Roller Coaster Trading

Trader's Mindset

Nov 18

Win or lose– does your trading make you feel like you are on a roller coaster? There is a reason for that.

I kinda knew the why in the back of my mind. But, Mark Melnick from T3 Live in his YouTube videos made it abundantly clear to me as to the why.

First, let us imagine you win 60% of your trades and your risk and reward are about even. That is a good sized edge. Most common technical methods will only yield a 2% to 5% edge, i.e. 55% win ratio. So, you have a real edge here.

It sounds good on paper, at least. However, let’s imagine you want to target a fairly modest 50k per year trading.

If we assume that your system generates 100 trades per year then you will need to risk $2,500 per trade and your expectancy will be $500 per trade. You will risk a total of $2500*100=$250,000 on this one system to achieve that return.

However, here’s the rub: with a 60% win ratio then you are only expected to win 1 trade more then chance would imply out of a 10 trade sequence. Out of a 100 trades, your expected profits essentially come from winning 10 trades more then chance! Of course, this also means you have 10 less losers.

The point that any trade is pretty much a coin flip. In fact, in the first simulation, one of the first 10 trade sequences was all winners. The fact is that if most traders won their first 10 trades with a new method they would think they hit the holy grail! But, another starting sequence was mostly losers!

If you lost 8 of your first 10 trades, would you be inclined to stop trading or question your method? Would you be able to keep trading even?

It gets worse though. We can never know whether or not we have an edge in the markets and most technical systems are necessarily simplified models. It means one should never blindly believe a model.

Notice, this is one of the big differences in discretionary vs system trading. In discretionary trading, if you have proven an ability to read and call markets over a long time then you can have higher confidence in your model but less certainty of the expectancy. A systematic strategy provides you the trader with greater certainty of the expectancy but less faith in the model. The goal is the right balance in model confidence and expectancy confidence.

So, what are you potential solutions to getting off the roller coaster?

  1. The risk first approach. This method is focused on account preservation. You determine in advance what you will risk of your account and let the profits fall where they may. This is a sensible approach regardless of tactics. Unfortunately, it is also likely to limit your returns.
  2. Develop & trade more systems: also known as the big boy solution, i.e. portfolio approach. You can either divide up the account into multiple allocations, i.e. limiting the total risk of your portfolio to a given system and if your systems are uncorrelated then you can trade more systems on a similar capital (i.e trading 2 systems doesn’t double the capital requirement). Before the e-micros, you needed a large account to play this game. Now, with the e-micros it is possible for anyone. As system developer Kevin Davey has expressed that he wants lots of uncorrelated systems. Something to note is that it is very difficult to make even a single quality system and so building a portfolio of high quality systems is not something you can expect to do overnight. As such, it might be more realistic to start with a goal to develop 4 to 8 quality systems. Even 8 systems would allow you to limit risk to 12% per system. While a sensible approach, your returns are probably going to be less too.
  3. Breakout of the 43/57 paradigm. Mark Melnick correctly identifies that the cause of the roller coaster of emotions is being in that 43% to 57% paradigm. If you can win 80% of the time, your probability of having 2 losses in a row drops to 4%. Of course, you cannot know the win ratio until after the fact– that’s a problem. You might use a fractional Kelly betting method, i.e. bet less if you start to lose.
  4. Seek higher R trades. One of the problems with day trading is that it is harder to get high R trades with day trading. If you can get into big moves early then obviously you do not need to win as often. Let’s imagine your average winner was 1.5x your average loser. Keeping all else equal, you would only need to risk $1,000 per trade to hit that 50k target.
  5. Find & take more quality trades. The whole reason we had to risk $2500 per trade is that we only had 100 trades. If we had 500 trades per year then we could have hit that goal with only $500 risk per trade and our expectancy per trade would be $100. However, taking more trades will likely result in taking worse trades unless we have more systems. So, this is essentially a trade more systems approach, as well.
  6. Find ways to combine factors. i.e. systematic and discretionary biases. This is one of the premises behind graybox trading.

Let us look at the trade more systems approach. In this same example, what if we only took the best 30 trades the system produced. Do you think our metrics would be slightly better? They should be.

Let’s imagine it boost our win ratio to 65% and then it follows we will only need to risk $1650 per trade. Of course, to hit our 50k goal we will need ~4 systems. The other benefit is we are only risking 50k per system versus 250k (30*1650=$49.5k versus 2500*100=$250k in the prior example). The risk per trade still seems high to me– so it also suggests you need more total trades or higher R factor, as well.

The solution seems to be simply this:

For your discretionary trades, you must find ways to get more edge. You need to take higher quality trades and boost your win ratio. If you are struggling then I can guarantee you aren’t winning 80% of the time.

For your systematic trades, then you must improve your systems to take higher quality trades and build & trade more systems.

About the Author

The author is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact the author at