Discussing the article: "MetaTrader 5 Machine Learning Blueprint (Part 14): Transaction Cost Modeling for Triple-Barrier Labels in MQL5"

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Check out the new article: MetaTrader 5 Machine Learning Blueprint (Part 14): Transaction Cost Modeling for Triple-Barrier Labels in MQL5.

The article replaces hardcoded cost assumptions in triple-barrier labeling with measured inputs. An MQL5 script captures spread distribution, swap rates, and symbol metadata from your broker, and a Python model converts them into a broker-calibrated min ret you can pass to get events. Labels then reflect the actual round-trip friction for your instrument and holding period.

Imagine building a bridge but omitting the structure's own weight from your load calculations: the design fails for a cost that was visible from the start. The same operational blindness affects many triple‑barrier labeling pipelines. Researchers commonly set min_ret to an arbitrary constant (0.5–1%) or to legacy spread/commission assumptions, then treat every historical price move above that threshold as genuine signal. The missing step is a disciplined, repeatable way to answer: what is the actual round‑trip transaction cost for this symbol, at this broker, given my strategy's typical holding period and entry hours?

Figure 1. 2-stage pipeline from broker data collection to labeling threshold

• Stage 1 (MQL5): TransactionCostCollector.mq5 runs on any chart, samples CopySpread() history, reads swap rates and symbol properties via SymbolInfoDouble(), and writes a structured CSV to the terminal's Files directory.
• Stage 2 (Python): load_cost_model() parses the CSV into a TransactionCostModel, which exposes min_ret_for_symbol() for the labeling pipeline and summary() for cost inspection before committing to a threshold.

This article gives that answer. It presents a reproducible two-stage pipeline: (1) a compact MQL5 script that samples the broker's spread history, swap rates, and symbol properties and writes a structured CSV; and (2) a Python TransactionCostModel that ingests the CSV, converts all components to a common fractional‑return unit, and exposes min ret for symbol() plus diagnostic routines. The output you should expect to obtain and use is explicit: a CSV with spread percentiles and hourly means, a model object that returns per‑trade cost breakdowns, a cost‑calibrated min ret ready for get events(), and the same parameters to pass into your per‑trade P&L calculation. Note: min ret here is a labeling threshold (for training data construction), not the execution profit‑taking target an EA will place in the terminal.

Author: Patrick Murimi Njoroge