Normalizing Multi-Broker Trading Data for Cross-Platform Analysis
Summary
This article explains why CSV exports from different brokers can produce misleading combined analyses even when they load without errors. It identifies common divergences: pip precision, commission treatment, symbol aliases, server time zones, and account currencies. Its proposed solution is a canonical dataset backed by broker profiles that specify each environment’s conventions and mappings. The normalized output standardizes symbols, timestamps to UTC, profit and commission into a common currency, and slippage points, while retaining metrics that do not need conversion.
The article also describes adding broker metadata to exports so the processing pipeline can identify the source environment, then validating transformations such as currency conversion, pip scaling, and timestamp offsets. The evidence is procedural: it presents schemas, sample profiles, and a validation approach, rather than comparative trading results. Correct normalization still depends on accurate profiles, appropriate exchange rates, and handling seasonal server-time changes and variable commission schedules; errors in those assumptions can remain hidden in downstream analysis.
Key ideas
- Broker-specific precision can change reported point-based performance by a scaling factor.
- Commission models must be reconciled to compare net profit consistently.
- Symbol aliases should map to canonical instrument names before grouping results.
- Convert broker server timestamps to UTC before analyzing sessions or aligning trades.
- Broker profiles and validation checks make normalization rules explicit and maintainable.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.