It seems some have a tunnel vision obsession on the notion of win:loss ratio. Some drone on about how it is simple mathematics. 7th grade algebra is their alter. There is reason to consider other math disciplines that can be applied to system development. To Wit: A single trade is not an adequate paradigm to build a strategy. A strategy should be built on representative sets, say 200-500 or 1000 trades*. Trades do not move directly to a win or loss. They spend a lot of time in-between. In that time exits can be adjusted. TIME is a real factor. Don't confuse "in theory" and "in practice". Certainly not "theory => practice", BUT also "practice => theory" for designing your system. They are two different models. I.e. build both not one and confuse the two. Going beyond the 7th grade, simple statistics are better suited to "sets" of trades and should be considered as a "wrapper" for the algebra**. I will stop with a comparison of two results where the MAE is the Loss, for simplification. 1) W/L ration 2:1 and a win probability of 40%; 2) W/L ration 1:15 and a win probability of 97%; Consider: What probabilities really mean. Theory and practice are NOT the same. You do have control on which trades are opened, which is another abstraction layer in the strategy. That plan should be logically and statically orthogonal. i.e. NOT THE SAME "plan" In any case, just food for thought and not advocating any approach, but trying to frame the issue instead of pontificating one or the other. Lastly, easy-simple is not the same as complete or adequate. Easy-simple models come from unifying the numerous details and exceptions, not by ignoring them. Feel free to flame away at your ego's peril, or not *Unless you think each trade is uniformly the same, which, imo, is an absurd assumptions usually adopted because it is "easy" to model. ** There are other wrappers e.g. advanced statistics that can wrap first order parametric statistical systems. And models outside statistics that wrap entire statistical systems.