Two Traders, Same Return, Different Skill
Trader A made 20% last year. Trader B made 8%. Who is the better trader?
The instinctive answer is Trader A. The correct answer is: you cannot tell from this information alone.
Trader A made 20% during a year when the ASX 200 gained 18%. They held concentrated positions with no defined stops, averaged down on losers, and took maximum portfolio heat of 85% in a single sector. Their 20% was 2 percentage points of alpha above the index -- achievable by simply buying a leveraged ETF.
Trader B made 8% during a choppy, range-bound market where the ASX 200 was flat. They risked 1% per trade, followed their rules on 92% of trades, ran two strategies with positive expectancy, and never had a drawdown exceeding 5%. Their 8% represented genuine edge extracted from a difficult market.
Trader B is the better trader. Trader A got lucky in a favourable market and took risks that will eventually produce a serious loss.
Portfolio returns cannot distinguish between these two situations.
Why Returns Are a Flawed Measure
Returns Ignore Risk Taken
Two traders both make $5,000 on a trade. Trader C risked $1,000 to make that $5,000 (a 5R winner). Trader D risked $10,000 to make the same $5,000 (a 0.5R winner). The dollar profit is identical. The quality of the trade is not even close.
R-multiples -- the ratio of profit to initial risk -- normalise returns against the risk taken to achieve them. A portfolio tracker shows the $5,000 profit for both traders and calls it equal. A journal that tracks initial risk reveals that Trader C found a five-to-one opportunity while Trader D took a coin flip.
Professional trading desks evaluate traders on risk-adjusted returns, not gross P&L. A prop firm would promote Trader C and put Trader D on review.
Returns Do Not Account for Market Conditions
Making 15% when everything is going up is not the same as making 15% when markets are choppy, volatile, or falling. A rising market lifts all boats, including poorly constructed portfolios.
The relevant question is: how much of your return came from market direction (beta) versus your own trading decisions (alpha)? During the 2024-2025 bull run on the ASX, many traders posted impressive returns that disappeared or reversed when conditions changed. Their "skill" was really market exposure.
A trading journal cannot perfectly isolate alpha from beta, but it provides data points that help: win rate across different market conditions, R-multiples during trending versus ranging periods, and strategy performance that can be compared independently of the index.
Returns Hide Process Quality
A trader who makes 25% by breaking every rule they set -- moving stops, oversizing positions, revenge trading after losses -- has a process that will produce ruin eventually. The 25% return makes them feel validated, which reinforces the bad behaviour.
A trader who makes 10% by following their rules consistently, managing risk, and executing their strategy as designed has a process that compounds over time. The modest return might feel disappointing, but the foundation is sound.
Returns measure the outcome of a single period. Process quality predicts outcomes across many periods. The two are not the same thing, especially over short time frames.
How to Measure Actual Trading Skill
If returns alone are insufficient, what should you measure? Here are the metrics that professionals use to evaluate trading performance.
Expectancy
Expectancy is the average R-multiple across all trades. It answers the question: for every dollar of risk, how much do I expect to make (or lose) on average?
Formula: (Win Rate x Average Winner in R) - (Loss Rate x Average Loser in R)
Example: Win rate 45%, average winner 2.0R, average loser 0.8R Expectancy = (0.45 x 2.0) - (0.55 x 0.8) = 0.90 - 0.44 = +0.46R
A positive expectancy means your system makes money over time. The higher the expectancy, the stronger the edge. An expectancy of +0.46R means that for every dollar risked, you expect to gain 46 cents on average across many trades.
Expectancy is the single most important number in trading performance measurement. It combines win rate, reward size, and risk management into one figure.
Average R-Multiple
Your average R-multiple across all trades (winners and losers combined) should be positive. More importantly, track it over rolling periods (last 30 trades, last 60 trades) to see if it is stable, improving, or declining.
A declining average R often signals behavioural drift: cutting winners shorter, moving stops further away, or taking lower-quality setups. The decline shows up in R-multiple data before it shows up in portfolio returns.
Win Rate by Strategy
Overall win rate is marginally useful. Win rate broken down by strategy is much more informative.
A trader with a 50% overall win rate might have:
- Strategy A: 65% win rate (28 trades)
- Strategy B: 40% win rate (30 trades)
- Strategy C: 30% win rate (15 trades)
Strategy C is either fundamentally flawed or being executed poorly. The overall 50% win rate masks this problem. A portfolio tracker shows the 50% (if it shows win rate at all). A journal shows the per-strategy breakdown.
Consistency of Process
This is the hardest metric to quantify but one of the most predictive. How consistently do you follow your own rules?
Track these for each trade:
- Did you enter at the planned level (or close to it)?
- Was your stop loss set before entry?
- Did you honour the stop, or did you move it?
- Did you exit at your target, or did you close early/late?
- Was the position size consistent with your risk rules?
A "rule adherence score" -- the percentage of trades where you followed your plan -- correlates strongly with long-term profitability. Traders above 85% adherence tend to improve over time. Traders below 60% tend to plateau or decline, regardless of their strategy quality.
Largest Winner to Largest Loser Ratio
Your largest winner should be significantly bigger than your largest loser. A ratio of 3:1 or higher suggests your risk management is working -- you are letting winners run while cutting losses. A ratio close to 1:1 or below means your biggest winning trade barely compensates for your biggest losing trade, which is a structural problem.
Consecutive Loss Analysis
How you handle losing streaks reveals more about your trading skill than how you handle winning streaks. Track:
- Maximum consecutive losses
- Average drawdown during losing streaks
- Behaviour change during losing streaks (do you increase size? Change strategy? Stop trading?)
A skilled trader has losing streaks -- they are statistically inevitable with any win rate below 100%. The difference is that skilled traders keep their losing streaks contained (-1R per loss, no size increase, no rule violations) while unskilled traders compound their losses through behavioural deterioration.
Why This Matters Practically
None of this is academic. The metrics above are calculable from trade journal data, and they predict future performance far better than past returns.
A trader who posts a 30% return but has negative expectancy on half their strategies, a declining average R, and a rule adherence score of 55% is heading for trouble. The 30% return will not repeat.
A trader who posts a 10% return with positive expectancy across all strategies, a stable average R, and 90% rule adherence is building something durable. Their returns will likely grow as their sample size increases and they refine their approach.
The difference between these two traders is invisible in a portfolio tracker. It is visible on the first page of a trading journal.
Starting to Measure What Matters
If you currently only track portfolio returns, here is how to start measuring trading skill:
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Record your initial risk on every trade. Before you enter, write down your stop loss level and calculate the dollar amount you are risking. This is the denominator of every R-multiple calculation.
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Tag every trade to a strategy. Even if you only trade one strategy, name it and track it. You will eventually trade more than one, and the per-strategy data becomes indispensable.
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Note whether you followed your rules. A simple yes/no after each trade on whether you executed as planned. Over 30 trades, this becomes a percentage that tells you a lot about your discipline.
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Review monthly. Look at expectancy, average R, and rule adherence across the past 30 trades. Are they stable? Improving? Declining?
SwingFolio calculates all of these metrics automatically from your trade data. But even if you use a spreadsheet, the act of tracking risk and process alongside returns will change how you evaluate your own trading.
The goal is not to ignore returns. Returns matter -- they are what pays the bills. The goal is to understand the process behind the returns, because process is what you can control, and process is what predicts whether your returns will continue.
Disclaimer: This article is general information only and does not constitute financial advice. The examples and trader profiles are hypothetical. Past performance does not guarantee future results. Trading involves significant risk of loss. Always do your own research and consider your personal financial situation before making trading decisions.