I have essentially identified 3 paths to edge among 7 figure traders from information readily available on the web and my own insights into trading edge:
1. Fully systematic and quantitative decision making. No discretionary decision making involved. While, I do not know any 7 figure traders with verified records that are fully systematic, it is the dominant methodology among professionals today and there are indeed reports of much bigger then 7 figure traders using quantitative methods. This is a simple method to understand because you program systems and the computer takes all the trades. This is probably the easiest and quickest path to getting into the top 1/3rd of traders.
The real benefit for this style of trading can be recognized when we realize that let’s say you want to make 150k trading and you earn 50 cents for every dollar risked. Even if you have a method that can do that, if you only have that one method or system then you will need to risk 300k over the year. That’s a lot of risk. The real advantage to system trading and something Kevin Davey has advocated– and that I admit I didn’t fully understand the power of is the capability to trade many systems, preferably uncorrelated. A simple and powerful way of seeing the advantage of trading multiple systems is going back to the 300k example: with 10 systems you only need to risk 30k per system. Other significant benefits is the ability to program the system to take only the best quality trades and the ability to understand what the risk should be given a prior that the system continues to work like it did in the past.
The downside is that while systematic trading doesn’t have to be bad– the market cognition is often channeled down restricted paths because of limitations of retail platforms. Recently, for example, it makes sense to be able to factor in Trump tweets but that’s not something easily supported out of the box from something like Tradestation. Yes, it can be programmed but most traders will start with moving average cross over systems.
2. Synthesis, Understanding, Events, What Changed, Unique Situations. This style of trading is discretionary and is based on understanding what is going on in the market and taking advantage of it. For example, in the Quantitative Collaborative, I recently shared the idea that UBER could outperform once all the people who wanted to exit had exited and we could get a post IPO lockup bounce. This was a simple idea.
The trader can then combine that “real information” with pattern recognition and risk management. What makes this form of discretionary trading potentially more robust is that it is possible to identify what’s really driving markets and take advantage of it. Of course, most ideas can be programmed but it is not clear what will be important until it happens. And, it can be difficult to anticipate in advance what will drive the markets. Another example of this sort of trading is identifying early that an asset like Gold is acting as a safe haven or trading a stock that had an analyst upgrade.
What’s appealing to me about this form of trading is that it is based on real factors versus trader behavior. The key with this style of trading is being able to identify when your ideas are actually “in play” and being respected by the market.
When it works well, it feels like one can take any stupid off the wall idea and make it work. However, there are a lot of risk with this style of opportunistic trading. And, position sizing is very important. As an example, one year I called a big rally in the dollar, and I timed it perfectly but we were sized tiny. We had a tiny win for a huge call.
This is why some traders may combine speculation with ad-hoc quantitative analysis. It can be one of the most difficult ways to trade because every situation is different.
Another discretionary method contained within this general paradigm would be day trading where the trader attempts to identify and understand the general market environment and then seek to trade that environment with dynamic leverage. This really describes my old style of trading. But, I feel like that’s 50% trading– meaning simply that such methods are unlikely to yield more then 50% per year returns. It is trading something that is on the edge of random. So, that is why I have moved away from that form of trading. I am not aware of any 7 figure traders who trade that way, either.
3. Combination programmed pattern setups / quantitative setups and discretionary execution, i.e. graybox. I feel like I am seeing more 7 figures using methods like these. They take something that is programmed and statistically demonstrated and then trade it with discretion. My BeyondBot software was designed exclusively for this purpose. The real advantage with this method is that you reduce the universe of possibilities to focus on very specific things that can be quantitatively identified. In addition, you can gain some experience trading these specific patterns. This style of trading is very simple because you know exactly what you are aiming to do. As I shared, it makes way more sense to “tape read” the market in the context of quantitative edge then being fool into taking phantom trade ideas all day.
The downside is that it is harder to scale out. Once you bring back in the discretionary aspect, this makes it a more involved style. Also, you lose some flexibility. So, you need at least a few setups to be able to harness the discretionary power.
And sometimes it is easier to find a programming filter that can do a better job anyway.
For example, in one case I was able to identify a certain type of market where my signal did not work well. This involved very complex pattern recognition skill that would not be possible to be programmed using traditional methods. However, after some thoughts and work, I was able to find a quantitative indicator that worked in a different way but did as good or better job then I did.
So, yes while the upside is the statistical nature, the downside is just that too because markets have statistical, speculative (forward looking), and unpredictable (we call it random) natures. The statistical lens is microscopic and often incomplete.
In my own trading, I am structuring primarily along types 1 and 3. And, I feel like it is often easier to trade with discretion when one has a system producing consistent profits. However, I know that I am also good at type 2 trading because I have had good experience with synthesis. But, I feel like one needs a solid base and type 1 and type 3 is more likely to provide that.
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 email@example.com.
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