Discussing the article: "From Novice to Expert: Weekend Gap Size Effect Research Using MQL5 and Python"
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Check out the new article: From Novice to Expert: Weekend Gap Size Effect Research Using MQL5 and Python.
The article provides a practical research setup for weekend gap analysis: MQL5 extracts precise pip‑based gaps and tracks fills, while Python performs statistical testing and visualization. You will compute fill rates by gap buckets, model fill probability with logistic regression, and assess time-to-fill via Kaplan–Meier curves. All steps are configurable and reproducible for EURUSD, GBPUSD, USDJPY and beyond.
Traders often rely on the intuitive rule that weekend gaps “fill” before price continues in the original direction. That intuition is useful as a hypothesis, but an algorithmic trader needs measurable answers: can gap‑fill be used as a reliable, parameterized signal for entry, risk management, or position sizing? This study reframes the question into an operational, reproducible testable specification.
Concretely, we define a gap fill as a return of intraday price to the Friday close (measured in pips), measured from the Monday open. To avoid ambiguous cases we exclude gaps where minute‑level data are missing ("NO_DATA"), parameterize gap size in pips, and bound the observation window (default: 168 hours). Our goals are explicit and actionable: (1) measure the fraction of weekend gaps that reach the Friday close and the distribution of time‑to‑fill; (2) test whether fill probability and fill speed vary with gap size; and (3) deliver a reproducible pipeline (MT5 Expert Advisor → CSV schema → Python analysis) so practitioners can reproduce, update, and extend the results for their instruments and parameter choices. The remainder of the article describes that pipeline, the statistical tests, and the outputs you can reuse directly.
Author: Clemence Benjamin