Win rate and risk-reward are locked in a tradeoff: improving one usually worsens the other. A closer take profit is hit more often but banks less per win. A distant take profit pays more per win but is reached less often. Neither is good or bad alone - only their combined effect on expectancy tells you which setting is best.
Why they trade off
Move your target from 1:1 to 1:3 and price now has to travel three times as far before you get paid. More trades will stall, reverse, and miss, so your win rate drops. In exchange, the wins that do land are three times larger. Whether that is a good deal depends entirely on how much the win rate falls. Sometimes the bigger winners more than pay for the lower hit rate. Sometimes they do not.
The highest win rate and the highest reward-to-risk both lose to the balanced 1:2, which produces the best expectancy. The middle often wins.
Expectancy is the referee
Because win rate and risk-reward move against each other, you cannot judge a change by looking at either in isolation. Expectancy folds both into one number: (win rate x average win) - (loss rate x average loss). When you tweak the target, recalculate expectancy. If it rose, the tradeoff was worth it. If it fell, revert. This is the only honest scoreboard for the tradeoff.
Important: do not let personality pick the target for you. Traders who hate losing gravitate to tight targets for the comfortable high win rate, even when a wider target has better expectancy. Let the data choose, then train yourself to hold it.
The psychology trap
High win rates feel good, so many traders unconsciously optimize for feeling right rather than for making money. They cut winners early to lock the win, quietly wrecking their risk-reward. Understanding this tradeoff protects you from it: you learn to accept a lower win rate on purpose because the math behind it is stronger. Managing that discomfort is closely tied to improving win rate the right way - through filters, not through shrinking targets.
Finding your balance point
The practical method is to test one setup at several target distances and compare expectancy at each. Keep entries and stops identical; only move the take profit. The distance that maximizes expectancy across a large sample is your answer, even if it is not the flashiest win rate or the biggest reward-to-risk. This is exactly what an R:R optimization view is for.
Test the tradeoff instead of guessing it
You cannot feel your way to the right balance - you have to measure it. When you backtest in a simulator, the report can show how expectancy changes across different reward-to-risk targets for the same trades, so you can see the exact point where win rate and risk-reward combine into the best result. That turns a vague debate into a settled number.
Win rate vs risk-reward FAQ
Is win rate or risk-reward more important?
Neither alone. They only mean something together, through expectancy. A high win rate with poor risk-reward can lose, and a low win rate with strong risk-reward can be very profitable.
Why does a wider take profit lower my win rate?
Price has to travel further before you get paid, so more trades fall short. You gain size on your wins but win less often.
How do I find the best balance?
Backtest the same setup at several target distances and pick the one with the highest expectancy across a large sample.