Backing Into Positive Expectancy on a Discretionary Trade

The broad stock market remains weak, so we continue to look for momentum opportunities in commodity markets instead of playing for income in indexes.

We spend some time in this session going over the math and tracking process used to help discretionary traders identify the trade type, stop loss, and profit target that will allow them to have positive expectancy even without a study of that specific trade setup. Then we take a look at some historic examples of applying stop losses and profit targets to options trades to get an idea of the impact of using these instead of the over-simplified probability at expiration.


I’m going to give myself a C- on the presentation quality in this session. But I do believe the Big Idea is hugely valuable. So, the notes for this session have the goal of re-summarizing and clarifying the most important points.

How to Estimate Expectancy in Discretionary Trades

Trade types can guide expectancy. For example, mean reversion strategies can be estimated between 60% and 70% accurate. Momentum and breakout strategies should have the inverse. Positive Theta options trades should have at least a 60% win rate regardless of the directional bias.

If it’s a combined mean reversion plus positive Theta trade, then the probability is likely higher than 60% to 70%. But, it doesn’t just instantly mean your profitability is higher.

Did you catch that?

Profitability and Probability are not the same things. Which one do you really want? It’s not a trick question. You can’t withdraw win rate from your account. You cannot pass on win rate to your grandchildren.

It doesn’t mean that high probability and mean-reversion are bad things. They are simply incomplete until you also know the profitability and long-term expectancy. I prefer to avoid purely systematic mean reversion.

Which brings us back to the main topic… How do you get that expectancy value without a list of historic trades? It comes from a broad knowledge of the variables in the trade. That mean you know the tendency of the market you trade (tends to mean revert, tends to grind down and crash up, or crash both ways, or mean revert instead of trend, etc.) You know the general stats of your directional bias, the trade configuration, and the impact of including a stop loss and profit target.

The more you study options systems, the faster and more accurately you can predict what the stats of a given set of rules will provide. We can generally expect mean reversion, long only, positive Theta options trades on broad-based stock indexes to be profitable.

Rather than guessing where the market will trade to and where it won’t stop you out. Determine the appropriate stop for the XX% of trades that are likely to be stopped out by this idea and the most appropriate limit for the YY% of trades that are likely to reach the profit target instead.

Many traders fall into the trap of thinking too hard about THIS TRADE instead of the NEXT 100 TRADES.

If you repeat the probability, stop loss, and profit target 100 or 1,000 times… what would the expectancy be? This is an exercise for you to work out. If you need help, then chat in to ask for guidance on generating a random set of returns using expectancy.

Ongoing evaluation.

If systematic trading is more like science and engineering, then discretionary trading is more like athletics.

In sports, the athlete’s real-world stats are all that matter. The stats reveal the weaknesses, strengths, and areas for improvement.

When you collect 20, 50, or 100 trades, you can begin to see what your actual win rate is. If your win rate for a certain type of trade is relatively high, then it may be too difficult to try to increase your win rate even more.  Instead, your best way to improve may be to either increase your profit target and/or reduce your stop loss.

If your win rate is relatively low for the trade type, then improving your entry criteria may be the fastest way to improve. Collecting data from several trades is what provides discretionary traders the ability to spend less time predicting what the market is going to do and more time improving their process to trade with positive expectancy.