Review portfolio backtests as assumption-sensitive historical simulations, not forecasts or investment advice.
Examine how financial data, strategy rules, and simulation choices combine in a backtest. Learners distinguish a portfolio backtest from a single-strategy example, trace a Python-oriented implementation workflow, and scrutinize whether a test avoids look-ahead bias, survivorship bias, and unrealistic execution assumptions. The course treats attention-grabbing claims as claims to investigate, not established results. Its assignments use hypothetical data and stress transaction costs, time-aware validation, benchmark comparisons, risk measures, and the limits of extrapolating historical performance. This course is educational and is not investment advice.
Specify a backtest's data universe, timing, rebalance rules, benchmark, and transaction-cost assumptions. Identify look-ahead, survivorship, selection, and execution biases that can make simulated performance misleading. Communicate backtest uncertainty and limitations without presenting historical returns as a promise of future results.
Basic Python or spreadsheet skills, percentages, and familiarity with returns are recommended. The assignments can be completed using hypothetical examples without real trading. This course is for education only, not financial, legal, or investment advice; do not use a course exercise as a basis for a financial decision.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Intermediate · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.