Explore 58 trading metrics for evaluating profitability, risk, consistency, and execution. Use the library to understand what each number reveals before building a deeper review process around it.
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Measure profitability, edge, growth, payoff quality, and return efficiency.
A good average profit per trade is at least 2-3x your total costs (commissions + slippage) per trade. For active traders, $50-$150 per trade after costs indicates a healthy edge.
A useful average winner is commonly larger than the average loser. Review the winner-to-loser ratio together with win rate, costs, and the influence of unusually large trades.
Your break-even win rate depends on your risk-reward ratio. At 1:1 R:R you need 50%, at 1:2 you need 33.3%, and at 1:3 you need 25%. Profitable traders maintain a win rate above their break-even.
A good CAGR for active traders is 15-25% annually, outperforming the S&P 500 long-term average of roughly 10%. Elite traders may sustain 30%+ CAGR, but anything above 50% is difficult to maintain.
An edge ratio above 1.0 confirms a structural edge — trades move further in your favor on average than against you. Above 1.5 is considered tradeable by most prop firm standards; above 2.0 is strong.
Expectancy is the average dollar amount you expect to make per trade. A positive expectancy means the strategy has an edge; negative means it loses money.
A healthy trading strategy keeps net profit above 70% of gross profit. If costs consume more than 30% of gross gains, your strategy may not be viable long-term.
A healthy capture rate is 55–70% of Maximum Run-Up. If you consistently capture under 40%, your trailing stops are too tight relative to the instrument's ATR, costing you on every winner.
Net expectancy measures the average result per trade after costs. Track it over rolling samples and by strategy so a profitable headline result is not hiding a deteriorating edge.
A good net profit margin per trade is above 0.5% of position value for day trades and above 1.0% for swing trades, after all commissions, fees, slippage, and financing costs are deducted.
Payoff ratio is your average winning trade divided by your average losing trade. A ratio above 1.5 is good; above 2.0 is excellent. It must be paired with win rate.
Profit factor is gross profits divided by gross losses. Above 1.0 means profitable; above 1.5 is good; above 2.0 is excellent. Below 1.0 means losing money.
A good Profit Per Day depends on account size and goals. Day traders targeting $50,000/year need roughly $200 PPD across 250 trading days. Always report PPD alongside daily P&L standard deviation.
A good trading ROI depends on timeframe. Annualized, 15-30% is strong for active traders. Above 30% is exceptional but hard to sustain, while below 5% underperforms passive index investing.
A good Return on Risk is 30% or higher per period, meaning you generate at least $0.30 of net profit for every $1.00 of total capital risked across your trades.
A good SQN is 2.5 or above over at least 100 trades. Scores of 3.0–5.0 are excellent. Anything below 2.0 on 100+ live trades signals the edge is too thin to trade with confidence.
A good volatility-adjusted return score depends on the method, but using Sharpe Ratio as the standard, above 1.0 is acceptable and above 2.0 is excellent for most trading strategies.
A good win/loss ratio is above 2.0, meaning you have twice as many winning trades as losing ones — but it must be evaluated alongside your payoff ratio to gauge true profitability.
Win rate is the percentage of trades closed at a profit. A good win rate depends on your risk-reward ratio — 40-50% is strong with 2:1 R:R or better.
Understand drawdowns, capital exposure, downside behaviour, and survival risk.
A good average losing trade is smaller than your average winning trade. Most profitable traders keep their average loss below 1R, meaning each loss stays within their predefined risk per trade.
The Calmar Ratio compares annualised return with maximum drawdown. A higher value indicates that returns have compensated more effectively for the deepest observed equity decline.
A good maximum drawdown duration is under 30 trading days. Consistently recovering within 10-20 days signals strong risk management and psychological resilience.
Most traders should use fractional Kelly (25-50% of the full Kelly percentage). Full Kelly maximizes long-term growth but causes severe drawdowns, so half-Kelly is the practical standard.
The longest drawdown period measures how long equity remains below its previous peak. Compare recovery time with the strategy’s normal holding cycle and your capital commitments.
A MAR ratio above 1.0 is institutional-grade. Most retail systematic traders land between 0.5 and 1.0. Below 0.5 signals the strategy destroys capital faster than it recovers it.
Maximum drawdown is the largest percentage drop from a peak to a trough in your account. Keeping it below 20% is critical for capital preservation.
Keep total portfolio heat below 6-10% of account equity. For a $50,000 account, that means no more than $3,000–$5,000 at risk across all open trades at any one time.
A good risk-adjusted return means your ratio scores (Sharpe > 1.0, Sortino > 1.5, Calmar > 3.0) consistently show profits that more than compensate for the volatility and drawdowns you endure.
Target below 1%. A 50% win rate with 1.1:1 payoff risking 1% per trade yields 0.005% ruin probability. The same edge at 5% risk jumps to 13.6% — a 2,700× increase from position size alone.
Risk of ruin should be below 1% to ensure long-term survival.
A good risk per trade is 1-2% of account equity. Risking more than 2% per trade significantly increases the probability of large drawdowns and account ruin.
A good risk-reward ratio is 1:2 or higher, meaning your potential profit is at least twice your potential loss on each trade, allowing profitability even with a win rate below 50%.
Return skewness shows whether results contain larger upside or downside outliers. Strong negative skew deserves scrutiny because frequent small gains may be vulnerable to occasional severe losses.
A good Treynor ratio exceeds the market benchmark of roughly 0.05–0.07. A Treynor above 0.10 indicates strong risk-adjusted performance relative to systematic market exposure.
A good Ulcer Index is below 5, indicating shallow, short-lived drawdowns. Values above 10 suggest deep or prolonged equity declines requiring strategy review.
A good daily VaR at 95% confidence should be 1-2% of account equity, meaning on 19 out of 20 days your losses should not exceed that threshold.
Evaluate the stability of returns across days, months, setups, and trading streaks.
A good daily P&L volatility shows a Coefficient of Variation (CV) below 1.0, meaning your standard deviation of daily P&L is smaller than your average daily profit.
Day-of-week performance compares results across weekdays. Use a meaningful sample and net expectancy—not one memorable session—before changing participation or position size.
Equity curve analysis is the visual tracking of account equity over time. A smooth upward curve signals a consistent edge; jagged or flat curves signal problems.
A good consistency ratio is above 1.5, with 65%+ profitable months for day traders, 60%+ for swing traders, and monthly return standard deviation under 6% for retail-pro level performance.
Strong active day traders achieve 55-65% profitable days. Below 50% over a rolling 20-day window signals a consistency problem — regardless of overall P&L — and is a common prop firm evaluation.
Measure win rate, payoff, expectancy, and rule adherence separately for each setup. A setup with negative expectancy can weaken the portfolio even when overall results remain positive.
A losing streak is statistically normal until it exceeds log(N)/log(1/(1-p)) trades. Beyond that threshold, run the Wald-Wolfowitz test — if |Z| exceeds 1.96, your outcomes are clustering.
Review timing, trade duration, slippage, realised risk, and profit-capture efficiency.
Average bars in trade should match the strategy’s intended timeframe. Compare winners and losers separately to identify premature exits or positions that remain open after the original edge has expired.
A healthy hold time ratio is above 2.0 for trend-following strategies and 1.0–1.5 for mean-reversion. Any ratio below 1.0 means you are holding losers longer than winners — the disposition effect.
Average R:R is the mean ratio of average win size to average loss size across all trades. An average R:R above 1.5:1 supports profitability at moderate win rates.
A healthy duration ratio (avg loser duration / avg winner duration) is below 1.0, meaning winners are held at least as long as losers. A ratio above 1.5 signals problematic hope-trading behavior.
Average win vs loss ratio should be tracked alongside win rate for full picture.
Cost per trade combines commissions, fees, spread, and slippage. Compare total friction with average gross profit to confirm that the strategy remains viable after execution expenses.
Good exposure time depends on style: scalpers target 60-80%, intraday traders 15-35%, swing traders 10-20%. Pair it with P&L per exposure minute to measure edge density regardless of session length.
A good MAE threshold is the highest unrealized loss your winning trades typically reach — any trade exceeding that level has historically low recovery odds and should be cut.
MFE records the largest unrealised profit reached during a trade. Comparing realised profit with MFE reveals whether exits consistently capture or surrender available movement.
A good Risk Deviation Ratio is 0.9–1.1x, meaning your actual exit risk is within 10% of your planned stop loss. Ratios above 1.2x indicate chronic stop-widening; below 0.8x suggests panic exits.
A good realized-to-planned R:R ratio is 0.8 or above, meaning you capture at least 80% of your planned risk-reward on average. Below 0.6 signals serious execution issues.
Setup accuracy measures how often your identified setups play out as expected.
Slippage analysis compares expected prices with actual fills. Review it by instrument, order type, session, volatility, and size to find where execution drag is consuming the edge.
Time in market is the percentage of the measurement period with capital exposed. Evaluate it alongside return, drawdown, and profit per exposure hour rather than assuming more exposure is better.
Trade efficiency compares captured movement with the movement theoretically available between entry and exit extremes. Low efficiency can expose recurring timing or management problems.
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