Trading Mastery Score

Trading Mastery is a 0–100 summary of five complementary dimensions. Its purpose is to prevent one impressive statistic from representing the whole trading process.

The score is available with Pro on Performance. It is a structured summary of the selected history, not a prediction, certification, or promise of future results.

The five dimensions

The inputs deliberately span edge, path risk, month-to-month downside, repeatability, and execution of loss limits. Win rate, profit factor, total profit, and recovery factor remain useful supporting metrics, but they are not independently added to Mastery because they would duplicate parts of the same outcome or reward longer histories.

Edge

Edge uses average realized R per observed trade:

R expectancy = sum(observed Net R) / observed R trade count

It is mapped linearly between these anchors:

Values between anchors are interpolated. Values outside the scale are clamped. Using R reduces the influence of account size and position size, but the result still depends on the quality and consistency of recorded initial risk.

Resilience

Resilience normalizes Calmar, which compares annualized compounded percentage return with maximum percentage drawdown:

Calmar needs a valid percentage-return path, elapsed time, enough trades, and a drawdown denominator. For Mastery only, a positive-growth history with sufficient evidence and genuinely zero drawdown receives a favorable score rather than failing solely because standalone Calmar would divide by zero.

Downside Performance

Downside Performance applies the same 0, 0.5, 1, 2, 3, 5 score anchors to Sortino. Sortino compares mean complete-month percentage return with downside deviation below the fixed 0% target.

For Mastery only, a positive mean with sufficient evidence and no downside months receives a favorable score even though standalone Sortino has a zero downside denominator. This prevents an otherwise strong history from becoming unscorable merely because no negative complete month exists, while retaining all evidence requirements.

Reliability

Reliability uses the 0–100 Monthly Return Reliability result directly. It is not normalized a second time.

Reliability evaluates complete active months, calculates a dependable monthly R floor, adjusts each month's target for its opportunity count, and scores downside shortfall relative to normal trade-level variation. Exceptional upside does not compensate for a different month missing its target.

Risk Control

Risk Control uses Average Excess Loss measured in Pip R. For each observed losing trade:

excess loss = max(0, abs(realized Pip R) - 1R)
Average Excess Loss = sum(excess loss) / observed losing trade count

Losses contained within the initial stop contribute 0R. Breaches contribute only the amount beyond -1R. Lower raw values are therefore better:

No observed losing trades can use the favorable 0R case only when the wider evidence requirements are satisfied. Otherwise, absence of losses in a very small sample would be mistaken for proven risk control.

Pip R isolates the price movement against the recorded initial stop. It is intentionally not Net R, which can include costs and other effects outside the stop-containment question.

How the total is combined

Mastery does not use a normal weighted arithmetic average. An arithmetic mean could allow excellent scores to fully offset a failed dimension. Instead, each score is regularized and combined geometrically:

x[i] = 0.01 + 0.99 * score[i] / 100

g = product(x[i] ^ weight[i])

Mastery total = 100 * (g - 0.01) / 0.99

The small 0.01 floor avoids a literal zero-product collapse while retaining a strong penalty for a failed dimension.

Why this matters

Suppose a trader has very strong Edge, Resilience, Downside Performance, and Reliability but extremely poor Risk Control. An arithmetic total could still look excellent. The geometric structure keeps the total meaningfully lower, reflecting that repeated uncontrolled losses can undermine an otherwise strong history.

The dimensions are not intended to be interchangeable. Improve the weakest component by investigating its underlying statistic rather than optimizing the headline score directly.

Evidence and availability

The total requires all five dimensions plus shared evidence:

• At least 30 selected trades (50 recommended)
• At least 80% R coverage (90% recommended)
• At least 80% percentage-return coverage (90% recommended)
• Every component's observation-window and data requirements

A component can be available, limited, or unavailable. If any component is unavailable, the total is unavailable. If a component is limited, the total is provisional even when a numerical score can be calculated.

This is intentional. A score without sufficient history can appear precise while being highly sensitive to one trade or month.

Interpreting Mastery responsibly

• Read the total as a balanced summary, not a grade of the trader.
• Compare like-for-like filters and meaningful date ranges.
• Open the weakest dimension and inspect its raw statistic and evidence.
• Treat limited scores as provisional.
• Do not assume a high historical score predicts future market conditions.
• Avoid changing trade records merely to improve the score. Improve the underlying process and let later evidence update it.

Use Performance for edge and break-even context, Risk for Calmar, Sortino, and loss containment, and Monthly Return Reliability for the full Reliability calculation.