Seasonality Analysis
See each instrument's month-by-month tendencies (seasonality). We aggregate monthly returns across several years so you can tell at a glance which months tend to rise, which tend to fall, and how often each month actually finished positive.
How to Read & Use This Tool
From what seasonality means, through reading the charts, to putting the patterns to work in your trading.
14 Key Terms to Know First
Four terms worth understanding before you use this tool.
1. Seasonality
The tendency for price moves to cluster in particular months or times of year. "Stocks are weak in September" and "crude tends to rise in spring" are classic examples. Averaging past monthly returns turns that impression into a number.
2. Monthly return
How much the price moved during a given month, measured between month-end closes. Every figure on this page is built from these monthly returns.
3. Average return
The mean of a given month across all years in the period — how strong or weak that month typically is. Be aware that a single extreme year can drag the average a long way.
4. Win rate
The share of years in which that month finished positive. A high average paired with a win rate near 50% suggests the average is being carried by one or two big years. Always read the average and the win rate together.
The same average can mean completely different things
Both months below average exactly +2.0%. Look inside them, though, and they mean very different things.
Each bar is one year's monthly return. Up (blue) = positive, down (red) = negative.
On average alone, A and B look identical — but B has no repeatability. Always check the win rate alongside it.
2What Can This Tool Do?
Identify strong and weak months
See in numbers which months of the year have tended to rise and which have tended to fall.
Check whether a pattern repeats
Reading down a column of the heat map tells you whether a month's tendency recurs year after year or was simply chance.
Compare across instruments
Indices, precious metals, energy, FX and crypto have very different seasonal shapes. Switch instruments to compare them.
3How to Read the 2 Charts
Average return by month (bar chart)
Each month's typical move, drawn up or down from the zero line. Blue pointing up means the month averaged a gain; red pointing down means it averaged a loss. Bar length is the size of that average. Below the chart we summarise the strongest month, the weakest month and the month with the highest win rate.
How to read: a longer bar means a bigger typical move — but length is not certainty.Year × month heat map
Every year's monthly returns as a grid of colour. Read across for one year's path through the calendar, and down for how the same month behaved year after year. For seasonality, always read down. The rightmost column is the annual return, compounded from that year's monthly returns.
How to read: a column of consistent colour is a repeatable seasonal pattern. Mixed colour means the month was driven by year-specific events.45-Step Usage Workflow
5Where Seasonality Comes From
Seasonal patterns have real causes behind them. Understanding the cause helps you judge whether the pattern is likely to persist.
Seasonal supply and demand
Natural gas has winter heating demand, gasoline has the summer driving season. Commodities whose actual consumption varies through the year develop the clearest seasonality.
Corporate and investor calendars
Quarterly earnings, institutional fiscal year-ends (March in Japan, December for most of the US) and year-end tax-loss selling all bias price behaviour.
Habits in money flow
New money arriving at the start of the year, thin summer trading, bonus-season buying — the customary rhythm of flows also contributes.
Beware patterns with no cause
A seasonal pattern you cannot explain may simply be what fell out of the data by chance (curve fitting). Whether the cause is explainable is an important test.
6Do's and Don'ts
7Frequently Asked Questions (FAQ)
How are monthly returns calculated?
Each month's closing price is compared with the previous month's close, and the percentage change is taken as the monthly return. The "Year" column compounds that year's twelve monthly returns into an annual figure.
How should I interpret a month with a win rate near 50%?
As a month with no directional bias — in other words, no detectable seasonality. Even if the average return is positive, a win rate near 50% suggests a few large years are doing the work.
Why do the past 5 years and past 10 years differ?
Because market conditions change over time. Monetary policy, the mix of participants and the supply-demand structure of a commodity all shift from year to year. A big difference between periods is a sign the pattern is not stable. Conversely, a month that shows the same tendency across both periods is comparatively reliable.
How reliable is seasonality?
Seasonality is a mild probabilistic tilt, not a guarantee. Even a month with a 70% win rate moved the other way in 3 years out of 10. Use it as supporting context for an entry, and always decide your stop loss in advance.
Are these figures real market data?
Yes — these are real market figures calculated from each instrument's month-end closing prices. The aggregation refreshes daily, though the underlying values only change when a calendar month closes.
8Glossary
- Seasonality
- The tendency for price moves to cluster in particular months or times of year. Also called a seasonal anomaly.
- Monthly return
- How much the price moved in a given month, measured between month-end closes.
- Average return
- The mean of a given month's returns across the period, showing how strong or weak that month typically is.
- Win rate
- The share of years in which that month finished positive. Read it together with the average.
- Anomaly
- A recurring bias in price behaviour that theory does not fully explain. Seasonality is one kind.
- Tax-loss selling
- Selling losing positions near year-end for tax purposes, which can affect year-end and new-year price behaviour.
- Curve fitting
- Tuning a rule so tightly to past data that it stops working on anything else.
- Sample size
- The number of years used in the aggregation. The fewer there are, the more chance distorts the result.
Method: We calculate each year's monthly returns from month-end closing prices, then aggregate the average and win rate for each month across the selected number of years. The "Year" column compounds that year's monthly returns into an annual return.
About the data: Figures are calculated from each instrument's month-end closing prices. The aggregation window is the most recent 10 completed years relative to that instrument's own latest available data. For instruments with a shorter listed history, the aggregation uses however much history is available.
The Titan FX Research Hub purpose is to provide solely informational and educational content to its users, and not investment, legal, financial, tax or any type of personalised advice. Seasonality is an aggregation of past data and neither guarantees nor implies future price behaviour. This tool does not recommend or solicit the purchase or sale of any financial product, and all trading decisions remain your own responsibility.