Skip to main content

GBTC Grayscale Bitcoin Trust ETF

ETF

63.54

Close 10/9

Loading candles…

Price and fund-flow snapshot

As of 2026-10-09

Risk / liquidity: Weak.

Momentum Neutral 60-day return is Top, but 240-day return is Lagging.
60-day return (%) 27.64 96th percentile Top
240-day return (%) -26.7 8th percentile Lagging
Risk / liquidity Weak 60-day annualized volatility is Average; 20-day dollar volume is Lagging.
60-day annualized volatility (%) 37.42 79th percentile Average
Distance from peak (%) -35.45 19th percentile Lagging
20-day dollar volume 109,174,953 12th percentile Lagging

Grayscale Bitcoin Trust ETF (GBTC) today

1 quant signals active · As of 2026-10-09

Win rate and excess are historical post-trigger stats vs the market. Click a signal for its full definition and samples; history does not predict future results.

Grayscale Bitcoin Trust ETF (GBTC) peers and tracking

Watchlist

This page refreshes daily after the close. Add Grayscale Bitcoin Trust ETF to your watchlist to track signal changes on your next visit.

Grayscale Bitcoin Trust ETF (GBTC) quant signal backtests

Signals active now

Each active condition is tested against this ETF’s own history and benchmarked versus the S&P 500.

20% below its peak

Active · now -35.45%

Adjusted close first falls 20% or more below its highest level so far; repeated hits within 20 days count once.

19 historical occurrences (since 2016-01-15)
Average forward return after 20% below its peak。20-day: Win 36.8%, Sample 19, Median -3.04%, Excess -2.57%, Beat mkt rate 36.8%；60-day: Win 63.2%, Sample 19, Median +2.87%, Excess +20.53%, Beat mkt rate 42.1%；120-day: Win 63.2%, Sample 19, Median +10.68%, Excess +65.26%, Beat mkt rate 52.6%

120 days after: historical avg +73.73%

20-day -1.26%
60-day +24.58%
120-day +73.73%
20-day
60-day
120-day
Win
36.8%
63.2%
63.2%
Sample
19
19
19
Median
-3.04%
+2.87%
+10.68%
Excess
-2.57%
+20.53%
+65.26%
Beat mkt rate
36.8%
42.1%
52.6%

Over 19 occurrences, 120-day forward avg gained 73.73%, beat the market by 65.26 pts; win rate 63.2%.

More history: Holding-period returns in its own history, Signals not active now

Holding-period returns in its own history

Since 2016-01-04, 67.3% of 2,456 one-year holding periods ended with a gain; the median one-year return was +56.9%. Over three-year holding periods, 94.5% of 1,952 ended with a gain (median +175.7%).

A holding period starts on every trading day, so periods overlap; they describe the historical distribution rather than independent outcomes.

Signals not active now

Inactive signals still show this ETF’s historical forward-return distribution for context.

52-week adjusted-price high

Pending

Adjusted close first reaches a 252-trading-day high; repeated hits within 20 days count once.

16 times historically; 60-day forward avg +29.97%, win rate 75%, beating the market by +24.43 pts on average.

Back above the 200-day average

Pending

Adjusted close crosses from below to above its 200-day moving average; repeated crosses within 20 days count once.

16 times historically; 60-day forward avg +10.48%, win rate 60%, beating the market by +6.77 pts on average.

Top-quintile 6-month momentum

Pending

The ETF ranks in the top 20% of liquid US-listed ETFs by 126-day adjusted return.

15 times historically; 60-day forward avg +3.57%, win rate 61.5%, beating the market by -1.1 pts on average.

Continue researching Grayscale Bitcoin Trust ETF with the evidence above.

Continue in Studio ↗

Method and limits

  • Returns use us_fund_price:adj_close (finlab data.set_market('us_fund')), adjusted for splits and for distributions where the data includes them.
  • Excess returns use world_index:^GSPC, the S&P 500 price index without dividends, as the benchmark.
  • Percentiles and the 6-month momentum signal compare against the most liquid US-listed ETFs by 20-day dollar volume; mutual funds are excluded.
  • Category labels come from the fund data where it provides one (bond, income, crypto) and from the fund name for leveraged, inverse and commodity ETFs.
  • Overlapping event windows are descriptive distributions, not independent samples.

This page is for historical data analysis and education only. It is not investment advice. Backtests and historical return distributions do not predict future performance; evaluate risk independently before trading.