Many traders on Twitter are using stats for their trading. One of the simplest examples is the gap fill stat. Many years ago traders would fade gaps because they were often filled. That’s changed in the past few years so be careful if you do that.
Lawrence Chan has been a pioneer in this area and he has written a couple of interesting books on using stats in your trading, specifically two are for the S&P 500. Before you rush to purchase these, I suggest reading ahead. I think the books are worth their price, but they’re not the holy grail. I believe most of the stats are not very applicable and I’ve found one to be invalid. More on that later.
At first I was intrigued by the stats in the first S&P book (the 2nd is relatively new). Some of them have 75% probabilities. So easy right? Just put on the trade and you have a 75% win rate.
Not so fast. Trade expectancy is based on two levers which are the Risk-Reward and the win rate. Expectancy of a trade is it’s reward * win rate – risk * loss rate. So if we have a target twice as big as the stop, the R:R is 2:1 (we usually right the reward first even though we say “risk-reward”). If the win rate is 40% then the expectancy is:
2*0.40 – 1*0.60 = 0.20 pts/trade.
So what can a trader control? We can only control the risk and reward. We can’t control the win rate. However, the market is efficient in that if we take random trades, the win rate will be exactly the win rate required for us to end up exactly zero*. I’ve proven this with random trade simulations and it’s quite fascinating. And logical if you think about it.
So how do we make money? We make money by controlling the R:R and getting a win rate above random win rate. If we use 2:1 the random win rate is 33%. So to be profitable, we must be this. In our example we had a positive expectancy of 0.20 pts because our win rate was slightly higher than average (40% vs. 33%). The edge was effectively slightly less than 1 tick, which coincidentally is the edge that I’ve often observed in my own trading (I wrote quite a lot about that over the past couple years). Don Miller, a famous retail trader, has a similar edge. It’s hard to get a win rate greater than random in something as unpredictable as the financial markets. And I think many traders may only have an edge of a few ticks. That means to make any reasonable money, one must trade size. Which is exactly how Don Miller makes millions. But trading size is not as easy as changing the order quantity on your DOM, but that’ll be for another post.
So back to our stats, this is is why having inverted R:R where risk is bigger than the target is ill-advised. If our R:R is 1:2 we need 66% win rate. And that’s difficult to do. If you trade with very large stops or no stops at all, the required win rate is 85% or higher. And that one loss will wipe out all the winnings. This is why moving stops and cutting winners short does not work.
So how do we get a win rate above average? We don’t enter random trades. We enter only when we have a well-defined edge. I say “well-defined” because intuition doesn’t work very well with trading. When we think we have an edge, we’re often being suckered by someone who knows they have an edge.
So to be well-defined we have to be in a situation we’ve seen before, something repeatable. And that’s where the trading plan comes in. This has been a constant challenge for me because my trading was always so complex and had too much discretion, and therefore was not repeatable. I’d have a good month followed by a bad month.
On the other hand, a simple setup or pattern that anyone could do isn’t going to work. The market is too efficient for that. And so coming up with your well-defined edge is a long trial & error process.
Stats can be used to help increase the win rate, and I’ll write more about this in a future post.
*I didn’t include the impact of commissions which will gaurantee any random trading will result in a loss.