Direct answer
A worked example of performance statistics is a fully numeric walkthrough that shows how common performance metrics are calculated from a clearly defined set of trade outcomes. It states every assumption (for example, how many trades, what profit-and-loss figure is used, and whether costs are included). This makes it possible to independently verify the calculations and to understand what the metrics do—and do not—measure.
Mechanism or definition
Performance statistics are metrics that summarize trading results over a period. Typical examples include total return, average return per trade, win rate, and drawdown. To make a worked example meaningful, you must separate stable mechanics (the math and definitions) from variable conditions (market behavior, execution quality, and costs).
A worked example usually includes:
- A dataset: a list of trade outcomes (profit or loss per trade) and the timeframe.
- Clear definitions: what “profit,” “return,” and “drawdown” mean in that example.
- Assumptions: whether the figures are gross or net of costs, and whether returns are measured consistently (for example, in account currency or as percentages).
Example dataset (assumptions stated)
Assume an account starts at $10,000. Over 10 trades, each trade outcome is either +$250 or −$200, with 6 wins and 4 losses. For simplicity, assume these outcomes are net of trading costs (so no additional fees or spread effects are added later). Also assume no deposits or withdrawals occur during the 10 trades.
Calculations (worked)
- Total profit
- Wins: 6 × $250 = $1,500
- Losses: 4 × (−$200) = −$800
- Total profit = $1,500 − $800 = $700
- Final account value
- Final value = $10,000 + $700 = $10,700
- Total return (percentage)
- Total return = $700 / $10,000 = 0.07 = 7%
- Win rate
- Win rate = 6 / 10 = 60%
- Average profit per trade
- Average = $700 / 10 = $70
- Drawdown (one material limitation: ordering matters) To compute drawdown, the sequence of wins and losses matters because drawdown depends on peak-to-trough movement over time.
Assume the worst-case ordering for this same set of outcomes: the 4 losses occur first, then the 6 wins.
- After 4 losses: $10,000 − 4×$200 = $10,000 − $800 = $9,200
- Peak prior to drawdown = $10,000
- Maximum drawdown in dollars = $10,000 − $9,200 = $800
- Maximum drawdown in percentage = $800 / $10,000 = 8%
Notice: with the same win rate and total profit, a different ordering could produce a smaller or larger drawdown.
Limitations and risks
Performance statistics can be precise in calculation but uncertain in interpretation.
1) Costs and execution are often variable
Even if a worked example assumes net outcomes, real records may be affected by spreads, commissions, slippage, and differing execution quality. If the dataset uses gross results or mixes calculation methods, metrics like total return and drawdown can change.
2) The sample can be too small
In the example, 10 trades is a limited sample. Metrics such as win rate and average profit per trade can fluctuate substantially with new trades.
3) Market conditions change
Historical relationships do not guarantee future results. A strategy that performed under one set of market regimes can behave differently under another.
4) Drawdown depends on the path
As shown, drawdown requires a specific trade sequence assumption. Many performance reports summarize drawdown but do not always make the underlying ordering and data assumptions obvious.
Verification or next question
To verify a worked example, recreate the steps using the same inputs and definitions: confirm the trade list, confirm whether figures are gross or net of costs, compute totals and percentages from the starting value, and verify drawdown using the stated sequence. A helpful next question is: what data definitions were used to generate the performance statistics (net vs gross, and how drawdown was computed)?