ORB Trading Expectancy

Costs are the least interesting part of a trading strategy and one of the few parts you can actually control. They are also the reason a great many methods that look viable on paper are not viable in an account. The mechanism is simple and unforgiving. Every cost is deducted from the same average result the strategy is trying to generate, and it is deducted on every trade regardless of how the trade went.

Costs Are Not a Separate Line Item

It is tempting to think of commissions as overheads, paid out of profits at the end of a period. That framing hides what is happening. Each cost reduces the result of the individual trade it belongs to, which reduces the average win, increases the average loss, and lowers the expectancy figure directly.

The consequence is that costs damage a losing trade as well as a winning one. A strategy with a low win rate pays them on every failed attempt, and those payments accumulate through exactly the stretches where the account can least tolerate them.

Three Different Costs, Three Different Behaviours

Commissions and exchange fees are the visible portion and the easiest to model, because they are known in advance and generally fixed per unit traded. The spread is the second, paid at both ends, and it widens at precisely the moments an opening range strategy is active, since the first minutes of a session are frequently the least orderly.

Slippage is the third and the one that resists measurement. It is the difference between the price you intended and the price you received, and it is systematically worse for breakout entries than for most other approaches, because a breakout entry is by construction an order placed into a market that is already moving in that direction. Stop orders sitting at obvious levels tend to fill worst on the sessions that move most.

Why a Small Edge Suffers Disproportionately

Costs are close to fixed per trade while the edge is a small residue. That relationship is the whole problem. A method whose average result per trade is comfortably large treats transaction costs as a minor deduction. A method whose average is slim can find that the same absolute deduction consumes a substantial share of it.

Two further factors make it worse. Frequency multiplies the cost without necessarily multiplying the edge, so a strategy that takes several attempts per session pays several times over for a single day's opportunity. And the smaller the intended move, the larger costs loom relative to it, which is why scalping variants of a breakout are far more cost sensitive than versions holding for a larger portion of the session.

Measure Them From Fills, Not From Assumptions

The commission schedule is published and the spread is observable, so both can be estimated reasonably well in advance. Slippage cannot, and the only credible source is your own fills. Recording the intended price alongside the received price on every trade builds that dataset over time and turns a guess into a measurement.

What usually emerges is that slippage is not evenly distributed. It concentrates in identifiable circumstances, wide ranges, the first minutes after the open, sessions following an overnight development. Knowing where it clusters is more actionable than knowing its average, because a cluster can sometimes be avoided while an average can only be accepted.

What Can Actually Be Reduced

Commission rates are negotiable at some brokers and vary considerably between them, and for a frequent strategy that difference alone can matter more than most changes to the entry rules. Trading fewer, better qualified setups reduces total cost directly and is usually available without any change to the method beyond enforcing conditions already written down.

Order type is the other lever. An entry that can be placed as a resting order rather than crossing the spread avoids part of the cost, though it introduces the risk of not being filled on the moves that run without pausing. That is a genuine trade off rather than a free improvement, and which side of it is correct depends on how often your strategy's winners come from the fast breaks.

The habit worth building is simple. Whenever expectancy is calculated, calculate it net, and never quote the gross figure even privately. The gross number describes a strategy nobody is able to trade, and the difference between the two is the part that decides whether the method belongs in an account.

The calculation itself is not the hard part. Multiply the proportion of trades that won by the average size of a win, subtract the proportion that lost multiplied by the average size of a loss, and what remains is the average outcome of taking one more trade under the same conditions. Anyone can do the arithmetic. The difficulty is that the arithmetic is only as honest as the log feeding it, and most logs are not honest by accident.

What the Log Has to Contain

Every trade needs a date, a direction, an entry, an exit, a size and a net result after costs. That is the minimum for the calculation to run at all. Useful beyond that are the planned stop, so you can tell a full loss from a partial one, and a flag for whether the trade followed the plan, which lets you compute the figure twice and compare.

Skipped sessions matter less than people expect for this particular number, since expectancy is per trade rather than per session. They matter enormously for interpreting it, because a strategy that only trades under specific conditions has an expectancy that applies only to trades taken under those conditions, and a log that does not record what was filtered out cannot tell you what the number is conditional on.

Express It in Risk, Not Currency

A figure in currency terms is unstable, because it moves whenever your position size moves, and position size moves whenever the range height moves. Two identical trades a month apart can show very different money results purely because the stop distance differed, and averaging those together produces a number that describes your sizing history rather than your method.

The alternative is to express each result as a multiple of the risk taken on that trade. A trade stopped at the planned stop counts as one unit lost. One that returned twice the planned risk counts as two units gained. The average of those multiples is comparable across sessions, across instruments and across changes in account size, and it isolates the thing you are trying to measure.

The Decisions the Calculation Forces

Several judgement calls appear the moment you sit down to compute. A trade closed manually before either the stop or the target was reached is neither a clean win nor a clean loss, and whichever bucket you put it in will shift the averages. A trade that was scratched at roughly break even sits awkwardly in a two outcome model.

There is no universally right answer, but there is a right procedure, which is to decide the treatment once, write it down, and apply it to every trade including the ones where the choice hurts. Deciding case by case is how a log drifts toward flattery, one reasonable exception at a time.

How Many Trades Before the Number Means Anything

Expectancy computed over a handful of trades is not a measurement, it is a description of those particular trades. The average win and the average loss are both averages, and averages taken from small samples move sharply when one more result arrives. A single unusually large win can carry a small sample from negative to positive, and it will carry it back on the next recalculation.

The practical sign that a sample is still too small is that the figure swings noticeably each time you add a trade. When additional trades stop moving it much, you have something worth interpreting. That point arrives later than most traders want, and there is no way to reach it faster except by continuing to trade the method the same way.

The Ways a Log Flatters

The most common distortion is omission. Trades that ended badly, particularly ones that broke the rules, are the least pleasant to record and the most likely to go missing. A log with holes always overstates the edge, and the holes cluster exactly where the information is most useful.

Second is recording gross rather than net results, which leaves the costs out of the number entirely. Third is quietly excluding a period as unrepresentative, usually a bad one. If a stretch is genuinely outside the strategy's conditions, that exclusion should follow a written rule that would also have excluded a good stretch under the same circumstances.

Computed carefully, the figure has one job. It tells you whether repeating this behaviour has a positive average result, and roughly how positive. It does not tell you what the next trade will do, and it never has, which is the part that makes it useful and the part that makes it hard to sit with.

Suppose the number came back positive and small. The method works, in the sense that repeating it has a better than even average result, and the margin is slim. That is the ordinary condition of a real strategy, and almost nothing about the day to day experience of running one resembles the tidy conclusion the arithmetic supports.

The Average Is Not Available Anywhere

An expectancy figure describes the mean of a distribution. No individual trade returns the mean. Every trade returns either a win of some size or a loss of some size, and the mean is a property of the collection rather than of any member of it. This sounds obvious written down and is remarkably difficult to hold on to at the end of a session that produced a loss.

With a thin edge the gap between the average and the individual results is proportionally enormous. The wins and losses are large relative to the small residue left after they cancel. So the signal you are trading is quiet and the noise around it is loud, and you experience the noise continuously and the signal never directly.

Losing Runs Are Longer Than Intuition Allows

People substantially underestimate how long a run of losses can be in a process that is genuinely positive. Sequences that feel like clear evidence of a broken method are routine features of a small edge, and they occur often enough that any trader running one for a reasonable period will meet several.

The trouble is that a broken method and a normal losing run look identical while you are inside them. There is no reliable way to tell from the experience alone, which is why the judgement has to be made against a written threshold set in advance rather than in response to how the current stretch feels. A trader without that threshold will abandon a working method, and the abandonment will happen at a low point rather than a random one.

Good Weeks Mislead in the Same Way

The reverse error attracts less attention and does comparable damage. A short winning run under a thin edge feels like the method has finally clicked, and the natural response is to size up, trade more often, or relax the conditions that filtered out marginal setups.

Each of those changes the strategy at the moment its results were most flattering, which is the least informative moment available. A run of wins with a small edge is not evidence of anything except variance breaking in your favour for a while, and treating it as confirmation is the same statistical error as treating a losing run as refutation, just more pleasant.

Small Improvements Matter More Than They Should

One genuinely encouraging property of a thin edge is its sensitivity. Because the margin is narrow, changes that would be irrelevant to a robust strategy are consequential here. Cutting the trades that were taken outside the conditions, tightening execution so fills land closer to the intended price, or removing the worst session type from the schedule can each shift the number by a meaningful proportion of itself.

This is where reviewing the log earns its keep. The candidates for improvement are usually visible as clusters of poor results sharing an identifiable feature, and with a thin edge you do not need to find many of them. The same sensitivity works in the other direction, which is why casual rule breaking is so much more expensive here than it would be under a wide margin.

Sizing Is What Makes It Survivable

A thin edge only pays if you are still running it when the sample gets large. That is a statement about position size before it is a statement about patience. Sizing that lets a normal losing run reduce the account severely will end the experiment before the average has a chance to show up, and the strategy will be recorded as a failure when what failed was the sizing.

Trading a small edge is therefore mostly an exercise in remaining unremarkable for a long time. The behaviour that produces the result is repetitive, the feedback is poor, and the confirmation you want does not arrive on any schedule you can plan around. Knowing that in advance does not make it comfortable, but it does make the discomfort expected, which is worth something on the mornings it shows up.