- Trading has some similarities to retail shopping and queue hopping
- We all know when the markets we trade are likely to be volatile, or slower
- Time of day can influence whether a trade is profitable
- Flow-on effects from Asia can influence early trading in European markets
There are a surprising number of methods for calculating probability.
Some are are mere experience events. For instance, you look at a queue of shoppers at various tills and determine which queue is probably the quickest to clear. We can from experience determine a multitude of factors that would influence our choice – which shopper has the largest purchase going through or ages of shopper that may slow down the transaction. Who has distracting influences to slow the process such as very young children, or even the presence of a trainee cashier at the register.
So experience teaches us a version of quantification which we have all been wrong with. We failed to see a flaw in our queue selection. Once we join a queue and wait patiently we might have an additional influence that occurs that would have changed our original selection. A change of cashier occurs whilst you are queuing or an earlier customer finds an error with their purchase. All of the above are real time events that we respond to and transact with.
Various factors can influence the time we spend in a queue
and the queue choice decisions we make. Photo: iStock
How we handle the changes involves yet more choice. Probability would suggest you stay with your original selection because the cost in time to move all your purchases to another queue and re-assess how fast each queue is moving and so on is detrimental to your objective of moving through the process quickly.
Trading is to some no different to humble shopping. We open a trade and await its outcome. This is trading probability through experience. Certainly there many who can (time and again) be consistent enough to succeed and grow their trade account. But the vast majority will not.
So how do we determine a numerical value to our probability of success? Will it enhance our ''odds'' of winning?
Let's reconsider the shoppers in the above example. Would our transaction be simpler by shopping at a quieter time of day? Would we find shopping for our weekly fresh groceries simpler on a Thursday as opposed to a Friday or Saturday? Changing the simplest part of the transaction can enhance the speed of the whole process.
Trading is no different. We all know when the markets we trade are likely to be volatile, or slower. Depending on our trade structure, for example, options require volatility or time decay to really earn for us, then we select our timings in accordance with this.
Typically time decay trades perform better 53 down to 45 days away from expiry for maximum potential profit, but volatility is often an opposing contra trade when volatility is at its busiest such as selling the VIX when the market is at max panic or if you like when all the tills are working and have maximum queues !
But that is just experience again. Sure have some quantification but that is for option players not CFD. Let's puts some numbers to our craft.
I mentioned that we use experience in the above commentary, but let's ask how much experience.
- Determine exactly how many times you have done what ever event.
- Furthermore exactly how many times was the trade successful?
From these two simple pieces of information, we have the ability to say as a percentage just what success rate we have for opening and closing a trade in profit.
Let's be honest a percentage of having a successful trade isn't enough for any trade as it doesn't tell us the real history.
- How many trades were successful is relevant.
- But what profits were made?
- How much of a drawdown occurred?
- What margin did you use in relation to the profit gained?
- How many consecutive trades (maximum and minimum) were successful?
Then look at the opposite...
- What losses were incurred?
- How frequent were those losses...did they occur consecutively?
Again we can improve our percentile overview. Could we reduce the stop requirement to ensure our profit margin increases or would this mean a greater frequency of losses?
Now we need to consider the timings of the trade and consider whether there is any correlation to your trade timings to the trade results. Are more trades successful on certain days with less drawdown like say the shoppers above seeking their weekly groceries on a Thursday as opposed to a weekend?
See this earlier article as well: Adding bell curves and whistles to your trading strategy
. Morning skew
Bayes Theorem on probability is based on the likelihood (or if you like the probability) of an event occurring given that another event has already happened. So we look at our data and see we have made a series of trades in the mornings and see the markets moves beyond say the second standard deviation level more frequently than the afternoons.
Because there are greater influences to the market, we have the follow on effect from Asia moving the European marketplace and the opening market moves of Europe first thing as they readjust to world pricing events. In other words we have the equivalent of weekend shoppers altogether pushing market prices up or down. Which using Bayes theorem suggest we have the highest probability of success. (due to market skew!).
Frequency is the fastest determination of probability. It is low level mathematics that is usually regarded as the real time acid test as to whether something works.
Follow-on effect from the Asian trading day can influence European markets at the start of trading, as markets adjust to world pricing events. Photo: iStock
We know that 53.1% of the time, the S&P500
will rally or move sideways (using 5 years/10years/15 years and 30 years of data this figure/percentage has remained robust) and frequency also tells us that the minimum move was 2.5 points. So a simple long at the close would 53.1% of the time be successful.
However as a trade structure this is useless as drawdowns would be corrosive. And losing days fall far further than the potential gain. The average drop is 11 points. So now we have a problem. We can lose on average more than we can gain....out of every 100 trades we have;
- 53.1 succeeding with an average 2.5 points profit, which equals 132.75 points
- 46.9 trades failed with an average 11pts loss = 515.90 points lost
Frequency obviously highlights the problem.
However frequency also gives us the answer for resolving the losses. We will know from our frequency data that losing trades occurs a maximum of two consecutive occurrences on extremely rare occurrences three times. So if we waited for two consecutive duff (that is poor quality) trades, and then started trading, we can expect a profit.
But how much profit?
Frequency tells us that the average run of successful trades is seven consecutive days, so we trade for that with expectation that on average we will gain 7 x 2.5 points equals 17.5 points profit, then we await the two duff trading events before resuming our trades. On average we do not incur losses. However they still will occur as no trade model is perfect so how do we minimise the effects further?
Frequency will show that the S&P500 will drop often by a maximum 8 points before it recovers to make 2.5 points profit for us, due to overnight market moves. Thus place your stop at 9ts so we ensure our stops are tighter than the average losing day drop.
Again probability and frequency can improve your profits. As on balance more days are bullish than bearish we can stagger our trades. Open half stake size as normal seeking the 2.5 points and then open another at 5 points lower, both with exact same stop level as described. The 5 points lower has the highest frequency for dips so is the optimum level to enter and enhances profits up to a sum total of 5 points
- For example, 2.5pts x half stake plus 7.5pts x the other half stake size = total 5pts.
Finally probability shows that a move beyond 9ts dip is bearish and the market has further to fall which gives us a whole new array of strategies but that's a whole new series of frequency data to re-assess.
Next time you visit your supermarket consider probability. If you are a scalper, you want a wift in and out transaction and will often seek the mispriced market value. So you will seek out the end of line bargain(s) a position player will buy on a Monday or Thursday for efficiency. Relative value traders seek economy of scale; eg wine bought by the box not just a random bottle purchase. Look and you will see all sorts of real time shopper/traders LOL.
*fxtime is an Alias
— Edited by Robert Ryan