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SGOV iShares 0-3 Month Treasury Bond ETF

ETF

100.45 USD Close 11/4
Since 2024-12-05 +7.8%

Close 11/4

FinLab Studio

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AI finds several complementary traits of iShares 0-3 Month Treasury Bond ETF across different dimensions, combines them into a multifactor strategy, and backtests it against the market.

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Price and fund-flow snapshot

As of 2026-11-04

Risk / liquidity: Strong.

Momentum Neutral 60-day return is Good, but 240-day return is Average.
60-day return (%) 0.9 74th percentile · Good
240-day return (%) 3.6 43th percentile · Average
Momentum Trading Strategy: A 12-1 Momentum Backtest
Risk / liquidity Strong All 3 indicators are Top.
60-day annualized volatility (%) 0.2 0th percentile · Top
Distance from peak (%) 0.0 93th percentile · Top
20-day dollar volume 2,169,367,372.0 94th percentile · Top
Factor Investing Explained: 10 Classic Factors in US Stocks

iShares 0-3 Month Treasury Bond ETF (SGOV) today

1 quant signals active · As of 2026-11-04

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.

iShares 0-3 Month Treasury Bond ETF (SGOV) peers and tracking

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iShares 0-3 Month Treasury Bond ETF (SGOV) 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.

12 historical occurrences (since 2021-05-26)

Average forward return after 52-week adjusted-price high

20-day: Win 91.7%, Sample 12, Median +0.3%, Excess -1.8%, Beat mkt rate 33.3%

60-day: Win 100.0%, Sample 12, Median +1.0%, Excess -3.6%, Beat mkt rate 25.0%

120-day: Win 100.0%, Sample 12, Median +2.1%, Excess -4.2%, Beat mkt rate 16.7%

120 days after: historical avg +1.8%

20-day +0.3%
60-day +0.8%
120-day +1.8%
20-day
60-day
120-day
Win
91.7%
100.0%
100.0%
Sample
12
12
12
Median
+0.3%
+1.0%
+2.1%
Excess
-1.8%
-3.6%
-4.2%
Beat mkt rate
33.3%
25.0%
16.7%

Over 12 occurrences, 120-day forward avg gained 1.8%, lagged the market by 4.2 pts; win rate 100.0%.

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

Holding-period returns in its own history

Since 2020-05-28, 100.0% of 1,367 one-year holding periods ended with a gain; the median one-year return was +4.1%. Over three-year holding periods, 100.0% of 863 ended with a gain (median +13.0%).

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.
  • us_fund_price:adj_close records this ETF’s distributions regularly only from 2022-07-01; returns before that date mostly reflect price changes and splits, so they understate its total return.

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.