Skip to main content

BOXX Alpha Architect 1-3 Month Box ETF

ETF

118.82 Close 10/30
Since 2024-12-02 +8.2%

Price and fund-flow snapshot

As of 2026-10-30

Momentum: Good, Risk / liquidity: Strong.

Momentum Good 60-day return is Good, but 240-day return is Average.
60-day return (%) 0.93 78th percentile Good
240-day return (%) 3.85 46th percentile Average
Momentum Trading Strategy: A 12-1 Momentum Backtest
Risk / liquidity Strong 60-day annualized volatility is Top; 20-day dollar volume is Good.
60-day annualized volatility (%) 0.37 3th percentile Top
Distance from peak (%) 0 96th percentile Top
20-day dollar volume 434,304,121 73th percentile Good
Factor Investing Explained: 10 Classic Factors in US Stocks

Alpha Architect 1-3 Month Box ETF (BOXX) today

1 quant signals active · As of 2026-10-30

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.

FinLab Studio

From Alpha Architect 1-3 Month Box ETF to every stock like it

AI finds several complementary traits of Alpha Architect 1-3 Month Box ETF across different dimensions, combines them into a multifactor strategy, and backtests it against the market.

Screen stocks like Alpha Architect 1-3 Month Box ETF

Alpha Architect 1-3 Month Box ETF (BOXX) peers and tracking

Watchlist

This page refreshes daily after the close. Add Alpha Architect 1-3 Month Box ETF to your watchlist to track signal changes on your next visit.

Alpha Architect 1-3 Month Box ETF (BOXX) quant signal backtests

Signals active now

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

52-week adjusted-price high

Active

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

16 historical occurrences (since 2023-12-28)

Average forward return after 52-week adjusted-price high

20-day: Win 100%, Sample 16, Median +0.33%, Excess -0.06%, Beat mkt rate 37.5%

60-day: Win 100%, Sample 16, Median +1.07%, Excess -2.47%, Beat mkt rate 18.8%

120-day: Win 100%, Sample 14, Median +2.13%, Excess -5.78%, Beat mkt rate 14.3%

120 days after: historical avg +2.16%

20-day +0.35%
60-day +1.07%
120-day +2.16%
20-day
60-day
120-day
Win
100%
100%
100%
Sample
16
16
14
Median
+0.33%
+1.07%
+2.13%
Excess
-0.06%
-2.47%
-5.78%
Beat mkt rate
37.5%
18.8%
14.3%

Over 14 occurrences, 120-day forward avg gained 2.16%, lagged the market by 5.78 pts; win rate 100%.

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

Holding-period returns in its own history

Since 2022-12-28, 100.0% of 712 one-year holding periods ended with a gain; the median one-year return was +4.9%. Over three-year holding periods, 100.0% of 208 ended with a gain (median +15.0%).

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.

20% below its peak

Pending

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

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.

Top-quintile 6-month momentum

Pending

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

Momentum Trading Strategy: A 12-1 Momentum Backtest

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.