The Trading Game: By Rajan Dhall MSTA
Randomness is one of my favourite subjects and I don’t really fancy getting into the subject of the efficient market hypothesis but I want to explain a story about technicals and odds analysis. At one of my previous places of employment I was responsible for looking after the interns for one month and teaching them some of the fundamentals of trading. These fresh-faced university graduates used to come to me and ask me about why I would take some of the trades I took, what factors I would consider in entry, position management and exit.
I proceeded to talk to them about the importance of account and position management, two very different things in my book. Some of the interns looked at me like I was crazy, ‘so you place more emphasis on trade management than taking the trades?’ I then said of course ‘a monkey can place a good trade but they cannot tell you when to exit or tell you how to manage a position’. So they then proceeded to ask me why, I explained at any moment and at any time there could be news or a major development that could change my whole trade. Case in point I once had a great AUD/USD position in great profit, walked into a meeting with a manger and came out and found it in a loss. This was all due to the governor of the RBA speaking at an unscheduled event in the USA, not even his time zone!
It was then I devised a plan, we fabricated a game based on the role of some dice:
I had 10 interns and split them into groups of 5.
Group 1 traded a 1hr chart, group 2 a 4hr, group 3 a daily chart and the
last group the weekly chart.
We then rolled the dice and anything under 6 was a short position and
anything over was a long position.
Then if the number was under 3 the risk to reward was 1 – 3 and between
3/6 was a 1 – 1 risk to reward.
Above 6 long with risk reward of 1 – 1 and between 9 and 12 the risk to
reward was 1 -3.
This went over all 4 time frames.
I must also explain some of the variables – the asset classes where Gold/EURUSD/Nasdaq futures/WTI there was one roll per asset and the size of the risk was dependent on the volatility. Example on the 1hr EURUSD the rise was 50 pips. Which was 50% of the average daily range on a 10 day period at the time. Basically it was based on a variation of an ATR function.
Now this experiment was over 4 years ago but I remember the results as clear as day. The highest time frame (weekly) based on 1 -3 risk to reward was the most successful; in second place was the daily time frame with a 1 -3 risk reward.
Most all of the lower time frame trades where losing especially those on the 1 – 3 risk reward. Interestingly on the lower time frames the 1 – 1 risk to reward trades where slightly more successful.
This was all based on random trade selection but with controlled risk to reward.
In conclusion, we then proceeded to do this but using technical levels to identify the risk parameters. The traders soon learned that levels on the charts were much better than the randomness of the dice. One lesson remained with them, the future is something that cannot be controlled, market forces shift but technicals helped us control risk and become more selective for entry. I am happy to say that many of them lasted in the industry and may even read this article and remember the test. One thing I can say it was a good learning experience for me as well as the
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