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Learning Module 04 · Week 4 · Individual mission

Build a Defensible Trading Decision Pipeline

Convert market observations into a governed decision without confusing a backtest with a tradable result.

About 8 minutes. There is no timer.Formative practice only. No assessment marks.1 Insight Brief on completionLO1LO3
Mission statusNot started
  1. 1BriefingCurrent
  2. 2Worked exampleNext
  3. 3Case boardNext
  4. 4CheckpointsNext
  5. 5ReflectionNext
  6. 6CompleteNext

Start here

Briefing

A strong backtest is not yet a trading decision. Data timing, rule definition, costs, execution, and governance all affect whether a result can survive outside the sample.

Audit each link of the conceptual pipeline and choose the smallest defensible next test. Also test whether a short-horizon order-book edge survives real latency.

Practice boundary

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Game economy

Build Evidence Momentum without risking Decision XP

+3Correct checkpoint
−1First incorrect check in a checkpoint
100 Decision XPAlways recoverable through retry

You begin with 3 Evidence Momentum. Any held points return when you solve that checkpoint. Evidence Momentum never falls below zero and never changes assessment marks. Complete the mission to open one transparent reward draw.

What you will practise

  1. Sequence a conceptual pipeline from source and cleaning to signal, rule, execution, and review.LO3
  2. Explain how efficiency, transaction costs, and market impact constrain a trading claim.LO1

6 terms for this mission

Market microstructure
How trading rules, participants, orders, and venues shape prices and execution.
Efficient Market Hypothesis
A family of hypotheses about how quickly available information is reflected in prices.
Slippage
The difference between an expected trade price and the price actually achieved.
Market impact
The price movement caused by attempting to trade.
Order-book imbalance
The relative weight of resting buy versus sell orders, sometimes read as a short-horizon signal of near-term price pressure.
Latency
The delay between a signal and the order reaching the market; very low latency is what high-frequency and algorithmic trading exploit.

Every organisation, person, figure, and dataset in this mission's scenario is invented. Real institutions, laws, and standards are named only as general context.

Sources and further reading

Every organisation, person, figure, and dataset in this mission's scenario is invented. Real institutions, laws, and standards named in this mission, including in the sources below, appear only as general context, and the sources support the concepts and methods, not the events in the scenario.

  1. Foucault, T., Pagano, M., & Röell, A. (2023). Market Liquidity: Theory, Evidence, and Policy (2nd ed.). Oxford University Press. doi:10.1093/oso/9780197542064.001.0001

    The market-microstructure treatment of limit order book markets, trade size, market depth, market impact, and algorithmic and high-frequency trading, which underpins the execution, depth and latency checks.

  2. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. doi:10.1111/j.1540-6261.1970.tb00518.x

    The foundational statement of the weak, semi-strong and strong forms of efficiency, and of the point that efficiency can only be tested jointly with an assumed model of expected returns.

  3. Bailey, D. H., Borwein, J. M., López de Prado, M., & Zhu, Q. J. (2014). Pseudo-mathematics and financial charlatanism: The effects of backtest overfitting on out-of-sample performance. Notices of the American Mathematical Society, 61(5), 458-471. doi:10.1090/noti1105

    Shows how repeated trials over the same data inflate reported backtest performance and why results deteriorate out of sample, which supports separating a backtest result from a tradable one.

  4. Walker, T., Davis, F., & Schwartz, T. (Eds.) (2022). Big Data in Finance: Opportunities and Challenges of Financial Digitalization (1st ed.). Palgrave Macmillan. doi:10.1007/978-3-031-12240-8

    Its financial-markets section covers alternative data, algorithmic trading strategy design and high-frequency trading in relation to market efficiency.