Historical Performance
Bitcoin Monthly Returns Heatmap
Bitcoin's monthly closing-price returns since July 2010, organized in a year×month matrix. Darker colors indicate larger swings.
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Annual |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026 | -2-1.5% | -22-21.9% | -3-2.5% | +16+15.8% | -1-0.8% | -23-22.8% | +9+9.1% | +23+22.9% | +7+6.5% | -2-2.0% | — | — | -6.8% |
| 2025 | +9+9.3% | -6-6.1% | -14-14.4% | +14+13.6% | +16+16.1% | -0-0.5% | +10+10.3% | -9-9.4% | +4+3.7% | +2+1.9% | -21-21.0% | -3-2.7% | -6.4% |
| 2024 | -1-0.5% | +23+23.1% | +38+37.9% | -12-11.5% | +9+8.6% | -9-8.5% | +9+8.9% | -6-5.7% | +2+2.2% | +3+3.5% | +44+43.8% | -4-4.0% | +122.3% |
| 2023 | +41+41.2% | -1-0.9% | +19+18.8% | +5+4.5% | -4-4.0% | +9+8.5% | -4-4.0% | -11-10.9% | +1+0.6% | +32+31.5% | +9+8.5% | +13+12.8% | +151.0% |
| 2022 | -24-24.3% | -1-1.2% | +24+24.2% | -16-15.7% | -25-25.4% | -29-28.6% | +11+11.0% | -16-15.9% | -4-4.2% | +10+9.8% | -20-20.3% | +2+2.4% | -66.6% |
| 2021 | +31+31.2% | +35+34.7% | +21+20.6% | -10-9.8% | -32-31.9% | -8-8.1% | +7+7.2% | +43+43.4% | -12-12.3% | +43+43.4% | -11-10.5% | -8-8.1% | +91.1% |
| 2020 | +14+14.2% | +16+16.1% | -36-35.7% | +21+21.1% | +28+28.4% | -7-6.9% | +8+7.6% | +19+18.6% | -7-6.7% | +22+21.8% | +36+36.2% | +48+48.1% | +260.7% |
| 2019 | -4-3.9% | +6+6.3% | +9+8.8% | +26+25.8% | +68+67.9% | +26+26.3% | -13-13.1% | +6+6.0% | -20-19.7% | +13+13.4% | -20-20.4% | +0+0.1% | +98.4% |
| 2018 | -16-15.6% | -18-18.0% | -12-11.9% | +11+11.2% | -22-21.8% | -16-16.3% | +34+33.6% | -18-18.4% | -2-1.6% | -2-2.4% | -41-41.2% | -3-2.8% | -73.4% |
| 2017 | +2+2.2% | +29+29.1% | -18-18.2% | +40+39.6% | +64+63.8% | +18+17.5% | +6+5.8% | +60+60.0% | -17-16.7% | +68+67.7% | +52+51.9% | +49+48.6% | +1449.3% |
| 2016 | -13-13.1% | +18+17.8% | -2-1.7% | +8+8.2% | +14+13.8% | +20+20.3% | -1-0.7% | -8-8.4% | +4+4.5% | +16+16.2% | +5+4.6% | +23+22.8% | +111.7% |
| 2015 | -20-19.7% | -7-7.5% | +3+2.8% | -10-9.6% | +5+5.1% | +8+7.9% | +18+17.6% | -22-21.6% | +2+1.9% | +22+21.6% | +31+31.4% | +14+13.6% | +33.4% |
| 2014 | +18+18.1% | -31-31.3% | -24-23.6% | -7-7.0% | +33+32.7% | +5+5.0% | -1-1.3% | -19-19.1% | -21-21.3% | -7-6.6% | +7+7.3% | -16-16.0% | -57.5% |
| 2013 | +32+32.5% | +68+67.7% | +211+211.2% | +45+44.5% | -1-0.7% | -27-27.0% | -6-5.5% | +21+21.4% | +12+12.0% | +48+48.3% | +305+304.6% | -1-0.8% | +5439.2% |
| 2012 | +27+27.5% | -9-8.6% | -8-7.5% | +8+7.7% | +5+4.9% | +24+23.5% | +37+37.2% | +22+21.8% | +17+16.9% | -14-13.7% | +17+16.6% | +8+7.8% | +218.7% |
| 2011 | +85+84.6% | +85+85.4% | -8-7.9% | +99+98.8% | +417+417.2% | +95+95.1% | -19-18.8% | -32-32.1% | -41-41.2% | -39-38.6% | -24-24.2% | +70+70.2% | +1523.3% |
| 2010 | — | — | — | — | — | — | — | +20+20.0% | +0+0.0% | +217+216.7% | +42+42.1% | -4-3.7% | +420.0% |
Average Return by Month · Seasonality
We grouped the same calendar months together to compute the average, median, and win rate. Compare which months have historically been strong.
Average return and win rate by month
| Month | Avg | Median | Win rate | Best | Worst |
|---|---|---|---|---|---|
| Jan | +11.4% | +5.7% | 56% | +84.6% | -24.3% |
| Feb | +11.6% | +2.7% | 50% | +85.4% | -31.3% |
| Mar | +12.6% | -2.1% | 44% | +211.2% | -35.7% |
| Apr | +14.8% | +9.7% | 69% | +98.8% | -15.7% |
| May | +35.9% | +6.8% | 63% | +417.2% | -31.9% |
| Jun | +5.3% | +2.3% | 50% | +95.1% | -28.6% |
| Jul | +6.6% | +7.4% | 63% | +37.2% | -18.8% |
| Aug | +4.3% | -5.7% | 47% | +60.0% | -32.1% |
| Sep | -4.4% | +0.0% | 47% | +16.9% | -41.2% |
| Oct | +25.4% | +13.4% | 71% | +216.7% | -38.6% |
| Nov | +25.6% | +7.9% | 63% | +304.6% | -41.2% |
| Dec | +11.8% | +1.3% | 56% | +70.2% | -16.0% |
Key Patterns in the Heatmap
We computed recurring patterns directly from the data on this page. The figures below update whenever the heatmap data refreshes.
Extreme swings of the early market
The darkest green cell on the heatmap is from May 2011. In the early market, tiny liquidity produced monthly gains in the hundreds of percent, which pulls the overall average sharply upward. A market of today's size rarely repeats such extreme monthly gains.
When reading the average return, get in the habit of comparing the all-time average with the recent-years average separately. Plan expectations around recent levels, and avoid expecting double-digit monthly gains just because the early market once produced them.
The deepest drawdown month
The darkest red cell is from Sep 2011. Huge rallies have historically been followed by equally deep corrections, and heatmap stretches where green and red alternate sharply mark the bubble-and-bust eras.
Instead of buying heavily right after a big up month, many investors prefer scheduled or staged buying that spaces entries out. A lump-sum entry made just before a deep red month is far harder to sit through, and often ends in selling near the bottom.
Autumn strength: October–November
October and November combined average +25.5%, above the all-time average of +13.3%. Historically, post-halving demand expansion and year-end flows have clustered gains here, though some autumns closed lower — it is a tendency, not a rule.
Always read the average (+25.5%) together with the win rate (67%). A high average alongside a ~60% win rate means four autumns in ten closed lower. If you tilt exposure toward a season, spread it across several months and size it so a losing autumn stays tolerable.
Weak month: Sep
Sep tend to show red more often on the heatmap. What looks like a repeating annual pattern may simply be statistical noise from a small sample, so treat seasonality as a reference signal only.
Rather than timing buys and sells around a supposedly weak month, use it as a reference when scheduling rebalancing or recurring buys. A given month's color has flipped from era to era, so mechanical "sell before this month" rules rest on thin evidence.
Long-term decline in volatility
Average monthly swings were about 55% in the earliest years (2010, 2011, 2012, 2013) but around 11% in the most recent four (2023, 2024, 2025, 2026). The fading colors toward the bottom of the heatmap reflect this declining volatility as market size and liquidity grew — still tens of times larger than traditional assets.
The shrinking swing matters directly for position sizing. Sizing against the early era would overstate today's risk, while sizing only on recent years leaves no room for another violent stretch. Base position sizes and cash buffers on the recent years' range, while remembering Bitcoin still moves tens of times more than traditional assets.
Boom eras and quiet eras
Reading the heatmap row by row, halving aftermath years cluster with green while late-cycle and bear-market years mix pale and red tones. This shows Bitcoin's returns concentrate in specific phases of the cycle rather than accruing evenly, cross-checking the yearly returns and halving cycle figures above.
Because returns cluster in specific cycle phases, plan for cycle-phase returns rather than monthly income: spread the buy budget across phases instead of deploying it at once, and keep cash reserves for drawdown stretches. Cross-check your own plan phases against the yearly figures in the halving cycle section.
These patterns are observations computed from past data and do not predict the future. Seasonality and volatility patterns in particular can shift sharply given the small sample size. The usage examples on each card are learning illustrations of how to read the pattern, not recommendations of any specific strategy or investment advice.
Yearly Returns and the Halving Cycle
Bitcoin's price cycles have historically revolved around the halving, which cuts the mining reward in half roughly every four years. We compared performance in halving years versus the year after.
Historically, halving years and the following year tended to post strong gains, but there is no guarantee the cycle repeats. See the halving page for the mechanism and the next halving schedule, and the chart page for the long-term price trend.
How Investors Can Use This Data
Seasonality is a reference, not a rule
Monthly averages are computed from only a dozen or so samples. A historically strong month is not guaranteed to repeat, and macro or regulatory events easily override seasonality.
Viewing it through a DCA lens
With dollar-cost averaging, your accumulated average cost matters more than any single month's move. Deep red months can be opportunities to accumulate at lower prices.
A gauge for volatility
From +470% gains to -40% losses, Bitcoin's monthly swings dwarf traditional assets. The heatmap's color spread shows the scale of volatility you must be able to stomach.
Understand cycles over timing
Rather than timing individual months, use this to locate where we are within the halving cycle and long-term trend. Read monthly returns like the seasons of a cycle.
This page is for informational purposes only and is not investment advice. Past returns do not guarantee future results, and you are solely responsible for your investment decisions.
Frequently Asked Questions
›Which month does Bitcoin rise the most?
Since 2010, May has posted the highest average return at +35.9%. However, with only a dozen or so samples and wide year-to-year variation, no month is guaranteed to be positive.
›How are monthly returns calculated?
Each month's return is the change in closing price versus the previous month's close. For example, March's return compares the end-of-March close to the end-of-February close, and the annual figure compounds the twelve monthly returns.
›How does the halving relate to monthly returns?
The halving cuts new Bitcoin supply in half roughly every four years. Historically, halving years and the following year tended to see strong bull markets, but with only four past halvings, statistical confidence is limited.
›Can past monthly returns predict future gains?
No. Monthly returns are a historical record, not a predictive indicator. Treat seasonal patterns as reference material only, and make investment decisions based on your own judgment.
›Where does the data come from?
Data is based on the CoinGecko API and public historical price data, aggregated since trading began in July 2010. Figures for the most recent month may change until its closing price is finalized.
How to read · Data notes
- Each cell shows the monthly return based on closing price for that month.
- Green shades mean gains, red shades mean losses; darker colors indicate larger swings.
- 2010 is counted only from the start of trading in July, so some months are empty.
- The most recent month's closing price may not yet be finalized, so treat it as a reference only.
- Data source: CoinGecko API and public historical price data.