• Horse

Riffing on Risk

Updated: Jan 7

Since I started sharing with people how I trade, I've learned something that kind of surprised me:


Risk management is a difficult topic to discuss.


Probably because it means so many different things to different people. I often find myself feeling bad because I struggle to answer the most basic of questions people ask me, like “How many points should I risk on each trade?” or “How many contracts should I trade?”


I find myself thinking “Dude…I don’t know!” I’m not you, I have no idea how you trade, what your goals are, what your experience is, etc. etc. etc.


This is the conundrum with risk management: It’s arguably the most important part of trading yet so universally difficult to teach because no two people trade exactly alike and there’s no standard definition of what risk management means.


To help establish a baseline for this blog post, I’ll define risk management in the most basic sense as the process by which a trader attempts to avoid losing more than they make.


If we can all agree on that basic definition, I’d like to share with you how I think about risk as a trader. As in, how I view risk management as a whole, not in regards to specific trades. As some of you know, I primarily trade futures intraday, but I also swing stocks and buy/sell options premium occasionally. So this blog post is about how I approach risk management in the long-term, which might help explain why I trade the way I do.


Ok, introduction over…let’s get into it.


Let’s start with 2 very basic factors in determining positional risk:

  1. Likelihood that I am correct in my thesis

  2. My position size


Logically, there should be a positive linear relationship between these 2 factors. The more likely I am to be correct when entering a trade, the larger my position size should be.


Some of you have watched the “How I Day Trade” videos I released and it should be no surprise that I approach trading like gambling. If you haven’t seen the videos, I use the analogy of poker. In poker, you start with a stack of chips (i.e. your trading capital), and good poker players wait for hands where they know they have a statistical edge based on the strength of the cards. When they receive a “strong” hand they bet accordingly (i.e. heavily). During tournament poker, a key part of any winning strategy is the ability to fold hands that are unlikely to win. To me, it’s the same in trading. You have to be willing to cut losers and/or wait patiently for a setup that has demonstrated a high likelihood of success in the past. This core concept is what I’m referring to in Factor #1 (the likelihood that I’m correct in my thesis). If I’m entering a trade in the middle of nowhere, with no key Support/Resistance, no clear order flow information, etc., well that trade is essentially a coin toss…the market is just as likely to move against me as it is to move in my favor. To stay with the poker analogy, that’s a hand I would fold.


But let’s say the market is at a key area of confluence, and there’s a VERY good change it’ll change direction, and I’m seeing order flow to support that thesis…well now I have a slight edge, so technically my position size should increase from whatever my minimum size currently is. We can represent this thinking with a basic graph:


Wouldn’t it be nice if risk management was just this simple? It’s not. Now we have to introduce a third factor on our Z axis: Time.

Specifically, the time it will take me to know if I am right or wrong in my thesis.


You might be thinking, “Horse what the hell does time have to do with risk management?”


That’s a valid question. It has to do with opportunity cost.


Again referencing our poker analogy, I have a limited stack of chips. I cannot place unlimited large bets with high conviction. I have to “manage” my chip stack, my capital. Therefore, I can’t have too much tied up for an indeterminate amount of time hoping my thesis is correct. For me, it’s sort of a “shit or get off the pot” scenario. The longer I have to wait to know if I’m correct absolutely impacts my likelihood of being correct AND my position size. In my opinion, this is why macro investing is so difficult. You could enter a position with a very sound thesis, but over time things change and you end up being dead-ass wrong…through no fault of your own. The more time that elapses, the more opportunity for things to change that impact your initial thesis. Sometimes those unknown factors are positives, but more often than not they seem to be negatives because your initial thesis generally accounted for the positives, right?


Earlier I used the word “process” to describe risk management. I absolutely view it as an ongoing process. Things change in markets—therefore I need to be prepared to change as well in order to properly manage risk in the long term.


So back to opportunity cost. I'm a trader in this for the long haul, I need to adjust position sizes and stoplosses based on new information and time.


Here’s a couple examples:


DWAC


One of my best equity trades this year was the DWAC SPAC (Note: Unintentional yet hilarious rhyme). Here’s how it went down and how I thought about it in my weird “mental risk model” that we’re discussing:


I developed a thesis for the trade after the markets were closed for the day. I heard the news about Trump possibly getting involved with a SPAC deal. I checked the price of DWAC after-hours and it was sitting almost at NAV (~$10), which had me scratching my head…why hadn’t this thing run? Well the answer was simple: The news JUST broke and it wasn’t priced in yet. So I decided that I’d take a very large position size pre-market the next day because I figured my likelihood of being correct (e.g. that it would pump on the news) was very high. I also factored in the time for our thesis to play out (our z axis) and determined that I’d know RIGHT away if I was right or not. The likelihood of it falling below NAV was extremely low, so it was “all systems go” for me and time to make an aggressive trade. The rest, as they say, is history.


GOLD


Another example that better showcases adjusting my risk management approach using the 3 factors we’ve discussed is a recent gold swing trade I took. In the final days of September, I started a large swing trade in gold micro futures (more on why I like the micros for gold some other time). The inflation narrative was picking back up, and even though gold is a bona fide piece of shit for an inflation hedge, my thesis was that the general public is too dumb to know that, plus gold had recently been beaten to a pulp and I figured it was oversold and there was an opportunity to BTFD. I also figured portfolios would be getting rebalanced Sept