In the latest of our 'How I Look For Ideas' series, we will look at the use of models and systems and how they can help traders spot trades and assess the markets.
Models & systems can help with both trade timing and the confirmation or denial of trade signals.
The concept of a model in the financial markets sounds complicated but in reality, what we are talking about is simply a system or set of rules which treats all instruments under observation in the same fashion, and in doing so allows us to compare like with like or apples with apples if you prefer.
Such systems allow us to adopt a systematic approach to the discovery of trade opportunities and ideas.
The consistent approach allows us to become familiar with the traits and characteristics of the individual instruments within our trading universe over time.
With that in mind, one of the first tips I have and one of the lessons I have learnt over the last 30 years is to keep that trading universe manageable.
A good rule of thumb is to keep your core trading universe to a sensible number of instruments, that you can digest in one go.
What might that look like?
Well the FTSE 100 stocks are probably one of the best examples, with just over 100 individual equities (there are two flavours of Royal Dutch Shell before you ask).
A list of this size size can be sorted and filtered easily and displayed on charts and graphs without them becoming overcrowded and impossible to read.
So how would we begin to build our system or model?
We’ll stay with the FTSE 100 as an example. Below is a screenshot of a spreadsheet that I use to track what’s happening with the top 100 UK stocks.
For convenience’s sake, I have used the first 20 rows of the sheet and just columns A to K in this image.
As you can see I track a variety of metrics including high, low, open and close data and I also track the volume traded and what that volume represents as a percentage of stocks rolling average daily volume.
As I said before what’s most important about a model or system is that is should be uniform in the way that treats the instruments within it.
This next statement might sound like hearsay or stupidity but I would go so far as to say that its doesn't actually matter whether the model contains inaccuracies or incorrect data - as long as those faults are uniform across the whole universe.
They can be corrected or allowed for in our judgements and interpretation of the data.
What does invalidate a model is when instruments within it are being treated in different ways or under differing rules.
At this point it breaks down and any comparison between instruments becomes meaningless - just like a conversation between two people, each speaking in a different language whilst not understanding the others diction.
Let’s expand on the table above and add-in columns that track and calculate change and percentage change in price. If we add those fields in we can start to look for outliers within the universe over a couple of factors.
When making a comparison with a model, percentage changes are always preferable because they allow us to make relative judgements and observations, rather than absolute ones.
Relative information is information that contains context and that is always more valuable than a series of outright values.
By adding a percentage price change column to our spreadsheet we are able to plot a chart like the one below;
This simple bubble plot shows us the percentage price change in each of the FTSE 100 stocks compared to the % of average daily volume they have traded.
For example, Lloyds Banking Group to the far right of the chart has traded over 89% of its ADV but its price has fallen by some 4.13% in the process.
Whilst on the far left of the chart we find B&M European Retail which has traded just 10.47% of its average daily volume, whilst its price has risen by some 1.55%
By activating a simple filter in the spreadsheet we can screen for stocks that are rising in price and look for those which are doing so with good volume, a sure sign of positive price momentum.
Here is the chart again filtered to show us only those stocks that are up by 1% or more on the day. To the far right of the chart, we find stocks which have traded higher percentages of their average daily volumes. Whilst to the middle of the chart we can find a clutch of stocks that have made solid price moves but which have done so on thin volume.
Both groups might be worth further investigation.
Could the stocks with positive price changes and good volume be worth pursuing on the upside?
Might the group who have had larger prices moves on thin volume be subject to a subsequent correction on a better volume day? I.E. has the price moved out of equilibrium between supply and demand and should it now correct back to an equilibrium level?
We can’t determine the answers to those question without further investigation but what we have been able to do is create a system which has generated those questions, which we might attempt to answer by looking at say an hourly candle chart of the stocks in question.
Or by scouring newsflow, Twitter and blogospheres related to those names, that may help to explain those moves.
We have created a starting point for further observation and investigation and piquing a traders interest is exactly what a model should be designed to do.
In part four we will look at how we can add some more complexity to our model without making it unusable.
Recap: Parts 1 & 2