📅 Seasonality in the Tool

The Seasonality Tab in sTraderZ.com is accessible from two locations. The primary entry point is the Cycle Modal in the Tradelog — there the tab has the most context because it belongs directly to an active cycle. The second entry point is the Trade-Check / Symbol View, which works symbol-centrically and without cycle context.

Entry Point 1: Tradelog → Cycle Modal

1. Open Tradelog — via the main navigation "Tradelog" or the "Underlyings View" subpage.
2. Click the 🔭 Cycle Icon — click the telescope icon in the trade row or the underlyings card. The Cycle Modal opens.
3. Select the "📅 Seasonality" tab — use the tab bar at the top of the modal. The tab is only visible if the underlying has sufficient price data (at least 5 years in the candles table).

Tab not visible? The underlying has no or too little price data. Solution: Admin → Update Price Data → search for the market and load historical data.

Entry Point 2: Trade-Check / Symbol View

Accessible via the ticker search or Symbol View. This entry shows seasonality without cycle context — purely symbol-centric. Suitable for quickly checking an underlying before opening a trade.

Quick-Start in 3 Steps

1. Tradelog → Underlyings View — select the underlying you want to analyse.
2. 🔭 Open Cycle Modal → "📅 Seasonality" tab — the tab loads the historical price curve and automatically calculates the seasonal patterns.
3. Check parameters — by default 20 years and Detrend mode are loaded. For getting started this is the recommended setting (details in section 19.5).

The seasonal curve shows the cumulative average within-year return — that is, how an instrument has developed on average across the months of a year, measured from 1 January (or the first trading day of the year) to year-end. Day 1 corresponds to the start of the year; the X-axis spans ~252 trading days. The Y-axis shows the average return relative to the instrument's own year-start level.

Detrending: Why It Matters

Without detrending the curve would always end with a slight positive slope — because stock markets rise over the long term. But that tells you nothing about the intra-year entry point. The Detrend mode removes the long-term uptrend. What remains is the pure seasonal pattern: which months have historically been weaker than the linear trend, and which stronger?

An example: without detrending the S&P 500 looks almost always positive (long-term bias). With detrending you can see that September–October has historically tended to be below the annual trend — that is the real seasonality information.

Main Line vs. Year Overlay

The curve shows two lines:

• Main Line (Historical Average): The average path across all selected years (default: 20 years, detrended). This is the reference line.
• Bright Line (Current Year): The year-to-date path of the current year. If it is above the main line, the instrument is performing better than the historical pattern so far. If it is below, there is either catch-up potential — or the current year is breaking the pattern (e.g. during crises).

Common reading mistake: interpreting the slope of the curve at a given point as a buy signal. The curve shows historical probabilities — not a deterministic pattern. A steep rise from November to April means: in the past this period has often produced positive returns. Whether that holds this year depends on trend, sentiment and fundamentals.

Below the seasonal curve sTraderZ.com shows a 4 × 12 heatmap: four rows for different statistical metrics, twelve columns for the months January through December. The colour coding makes it immediately visible which months have historically been particularly strong or weak.

Colour Coding

Green signals positive or high values (depending on the row: positive average return, high win rate, especially strong best year). Red signals negative or low values. The intensity of the colour scales with the magnitude of the value compared to all twelve months.

Reading Win Rate Correctly

A win rate of 65 % in November means: in 65 out of 100 November months (of the selected time series) the return was positive. This is a probability statement, not a trade command. In the remaining 35 % of cases November was negative — often when macroeconomic shocks, geopolitics or interest-rate changes overrode the seasonal pattern.

Outlier Years and Data Depth

March 2020 (Corona crash, −12 % in one month) or October 2008 (Lehman aftermath) appear as worst-year entries and distort the average return figure considerably. For a robust interpretation at least 10, preferably 20 years are recommended. The year slider in the tool (section 19.5) allows adjustment. With more years the sample size grows — the pattern becomes statistically more robust.

In addition to the seasonal curve and the heatmap, sTraderZ.com automatically calculates the strongest and weakest period of the year — displayed as coloured badges with a band overlay on the curve.

How Badges Are Calculated: Sliding Window

The tool applies a sliding-window approach: it calculates for every possible contiguous time window of the selected width (default: 8 weeks) the historically cumulated average return. The window with the highest average return is highlighted as "Strongest Period" (green badge). The window with the lowest average return receives the "Weakest Period" badge (red badge).

Band Overlay on the Curve

The strongest and weakest periods are drawn as coloured bands directly onto the seasonal curve:

• Green Band: Time span of the historically strongest period. The market has historically closed positively above-average during this window.
• Red Band: Time span of the historically weakest period. This is where the most declines or lowest returns have historically occurred.

Interpretation in Practical Context

If you are currently in the middle of the green band you have seasonal tailwind: the statistics point to a historically strong phase. At the start of the red band there is seasonal headwind: historically this period has been weak — a reason for increased caution when building positions, not an automatic short signal.

Important: The badges are probability markers, not trade recommendations. A green band does not mean the market must rise — it means that in comparable historical phases it has risen above average more often than not.

Adjusting Window Width

In the "Window Width" control area you can change the width of the sliding-window calculation: 4 weeks for very specific, short-term patterns (more noise, less robust), 8 weeks as the default value (good compromise), and 12 weeks for seasonal quarterly patterns (more robust, but less precise in timing). For most analyses the 8-week setting is the best starting point.

Two parameters decisively control the appearance of the seasonal curve: the year slider and the detrend toggle. Setting them correctly determines whether you see a robust long-term pattern or a short-term regime picture.

Year Slider

Detrend On / Off

Detrend mode is enabled by default and is the right setting for most use cases:

• Detrend on (default): The long-term uptrend is removed. Only the pure seasonal intra-year pattern remains visible. This allows you to see whether an entry point is seasonally favoured or disadvantaged — independent of the general market bias.
• Detrend off: The curve shows the raw historical average return including the trend. Useful when you want to see what absolute excess returns were historically achieved in certain periods — e.g. for long-term allocation decisions.

Practical recommendation: Always start with 20 years + detrend on. Switch to 5 or 10 years only when you deliberately want to analyse a more recent regime (e.g. post-2020 market structure for tech ETFs).

The Window Statistics Panel shows aggregated metrics for a selected time period — either automatically for the strongest or weakest period (click on badge) or for a manually chosen window.

What the Panel Shows

• Avg Return: Average return of all historical instances of this window across the selected years.
• Win Rate: Share of window instances with a positive return, expressed as a percentage and as a ratio (e.g. 15/20 positive).
• Number of Data Points: How many historical windows were included in the calculation. The more, the more reliable the statement.

Example: Panel shows "Nov–Apr, 20 years: Avg +6.2 %, Win Rate 75 % (15/20 positive)". This means: in 15 of the last 20 November–April periods the cumulated return was positive, averaging +6.2 %. This is strong statistical evidence for seasonal tailwind in this time window.

Combination with Badges

Clicking the "Strongest Period" badge automatically updates the panel and shows the metrics for exactly that period. The same applies to the "Weakest Period" badge. You can see at a glance how strong or weak the statistical evidence for a period actually is — not just visually via the band overlay, but as concrete numbers.

Tip: A win rate above 65 % combined with an average return above 2 % and at least 15 data points is considered a statistically robust seasonal pattern. Below 10 data points the figures should be treated as orientation, not evidence.

The following three workflows show how to embed the Seasonality Tab in sTraderZ.com into real analysis processes. Seasonality is always a probability filter, never a standalone trade signal.

Workflow 1: Checking the Entry Timing

1. Enter symbol — Tradelog → Underlyings View → 🔭 Cycle Modal → open "📅 Seasonality" tab.
2. 20 years + detrend on — default parameters for robust analysis (section 19.5).
3. Check window statistics: Win Rate > 60 % AND avg return > 1 %? → Seasonality supports the entry.
4. Combine with trend: Only trade when chart technicals (primary uptrend) also confirm the seasonal signal.

Workflow 2: Sector Rotation

1. Open sector ETF — e.g. XLE (Energy), XLK (Technology), XLV (Healthcare), XLF (Financials).
2. Identify strongest period — the green badge and band overlay show the historically preferred entry window.
3. Cross-check with theory from 5.7 — Module 5.7 explains the underlying calendar anomalies (Halloween Effect, commodity seasonality, January Effect). Agreement between the empirical pattern and a theoretical reason increases confidence.
4. Compare multiple sectors — which sector is currently in its historically strongest window? This supports the decision to overweight it in the portfolio mix.

Workflow 3: Seasonality + Sentiment Confluence

The strongest statistical combination arises when seasonality and sentiment point in the same direction simultaneously:

• Seasonality strongly positive (Win Rate > 65 %, avg return > 2 %) AND
• Fear & Greed < 30 (contrarian buy signal, market excessively pessimistic) →
• Particularly attractive setup: seasonal tailwind meets exaggerated pessimism readings. Historically many of the best entry points arise exactly in this constellation (e.g. October–November 2022, October 2023).

Avoiding Common Mistakes

• Using only 5 years of data: Too few data points — individual outlier years dominate the pattern. Always start with 20 years.
• Treating seasonality as a buy signal in a primary bear market: In an established downtrend, seasonal strength periods are often negated or significantly diminished. Trend beats seasonality.
• Looking only at badges without the overall curve: The badges show the strongest window — but if the overall curve shows no clear pattern (flat, random), the window has little informational value either. Always evaluate the overall curve and the window statistics together.
• Using seasonality as the sole reason to trade: A single factor is not enough. Seasonality is the final confirmation filter — not the primary trigger.