TLT

iShares 20+ Year Treasury Bond ETF Price

TLT
$86,56
-$0,12(-%0,13)

*Data last updated: 2026-04-07 19:46 (UTC+8)

As of 2026-04-07 19:46, iShares 20+ Year Treasury Bond ETF (TLT) is priced at $86,56, with a total market cap of $41,96B, a P/E ratio of 0,00, and a dividend yield of %0,00. Today, the stock price fluctuated between $85,91 and $86,74. The current price is %0,75 above the day's low and %0,20 below the day's high, with a trading volume of 3,95M. Over the past 52 weeks, TLT has traded between $83,29 to $92,18, and the current price is -%6,09 away from the 52-week high.

TLT Key Stats

Yesterday's Close$86,65
Market Cap$41,96B
Volume3,95M
P/E Ratio0,00
Dividend Yield (TTM)%0,00
Dividend Amount$0,34
Net Income (FY)$0,00
Revenue (FY)$0,00
Revenue Estimate$0,00
Shares Outstanding484,34M
Beta (1Y)2.38
Ex-Dividend Date2026-04-01
Dividend Payment Date2026-04-07

About TLT

The iShares 20+ Year Treasury Bond ETF seeks to track the investment results of an index composed of U.S. Treasury bonds with remaining maturities greater than twenty years.
SectorFinancial Services
IndustryAsset Management - Bonds
HeadquartersSan Francisco,DE,US

iShares 20+ Year Treasury Bond ETF (TLT) FAQ

What's the stock price of iShares 20+ Year Treasury Bond ETF (TLT) today?

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iShares 20+ Year Treasury Bond ETF (TLT) is currently trading at $86,56, with a 24h change of -%0,13. The 52-week trading range is $83,29–$92,18.

What are the 52-week high and low prices for iShares 20+ Year Treasury Bond ETF (TLT)?

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What is the price-to-earnings (P/E) ratio of iShares 20+ Year Treasury Bond ETF (TLT)? What does it indicate?

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What is the market cap of iShares 20+ Year Treasury Bond ETF (TLT)?

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What is the most recent quarterly earnings per share (EPS) for iShares 20+ Year Treasury Bond ETF (TLT)?

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Should you buy or sell iShares 20+ Year Treasury Bond ETF (TLT) now?

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What factors can affect the stock price of iShares 20+ Year Treasury Bond ETF (TLT)?

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Hot Posts About iShares 20+ Year Treasury Bond ETF (TLT)

ChenDong'sTransactionNotes

ChenDong'sTransactionNotes

04-05 04:29
Is the "Head and Shoulders" pattern in gold emerging? Three signals point to the same target Technical formations, actual yields, support and resistance levels converge, making this rare triple alignment. If gold completes the right shoulder formation, the mid-range of $6,000 may no longer be just a dream... On Tuesday, gold prices broke through the $4,400 per ounce consolidation zone and moved up to around $4,700 (plus or minus $100). This area overlaps with the support/resistance level formed by 1.65 times the 3-year moving average of gold prices. After this consolidation near the zone ends, the next upward move is expected to retest the $5,000 level, which is related to the 3.00 multiple of the medium-term cycle level for gold. If this trend materializes, it will set the stage for a head and shoulders bottom pattern in gold. To confirm this pattern, we will observe whether gold consolidates sideways during the formation of the right shoulder and ultimately breaks above to reach a new all-time high. The projected upward target for this pattern is precisely in the mid-range of over $6,000, consistent with our independent analysis from last week. That analysis showed that the 10-year real yield dropping close to zero would support gold rising into the mid-$6,000 range. Additionally, two pairs of support/resistance levels that repeatedly appear are expected to converge in the same zone later in 2026. In other words, three independent analyses all point to the mid-$6,000 range as the upward target for gold. Gold options intrinsic value curve Currently, gold is trading near $4,700 per ounce, which closely aligns with the "maximum pain" price of the May 2026 gold options contracts. This means that before options-related pressure potentially hits gold prices, there is still ample room for upward movement in the short term. For example, even if gold rises to $5,000, the ΔIV (intrinsic value) of the May 2026 gold options contracts would only increase to about $400 million, which remains relatively low compared to recent historical levels. Factors driving gold As shown in Chart 8, since the establishment of the medium-term cycle low (ICL) in gold on March 23, the market has especially begun to price in higher future inflation expectations. This trend is expected to continue supporting gold prices. Another common driver of gold is the price/yield of the 10-year U.S. Treasury. Although recent contributions of this factor to gold price increases have been modest, it no longer exerts downward pressure. On March 27, the 10-year U.S. Treasury price formed a local low at its support/resistance level of 3.05 times, then rebounded to the top of that zone. Since the outbreak of war, the 10-year Treasury yield has risen from 3.97% to the latest 4.31%. Despite the yield increase, the circulation of the iShares 7-10 Year Treasury Bond ETF (IEF), a substitute for bonds, has continued to rise uninterrupted since the start of the year. This inflow may be driven by factors such as: first, expectations that yields will fall as the economy slows; second, funds moving out of declining stocks to traditional safe-haven assets in anticipation of an upcoming recession. In contrast, the circulation of the iShares 20+ Year Treasury Bond ETF (TLT), a substitute for 30-year Treasuries, has been declining since reaching a local high in November 2024. This highlights that different segments of the yield curve are attracting different levels of demand. Market participants are clearly favoring the 10-year segment, as longer-term bonds may be viewed as offering better risk-adjusted returns amid uncertainty and inflation risks that are not high enough to offset the potential yields. Silver Similar to gold, silver prices have also returned to near their "maximum pain" level, currently around $74. Over the next few weeks, silver is expected to gradually rebound to just over $80. The ΔIV of the 2.6x gold/silver futures contracts supporting this support/resistance level is pushed up to about $120 million, which remains quite low. In other words, even if this target is reached, it is unlikely to generate significant options-related pressure on the price.
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K-LinePoet

K-LinePoet

04-04 15:04
IT Home reported on February 28 that MIT News published a blog post on February 26, saying that the Massachusetts Institute of Technology (MIT), together with NVIDIA and other organizations, has released the “Tail Taming (TLT)” technology, which can significantly improve the training efficiency of reasoning large language models (LLMs).   Citing details from the blog post, IT Home notes that reasoning large models are good at solving complex problems by breaking them down into steps, but during reinforcement learning (RL) training, the consumption of computing power and energy is extremely high.   The research team found that the “rollout” stage, in which multiple candidate answers are generated, accounts for as much as 85% of the training time. Because different processors generate responses of varying lengths, the processors that finish sooner can only be forced into idle waiting, while they wait for other processors to complete long-text tasks—creating a serious efficiency bottleneck.   To address this pain point, MIT researchers, together with NVIDIA, the Swiss Federal Institute of Technology, and other institutions, proposed an adaptive solution called “Tail Taming (TLT).”   The core of the approach is to innovate by applying “speculative decoding” technology—training a smaller “draft model” (drafter) to quickly predict the large model’s future outputs, and then having the large model batch-validate these guesses. In this way, the large model no longer needs to generate outputs one by one in sequence, greatly speeding up processing.   In conventional speculative decoding, the draft model is usually trained only once and kept static. However, in reinforcement learning, the main model needs to be updated thousands of times, and a static draft model quickly becomes ineffective.   Therefore, the TLT system introduces an “adaptive draft trainer.” Once some processors finish short queries and enter an idle state, the system immediately schedules them to train the draft model in real time.   At the same time, an “adaptive rollout engine” automatically adjusts the decoding strategy based on workload characteristics to ensure that the draft model stays highly synchronized with the target large model, without adding extra computational overhead.   Tests based on real-world datasets show that, while maintaining model accuracy with absolutely no loss, TLT technology increases the training speed of multiple reasoning large language models by 70% to 210%.   Not only that, the lightweight draft model obtained through training can also serve as a free byproduct and be directly used for later efficient deployment. In the future, the research team plans to integrate this technology into more training and inference frameworks, further reducing AI development costs and improving energy utilization.
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