Measure the process without overstating the result.

Build a reproducible trading backtest with explicit data, timing, fills and costs. Use a worked example to spot look-ahead bias and unrealistic order assumptions.

Learn how trading Monte Carlo simulations resample outcomes, model drawdowns and test assumptions. Compare shuffling, bootstrapping and stress scenarios.

Compare Sharpe and Sortino ratios with a worked return series. Check benchmarks, downside targets, annualization and what each statistic leaves out.

Calculate trading expectancy from average wins, losses and costs. See why a high win rate can lose money and how to review a sample without overclaiming.

Design a walk-forward trading test with fixed training and evaluation windows. Learn how to combine results, avoid leakage and document parameter changes.

Compare arithmetic mean returns with compounded geometric returns using transparent examples, consistent periods and cash-flow limitations.

Understand how repeated strategy searches select noise, with a transparent multiple-testing example and a practical experiment ledger for honest evaluation.

Calculate compound annual growth from beginning value, ending value and elapsed years. Check cash flows, short samples and path information CAGR omits.

Calculate covariance and correlation from paired returns, understand units, missing-data choices and nonlinear relationships, and avoid false hedge conclusions.

Calculate downside deviation with a transparent target and full-series denominator. Compare subset conventions, annualization and zero-result limitations.

Audit decision timestamps, data availability, revised values and same-bar execution with original examples that expose future-information leakage.

Calculate maximum adverse and favorable excursion with a defined trade window, price basis, quantity and costs. Includes long and short examples.

Design a chronological evaluation with frozen rules, data boundaries and cost assumptions. Understand walk-forward reuse, overlapping labels and failure cases.

Calculate realized R-multiples using a documented initial risk denominator, including costs, partial exits and examples where money and R averages differ.

Understand lagged return correlation with worked lag-one examples, consistent sampling and limitations for confidence intervals and annualized risk.

Understand return asymmetry and fourth-moment tail sensitivity, with an explicit population-moment example and ordinary-versus-excess kurtosis conventions.

Work through sample standard deviation of returns, distinguish percent units from decimal units, and understand annualization and tail-risk limitations.

Understand survivorship bias through an original account-cohort example and an auditable inclusion ledger, with limits on missing-history conclusions.

Compare identical observed win rates with different sample sizes, interpret Wilson intervals and understand dependence, selection and payoff limitations.

Find the two-outcome win rate needed for zero modeled expectancy using average gross win, average gross loss and per-trade cost, including impossible cases.

Compare an equity peak with current equity to calculate drawdown and the gain needed to recover. Handles zero equity and explains cash-flow limitations.

Calculate profit factor from realized winning and losing amounts, with transparent cost conventions, original ledger examples and zero-loss handling.

Calculate arithmetic expectancy from assumed win rate, average gross win, average gross loss and cost. Understand sample definitions and model limitations.