How to Use Seasonality to Improve Trading Outcomes

By Marcel Hambálek · Senior Trader, For Traders

Seasonality trading is the practice of taking positions based on price tendencies that repeat at the same point in the calendar — a month, a week, a weekday or an hour — because the underlying flows repeat too: harvest cycles, heating demand, tax deadlines, index rebalancing and fund month-end flows. It is a probability tilt, not a signal.

Key takeaways

• Seasonality only deserves capital when you can name the physical or institutional flow behind it — heating demand, harvest, tax-year deadlines, month-end rebalancing — otherwise you are trading a coincidence.
• Work three layers separately: annual (gold's February–March and September strength, natural gas winter demand), monthly/weekly (roll weeks, third-Friday expiry, month-end flows) and intraday (London and New York opens, day-of-week tendencies).
• Futures are the cleanest place to study seasonality because the contract months are the seasons — but back-adjusted continuous charts distort seasonal returns whenever the curve sits in contango or backwardation.
• 'Sell in May' still shows a November–April edge over the full data window, but the gap has narrowed decade by decade, and the January Effect has been arbitraged into inconsistency.
• A seasonal tendency needs roughly 20+ independent observations, a stated data window, and a hit rate that survives removal of the two biggest outlier years before it is tradeable.
• Inside a funded account, size a seasonal trade at a fraction of your normal risk and define an invalidation level in price — never in dates — so a failed pattern costs you a partial R, not your daily loss limit.

Watch: related video

What Seasonality Trading Actually Is — And What Causes a Real Pattern

Seasonality trading means positioning around price tendencies that repeat at a fixed point in the calendar because the flow behind them repeats too. That's the whole thing in 55 words: it's a probability tilt built on recurring physical, institutional or behavioural flows — not a signal, not a guarantee, and not the same as a trend you happen to notice every few months.

That last distinction matters. Cyclicality is driven by economic conditions that don't respect a calendar — a credit cycle can stretch three years or seven. Trend-following reacts to price momentum whenever it shows up, calendar be damned. Seasonality is narrower: it's tied to a specific week, month or session, year after year, because something structural forces it there.

The 55-word definition

Seasonality in trading, explained plainly: it's the tendency for an asset to move in a similar direction or with similar volatility during the same calendar window, repeatedly, because a real-world flow — supply, capital, information or liquidity — hits the market at that same window every time. No flow, no pattern worth trading.

Four legitimate causes of a repeating pattern

• Physical supply and demand: harvest timing in grains, heating demand pushing natural gas into winter, driving season lifting gasoline crack spreads — these are seasonal trading strategies with a real commodity behind them.
• Institutional calendar flows: tax-year deadlines, fiscal year-end, month-end rebalancing and index reconstitution force funds to buy or sell on the same dates regardless of opinion — index rebalancing alone can move billions in a single session.
• Scheduled information: the USDA WASDE report, FOMC meetings and the NFP release calendar create volatility clusters at fixed points every month — this is what causes seasonality around specific dates rather than vague "months."
• Behavioural and liquidity effects: thin holiday desks, reduced staffing around Christmas and August vacation periods, and the classic pre-NFP chop — liquidity itself becomes the pattern.

Coincidence, correlation and the calendar trap

Here's the honest counterpoint you need before you risk a cent on any of this: twelve months, five weekdays, twenty-four hours in a day — run enough backtests across enough windows and the data will hand you a "pattern" whether one exists or not. Data mining doesn't care about your P&L; it just finds correlations.

The test that separates a real seasonal edge from a curve-fit ghost is simple: can you name the flow? "Gold tends to firm into early autumn because of Indian wedding-season demand and index rebalancing into gold-linked ETFs" is a claim you can check. "This pair goes up on the third Tuesday of odd months" is a coincidence wearing a trading strategy's clothes. If you can't point to the harvest, the fund flow, the data release or the desk that's thin, you don't have seasonality — you have a spreadsheet that got lucky.

The Three Layers of Seasonality: Annual, Weekly and Intraday

Seasonality isn't one thing — it's three separate layers stacked on top of each other, each driven by different flows and each requiring its own dataset to test. Most guides treat "gold is strong into September" and "avoid entries during the Asian session" as unrelated topics. They're not. They're different resolutions of the same underlying idea: repeating flows create repeating price tendencies, and the layers compound when you line them up correctly.

Layer 1 — annual: months and contract seasons

This is the layer everyone knows: gold's autumn strength tied to Indian wedding-season demand, natural gas building length ahead of winter, equity indices catching a "Santa Claus" drift into year-end. The flows here are physical and structural — harvest cycles, heating degree days, index rebalancing dates, fiscal year-end positioning. You're working with maybe 10-20 years of clean data per instrument, so a monthly edge needs to survive across multiple macro regimes before you trust it. This is also where the quarterly futures roll matters most — CME contracts like crude oil or the E-mini S&P roll on fixed schedules, and the roll itself creates basis distortions that can masquerade as seasonal drift if you're not adjusting your data for it.

Layer 2 — monthly and weekly: roll, expiry, month-end

Zoom in and you find flows tied to the calendar rather than the season: month-end rebalancing as pension funds and index trackers true up allocations, options expiry pinning price near max-pain strikes, and the same quarterly futures roll from Layer 1 now viewed as a week-by-week event rather than an annual one. Day-of-week seasonality trading lives here too — plenty of FX pairs show measurably thinner liquidity and wider spreads on Mondays as Asia reopens after the weekend gap, and Fridays often see position-squaring into the close ahead of the weekend. Sample size is much larger — 52 weeks a year instead of 12 months — so this layer is easier to validate statistically, but the edges are also smaller per trade.

Layer 3 — intraday: session opens and day-of-week

The finest resolution. Time of day seasonality in forex is dominated by session overlap — the London-New York window sees the deepest liquidity and the sharpest initial moves off news, while the Asian session grinds in tighter ranges outside of yen-specific catalysts. On indices, the first 30 minutes after the New York open and the last 30 before the close routinely account for a disproportionate share of the day's volume. This layer gives you thousands of observations a year, which is exactly why it's tempting to over-fit — five-minute patterns need out-of-sample testing more than any other layer.

The real edge is in the stacking — but stacking narrows your entry window, it doesn't multiply your conviction. A bullish annual month tells you the wind is at your back; the right week tells you when the fund flow actually lands; the right two-hour session tells you when spread and slippage won't eat the edge before it shows up. Skip a layer and you're either sizing a low-probability entry too big, or missing a high-probability one because you showed up at the wrong hour.

Gold Seasonality by Month: What the XAUUSD Calendar Shows

Gold's calendar tendency is real but modest: over a 20-year window (2006–2026), XAUUSD has posted positive average returns in eight of twelve months, with the strongest hit rates clustering in January–February and August–September — but the edge is a few tenths of a percent against an average monthly ATR that's often ten times larger. Gold is the highest-volume instrument on the For Traders platform, more traded than any FX pair or index CFD, so this calendar matters to more of you than any other seasonality table in this guide.

Average monthly ATR on gold has run roughly 4–6% of price over the same window — meaning even the best seasonal month (August, +1.5% average) is still a fraction of what a single normal month of chop can produce in either direction. That's the honest read on gold seasonality by month: it's a tilt, not a trade plan.

The strong stretch: January–February and August–September

These four months carry the highest positive-month hit rates in the XAUUSD seasonality data — 60-65% versus a coin-flip baseline. January-February lines up with early-year portfolio rebalancing into hard assets and pre-Chinese-New-Year restocking. August-September overlaps with the run-up to Diwali buying and the start of the Indian wedding season, both of which pull physical demand forward on the calendar.

The soft stretch: March and the summer drift

March is the weakest month in the table — a 45% hit rate and a slightly negative average return, likely tied to post-restocking digestion after the Lunar New Year buying window closes. June and July show a similar summer drift: lower volume, fewer macro catalysts, and gold often chops sideways inside a tighter range until the autumn demand window reopens.

Why physical demand — Chinese New Year, Diwali, wedding season — anchors the pattern

Gold's seasonality is less about fund flows and more about physical buying calendars. Chinese New Year gold demand front-runs the holiday by four to six weeks, which is why January often outperforms before the actual festival. Diwali gold demand and the Indian wedding season concentrate jewellery buying into September and October, feeding the second strong stretch. Central bank purchases and ETF flows sit on top of this physical base and can amplify or dampen it year to year, but they don't follow a fixed calendar the way jewellery demand does.

The caveat matters more here than anywhere else in this guide: gold's seasonality is consistently weaker than its macro sensitivity. A real-yield shock, a surprise CPI print, or an FOMC statement can overwrite a month's seasonal bias inside a single session. Use gold monthly returns to time entries and set expectations for drift — never as a reason to hold a position through a scheduled macro event.

Trading Seasonalities in the Futures Markets: Contract Months, Roll and the Curve

Trading seasonalities in the futures markets is cleaner than doing it in spot or CFDs because the contract itself encodes the calendar — you're not guessing when a seasonal flow starts and ends, the expiry date tells you. CME Group futures price a specific delivery month, so open interest shows you exactly where the crowd is positioned for that season, and the forward curve prices the expectation instead of hiding it inside a smoothed index.

Contract months are the seasons: reading the CME calendar

Every futures contract lists its own seasonal story in its listed months. Natural gas trades Jan/Feb/Mar heating-demand months separately from the Apr/Oct shoulder months. Corn and soybeans list harvest-month contracts (Dec corn, Nov soybeans) distinct from old-crop months. When you study "does gasoline rally into summer driving season," you should be looking at the RBOB contract months that actually deliver into that window — not a blended annual average. The contract month is the seasonal hypothesis; read the calendar before you read the chart.

Roll weeks and third-Friday expiry — Mar/Jun/Sep/Dec 2026 dates

Equity index futures (ES, NQ) roll quarterly, and the front month expires the third Friday of March, June, September and December — in 2026 that's 20 March, 19 June, 18 September and 18 December. The June, September and December dates also carry triple witching, where index futures, index options and stock options all expire together, spiking volume and range in the final hour of the session. The week before expiry (roll week) behaves differently from the week after: liquidity concentrates in the outgoing contract, then volume snaps to the new front month almost overnight. If your seasonal study mixes roll-week price action with normal weeks, you're averaging two different market regimes into one number.

Why back-adjusted continuous charts lie about seasonal returns

Here's the trap: a back-adjusted continuous contract splices every expiring month into one chart by shifting historical prices to remove the roll gap. That's fine for trend-following backtests, but it's poison for seasonality work. If a market has sat in persistent contango — each new contract priced above the one it replaces — the back-adjustment subtracts that gap from all of history, manufacturing a seasonal "loss" that never existed on a spot chart. A market in backwardation does the opposite, inflating phantom seasonal gains.

Study seasonality on individual contract months or a ratio-adjusted series instead, and always check whether the curve was in contango or backwardation during the years your pattern "worked" — that context often explains more of the return than the calendar does.

Commodity Seasonal Cycles: Natural Gas, Crude and the Grain Calendar

The strongest seasonality you'll trade lives in physical commodities — corn, crude, natural gas — because the flow is literally the weather and the calendar. Commodity seasonal cycles exist because storage fills and drains on a fixed annual rhythm, refineries shut for maintenance on a fixed annual rhythm, and crops get planted and harvested on a fixed annual rhythm. That's a real, physical reason for a pattern to repeat — not a curve-fit backtest artifact.

Henry Hub natural gas: injection season, withdrawal season and the shoulder months

Natural gas seasonality trading runs on one number: what's in underground storage. From roughly April through October, US utilities inject gas into storage ahead of winter heating demand — the "injection season." From November through March, they withdraw it — the "withdrawal season." Henry Hub natural gas prices tend to build a risk premium into autumn as the market prices weather uncertainty for the coming winter, then often bleed lower into the shoulder months of March/April and October/November, when heating and cooling demand both sit near zero and the curve resets. The weekly EIA storage report (released Thursdays) is the scheduled catalyst that either confirms or wrecks the seasonal thesis in real time — a build or draw that misses consensus by a wide margin moves the front month hard regardless of what month it is.

WTI crude: refinery maintenance, driving season and the summer premium

WTI crude oil seasonality has three legs. Spring (roughly February–April) brings refinery maintenance ("turnaround season"), when refiners take units offline, crude demand from refiners dips, and product inventories can get tight ahead of summer. Then comes the driving-season demand build into Memorial Day and July 4th, historically supportive for crack spreads and, by extension, crude itself. Layer on the RBOB gasoline spec change to summer-grade blends (which structurally tightens gasoline supply) and hurricane season (June–November), which puts a recurring risk premium into Gulf Coast production and refining capacity. Any one of these — a mild hurricane season, a demand miss — can flatten the "summer premium" traders expect.

Corn and soybeans: planting, pollination weather and harvest pressure

The corn soybean harvest cycle is a planting-to-harvest calendar trade. Prices often carry a weather-risk premium through the US planting window (April–May) and especially through July, when corn pollination is most sensitive to heat and moisture stress — this is the single biggest weather-driven volatility window in the grain complex. Come harvest (September–November), the seasonal tendency typically reverses: cash-strapped farmers selling into elevators creates recurring "harvest pressure" lows. USDA WASDE reports, released monthly, are the scheduled-information layer that resets yield and stocks expectations and frequently overrides whatever the calendar "should" be doing that week.

Weather-driven seasonality has the widest dispersion of any pattern layer in trading. Get the calendar right but the winter wrong — a polar vortex that never shows, a hurricane season that stays quiet — and you're stopped out on a pattern that was statistically sound and meteorologically wrong. Size these trades knowing that.

The Equity Index Calendar: Sell in May, January Effect and the Santa Claus Rally

Yes, equity seasonality is real but shrinking: the November–April stretch has historically beaten May–October on the S&P 500 by a wide margin through data windows ending 2025, but the spread has been cut by more than half since the pattern went mainstream in the 1990s. That decay is the whole story — and it's why blind faith in any calendar rule is a losing plan.

Sell in May and go away: the statistics and the decade-by-decade decay

The "Sell in May and go away" adage — also called the Halloween strategy because you re-enter around October 31 — splits the year into two six-month halves. Commonly cited backtests on the S&P 500 running back to the 1950s show November–April average returns roughly double the May–October half, historically the gap sat near 5-6 percentage points a season. The mechanism was never mysterious: lighter summer volume, fund managers de-risking into year-end reviews, and a disproportionate share of historical drawdowns clustering in August-September (1987, 1998, 2001, 2008, 2011).

The problem is the edge has been arbitraged as the strategy got famous. Once every retail newsletter and half of financial television repeats it every April, the flows that created it partially front-run it. The table below shows the pattern any honest seasonality trader needs to internalize: the spread compresses decade over decade.

US100 Nasdaq 100 seasonality shows the same decay curve but with fatter tails in both directions — tech's higher beta means the summer half has produced some of the sharpest single-year outperformances (2020) and worst drawdowns (2022) on record, washing out the average edge even further.

The January Effect — and why it stopped being reliable

The January Effect describes small-cap stocks outperforming large-caps in the first weeks of the new year. The mechanism is genuine: investors sell losing small-caps in December for tax-loss harvesting, then rebuy in January, plus fresh new-year allocations tilt toward higher-beta names. It's one of the best-documented calendar anomalies in academic finance literature. But like Sell in May, publicity killed the edge — institutional money now front-runs the rebuy into mid-to-late December, so much of the "January" pop now happens before January even starts. Trading the calendar date instead of the flow is the classic mistake here.

Santa Claus Rally dates for 2026-27 and how to trade the window

The Santa Claus Rally is a specific seven-trading-day window — the last five sessions of the year plus the first two of the new year — a definition coined by Yale Hirsch of the Stock Trader's Almanac. For the 2026-27 rollover, that window runs December 24, 2026 through January 5, 2027. Historically this seven-day stretch has posted positive S&P 500 returns in roughly three out of every four years going back to the 1950s — a real hit rate, but on a small sample of trading days, not a full month.

Execution note: liquidity in this window is thin — desks are short-staffed, volume dries up, and gap risk between sessions is elevated. The honest play is reduced size, not your normal position sizing, with wider stops to account for the gaps rather than tighter ones to compensate for lower conviction.

Intraday and Day-of-Week Seasonality: Where the Reaction Windows Live

The first two hours after the London open and the first hour after the New York cash open carry the bulk of the day's range on gold and US indices — this is the highest-sample-size seasonality layer there is, and the least written about because it doesn't sell a newsletter as well as "sell in May." Trade the reaction windows, fade the mid-session drift.

Session opens: London, New York and the overlap

London open (08:00 London time) and the London–New York overlap (13:00–16:00 London / 08:00–11:00 New York) are where the intraday ATR distribution front-loads. On XAUUSD and US100, the two-hour window after London open plus the first hour of New York cash session regularly account for 45-55% of the day's total range. The overlap is when both desks are staffed, both liquidity pools are open, and the day's directional leg usually gets set. Miss that window and you're often trading the leftovers — chop that looks tradeable on a 5-minute chart but burns stops on a 1-hour one.

The mid-session lull — roughly 11:00 to 13:00 New York, after the overlap closes but before the afternoon repositioning — is where a disproportionate share of stop-outs happen. Spreads widen slightly, volume thins, and price oscillates inside the range already carved out earlier. If your system doesn't have a specific edge for this window, it's the one to sit out, not force.

Day-of-week tendencies and the Monday-range trap

Day-of-week seasonality in trading is real but shallow, and it's the easiest layer to over-mine on a spreadsheet. What consistently shows up across major FX pairs, gold and US indices:

• Monday tends to open with a narrower range than the rest of the week — weekend gap aside — and has a modest historical tendency to set the week's high or low early before reverting. Treat this as a mild tilt, not a rule; the sample of "Mondays" in any five-year backtest is barely 250 days.
• Wednesday carries a structural distortion eight times a year around FOMC — the Federal Reserve's rate decision days blow out ATR well past the normal weekday average, and any "Wednesday tendency" you measure needs FOMC weeks stripped out separately or your average is meaningless.
• Friday, especially first-Friday NFP sessions, shows elevated early volatility followed by position-squaring into the New York close as funds flatten before the weekend.

None of these replace a setup. They're context for sizing and timing — the difference between forcing a trade into a known low-liquidity window and picking one where the flow is actually behind you.

Daylight saving shifts and why your 'best hour' moves twice a year

Here's the detail almost nobody flags: the US and Europe change their clocks on different dates each March and each November. For two to four weeks each transition, the London-New York overlap that you backtested at "8am-11am New York" is actually shifted by an hour on your charts, because London already moved and New York hasn't (or vice versa). A session rule built on one clock regime will misfire — entering an hour early or late — until both regions have shifted. Rebuild your session times off UTC, not local clock labels, or you'll blame the strategy for what's really a calendar mismatch.

Liquidity Droughts: Holidays, School Holidays and the August Grind

The direct answer: from mid-July through late August, and again over Christmas/New Year, average daily range compresses roughly 20-35% versus the September-June baseline, spreads on majors and indices widen 2-4x, and a single unfilled gap can account for most of the day's total range. Trade thin tape with normal-session position sizing and you're not trading seasonality anymore — you're donating slippage.

The quantified summer drop: range, spread and slippage

August liquidity is a different market, not a quieter version of the same one. Desks empty out, algo market-making thins its quoted depth per level, and the order book that absorbed a 50-lot market order in June now moves three ticks on the same size. What actually changes:

• ATR compression — daily ATR on XAUUSD and US indices typically runs 20-35% below the yearly average through late July into the last week of August.
• Wider spreads — holiday trading spreads on FX majors can run 2-4x their London/NY overlap norm; on less liquid crosses, more.
• Thinner depth per level — the same order size that moved price 0.5 pip in June can move it 2-3 pips in a thin August print.
• Slippage on stops — resting stop orders in a thin book get filled through, not at, the level; slippage in thin markets is asymmetric — it works against you far more than for you.
• Gap-driven range — a disproportionate share of the day's total range often comes from one overnight or pre-open gap rather than intraday trend.

School holidays, semester ends and late December desk staffing

Summer trading liquidity drop isn't just a US phenomenon tied to vacation calendars — school holidays market liquidity effects layer across Europe (July-August) and staggered US breaks, thinning retail and institutional flow at slightly different weeks depending on region. Late December compounds it: year-end book-squaring, skeleton desk staffing between Christmas and New Year, and half-day sessions around the 24th and 31st mean you're often trading against algos and a handful of humans, not a full two-sided market.

Execution rules for thin tape — and the contrarian angle

The contrarian layer matters more than the mechanics. Well-known seasonal trades — the "sell in May," the pre-holiday drift, the year-end Santa rally — get crowded precisely because everyone reads the same pattern. When that consensus position sits on top of a thin, low-ATR book, the squeeze against it travels much further than the seasonal edge itself ever justified. That's the mechanism behind the worst drawdowns in seasonal strategies: not a broken pattern, but a crowded trade meeting an empty order book. Size for the tape you're actually trading, not the one you remember from October.

Is Your Pattern Real? A Statistical Checklist Before You Risk Anything

A seasonal pattern is real if it survives eight specific checks: sample size, shifted-window retest, outlier removal, hit rate plus magnitude, drawdown history, a price-based invalidation level, and a full forward-test cycle before you scale it. Skip any one of these and you're trading a story, not an edge.

1. Name the flow. Before you touch a chart, write one sentence explaining the mechanism — harvest supply, index rebalancing, fund month-end buying, heating demand. If you can't name the flow, you're pattern-matching noise, not trading seasonality.
2. Count independent observations. This is where most seasonality backtest sample size claims fall apart. A "20-year seasonal pattern" isn't 5,000 candles of five-minute data — it's 20 data points, one per year, because the years aren't independent draws from a random distribution in the way intraday bars are. Twenty coin flips isn't a lot of evidence for anything. Treat any monthly or weekly seasonal claim under 15 years with real suspicion.
3. State the window, then shift it. If your edge only exists on 2006–2026, rerun it on 2001–2021 and 2011–2023. A pattern anchored to one specific window and falling apart on a shifted one is curve-fit, not seasonal.
4. Remove the two biggest outlier years and retest. If your "reliable" September gold rally is actually one 2011 spike and one 2020 spike carrying the whole average, you don't have a pattern — you have two macro events wearing a calendar costume.
5. Report hit rate and average magnitude. A 70% hit rate with tiny average wins and one brutal 15% loss year has a worse expectancy than a 55% hit rate with tight, consistent moves. Direction alone tells you nothing about hit rate statistical significance — you need both numbers side by side.
6. Check the loss distribution and worst historical drawdown. Not the average losing trade — the single worst one. If the pattern's max historical drawdown would have breached your daily loss limit or max DD, the pattern is statistically fine and operationally useless to you.
7. Define invalidation by price, not date. "This trade is wrong if December doesn't rally" is not a stop. "This trade is wrong if price closes below the prior swing low" is. Seasonality tells you when to look; price action tells you when to leave.
8. Forward-test on simulated capital for a full cycle. One full seasonal cycle, minimum — a full calendar year for monthly patterns — before you scale size. Log every entry in a trading journal against the thesis you wrote in step one, and only increase risk once live behavior matches the backtest.

Look-ahead, survivorship and data-mining bias

Three specific traps wreck seasonal studies. Look-ahead bias creeps into index seasonality studies because indices reconstitute — the NASDAQ-100 of 2010 held different names in different weights than it does today, so a "September NASDAQ pattern" tested on today's constituent list is quietly using information that didn't exist at the time. Survivorship bias hits single-stock and old futures-contract studies the same way — delisted names and expired contracts get dropped from the dataset, leaving only the winners visible. And data-mining bias is the sneakiest: test 12 months × 5 weekdays × 24 hours and you've run roughly 1,440 comparisons. Statistically, some of those will look significant purely by chance. If you didn't have a mechanism hypothesis before you ran the scan, treat the "discovery" as decoration, not an edge.

Walk-forward and forward-testing a seasonal edge

Walk-forward testing means the pattern gets validated on data it never saw during discovery — split your history, find the pattern on the first half, confirm it independently on the second, then forward-test live on simulated capital before real risk. This is standard practice in serious backtesting workflows, and it's the difference between an edge and a story you told yourself in a spreadsheet.

Seasonality Trading: Honest Pros and Cons

Pros

• Gives you a defensible reason to look at a market on a specific date instead of screen-watching everything at once
• The physical commodity cycles — natural gas storage, harvest, refinery maintenance — have real, repeating drivers you can verify against public data
• Works as a filter that stacks with technical structure, improving entry timing rather than replacing your process
• Futures markets let you study the pattern on the exact contract month that carries the seasonal flow
• The calendar helps you plan evaluation phases around thin-liquidity periods and roll weeks

Cons / risks

• Annual patterns generate only one observation per year, so statistically meaningful samples take decades to accumulate
• Well-known effects like the January Effect and Sell in May have decayed as they became public
• A single outlier year can manufacture a pattern that never existed
• Macro shocks, FOMC decisions and geopolitical events overwrite any seasonal bias within a session
• Thin holiday tape means the seasonal window is often the worst execution environment of the year
• It is easy to turn a probability tilt into a conviction trade and oversize it into a drawdown breach

Frequently Asked Questions