The Stat Map Unicorn. The Stat Map Unicorn PDF is finally out!. I aimed to create something that serves as both an introduction and a complete guide for those diving into the OHLC Stat Map from @_joshuuu & @toodegrees team. Inside, you'll find the complete model I've been using

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How to use this:. 1) Wait for the first close outside Asian Range. 2) Align with probabilities (above → bullish bias, below → bearish bias). 3) Size risk based on average extensions. Not a signal, but a framework. Probabilities + risk management = edge.

Finally, extension stats. ▪️ Avg extension up: +0.73% (max +11.26%).▪️ Avg extension down: -0.86% (max -5.04%). This gives a framework for risk/reward once price breaks the range.

Looking at where the day actually closes vs the Asian Range:. ▪️ 62% of days that first closed above AR went on to finish above AR high. ▪️ 54% of days that first closed below AR finished below AR low. This helps gauge continuation after the breakout.

Breaking it down by weekdays . The tendencies hold pretty consistent across the board. ▪️ Around 67% of closes above AR finish green.▪️ Around 65% of closes below AR finish red. One nuance: Thursdays lean a bit more bearish historically.

When NQ first breaks and closes above the Asian Range . ▫️ 72% of the time the day closes green.▫️ 28% of the time it closes red.When it breaks and closes below . ▫️ 66% of the time the day closes red.▫️ 34% of the time it closes green. Probabilities give you the bias.

$NQ Asian Range Breakout Statistics using @edgeful . The Asian session range is one of the cleanest intraday references. I pulled a 1-year report on NQ using Edgeful, here’s what the stats say 🧵

1m entry off the Unicorn after price tapped the Stat Map, with the draw lining up to Smooth Edges at Asia highs.

$NQ – August 27th, 2025. Back from vacation, +5K across 5 FundedNext accounts. I’ll keep posting updates until payout. Haven’t shared a Stat Map setup in a while, process is still the same, boring, but effective. Execution chart below 🧵

Been off the grid for a few days, quick escape to Sardinia. Back tomorrow, ready to dive back in. Had to treat myself for surviving the first half of the year. Much love 🫶🏻

RT @FindYourProp: 🚨2 DAYS LEFT FOR OUR GIVEAWAY 🚨.

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How traders can apply seasonality:. 1.Context filter: avoid fighting historical weak months (e.g. September). 2.Bias builder: tilt bullish setups in historically strong months (e.g. November). 3.Risk mgmt: size down when probabilities don’t favor trend continuation. Seasonality.

Why might this happen?. • April weakness → earnings digestion + tax selling.• September weakness → low liquidity + portfolio rebalancing.• November strength → start of new fiscal year, fund inflows, “Santa rally” effect.

3-year seasonal averages (monthly returns):. ✅ Nov: +7.5%.✅ May: +4.9%.❌ Apr: −5.2%.❌ Sep: −5.1%. Probability of positive return in Nov >70%. Probability of red Apr/Sep >60%.

Looking across individual years:. •2023: +9.7% YTD with huge Oct (+10.5%) rally.•2022: brutal Apr (−13.6%) & Dec (−9.0%).•2024: steady recovery with +7.5% Nov & +6.0% Oct.•2025 (so far): weak summer (−7.1% Jul, −3.0% Jun). Patterns repeat more often than not.

From Dec ‘21 → Aug ‘25:. •Best months: Nov (+7.5%), May (+4.9%), Mar (+3.2%).•Worst months: Apr (−5.2%), Sep (−5.1%), Aug (−2.0%). That’s a statistical edge: probabilities of green/red vary strongly by month.

$NQ NQ Seasonality 🧵. How’s August performing for you? Did you know Aug. Seasonality Statistics?. Looking at the last 3–5 years of Nasdaq futures @edgeful data, certain months clearly outperform while others drag the index down.

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