over or under stating the respective underlying positions. It is always good to be aware of these things, and one can always do their own analysis with RAPA to help calibrate the overall beta’s of the portfolio.