Ask most traders what makes a strategy good, and they'll talk about win rate — how often the trade goes in their favor. But win rate alone tells you almost nothing about whether a strategy will make money over time. The number that actually determines long-term performance is the risk-reward ratio, and understanding how these two figures work together completely changes how you evaluate a trading strategy.
Defining Risk-Reward Ratio
The risk-reward ratio describes the relationship between the average profit on a winning trade and the average loss on a losing one. If your average winner nets you $200 and your average loser costs you $100, your risk-reward ratio is 2:1. The higher that ratio, the more favorable the strategy — at least in isolation.
The reason risk-reward ratio can't be looked at alone is that it's inseparable from win rate. A 2:1 ratio sounds excellent, but if you only win 20% of the time, the math still works against you. Conversely, a 1:1 ratio can be profitable if you win more than half the time. What you actually need to evaluate is the expected value of the strategy — the combined result of both numbers together.
Putting the Math Together
Expected value is the clearest way to see this. The formula is straightforward: multiply the win rate by the average gain, then subtract the loss rate multiplied by the average loss.
Take a strategy with a 40% win rate, average gain of $100, and average loss of $50. The expected value per trade is (0.40 × $100) − (0.60 × $50) = $40 − $30 = $10. Positive — this strategy makes money over time even though it loses more often than it wins.
Now flip the numbers: a 70% win rate, average gain of $30, average loss of $100. The expected value is (0.70 × $30) − (0.30 × $100) = $21 − $30 = −$9. Negative — despite winning seven out of ten trades, the strategy loses money in the long run.
This is the math behind one of the most common trading frustrations: "my win rate is great but my account keeps shrinking." Win rate and risk-reward ratio need to be read together, not separately.
Where This Became Critical in the Grid Strategy
The flaw described in the previous post — the 25-step grid where the risk-reward ratio quietly deteriorated in later stages — was fundamentally a problem of these two numbers falling out of alignment. The early stages had a roughly balanced 1:1 ratio. But as capital grew in the later stages, the maximum take-profit stayed fixed while the potential loss at the stop-loss level kept expanding. Translated into expected value terms, later stages were operating with a negative expected value embedded directly into the structure.
Fixing this required one firm principle: the risk-reward ratio had to remain reasonable and consistent at every possible exit point across the full grid, not just in the first few stages. Whether the trade exits at stage 3 or stage 12, the math had to hold.
One common mistake worth highlighting: in a multi-stage strategy like grid trading, you can't calculate risk-reward from the entry price of the current stage alone. You have to calculate it from the weighted average cost across all stages that have already filled. An exit price that looks profitable against stage one's entry might actually be a loss against the true blended average cost. Getting this calculation wrong produces a strategy that appears to work on paper but bleeds capital in practice.
Why Most Traders Focus on Win Rate Instead
Win rate is psychologically satisfying in a way that risk-reward ratio isn't. Every win feels like a confirmation that the strategy works. Every loss feels like an exception. This makes it easy to keep a mental tally of wins, point to it as evidence of skill, and quietly ignore the fact that the losses were each twice the size.
Risk-reward ratio is uncomfortable to focus on because it forces you to confront loss size directly — not just frequency. Building a strategy around maintaining a healthy risk-reward ratio means being willing to take losses of a defined size consistently, in exchange for the expectation that winners will be proportionally larger. That kind of disciplined acceptance is harder to maintain than it sounds, which is another reason algorithmic systems — which execute the plan without second-guessing — have an advantage over manual trading.
Today's Investing Insight — The Kelly Criterion
When you know both your win rate and your risk-reward ratio, there's actually a mathematical formula for calculating the optimal fraction of your capital to deploy on any given trade: the Kelly Criterion. The formula is: Kelly % = Win Rate − (Loss Rate ÷ Risk-Reward Ratio). The result tells you, in theory, the exact bet size that maximizes long-term growth.
In practice, most traders who use Kelly apply it at a fraction of the full output — usually half or less — because the full Kelly size produces extreme volatility in account value, and because real-world win rates and risk-reward ratios are never as stable as the formula assumes. The Kelly Criterion is best understood not as a precise instruction but as a framework: it makes the relationship between position sizing, win rate, and risk-reward ratio concrete and calculable, which is more useful than most traders realize.
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This post documents a personal journey of building an algorithmic trading system and is not a recommendation of any specific strategy or position sizing method. All investment decisions and their outcomes are the sole responsibility of the investor.
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