‘I play poker.’

‘Ah you like a gamble then?’

‘No, I play it. It’s a business.’ Daniel Negreanu, professional poker player.

I have no idea if that interchange ever happened, but it most probably did. Anyone who has traded has probably heard the same thing, just replace poker with trading. It’s almost always followed by, ‘yeah, my Dad did that for a bit but he lost a load of cash,’ or some comment to try and bring you to a level of relativity, because put simply, they don’t understand risk, a tested sample size and the concept of explicit risk:reward.

Risk is quite a foreign concept to most. The way the majority of the world makes money is by having a job whereby a set salary is paid with the only variables on that salary changing being hours worked and performance ceterus paribus. Professional poker players and day traders can experience swings in income relative to risk taken. You can go long periods without earning anything. Why is this?

Trading and poker strategies are measures over large sample sizes. A greater sample size provides a lesser degree of error and generally allows for a p-value to be below the standard 5% significance level when accounted for sampling error, which is predetermined. However, you don’t necessarily need to delve into deep statistics in either to realise when a strategy provides significance. For example, if you have a trading strategy that over 1000 trades netted a win ratio of 61% and risk:reward of 1:2, that would be a profitable strategy. Because you know the final outcome over this sample size, you don’t need to hypothesise whether it is significant or not. The same goes for poker. If you have played 1,000,000 a lot of hands in a month (and professional players do this when including online tables as well) then you have a pretty good basis for your outcome to be significant.

When comparing poker to trading, however, it is probably more apt to compare online poker, since live poker can be less mechanical due to increased variables (you can see your player, their tells, you can get into their head etc). Live poker can also have these tells where you can see by how quickly they check, call, or raise as to how strong their hand is but the key here is that you can’t see your opponent when trading or playing online poker. However, live poker is heavily based on pot odds and betting strategies. Let’s take an example.

Let’s say you are dealt 8H 8S and on the flop you get 7S 2S 5H. Here you have two potential situations. You have four aces that can drop to give you two pair aces high with you holding second highest pair based on the flop, or you have an 8C 8D that can drop. You also have 10 spade cards than can drop to improve your hand on the turn. This means you have seen 5 cards and have 16 outs. Now we can calculate turn card odds. This leaves us with 21 ‘useful’ cards and 31 cards that are not useful. 31/21 is roughly 1.5. This means that for you to call the pot only has to be 1.5x bigger than the call amount. So if the current pot is £10 the call amount must be £6.60 or less to make it a statistically valid call based on the pot odds. The 1.5 figure is basically your chance of winning the pot at this stage. As soon as your pot odds drop below 1, this is a signal to go all in usually. This is very barebones, but it shows what goes through a poker players mind when calculating whether to take a risk or not, much like how a trader does.

Something else you’ll notice about poker players and traders is that they can have relatively aggressive (not as in they’ll punch your face in) personality types – highly competitive and quite alpha in nature. This most certainly adds to a risk taking nature due to the reward seemingly being more important than the amount risked. Slightly off topic, it’s also why men have a lower life expectancy than women. Men take risks more in their occupation choice than women.

Traders and poker players also have their favourite games, whether it’s a specific time frame or session to trade, or for poker players, a cash game or tournament type game. This edge building is vitally important provided that it is followed through over a full sample size. This edge preservation builds into the personality type as well. A good poker player can sit at a table and check out the other players. They can see who the loose and tight players are – who is scared and who is greedy. This is the same as in the markets. You can sense when fear is coming in and where people are excessively greedy. You then play off of this… or just consistently go against retail sentiment.