Mechanism and definition: what “seasonality” means for currency pairs
Currency pair seasonality means that a currency pair’s performance or behavior shows a repeating tendency across the calendar (for example, certain months, weeks, weekdays, or holiday periods) when you analyze historical data over many years.
To assess it, you must first decide what “behavior” is. Common measurable targets include:
- Price change (difference in closing price between two dates)
- Returns (percentage change over a defined interval)
- Volatility (variation of returns in a period)
- Range statistics (high–low behavior within a time window)
This choice matters because seasonality can appear in returns but not in levels, or in volatility but not in average returns.
Direct answer: the data inputs you need
At minimum, you need data that supports a repeatable calendar-aligned calculation of your chosen behavior metric.
1) Price or spread-compatible time series
You typically need one of the following, consistently recorded:
- Bid/ask quotes (to compute transaction-relevant mid prices and spreads)
- Mid price or close price series (to compute price changes/returns)
For seasonality, the key requirement is time alignment: every observation must map to a consistent timezone and trading-day or calendar convention.
2) A clearly defined sampling rule
Choose the interval and rule that converts raw data into the metric you will test. Examples of sampling rules include:
- Daily closes (one value per day)
- End-of-week returns (e.g., from week-end to week-end)
- Intraday windows aggregated to a fixed period (e.g., average within an hour)
State the exact start/end times used for each interval. Without a fixed rule, “seasonality” can be an artifact of inconsistent cutoffs.
3) A calendar mapping
Because seasonality is about the calendar, you need a calendar framework to label each observation by:
- Month, week-of-year, day-of-week
- Holiday flags (if you plan to test holiday effects)
- Trading session conventions (if your dataset includes only certain market hours)
This mapping lets you group results (for example, average returns by month) in a way that can be independently reproduced.
4) Data provenance and timeliness
Document where the time series came from and what was done to it:
- Source/provider (data vendor, broker feed, exchange source if applicable)
- Data processing steps (cleaning, interpolation, missing-value handling)
- Coverage period length (how many years)
- Version or update date (when the dataset was extracted)
If the provider’s methodology changes, the seasonality profile can change even when the “market” did not.
5) Quality checks and metadata required for reliable grouping
You should be able to verify the following before trusting any calendar pattern:
- Missing data handling: what happened on days with gaps?
- Timezone and daylight saving time: were timestamps normalized?
- Outliers: were bad ticks/quotes removed, and how?
- Corporate-action adjustments: less common for FX, but still check for unusual data artifacts
- Consistency: do the same conventions hold across the full lookback window?
6) Costs and execution assumptions (if you use tradable metrics)
Seasonality claims can fail when costs are ignored. To assess “behavior” in a way that can be checked against realistic outcomes, you need explicit assumptions about:
- Transaction costs model (even if simplified)
- Whether you use mid prices or bid/ask-derived measures
- Slippage assumptions (if you include any execution-aware metric)
If you cannot explain the cost/execution model, you cannot separate “calendar effect” from “market microstructure effect.”
Evidence or example: how the data is used in practice (with assumptions)
A typical workflow uses the chosen metric and groups observations by calendar label.
For example, suppose you decide to measure daily returns. You would:
- Compute daily returns from the price series using a consistent formula (define whether it’s simple or log return).
- Assign each day to a calendar category (e.g., month-of-year = 1 to 12).
- For each category, compute a summary statistic (mean return, median, or volatility).
- Compare categories using an explicit criterion (for instance, whether differences persist across multiple years).
Assumptions you must state for reproducibility include: the daily close definition, timezone normalization, and how missing days are treated. Also decide whether you exclude holidays or keep them in the same category—changing this decision can alter conclusions.
Limitations and risks: failure modes you must account for
At least one material limitation should guide how you interpret results.