Have you ever tested a strategy, a proper test that gives you statistical evidence that you have an actual edge in financial markets?
And after a few months of demo trading, the strategy is still making money?
It may seem like you’ve cracked the code, but here’s the trouble.
Sticking to the strategy is much harder than developing it. Your emotions will try to overpower your rational well thought out ideas, and eventually, they are likely to win.
During your trading life, even if you can act with no emotion, there will be an inevitable sequence of losses and winning trades that will ultimately test your trading psychology.
This is when you’ll start wondering if you really need to take the next trade, or whether you should stop trading and have a break. (None of which are rational ways of thinking if you believe in your strategy and the law of averages).
Of course, if you do give in to your emotions, and you don’t take the next trade, you probably miss that all important trade or sequence of trades that made your back-tested results profitable.
For example, if you are trading breakouts, and many of them are fake, leading to a loss, but eventually one of them leads to a monster move that would have paid for all your losses but you gave up and didn’t take it.
Does this sound familiar? I am sure that if you are already a seasoned trader, this has happened to you.
In short, to be successful in trading you should be super disciplined, and stronger than your emotions, which are always telling you to do the wrong thing.
Do not forget that this job goes against, our natural emotional structure. Every time you act as a good trader, you feel pain. Every time you are in profit you feel the irrational need to put that money in your pocket before it disappears, taking profit too quickly.
Following a trading strategy, even if it comes from rigorous backtesting, it is like swimming upstream.
How to trade with no emotion?
To sort this problem, the best way would be to get rid of our emotion, but becoming a cyborg is a too high price to pay to be a trader. Maybe it is better to give a robot, or simply to a computer, the exhausting task of following your trading strategy. It will execute it, without questioning it.
Most important benefit of Algorithmic Trading
Human emotions are reduced to zero. Our silicon made friends have of course other several advantages: they don’t sleep, hence more trades, many more, in many different markets.
Why is it so important having more trades?
Say your trading strategy, rigorously tested using 1000 trades over the last 5 years on a small timeframe, gives you a profit factor of 1.6 with a 50 % win ratio, you are still profitable because when you make a profit you make more money than when you are in loss, Lets now assume that you are a bit unlucky, and in the first month you took 20 trades, all in loss ☹ you are now ready to give up .
It’s like having 1000 balls, 500 red, and 500 black. There is a possibility that the first 20 you pull out are the same colour.
What if you have many strategies, all similar in terms of performance, working 24 hrs on different markets and different time frame. Your trades are not anymore 20 in the first month, but at least 200. The likelihood to have 100 losing and 100 winning is much higher.
Being a bit more technical, the trades you took are now a more representative sample of the initial population, which is made by trades coming from the backtesting. This is when the central limit theorem kicks in (but this is not a technical article, so this is where we stop)
In a nutshell, a computer has no emotion, getting rid of the biggest problem that any trader would face in his job and because of more trades, thanks to our friend central limit theorem, we are more likely to have months that have a profit factor similar to that one of the backtest done using 10 years.
Of course, we have simplified it a bit as not every strategy present on our computer can have the same metrics, but they altogether make a portfolio which should be representative of the back-test portfolio.
To learn more about developing and using algorithmic trading systems click here to find out more information about our programmes.
This article was written by Alberto Pallotta (a certified quant, university lecturer and experienced trader since 2003).