Bitcoin Monthly Seasonality — 10+ Years of BTC Data
Crypto Layer — Bitcoin Specific

Bitcoin Monthly Seasonality

Forget "Uptober" memes and "September is bearish" tweets. Below is the actual monthly seasonality for BTC, computed from our own historical dataset (yfinance-sourced, daily bars rolled to monthly). Every bar is a win rate + average change; every number comes from your AlphaTRADER database, not an influencer's screenshot.

"Crypto Twitter has more seasonality opinions than seasons. The data has one answer."

10
Years of data
Oct
Strongest month
Aug
Weakest month
4y
Halving cycle

BTC Monthly Seasonality — Live

Bars are color-coded by win rate (green ≥70%, gray neutral, red ≤30%). Hover any month for raw stats. Data refreshed daily from the seasonality engine.

Monthly Seasonality (Bitcoin)

12 months analyzed
Win Rate > 70%
Win Rate < 30%
Neutral
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Best Month
Oct (80%)
Worst Month
Aug (30%)
Current Month
Jun (50%)
Based on 10 years of historical monthly data

See the same widget on the BTC asset details page with extended day-of-week and weekly views.

Read This Before You Trade These Stats

BTC seasonality has a sample-size problem. Compared to equities:

SPX / DAX

  • 50+ years of clean data
  • 5+ full economic cycles
  • Single regime: financialized capitalism

BTC

  • ~12 years of liquid data (post-2014)
  • 3 halving cycles, partial 4th
  • Multiple regimes: retail-only → derivatives → ETF era

Practical rule: If a month shows ≥70% win rate with only 8 samples, that's 8 coin flips landing heads 6 times — interesting but not yet evidence. Demand 10+ samples before treating any month as a "high-edge" prior.

The Halving Cycle Confounder

Monthly seasonality blends very different macro regimes. Knowing which one you're in changes the read.

Post-halving Year (Y+0, Y+1)

  • Historical bull-market years (2013, 2017, 2021)
  • Q4 strength dominant, retail FOMO mid-year
  • Monthly seasonality SKEWED bullish — averaging across cycles overstates baseline

Mid-cycle / Bear (Y+2, Y+3)

  • Historical drawdown years (2014, 2018, 2022)
  • Long summer slumps, autumn capitulations
  • Monthly seasonality CONTRADICTS bull-cycle averages

How to use it: Before acting on a "bullish October" stat, ask which halving year you're in. If it's Y+2 of a bear cycle, the average is misleading — the bull years are dragging it up. Treat seasonality as a cycle-conditional prior, not a calendar oracle.

Stacking Seasonality with Wyckoff Setups

Seasonality alone is a base rate — Wyckoff provides the entry. Combine them as a sizing filter.

Setup Seasonal Context Size Reason
Spring (long) Bullish month Full size Structure + base rate aligned. Highest-conviction.
Spring (long) Neutral month ~80% size Structure carries it. Seasonality neither helps nor hurts.
Spring (long) Bearish month Half size Counter-seasonal. Take it (structure still valid) but cap downside.
UTAD (short) Bearish month Full size Distribution + bearish prior. Cleanest short setup.
UTAD (short) Bullish month Half size or skip Shorting into seasonal tailwind. Lower historical edge.

BTC vs SPX Seasonal Decoupling

When BTC and SPX seasonality point opposite directions in the same month, something specific is going on. Treat divergence as a regime signal.

Aligned

When: BTC and SPX both bullish or both bearish in the same month.

Read: Macro risk appetite is dominant. BTC is acting like a leveraged risk asset. Standard correlations hold.

Decoupled

When: BTC seasonally strong while SPX seasonally weak (or vice versa).

Read: Crypto-native flow dominant: ETF rotation, halving narrative, regulatory event. Don't apply standard risk-on/off logic.

Practical signal: If you're seeing strong BTC seasonality in a month where SPX is historically flat/weak, lean into the BTC-specific setup. The seasonal stat is being driven by something other than macro — usually crypto-native flows, which are stickier than equity flows.

BTC Seasonality — One-Page Cheatsheet

  1. Open the chart. Note current month's win rate and avg change. Anything ≥70% green or ≤30% red is meaningful — gray is noise.
  2. Check sample count. <8 years = ignore. 8–10 = suggestive. ≥10 = treat as base rate.
  3. Identify halving phase. Y+0/Y+1 post-halving = bull regime. Y+2/Y+3 = mid-cycle/bear. Adjust seasonal read accordingly.
  4. Match Wyckoff phase. Bullish month + Phase C Spring = full size. Bearish month + Phase C Spring = half size. Counter-seasonal UTAD short = skip.
  5. Compare BTC vs SPX. If divergent → crypto-native flows in play. Trust BTC stat. If aligned → macro risk regime.
  6. Never let seasonality override structure. Wyckoff entry first; seasonality is a size/conviction filter, not a trigger.

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