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Speculator Extremes: Steel, Palladium, EAFE & CAD lead Bullish Positions

The latest update for the weekly Commitment of Traders (COT) report was released by the Commodity Futures Trading Commission (CFTC) on Friday for data ending on January 27th.

This weekly Extreme Positions report highlights the Most Bullish and Most Bearish Positions for the speculator category. Extreme positioning in these markets can foreshadow strong moves in the underlying market.

To signify an extreme position, we use the Strength Index (also known as the COT Index) of each instrument, a common method of measuring COT data. The Strength Index is simply a comparison of current trader positions against the range of positions over the previous 3 years. We use over 80 percent as extremely bullish and under 20 percent as extremely bearish. (Compare Strength Index scores across all markets in the data table or cot leaders table)

Here Are This Week’s Most Bullish Speculator Positions:

Steel

The Steel speculator position comes in once again this week as the most bullish extreme standing as the Steel speculator level is currently at a maximum 100 percent score of its 3-year range.

The six-week trend for the strength score totaled a gain of 18 percentage points this week. The overall net speculator position was a total of 12,340 net contracts this week with a rise of 669 contract in the weekly speculator bets.

Speculators or Non-Commercials Notes:
Speculators, classified as non-commercial traders by the CFTC, are made up of large commodity funds, hedge funds and other significant for-profit participants. The Specs are generally regarded as trend-followers in their behavior towards price action – net speculator bets and prices tend to go in the same directions. These traders often look to buy when prices are rising and sell when prices are falling. To illustrate this point, many times speculator contracts can be found at their most extremes (bullish or bearish) when prices are also close to their highest or lowest levels.
These extreme levels can be dangerous for the large speculators as the trade is most crowded, there is less trading ammunition still sitting on the sidelines to push the trend further and prices have moved a significant distance. When the trend becomes exhausted, some speculators take profits while others look to also exit positions when prices fail to continue in the same direction. This process usually plays out over many months to years and can ultimately create a reverse effect where prices start to fall and speculators start a process of selling when prices are falling.

Palladium

The Palladium speculator position comes up next in the extreme standings this week. The Palladium speculator level is now at a 96 percent score of its 3-year range and the six-week trend for the strength score was a dip by -2 percentage points this week.

The speculator position registered 684 net contracts this week with a weekly decline of -204 contracts in speculator bets.

MSCI EAFE MINI

The MSCI EAFE MINI speculator position is third this week in the extreme standings with the MSCI EAFE-Mini speculator level residing at a 94 percent score of its 3-year range.

The six-week trend for the speculator strength score came in at plus 11 percentage points this week. The overall speculator position was 23,890 net contracts this week with a small dip of -679 contracts in the weekly speculator bets.

Canadian Dollar

The Canadian Dollar speculator position comes up number four in the extreme standings this week. The CAD speculator level is at a 89 percent score of its 3-year range.

The six-week trend for the speculator strength score jumped by a total of 35 percentage points this week while the overall speculator position was -16,046 net contracts this week with a strong gain of 25,739 contracts in the speculator bets.

Russell 2000 Mini

The Russell 2000 Mini speculator position rounds out the top five in this week’s bullish extreme standings. The Russell-Mini speculator level sits at a 88 percent score of its 3-year range. The six-week trend for the speculator strength score was a slight gain by 2 percentage points this week.

The speculator position was 12,327 net contracts this week but showed a drop of -8,236 contracts in the weekly speculator bets.

The Most Bearish Speculator Positions of the Week:

Cocoa Futures

The Cocoa Futures speculator position comes in as the most bearish extreme standing this week as the Cocoa speculator level is at just a 2 percent score of its 3-year range.

The six-week trend for the speculator strength score was a drop by -17 percentage points while the overall speculator position was -15,502 net contracts this week after an increase of 2,372 contracts in the speculator bets.

Sugar

The Sugar speculator position comes in next for the most bearish extreme standing on the week at a 7 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decrease by -2 percentage points while the speculator position was -167,753 net contracts this week with a gain of 10,595 contracts in the weekly speculator bets.

New Zealand Dollar

The New Zealand Dollar speculator position comes in as third most bearish extreme standing of the week. The NZD speculator level resides at a 10 percent score of its 3-year range while the six-week trend for the speculator strength score showed no change this week. The overall speculator position was -47,745 net contracts with a rise of 1,865 contracts in the speculator bets.

Swiss Franc

The Swiss Franc speculator position comes in as this week’s fourth most bearish extreme standing with the CHF speculator level at a 14 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decrease of -8 percentage points this week and the speculator position was -42,893 net contracts this week with a slight increase of 314 contracts in the weekly speculator bets.

Cotton

Finally, the Cotton speculator position comes in as the fifth most bearish extreme standing for this week as the Cotton speculator level stands at a 16 percent score of its 3-year range.

The six-week trend for the speculator strength score showed no change on the week while the overall speculator position was -38,967 net contracts this week with a drop by -13,143 contracts in the weekly speculator bets.

Article By InvestMacro – Receive our weekly COT Newsletter

*COT Report: The COT data, released weekly to the public each Friday, is updated through the most recent Tuesday (data is 3 days old) and shows a quick view of how large speculators or non-commercials (for-profit traders) were positioned in the futures markets.

The CFTC categorizes trader positions according to commercial hedgers (traders who use futures contracts for hedging as part of the business), non-commercials (large traders who speculate to realize trading profits) and nonreportable traders (usually small traders/speculators) as well as their open interest (contracts open in the market at time of reporting). See CFTC criteria here.

COT Updates Opinions

30 Jan

Americans want heat pumps – but high electricity prices may get in the way

By Roxana Shafiee, Harvard University; Harvard Kennedy School

Heat pumps can reduce carbon emissions associated with heating buildings, and many states have set aggressive targets to increase their use in the coming decades. But while heat pumps are often cheaper choices for new buildings, getting homeowners to install them in existing homes isn’t so easy.

Current energy prices, including the rising cost of electricity, mean that homeowners may experience higher heating bills by replacing their current heating systems with heat pumps – at least in some regions of the country.

Heat pumps, which use electricity to move heat from the outside in, are used in only 14% of U.S. households. They are common primarily in warm southern states such as Florida where winter heating needs are relatively low. In the Northeast, where winters are colder and longer, only about 5% of households use a heat pump.

In our new study, my co-author Dan Schrag and I examined how heat pump adoption would change annual heating bills for the average-size household in each county across the U.S. We wanted to understand where heat pumps may already be cost-effective and where other factors may be preventing households from making the switch.

Wide variation in home heating

Across the U.S., people heat their homes with a range of fuels, mainly because of differences in climate, pricing and infrastructure. In colder regions – northern states and states across the Rocky Mountains – most people use natural gas or propane to provide reliable winter heating. In California, most households also use natural gas for heating.

In warmer, southern states, including Florida and Texas, where electricity prices are cheaper, most households use electricity for heating – either in electric furnaces, baseboard resistance heating or to run heat pumps. In the Pacific northwest, where electricity prices are low due to abundant hydropower, electricity is also a dominant heating fuel.

The type of community also affects homes’ fuel choices. Homes in cities are more likely to use natural gas relative to rural areas, where natural gas distribution networks are not as well developed. In rural areas, homes are more likely to use heating oil and propane, which can be stored on property in tanks. Oil is also more commonly used in the Northeast, where properties are older – particularly in New England, where a third of households still rely on oil for heating.

Why heat pumps?

Instead of generating heat by burning fuels such as natural gas that directly emit carbon, heat pumps use electricity to move heat from one place to another. Air-source heat pumps extract the heat of outside air, and ground-source heat pumps, sometimes called geothermal heat pumps, extract heat stored in the ground.

Heat pump efficiency depends on the local climate: A heat pump operated in Florida will provide more heat per unit of electricity used than one in colder northern states such as Minnesota or Massachusetts.

But they are highly efficient: An air-source heat pump can reduce household heating energy use by roughly 30% to 50% relative to existing fossil-based systems and up to 75% relative to inefficient electric systems such as baseboard heaters.

Heat pumps can also reduce emissions of greenhouse gases, although that depends on how their electricity is generated – whether from fossil fuels or cleaner energy, such as wind and solar.

Heat pumps can lower heating bills

We found that for households currently using oil, propane or non-heat pump forms of electric heating – such as electric furnaces or baseboard resistive heaters – installing a heat pump would reduce heating bills across all parts of the country.

The amount a household can save on energy costs with a heat pump depends on region and heating type, averaging between $200 and $500 a year for the average-size household currently using propane or oil.

However, savings can be significantly greater: We found the greatest opportunity for savings in households using inefficient forms of electric heating in northern regions. High electricity prices in the Northeast, for example, mean that heat pumps can save consumers up to $3,000 a year over what they would pay to heat with an electric furnace or to use baseboard heating.

A challenge in converting homes using natural gas

Unfortunately for the households that use natural gas in colder, northern regions – making up around half of the country’s annual heating needs – installing a heat pump could raise their annual heating bills. Our analysis shows that bills could increase by as much as $1,200 per year in northern regions, where electricity costs are as much as five times greater than natural gas per kilowatt-hour.

Even households that install ground-source heat pumps, the most efficient type of heat pump, would still see bill increases in regions with the highest electricity prices relative to natural gas.

Installation costs

In parts of the country where households would see their energy costs drop after installing a heat pump, the savings would eventually offset the upfront costs. But those costs can be significant and discourage people from buying.

On average, it costs $17,000 to install an air-source heat pump and typically at least $30,000 to install a ground-source heat pump.

Some homes may also need upgrades to their electrical systems, which can increase the total installation price even more, by tens of thousands of dollars in some cases, if costly service upgrades are required.

In places where air conditioning is typical, homes may be able to offset some costs by using heat pumps to replace their air conditioning units as well as their heating systems. For instance, a new program in California aims to encourage homeowners who are installing central air conditioning or replacing broken AC systems to get energy-efficient heat pumps that provide both heating and cooling.

Rising costs of electricity

A main finding of our analysis was that the cost of electricity is key to encouraging people to install heat pumps.

Electricity prices have risen sharply across the U.S. in recent years, driven by factors such as extreme weather, aging infrastructure and increasing demand for electric power. New data center demand has added further pressure and raised questions about who bears these costs.

Heat pump installations will also increase electricity demand on the grid: The full electrification of home heating across the country would increase peak electricity demand by about 70%. But heat pumps – when used in concert with other technologies such as hot-water storage – can provide opportunities for grid balancing and be paired with discounted or time-of-use rate structures to reduce overall operating costs. In some states, regulators have ordered utilities to discount electricity costs for homes that use heat pumps.

But ultimately, encouraging households to embrace heat pumps and broader economy-wide electrification, including electric vehicles, will require more than just technological fixes and a lot more electricity – it will require lower power prices.

About the Author:

Roxana Shafiee, Environmental Fellow, Center for the Environment, Harvard University; Harvard Kennedy School

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Energy Opinions

29 Jan

America is falling behind in the global EV race – that’s going to cost the US auto industry

By Hengrui Liu, Tufts University and Kelly Sims Gallagher, Tufts University

At the 2026 Detroit Auto Show, the spotlight quietly shifted. Electric vehicles, once framed as the inevitable future of the industry, were no longer the centerpiece. Instead, automakers emphasized hybrids, updated gasoline models and incremental efficiency improvements.

The show, held in January, reflected an industry recalibration happening in real time: Ford and General Motors had recently announced US$19.5 billion and $6 billion in EV-related write-downs, respectively, reflecting the losses they expect as they unwind or delay parts of their electric vehicle plans.

The message from Detroit was unmistakable: The United States is pulling back from a transition that much of the world is accelerating.

Highlights from the Detroit Auto Show, starting with V-8 trucks, by the Detroit Free Press’ auto writer.

That retreat carries consequences far beyond showroom floors.

In China, Europe and a growing number of emerging markets, including Vietnam and Indonesia, electric vehicles now make up a higher share of new passenger vehicle sales than in the United States.

That means the U.S. pullback on EV production is not simply a climate problem – gasoline-powered vehicles are a major contributor to climate change – it is also an industrial competitiveness problem, with direct implications for the future of U.S. automakers, suppliers and autoworkers. Slower EV production and slower adoption in the U.S. can keep prices higher, delay improvements in batteries and software, and increase the risk that the next generation of automotive value creation will happen elsewhere.

Where EVs are taking over

In 2025, global EV registrations rose 20% to 20.7 million. Analysts with Benchmark Mineral Intelligence reported that China reached 12.9 million EV registrations, up 17% from the previous year; Europe recorded 4.3 million, up 33%; and the rest of the world added 1.7 million, up 48%.

By contrast, U.S. EV sales growth was essentially flat in 2025, at about 1%. U.S. automaker Tesla experienced declines in both scale and profitability – its vehicle deliveries fell 9% compared to 2024, the company’s net profit was down 46%, and CEO Elon Musk said it would put more of its focus on artificial intelligence and robotics.

Market share tells a similar story and also challenges the assumption that vehicle electrification would take time to expand from wealthy countries to emerging markets.

In 39 countries, EVs now exceed 10% of new car sales, including in Vietnam, Thailand and Indonesia, which reached 38%, 21% and 15%, respectively, in 2025, energy analysts at Ember report.

In the U.S., EVs accounted for less than 10% of new vehicle sales, by Ember’s estimates.

U.S. President Donald Trump came back into office in 2025 promising to end policies that supported EV production and sales and boost fossil fuels. But while the U.S. was curtailing federal consumer incentives, governments elsewhere largely continued a transition to electric vehicles.

Europe softened its goal for all vehicles to have zero emissions by 2035 at the urging of automakers, but its new target is still a 90% cut in automobiles’ carbon dioxide emissions by 2035.

Germany launched a program offering subsidies worth 1,500 to 6,000 euros per electric vehicle, aimed at small- and medium-income households.

In developing economies, EV policy has largely been sustained through industrial policies. In Brazil, the MOVER program offers tax credits explicitly linked to domestic EV production, research and development, and efficiency targets. South Africa is introducing a 150% investment allowance for EV and battery manufacturing, giving them a tax break starting in March 2026. Thailand has implemented subsidies and reduced excise tax tied to mandatory local production and export commitments.

In China, the EV industry has entered a phase of regulatory maturity. After a decade of subsidies and state-led investment that helped domestic firms undercut global competitors, the government’s focus is no longer on explosive growth at home.

With their domestic market saturated and competition fierce, Chinese automakers are pushing aggressively into global markets. Beijing has reinforced this shift by ending its full tax exemption for EV purchases and replacing it with a tapered 5% tax on EV buyers.

Consequences for US automakers

EV manufacturing is governed by steep learning curves and scale economies, meaning the more vehicles a company builds, the better it gets at making them faster and cheaper. Low domestic production and sales can mean higher costs for parts and weaker bargaining power for automakers in global supply chains.

The competitive landscape is already changing. In 2025, China exported 2.65 million EVs, doubling its 2024 exports, according to the China Association of Automobile Manufacturers. And BYD surpassed Tesla as the world’s largest EV maker in 2025.

The U.S. risks becoming a follower in the industry it once defined.

Some people argue that American consumers simply prefer trucks and hybrids. Others point to Chinese subsidies and overcapacity as distortions that justify U.S. industry caution. These concerns deserve consideration, but they do not outweigh the fundamental fact that, globally, the EV share of auto sales continues to rise.

What can the US do?

For U.S. automakers and workers to compete in this market, the government, in our view, will have to stop treating EVs as an ideological matter and start governing it like an industrial transition.

That starts with restoring regulatory credibility, something that seems unlikely right now as the Trump administration moves to roll back vehicle emissions standards. Performance standards are the quiet engine of industrial investment. When standards are predictable and enforced, manufacturers can plan, suppliers can invest in new businesses, and workers can train for reliable demand.

Governments at state and local levels and industry can also take important steps.

Focus on affordability and equity: The federal clean-vehicle tax credit that effectively gave EV buyers a discount expired in September 2025. An alternative is targeted, point-of-sale support for lower- and middle-income buyers. By moving away from blanket credits in favor of targeted incentives – a model already used in California and Pennsylvania – governments can ensure public funds are directed toward people who are currently priced out of the EV market. Additionally, interest-rate buydowns that allow buyers to reduce their loan payments and “green loan” programs can help, typically funded through state and local governments, utility companies or federal grants.

Keep building out the charging network: A federal judge ruled on Jan. 23, 2026, that the Trump administration violated the law when it suspended a $5 billion program for expanding the nation’s EV charger network. That expansion effort can be improved by shifting the focus from the number of ports installed to the number of working chargers, as California did in 2025. Enforcing reliability and clearing bottlenecks, such as electricity connections and payment systems, could help boost the number of functioning sites.

Use fleet procurement as a stabilizer for U.S. sales: When states, cities and companies provide a predictable volume of vehicle purchases, that helps manufacturers plan future investments. For example, Amazon’s 2019 order of 100,000 Rivian electric delivery vehicles to be delivered over the following decade gave the startup automaker the boost it needed.

Treat workforce transition as core infrastructure: This means giving workers skills they can carry from job to job, helping suppliers retool instead of shutting down, and coordinating training with employers’ needs. Done right, these investments turn economic change into a source of stable jobs and broad public support. Done poorly, they risk a political backlash.

The scene at the Detroit Auto Show should be a warning, not a verdict. The global auto industry is accelerating its EV transition. The question for the United States is whether it will shape that future – and ensure the technologies and jobs of the next automotive era are in the U.S. – or import it.

About the Author:

Hengrui Liu, Postdoctoral Scholar in Economics and Public Policy, The Fletcher School, Tufts University and Kelly Sims Gallagher, Professor of Energy and Environmental Policy, Director of the Climate Policy Lab and Center for International Environment and Resource Policy, The Fletcher School, Tufts University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Energy Opinions

27 Jan

Moore’s law: the famous rule of computing has reached the end of the road, so what comes next?

By Domenico Vicinanza, Anglia Ruskin University

For half a century, computing advanced in a reassuring, predictable way. Transistors – devices used to switch electrical signals on a computer chip – became smaller. Consequently, computer chips became faster, and society quietly assimilated the gains almost without noticing.

These faster chips enable greater computing power by allowing devices to perform tasks more efficiently. As a result, we saw scientific simulations improving, weather forecasts becoming more accurate, graphics more realistic, and later, machine learning systems being developed and flourishing. It looked as if computing power itself obeyed a natural law.

This phenomenon became known as Moore’s Law, after the businessman and scientist Gordon Moore. Moore’s Law summarised the empirical observation that the number of transistors on a chip approximately doubled every couple of years. This also allows the size of devices to shrink, so it drives miniaturisation.

That sense of certainty and predictability has now gone, and not because innovation has stopped, but because the physical assumptions that once underpinned it no longer hold.

So what replaces the old model of automatic speed increases? The answer is not a single breakthrough, but several overlapping strategies.

One involves new materials and transistor designs. Engineers are refining how transistors are built to reduce wasted energy and unwanted electrical leakage. These changes deliver smaller, more incremental improvements than in the past, but they help keep power use under control.

Another approach is changing how chips are physically organised. Rather than placing all components on a single flat surface, modern chips increasingly stack parts on top of each other or arrange them more closely. This reduces the distance that data has to travel, saving both time and energy.

Perhaps the most important shift is specialisation. Instead of one general-purpose processor trying to do everything, modern systems combine different kinds of processors. Traditional processing units or CPUs handle control and decision-making. Graphics processors, are powerful processing units that were originally designed to handle the demands of graphics for computer games and other tasks. AI accelerators (specialised hardware that speeds up AI tasks) focus on large numbers of simple calculations carried out in parallel. Performance now depends on how well these components work together, rather than on how fast any one of them is.

Alongside these developments, researchers are exploring more experimental technologies, including quantum processors (which harness the power of quantum science) and photonic processors, which use light instead of electricity.

These are not general-purpose computers, and they are unlikely to replace conventional machines. Their potential lies in very specific areas, such as certain optimisation or simulation problems where classical computers can struggle to explore large numbers of possible solutions efficiently. In practice, these technologies are best understood as specialised co-processors, used selectively and in combination with traditional systems.

For most everyday computing tasks, improvements in conventional processors, memory systems and software design will continue to matter far more than these experimental approaches.

For users, life after Moore’s Law does not mean that computers stop improving. It means that improvements arrive in more uneven and task-specific ways. Some applications, such as AI-powered tools, diagnostics, navigation, complex modelling, may see noticeable gains, while general-purpose performance increases more slowly.

New technologies

At the Supercomputing SC25 conference in St Louis, hybrid systems that mix CPUs (processors) and GPUs (graphics processing units) with emerging technologies such as quantum or photonic processors were increasingly presented and discussed as practical extensions of classical computing. For most everyday tasks, improvements in classical processors, memories and software will continue to deliver the biggest gains.

But there is growing interest in using quantum and photonic devices as co-
processors, not replacements. Their appeal lies in tackling specific classes of
problems, such as complex optimisation or routing tasks, where finding low-energy
or near-optimal solutions can be exponentially expensive for classical machines
alone.

In this supporting role, they offer a credible way to combine the reliability of
classical computing with new computational techniques that expand what these
systems can do.

Life after Moore’s Law is not a story of decline, but one that requires constant
transformation and evolution. Computing progress now depends on architectural
specialisation, careful energy management, and software that is deeply aware of
hardware constraints. The danger lies in confusing complexity with inevitability, or marketing narratives with solved problems.

The post-Moore era forces a more honest relationship with computation where performance is not anymore something we inherit automatically from smaller transistors, but it is something we must design, justify, and pay for, in energy, in complexity, and in trade-offs.

About the Author:

Domenico Vicinanza, Associate Professor of Intelligent Systems and Data Science, Anglia Ruskin University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Opinions Programming

25 Jan

Currency Speculators boost Australian Dollar bets to 58-Week High

By InvestMacro

Here are the latest charts and statistics for the Commitment of Traders (COT) data published by the Commodities Futures Trading Commission (CFTC).

The latest COT data is updated through Tuesday January 20th and shows a quick view of how large market participants (for-profit speculators and commercial traders) were positioned in the futures markets. All currency positions are in direct relation to the US dollar where, for example, a bet for the euro is a bet that the euro will rise versus the dollar while a bet against the euro will be a bet that the euro will decline versus the dollar.

Weekly Speculator Changes led by Australian Dollar & Mexican Peso

The COT currency market speculator bets were overall higher this week as seven out of the eleven currency markets we cover had higher positioning while the other four markets had lower speculator contracts.

Leading the gains for the currency markets was the Australian Dollar (4,835 contracts) with the Mexican Peso (3,595 contracts), the British Pound (3,290 contracts), the Canadian Dollar (465 contracts), the Japanese Yen (335 contracts), Bitcoin (229 contracts) and the Swiss Franc (185 contracts) also recording positive weeks.

The currencies seeing declines in speculator bets on the week were the EuroFX (-20,961 contracts), the US Dollar Index (-2,688 contracts), the New Zealand Dollar (-759 contracts) and with the Brazilian Real (-233 contracts) also registering lower bets on the week.

Highlighting the Currency Market Speculator Positions this week were the AUD, MXN, Euro & Dollar Index

The Australian Dollar speculative bets lead off the highlights this week as the AUD bets rose for an eighth consecutive week. Over this eight-week span, the Aussie Dollar speculative net position has improved by over 70,000 contracts. Despite that improvement, the Australian Dollar net position remains in bearish territory at -14,011 net positions at this time. This is actually the best standing for the Australian dollar speculative bets since all the way back to December of 2024, a span of 58 consecutive weeks that this currency has been in a bearish net position. The Australian Dollar, in the currency markets, has been on the rise and jumped this week by over 3%. It is now up by over 12% since January of 2025. Currently trading around 0.6887, the AUD is at its highest level since September of 2024 and with further upside momentum, we could see a challenge of the 0.70 significant psychological level soon.

Coming up next is the Mexican Peso, which saw speculator bets rise this week for the fourth time in the past five weeks, and for the tenth time over the past 14 weeks. The Peso has been in an overall bullish position for approximately one year now, dating back to January 21st of 2025. Peso positions have been gaining steadily over the past 52 weeks and have now been above the +100,000 net contract level for five consecutive weeks and for six out of the last seven weeks, indicating the strong sentiment for the MXN at this time. The Peso exchange rate is on a strong uptrend at the moment versus the US Dollar, and has seen a strong monthly gain to start the new year with gains in eight out of the last nine weeks. The MXN is now at the highest price level since June of 2024 and is up by over 20% in the last 52 weeks.

The Euro common currency’s speculative bets fell sharply for a second consecutive week, and have now declined by over -50,000 contracts in just the past two weeks. However, the Euro has been in a super strong position and indicates a likely profit-taking dip as the net speculative contracts have been above the +100,000 net contract level for 28 out of the last 32 weeks, including for the last eight consecutive weeks. The Euro currency closed out this week above the 1.18 level in the forex market after hitting support last week and rebounding off of the 1.1620 area. What a difference a year makes as last January, the Euro currency was trading around just 1.0250. And since then, the currency has risen by about 15%. Time will tell if the Euro can break above the 1.1865 resistance area that has stopped its ascent multiple times since June.

The US Dollar Index position dropped this week by over -2,500 contracts after seeing seven straight weeks of gains previously. The US Dollar Index net positions have now been in an overall bearish level for the past 32 consecutive weeks, dating back to June of 2025. The Dollar Index price has been on a strong downtrend for the past year and this week closed under the 97.50 level with an almost 2% drop on the week. Compared to last January, when this currency was trading around the 1.09 to 1.10 levels, USD Index is now currently lower by approximately 11%.

Currency Markets 5-Day Price Performance led by NZD & AUD

The best returning currency this week was the New Zealand Dollar which showed a 3.36% gain, while the Australian Dollar came in at a similar 3.13% rise over these past five days. The Swiss Franc was higher by 2.74%, followed by the British Pound with a 1.92% gain and the Euro with a 1.91% gain. The Brazilian Real was higher by 1.60%, while the Canadian Dollar was up by 1.59%. The Mexican Peso rose by 1.4%, and the Japanese Yen showed an increase by 1.45%.

On the downside, the US Dollar Index dropped by -1.90% over these past five days while Bitcoin saw the biggest decline with a -6.23% drop.

The leaders over the past 30 days are the Mexican Peso, with a gain of approximately 4% over that time, with a 3.8% rise, followed by the Australian Dollar, which is up by 3.45%. The Peso and the Australian Dollar also lead the past 90 days percent changes, with the Peso up by 5.7% over that time and the Australian Dollar higher by 4.26%.

Currencies Data:

Legend: Open Interest | Speculators Current Net Position | Weekly Specs Change | Specs Strength Score compared to last 3-Years (0-100 range)

Strength Scores led by Mexican Peso & Canadian Dollar

COT Strength Scores (a normalized measure of Speculator positions over a 3-Year range, from 0 to 100 where above 80 is Extreme-Bullish and below 20 is Extreme-Bearish) showed that the Mexican Peso (82 percent) and the Canadian Dollar (76 percent) lead the currency markets this week. The EuroFX (71 percent), Australian Dollar (66 percent) and Bitcoin (59 percent) come in as the next highest in the weekly strength scores.

On the downside, the New Zealand Dollar (8 percent) and the Swiss Franc (13 percent) come in at the lowest strength levels currently and are in Extreme-Bearish territory (below 20 percent). The next lowest strength scores are the US Dollar Index (27 percent) and the British Pound (30 percent).

3-Year Strength Statistics:
US Dollar Index (26.8 percent) vs US Dollar Index previous week (34.1 percent)
EuroFX (71.3 percent) vs EuroFX previous week (79.3 percent)
British Pound Sterling (30.3 percent) vs British Pound Sterling previous week (28.9 percent)
Japanese Yen (38.4 percent) vs Japanese Yen previous week (38.3 percent)
Swiss Franc (13.3 percent) vs Swiss Franc previous week (13.0 percent)
Canadian Dollar (76.2 percent) vs Canadian Dollar previous week (76.0 percent)
Australian Dollar (66.4 percent) vs Australian Dollar previous week (62.9 percent)
New Zealand Dollar (8.2 percent) vs New Zealand Dollar previous week (9.1 percent)
Mexican Peso (82.4 percent) vs Mexican Peso previous week (80.5 percent)
Brazilian Real (52.8 percent) vs Brazilian Real previous week (52.9 percent)
Bitcoin (59.0 percent) vs Bitcoin previous week (54.2 percent)

Canadian Dollar & Australian Dollar top the 6-Week Strength Trends

COT Strength Score Trends (or move index, calculates the 6-week changes in strength scores) showed that the Canadian Dollar (44 percent) and the Australian Dollar (35 percent) lead the past six weeks trends for the currencies. The British Pound (23 percent), the US Dollar Index (20 percent) and the New Zealand Dollar (8 percent) are the next highest positive movers in the 3-Year trends data.

The Brazilian Real (-29 percent) leads the downside trend scores currently with the Japanese Yen (-17 percent), EuroFX (-10 percent) and the Swiss Franc (-9 percent) following next with lower trend scores.

3-Year Strength Trends:
US Dollar Index (20.1 percent) vs US Dollar Index previous week (33.7 percent)
EuroFX (-10.3 percent) vs EuroFX previous week (9.2 percent)
British Pound Sterling (22.7 percent) vs British Pound Sterling previous week (23.3 percent)
Japanese Yen (-17.1 percent) vs Japanese Yen previous week (-22.4 percent)
Swiss Franc (-9.3 percent) vs Swiss Franc previous week (-15.4 percent)
Canadian Dollar (43.8 percent) vs Canadian Dollar previous week (53.2 percent)
Australian Dollar (34.7 percent) vs Australian Dollar previous week (45.8 percent)
New Zealand Dollar (8.2 percent) vs New Zealand Dollar previous week (4.9 percent)
Mexican Peso (0.1 percent) vs Mexican Peso previous week (2.5 percent)
Brazilian Real (-29.0 percent) vs Brazilian Real previous week (-31.3 percent)
Bitcoin (0.7 percent) vs Bitcoin previous week (-10.6 percent)

Individual COT Forex Markets:

US Dollar Index Futures:

The US Dollar Index large speculator standing this week came in at a net position of -6,418 contracts in the data reported through Tuesday. This was a weekly decline of -2,688 contracts from the previous week which had a total of -3,730 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 26.8 percent. The commercials are Bullish with a score of 73.8 percent and the small traders (not shown in chart) are Bearish with a score of 35.1 percent.

Price Trend-Following Model: Strong Downtrend

Our weekly trend-following model classifies the current market price position as: Strong Downtrend.

US DOLLAR INDEX Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	54.1	30.5	9.3
– Percent of Open Interest Shorts:	75.8	9.2	9.0
– Net Position:	-6,418	6,305	113
– Gross Longs:	16,003	9,023	2,762
– Gross Shorts:	22,421	2,718	2,649
– Long to Short Ratio:	0.7 to 1	3.3 to 1	1.0 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	26.8	73.8	35.1
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	20.1	-19.9	-2.0

Euro Currency Futures:

The Euro Currency large speculator standing this week came in at a net position of 111,695 contracts in the data reported through Tuesday. This was a weekly fall of -20,961 contracts from the previous week which had a total of 132,656 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 71.3 percent. The commercials are Bearish with a score of 28.2 percent and the small traders (not shown in chart) are Bullish with a score of 67.2 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

EURO Currency Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	31.2	55.4	10.6
– Percent of Open Interest Shorts:	18.6	73.1	5.6
– Net Position:	111,695	-155,596	43,901
– Gross Longs:	275,235	488,166	92,941
– Gross Shorts:	163,540	643,762	49,040
– Long to Short Ratio:	1.7 to 1	0.8 to 1	1.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	71.3	28.2	67.2
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-10.3	11.6	-14.6

British Pound Sterling Futures:

The British Pound Sterling large speculator standing this week came in at a net position of -21,980 contracts in the data reported through Tuesday. This was a weekly lift of 3,290 contracts from the previous week which had a total of -25,270 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 30.3 percent. The commercials are Bullish with a score of 66.9 percent and the small traders (not shown in chart) are Bullish with a score of 66.0 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

BRITISH POUND Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	39.2	43.8	16.0
– Percent of Open Interest Shorts:	49.8	35.6	13.7
– Net Position:	-21,980	17,082	4,898
– Gross Longs:	81,332	91,023	33,243
– Gross Shorts:	103,312	73,941	28,345
– Long to Short Ratio:	0.8 to 1	1.2 to 1	1.2 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	30.3	66.9	66.0
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	22.7	-26.2	35.5

Japanese Yen Futures:

The Japanese Yen large speculator standing this week came in at a net position of -44,829 contracts in the data reported through Tuesday. This was a weekly boost of 335 contracts from the previous week which had a total of -45,164 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 38.4 percent. The commercials are Bullish with a score of 61.3 percent and the small traders (not shown in chart) are Bearish with a score of 46.4 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

JAPANESE YEN Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	36.6	39.5	14.7
– Percent of Open Interest Shorts:	51.9	25.4	13.5
– Net Position:	-44,829	41,140	3,689
– Gross Longs:	107,139	115,583	43,047
– Gross Shorts:	151,968	74,443	39,358
– Long to Short Ratio:	0.7 to 1	1.6 to 1	1.1 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	38.4	61.3	46.4
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-17.1	14.9	7.9

Swiss Franc Futures:

The Swiss Franc large speculator standing this week came in at a net position of -43,207 contracts in the data reported through Tuesday. This was a weekly increase of 185 contracts from the previous week which had a total of -43,392 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 13.3 percent. The commercials are Bullish with a score of 77.7 percent and the small traders (not shown in chart) are Bullish with a score of 58.6 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

SWISS FRANC Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	12.5	73.3	14.1
– Percent of Open Interest Shorts:	56.5	24.4	19.0
– Net Position:	-43,207	47,972	-4,765
– Gross Longs:	12,257	71,873	13,860
– Gross Shorts:	55,464	23,901	18,625
– Long to Short Ratio:	0.2 to 1	3.0 to 1	0.7 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	13.3	77.7	58.6
– Strength Index Reading (3 Year Range):	Bearish-Extreme	Bullish	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-9.3	8.8	-3.5

Canadian Dollar Futures:

The Canadian Dollar large speculator standing this week came in at a net position of -41,785 contracts in the data reported through Tuesday. This was a weekly lift of 465 contracts from the previous week which had a total of -42,250 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 76.2 percent. The commercials are Bearish with a score of 30.5 percent and the small traders (not shown in chart) are Bearish with a score of 33.6 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

CANADIAN DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	27.8	56.2	13.0
– Percent of Open Interest Shorts:	47.4	34.7	15.0
– Net Position:	-41,785	45,990	-4,205
– Gross Longs:	59,456	120,142	27,744
– Gross Shorts:	101,241	74,152	31,949
– Long to Short Ratio:	0.6 to 1	1.6 to 1	0.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	76.2	30.5	33.6
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	43.8	-40.8	10.9

Australian Dollar Futures:

The Australian Dollar large speculator standing this week came in at a net position of -14,011 contracts in the data reported through Tuesday. This was a weekly gain of 4,835 contracts from the previous week which had a total of -18,846 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 66.4 percent. The commercials are Bearish with a score of 22.9 percent and the small traders (not shown in chart) are Bullish-Extreme with a score of 100.0 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

AUSTRALIAN DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	37.2	43.6	18.5
– Percent of Open Interest Shorts:	43.3	48.7	7.3
– Net Position:	-14,011	-11,787	25,798
– Gross Longs:	85,759	100,608	42,698
– Gross Shorts:	99,770	112,395	16,900
– Long to Short Ratio:	0.9 to 1	0.9 to 1	2.5 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	66.4	22.9	100.0
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bullish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	34.7	-33.9	17.9

New Zealand Dollar Futures:

The New Zealand Dollar large speculator standing this week came in at a net position of -49,610 contracts in the data reported through Tuesday. This was a weekly reduction of -759 contracts from the previous week which had a total of -48,851 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 8.2 percent. The commercials are Bullish-Extreme with a score of 91.2 percent and the small traders (not shown in chart) are Bearish with a score of 36.7 percent.

Price Trend-Following Model: Weak Downtrend

Our weekly trend-following model classifies the current market price position as: Weak Downtrend.

NEW ZEALAND DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	14.8	79.4	4.0
– Percent of Open Interest Shorts:	68.3	24.6	5.3
– Net Position:	-49,610	50,811	-1,201
– Gross Longs:	13,670	73,619	3,742
– Gross Shorts:	63,280	22,808	4,943
– Long to Short Ratio:	0.2 to 1	3.2 to 1	0.8 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	8.2	91.2	36.7
– Strength Index Reading (3 Year Range):	Bearish-Extreme	Bullish-Extreme	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	8.2	-8.7	6.9

Mexican Peso Futures:

The Mexican Peso large speculator standing this week came in at a net position of 107,153 contracts in the data reported through Tuesday. This was a weekly increase of 3,595 contracts from the previous week which had a total of 103,558 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish-Extreme with a score of 82.4 percent. The commercials are Bearish-Extreme with a score of 17.6 percent and the small traders (not shown in chart) are Bearish with a score of 49.3 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

MEXICAN PESO Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	63.9	32.4	3.2
– Percent of Open Interest Shorts:	19.3	79.1	1.2
– Net Position:	107,153	-111,938	4,785
– Gross Longs:	153,398	77,773	7,650
– Gross Shorts:	46,245	189,711	2,865
– Long to Short Ratio:	3.3 to 1	0.4 to 1	2.7 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	82.4	17.6	49.3
– Strength Index Reading (3 Year Range):	Bullish-Extreme	Bearish-Extreme	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	0.1	-0.6	5.4

Brazilian Real Futures:

The Brazilian Real large speculator standing this week came in at a net position of 17,641 contracts in the data reported through Tuesday. This was a weekly lowering of -233 contracts from the previous week which had a total of 17,874 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 52.8 percent. The commercials are Bearish with a score of 46.1 percent and the small traders (not shown in chart) are Bearish with a score of 41.3 percent.

Price Trend-Following Model: Weak Downtrend

Our weekly trend-following model classifies the current market price position as: Weak Downtrend.

BRAZIL REAL Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	66.0	26.9	5.7
– Percent of Open Interest Shorts:	44.3	53.0	1.2
– Net Position:	17,641	-21,265	3,624
– Gross Longs:	53,730	21,911	4,628
– Gross Shorts:	36,089	43,176	1,004
– Long to Short Ratio:	1.5 to 1	0.5 to 1	4.6 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	52.8	46.1	41.3
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-29.0	27.6	6.3

Bitcoin Futures:

The Bitcoin large speculator standing this week came in at a net position of 298 contracts in the data reported through Tuesday. This was a weekly lift of 229 contracts from the previous week which had a total of 69 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 59.0 percent. The commercials are Bearish with a score of 46.7 percent and the small traders (not shown in chart) are Bearish with a score of 45.0 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

BITCOIN Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	80.8	3.8	5.2
– Percent of Open Interest Shorts:	79.6	5.6	4.6
– Net Position:	298	-445	147
– Gross Longs:	19,841	940	1,285
– Gross Shorts:	19,543	1,385	1,138
– Long to Short Ratio:	1.0 to 1	0.7 to 1	1.1 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	59.0	46.7	45.0
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	0.7	-2.2	3.4

Article By InvestMacro – Receive our weekly COT Newsletter

COT Updates Forex and Currency News Opinions

25 Jan

Speculator Extremes: Steel, Palladium & Russell-2000 lead Bullish Positions

By InvestMacro

The latest update for the weekly Commitment of Traders (COT) report was released by the Commodity Futures Trading Commission (CFTC) on Friday for data ending on January 20th.

Here Are This Week’s Most Bullish Speculator Positions:

Steel

The Steel speculator position comes in as the most bullish extreme standing this week as the Steel speculator level is currently at a maximum 100 percent score of its 3-year range.

The six-week trend for the strength score totaled a gain of 20 percentage points this week while the overall net speculator position was a total of 11,671 net contracts this week with an increase by 649 contract in the weekly speculator bets.

Speculators or Non-Commercials Notes:
Speculators, classified as non-commercial traders by the CFTC, are made up of large commodity funds, hedge funds and other significant for-profit participants. The Specs are generally regarded as trend-followers in their behavior towards price action – net speculator bets and prices tend to go in the same directions. These traders often look to buy when prices are rising and sell when prices are falling. To illustrate this point, many times speculator contracts can be found at their most extremes (bullish or bearish) when prices are also close to their highest or lowest levels.
These extreme levels can be dangerous for the large speculators as the trade is most crowded, there is less trading ammunition still sitting on the sidelines to push the trend further and prices have moved a significant distance. When the trend becomes exhausted, some speculators take profits while others look to also exit positions when prices fail to continue in the same direction. This process usually plays out over many months to years and can ultimately create a reverse effect where prices start to fall and speculators start a process of selling when prices are falling.

Palladium

The Palladium speculator position comes up next in the extreme standings this week. The Palladium speculator level is now at a 98 percent score of its 3-year range while the six-week trend for the strength score was a rise by 6 percentage points this week.

The overall speculator position registered 888 net contracts this week with a weekly dip of -337 contracts in speculator bets.

Russell 2000 Mini

The Russell 2000 Mini speculator position comes in third this week in the extreme standings as the Russell speculator level resides at a 95 percent score of its 3-year range.

The six-week trend for the speculator strength score came in at a strong boost of 33 percentage points this week. The overall speculator position was 20,563 net contracts this week with a rise of 9,126 contracts in the weekly speculator bets.

MSCI EAFE MINI

The MSCI EAFE MINI speculator position comes up number four in the extreme standings this week. The MSCI EAFE-Mini speculator level is also at a 95 percent score of its 3-year range.

The six-week trend for the speculator strength score totaled an increase by 8 percentage points this week. The overall speculator position was 24,569 net contracts this week with a decline of -4,784 contracts in the speculator bets.

Copper

The Copper speculator position rounds out the top five in this week’s bullish extreme standings as the Copper speculator level sits at na 82 percent score of its 3-year range. The six-week trend for the speculator strength score was a drop of -9 percentage points this week.

The speculator position was 52,575 net contracts this week with a decline of -866 contracts in the weekly speculator bets.

The Most Bearish Speculator Positions of the Week:

Natural Gas

The Natural Gas speculator position comes in tied for the most bearish extreme standing on the week with the Natural Gas speculator level at a 0 percent score of its 3-year range.

The six-week trend for the speculator strength score was a drop by -61 percentage points this week. The overall net speculator position was -193,490 contracts this week with a decrease of -7,889 contracts in the weekly speculator bets.

Cocoa Futures

The Cocoa Futures speculator position comes in tied as the most bearish extreme standing this week as the Cocoa speculator level is at a minimum 0 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decline by -14 percentage points this week while the overall speculator position was -17,874 net contracts this week with a decrease of -8,378 contracts in the speculator bets.

Sugar

The Sugar speculator position comes in as third most bearish extreme standing of the week with the Sugar speculator level residing at a 5 percent score of its 3-year range.

The six-week trend for the speculator strength score was small bump up by 1 percentage points this week. The overall speculator position was -178,348 net contracts this week with a slide by -12,637 contracts in the speculator bets.

New Zealand Dollar

The New Zealand Dollar speculator position comes in as this week’s fourth most bearish extreme standing with the NZD speculator level sitting at an 8 percent score of its 3-year range.

The six-week trend for the speculator strength score was a gain by 8 percentage points this week while the speculator position was -49,610 net contracts this week with a change of -759 contracts in the weekly speculator bets.

WTI Crude Oil

The WTI Crude Oil speculator position comes in as the fifth most bearish extreme standing for this week. The WTI Crude speculator level is at just a 13 percent score of its 3-year range.

The six-week trend for the speculator strength score was a rise by 7 percentage points this week and the speculator position was 78,792 net contracts this week with a boost by 20,664 contracts in the weekly speculator bets.

Article By InvestMacro – Receive our weekly COT Newsletter

COT Updates Opinions

23 Jan

AI-induced cultural stagnation is no longer speculation − it’s already happening

By Ahmed Elgammal, Rutgers University

Generative AI was trained on centuries of art and writing produced by humans.

But scientists and critics have wondered what would happen once AI became widely adopted and started training on its outputs.

A new study points to some answers.

In January 2026, artificial intelligence researchers Arend Hintze, Frida Proschinger Åström and Jory Schossau published a study showing what happens when generative AI systems are allowed to run autonomously – generating and interpreting their own outputs without human intervention.

The researchers linked a text-to-image system with an image-to-text system and let them iterate – image, caption, image, caption – over and over and over.

Regardless of how diverse the starting prompts were – and regardless of how much randomness the systems were allowed – the outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings and pastoral landscapes. Even more striking, the system quickly “forgot” its starting prompt.

The researchers called the outcomes “visual elevator music” – pleasant and polished, yet devoid of any real meaning.

For example, they started with the image prompt, “The Prime Minister pored over strategy documents, trying to sell the public on a fragile peace deal while juggling the weight of his job amidst impending military action.” The resulting image was then captioned by AI. This caption was used as a prompt to generate the next image.

After repeating this loop, the researchers ended up with a bland image of a formal interior space – no people, no drama, no real sense of time and place.

A collage of AI-generated images that begins with a politician surrounded by policy papers and progresses to a room with fancy red curtains. — A prompt that begins with a prime minister under stress ends with an image of an empty room with fancy furnishings.
Arend Hintze, Frida Proschinger Åström and Jory Schossau, CC BY

As a computer scientist who studies generative models and creativity, I see the findings from this study as an important piece of the debate over whether AI will lead to cultural stagnation.

The results show that generative AI systems themselves tend toward homogenization when used autonomously and repeatedly. They even suggest that AI systems are currently operating in this way by default.

The familiar is the default

This experiment may appear beside the point: Most people don’t ask AI systems to endlessly describe and regenerate their own images. The convergence to a set of bland, stock images happened without retraining. No new data was added. Nothing was learned. The collapse emerged purely from repeated use.

But I think the setup of the experiment can be thought of as a diagnostic tool. It reveals what generative systems preserve when no one intervenes.

This has broader implications, because modern culture is increasingly influenced by exactly these kinds of pipelines. Images are summarized into text. Text is turned into images. Content is ranked, filtered and regenerated as it moves between words, images and videos. New articles on the web are now more likely to be written by AI than humans. Even when humans remain in the loop, they are often choosing from AI-generated options rather than starting from scratch.

The findings of this recent study show that the default behavior of these systems is to compress meaning toward what is most familiar, recognizable and easy to regenerate.

Cultural stagnation or acceleration?

For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation.

Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions.

What has been missing from this debate is empirical evidence showing where homogenization actually begins.

The new study does not test retraining on AI-generated data. Instead, it shows something more fundamental: Homogenization happens before retraining even enters the picture. The content that generative AI systems naturally produce – when used autonomously and repeatedly – is already compressed and generic.

This reframes the stagnation argument. The risk is not only that future models might train on AI-generated content, but that AI-mediated culture is already being filtered in ways that favor the familiar, the describable and the conventional.

Retraining would amplify this effect. But it is not its source.

This is no moral panic

Skeptics are right about one thing: Culture has always adapted to new technologies. Photography did not kill painting. Film did not kill theater. Digital tools have enabled new forms of expression.

But those earlier technologies never forced culture to be endlessly reshaped across various mediums at a global scale. They did not summarize, regenerate and rank cultural products – news stories, songs, memes, academic papers, photographs or social media posts – millions of times per day, guided by the same built-in assumptions about what is “typical.”

The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions.

This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risk – not a speculative fear – if generative systems are left to operate in their current iteration.

They also help clarify a common misconception about AI creativity: Producing endless variations is not the same as producing innovation. A system can generate millions of images while exploring only a tiny corner of cultural space.

In my own research on creative AI, I found that novelty requires designing AI systems with incentives to deviate from the norms. Without it, systems optimize for familiarity because familiarity is what they have learned best. The study reinforces this point empirically. Autonomy alone does not guarantee exploration. In some cases, it accelerates convergence.

This pattern already emerged in the real world: One study found that AI-generated lesson plans featured the same drift toward conventional, uninspiring content, underscoring that AI systems converge toward what’s typical rather than what’s unique or creative.

Lost in translation

Whenever you write a caption for an image, details will be lost. Likewise for generating an image from text. And this happens whether it’s being performed by a human or a machine.

In that sense, the convergence that took place is not a failure that’s unique to AI. It reflects a deeper property of bouncing from one medium to another. When meaning passes repeatedly through two different formats, only the most stable elements persist.

But by highlighting what survives during repeated translations between text and images, the authors are able to show that meaning is processed inside generative systems with a quiet pull toward the generic.

The implication is sobering: Even with human guidance – whether that means writing prompts, selecting outputs or refining results – these systems are still stripping away some details and amplifying others in ways that are oriented toward what’s “average.”

If generative AI is to enrich culture rather than flatten it, I think systems need to be designed in ways that resist convergence toward statistically average outputs. There can be rewards for deviation and support for less common and less mainstream forms of expression.

The study makes one thing clear: Absent these interventions, generative AI will continue to drift toward mediocre and uninspired content.

Cultural stagnation is no longer speculation. It’s already happening.

About the Author:

Ahmed Elgammal, Professor of Computer Science and Director of the Art & AI Lab, Rutgers University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Opinions Programming

22 Jan

Companies are already using agentic AI to make decisions, but governance is lagging behind

By Murugan Anandarajan, Drexel University

Businesses are acting fast to adopt agentic AI – artificial intelligence systems that work without human guidance – but have been much slower to put governance in place to oversee them, a new survey shows. That mismatch is a major source of risk in AI adoption. In my view, it’s also a business opportunity.

I’m a professor of management information systems at Drexel University’s LeBow College of Business, which recently surveyed more than 500 data professionals through its Center for Applied AI & Business Analytics. We found that 41% of organizations are using agentic AI in their daily operations. These aren’t just pilot projects or one-off tests. They’re part of regular workflows.

At the same time, governance is lagging. Only 27% of organizations say their governance frameworks are mature enough to monitor and manage these systems effectively.

In this context, governance is not about regulation or unnecessary rules. It means having policies and practices that let people clearly influence how autonomous systems work, including who is responsible for decisions, how behavior is checked, and when humans should get involved.

This mismatch can become a problem when autonomous systems act in real situations before anyone can intervene.

For example, during a recent power outage in San Francisco, autonomous robotaxis got stuck at intersections, blocking emergency vehicles and confusing other drivers. The situation showed that even when autonomous systems behave “as designed,” unexpected conditions can lead to undesirable outcomes.

This raises a big question: When something goes wrong with AI, who is responsible – and who can intervene?

Why governance matters

When AI systems act on their own, responsibility no longer lies where organizations expect it. Decisions still happen, but ownership is harder to trace. For instance, in financial services, fraud detection systems increasingly act in real time to block suspicious activity before a human ever reviews the case. Customers often only find out when their card is declined.

So, what if your card is mistakenly declined by an AI system? In that situation, the problem isn’t with the technology itself – it’s working as it was designed – but with accountability. Research on human-AI governance shows that problems happen when organizations don’t clearly define how people and autonomous systems should work together. This lack of clarity makes it hard to know who is responsible and when they should step in.

Without governance designed for autonomy, small issues can quietly snowball. Oversight becomes sporadic and trust weakens, not because systems fail outright, but because people struggle to explain or stand behind what the systems do.

When humans enter the loop too late

In many organizations, humans are technically “in the loop,” but only after autonomous systems have already acted. People tend to get involved once a problem becomes visible – when a price looks wrong, a transaction is flagged or a customer complains. By that point, the system has already been decided, and human review becomes corrective rather than supervisory.

Late intervention can limit the fallout from individual decisions, but it rarely clarifies who is accountable. Outcomes may be corrected, yet responsibility remains unclear.

Recent guidance shows that when authority is unclear, human oversight becomes informal and inconsistent. The problem is not human involvement, but timing. Without governance designed upfront, people act as a safety valve rather than as accountable decision-makers.

How governance determines who moves ahead

Agentic AI often brings fast, early results, especially when tasks are first automated. Our survey found that many companies see these early benefits. But as autonomous systems grow, organizations often add manual checks and approval steps to manage risk.

Over time, what was once simple slowly becomes more complicated. Decision-making slows down, work-arounds increase, and the benefits of automation fade. This happens not because the technology stops working, but because people never fully trust autonomous systems.

This slowdown doesn’t have to happen. Our survey shows a clear difference: Many organizations see early gains from autonomous AI, but those with stronger governance are much more likely to turn those gains into long-term results, such as greater efficiency and revenue growth. The key difference isn’t ambition or technical skills, but being prepared.

Good governance does not limit autonomy. It makes it workable by clarifying who owns decisions, how systems function is monitored, and when people should intervene. International guidance from the OECD – the Organization for Economic Cooperation and Development – emphasizes this point: Accountability and human oversight need to be designed into AI systems from the start, not added later.

Rather than slowing innovation, governance creates the confidence organizations need to extend autonomy instead of quietly pulling it back.

The next advantage is smarter governance

The next competitive advantage in AI will not come from faster adoption, but from smarter governance. As autonomous systems take on more responsibility, success will belong to organizations that clearly define ownership, oversight and intervention from the start.

In the era of agentic AI, confidence will accrue to the organizations that govern best, not simply those that adopt first.

About the Author:

Murugan Anandarajan, Professor of Decision Sciences and Management Information Systems, Drexel University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Opinions Programming

21 Jan

Despite its steep environmental costs, AI might also help save the planet

By Nir Kshetri, University of North Carolina – Greensboro

The rapid growth of artificial intelligence has sharply increased electricity and water consumption, raising concerns about the technology’s environmental footprint and carbon emissions. But the story is more complicated than that.

I study emerging technologies and how their development and deployment influence economic, institutional and societal outcomes, including environmental sustainability. From my research, I see that even as AI uses a lot of energy, it can also make systems cleaner and smarter.

AI is already helping to save energy and water, cut emissions and make businesses more efficient in agriculture, data centers, the energy industry, building heating and cooling, and aviation.

Agriculture

Agriculture is responsible for nearly 70% of the world’s freshwater use, and competition for water is growing.

AI is helping farmers use water more efficiently. Argentinian climate tech startup Kilimo, for example, tackles water scarcity with an AI-powered irrigation platform. The software uses large amounts of data, machine learning, and weather and satellite measurements to determine when and how much to water which areas of fields, ensuring that only the plants that actually need water receive it.

Chile’s Ministry of Agriculture has found that in that country’s Biobío region, farms using Kilimo’s precision irrigation systems have reduced water use by up to 30% while avoiding overirrigation. Using less water also reduces the amount of energy needed to pump it from the ground and around a farm.

Kilimo is one example that shows how AI can create economic incentives for sustainability: The amount of water farmers save from precision irrigation is verified, and credits for those savings are sold to local companies that want to offset some of their water use. The farmers then earn a profit – often 20% to 40% above their initial investment.

Data centers

U.S. data centers consumed about 176 terawatt-hours of electricity in 2023, accounting for roughly 4.4% of total U.S. electricity use. This number increased to 183 TWh in 2024. This growing energy footprint has made improving data center efficiency a critical priority for the operators of the data centers themselves, as well as the companies that rely on them – including cloud providers, tech firms and large enterprises running AI workloads – both to reduce costs and meet sustainability and regulatory goals.

AI is helping data centers become more efficient. The number of global internet users grew from 1.9 billion in 2010 to 5.6 billion in 2025. Global internet traffic surged from 20.2 exabytes per month in 2010 to 521.9 exabytes per month in 2025 – a more than 25-fold increase.

Despite the surge in internet traffic and users, data center electricity consumption has grown more moderately, rising from 1% of global electricity use in 2010 to 2% in 2025. Much of this is thanks to efficiency gains, including those enabled by AI.

AI systems analyze operational data in data centers – including workloads, temperature, cooling efficiency and energy use – to spot energy-hungry tasks. It adjusts computing resources to match demand and optimizes cooling. This lets data centers run smoothly without wasting electricity.

At Microsoft, AI is improving energy efficiency by using predictive analytics to schedule computing tasks. This lets servers enter low-power modes during periods of low demand, saving electricity during slower times. Meta uses AI to control cooling and airflow in its data centers. The systems stay safe while using less energy than they might otherwise.

In Frankfurt, Germany, Equinix uses AI to manage cooling and adjust energy use at its data center based on real-time weather. This improved operational efficiency by 9%, The New York Times reported.

Energy and fuels

Energy companies are using AI to boost efficiency and cut emissions. They deploy drones with cameras to inspect pipelines. AI systems analyze the images to more quickly detect corrosion, cracks, dents and leaks, which allows problems to be addressed before they escalate, improving overall safety and reliability.

Shell has AI systems that monitor methane emissions from its facilities by analyzing methane concentrations and wind data, such as speed and direction. This helps the system track how methane disperses, enabling it to pinpoint emission sources and optimize energy use. By identifying the largest leaks quickly, the system allows targeted maintenance and operational adjustments to further reduce emissions. Using that technology, the company says it aims to nearly eliminate methane leaks by 2030.

AI could speed up innovation in clean energy by improving solar panels, batteries and carbon-capture systems. In the longer term, it could enable major breakthroughs, including advanced biofuels or even usable nuclear fusion, while helping track and manage carbon-absorbing resources such as forests, wetlands and carbon storage facilities.

Shell uses AI across its operations to cut emissions. Its process optimizer for liquefied natural gas analyzes sensor data to find more efficient equipment settings, boosting energy efficiency and reducing emissions.

Buildings and district heating

The energy needed to heat, cool and power buildings is responsible for roughly 28% of total global emissions. AI initiatives are starting to reduce building emissions through smart management and predictive optimization.

In downtown Copenhagen, for instance, the local utility company HOFOR deployed thousands of sensors tracking temperatures, humidity and building energy flows. The system uses information about each building to forecast heating needs 24 hours in advance and automatically adjust supply to match demand.

The Copenhagen system was first piloted in schools and multifamily housing, with support from the Nordic Smart City Network and climate-innovation grants. It has since expanded to dozens of sites. Results were clear: Across participating buildings, energy use fell 15% to 25%, peak heating demand dropped by up to 30%, and carbon dioxide emissions decreased by around 10,000 tonnes per year.

AI can also help households and offices save energy. Smart home systems optimize heating, cooling and appliance use. Researchers at the Lawrence Berkeley National Laboratory found that by adopting AI, medium-sized office buildings in the U.S. could reduce energy use by 21% and cut carbon dioxide emissions by 35%.

Aviation

About 2% of all human-caused carbon dioxide emissions in 2023 came from aviation, which emitted about 882 megatons of carbon dioxide.

Contrails, the thin ice clouds formed when aircraft exhaust freezes at cruising altitudes, contribute more than one-third of aviation’s overall warming effect by trapping heat in the atmosphere. AI can optimize flight routes and altitudes in real time to reduce contrail formation by avoiding areas where the air is more humid and therefore more likely to produce contrails.

Airlines have also used AI to improve fuel efficiency. In 2023, Alaska Airlines used 1.2 million gallons less fuel by using AI to analyze weather, wind, turbulence, airspace restrictions and traffic to recommend the most efficient routes, saving around 5% on fuel and emissions for longer flights.

In short, AI affects the environment in both positive and negative ways. Already, it has helped industries cut energy use, lower emissions and use water more efficiently. Expanding these solutions could drive a cleaner, more sustainable planet.

About the Author:

Nir Kshetri, Professor of Management, University of North Carolina – Greensboro

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Opinions Programming

18 Jan

Currency Speculators bets improving for GBP, MXN and going bearish for JPY

By InvestMacro

Here are the latest charts and statistics for the Commitment of Traders (COT) data published by the Commodities Futures Trading Commission (CFTC).

The latest COT data is updated through Tuesday January 13th and shows a quick view of how large market participants (for-profit speculators and commercial traders) were positioned in the futures markets. All currency positions are in direct relation to the US dollar where, for example, a bet for the euro is a bet that the euro will rise versus the dollar while a bet against the euro will be a bet that the euro will decline versus the dollar.

Weekly Speculator Changes led by British Pound

The COT currency market speculator bets were overall lower this week as five out of the eleven currency markets we cover had higher positioning and the other six markets had lower speculator contracts.

Leading the gains for the currency markets was the British Pound (5,268 contracts) with Bitcoin (803 contracts), the Brazilian Real (257 contracts), the Australian Dollar (114 contracts), the US Dollar Index (101 contracts) and also showing positive weeks.

The currencies seeing declines in speculator bets on the week were the Japanese Yen (-53,979 contracts), the EuroFX (-30,156 contracts), the New Zealand Dollar (-5,488 contracts), the Mexican Peso (-5,743 contracts), the Swiss Franc (-3,126 contracts) and with the Canadian Dollar (-1,665 contracts) also recording lower bets on the week.

Speculators bets improving for GBP, MXN and going bearish for JPY

Highlighting the latest currency data is the British pound sterling seeing improved sentiment, the Mexican peso with net contracts above +100,000 positions and the Japanese yen which is shedding speculator contracts.

The British pound sterling which saw its speculator bets rise for a seventh straight week in the latest updated data. Over the last seven weeks, there have been +67,951 contracts added to the GBP speculator standing. This has taken the GBP positioning from a highly bearish -93,221 contracts to this week’s -25,270 contracts, which is the least bearish level of the last 11 weeks. The British pound sterling positioning has been consistently on the bearish side, dating back to July of 2025—a span of 25 weeks. The British pound exchange rate against the US Dollar has fluctuated since that time and is actually down by about 300 pips from the July 2025 high.

The Mexican peso futures speculator bets dipped this week for the first time in four weeks. However, the peso position is strongly bullish at the current moment, with the overall net position above the 100,000 contract level for a fourth straight week and for the fifth time out of the last six weeks. These are currently the highest and most bullish levels for the peso since June of 2024. In the currency market trading, the peso has been on a steady uptrend since bottoming in February 2025. Since hitting that bottom, the peso is up by roughly 20% against the US dollar and is up by over 2% to start 2026.

On the other end of the spectrum, the Japanese yen has seen its net speculator position fall into a bearish net standing this week at -45,164 contracts. This was because of a huge decline on the week of over -53,000 contracts. The sentiment for the Japanese yen has shifted sharply, as the yen bullish position was above +100,000 contracts consistently for 21 weeks last year from March all the way to July. Since then, there has been a steady decline week to week and month to month that has culminated in a negative bearish position for the yen. The yen futures price has also been on a stark downtrend and is touching the lowest levels since 2024. Despite recent interest rates in Japan rising (which is usually a boost for the home currency), the yen has been going the opposite way. Giving caution to the yen bulls is the outlook for the new prime minister possibly being implementing a dovish policy and hindering the Bank of Japan plans to hike the interest rate further.

The Euro speculative position saw a large reduction in bullish bets this week. The change in this week’s speculative position looks like a cool off from a very high position, as the euro speculative contracts have now been over +100,000 for seven straight weeks and above the +100,000 net contract level in 27 out of the last 31 weeks. Last week marked the highest level (+162,812 net contracts) for Euro speculative positions since August of 2023. The Euro currency price seems to be in consolidation mode between the 1.1900 level on the top side and the 1.1500 level on the lower support. Currently in the month of January, the Euro is down by -1.3% but recently hit its highest level since 2021 in November at the high of 1.1979.

Bitcoin leads 5-Day Price Performance Changes

Currency market price changes this week were led by Bitcoin, which rose by almost 6%. The Mexican Peso was next with a 1.95% change over the past five days. The US Dollar Index was higher by 0.37%, and the New Zealand Dollar was higher by 0.35%.

On the downside, the Canadian Dollar was virtually unchanged with a 0.02% decline, followed by the Australian Dollar with a 0.07% decrease. Next up, the Japanese Yen was lower by 0.12%, and the British Pound, as well as the Brazilian Real, were both down by 0.20%. The Swiss Franc fell by 0.29%, followed by the Euro, which fell a similar 0.30% over the past five days.

The biggest movers over the past 90 days have been the Mexican Peso, which is up by approximately 5%. The Brazilian Real is up by almost 2% over that same period, while on the downside, the Japanese Yen has fallen sharply by -6.90% in the past 90 days. The New Zealand Dollar is down by -3.69% in the past 90 days.

Currencies Data:

Legend: Open Interest | Speculators Current Net Position | Weekly Specs Change | Specs Strength Score compared to last 3-Years (0-100 range)

Strength Scores led by Mexican Peso & EuroFX

COT Strength Scores (a normalized measure of Speculator positions over a 3-Year range, from 0 to 100 where above 80 is Extreme-Bullish and below 20 is Extreme-Bearish) showed that the Mexican Peso (81 percent) and the EuroFX (79 percent) lead the currency markets this week. The Canadian Dollar (76 percent), the Australian Dollar (63 percent) and Bitcoin (54 percent) come in as the next highest in the weekly strength scores.

On the downside, the New Zealand Dollar (9 percent) and the Swiss Franc (13 percent) come in at the lowest strength levels currently and are in Extreme-Bearish territory (below 20 percent). The next lowest strength scores are the British Pound (29 percent) and the US Dollar Index (34 percent).

3-Year Strength Statistics:
US Dollar Index (34.1 percent) vs US Dollar Index previous week (33.8 percent)
EuroFX (79.3 percent) vs EuroFX previous week (90.8 percent)
British Pound Sterling (28.9 percent) vs British Pound Sterling previous week (26.6 percent)
Japanese Yen (38.3 percent) vs Japanese Yen previous week (53.1 percent)
Swiss Franc (13.0 percent) vs Swiss Franc previous week (19.3 percent)
Canadian Dollar (76.0 percent) vs Canadian Dollar previous week (76.8 percent)
Australian Dollar (62.9 percent) vs Australian Dollar previous week (62.8 percent)
New Zealand Dollar (9.1 percent) vs New Zealand Dollar previous week (15.3 percent)
Mexican Peso (80.8 percent) vs Mexican Peso previous week (83.9 percent)
Brazilian Real (52.9 percent) vs Brazilian Real previous week (52.7 percent)
Bitcoin (54.2 percent) vs Bitcoin previous week (37.1 percent)

Canadian Dollar & Australian Dollar top the 6-Week Strength Trends

COT Strength Score Trends (or move index, calculates the 6-week changes in strength scores) showed that the Canadian Dollar (53 percent) and the Australian Dollar (46 percent) lead the past six weeks trends for the currencies. The US Dollar Index (34 percent), the British Pound (23 percent) and the EuroFX (9 percent) are the next highest positive movers in the 3-Year trends data.

The Brazilian Real (-31 percent) leads the downside trend scores currently with the Japanese Yen (-22 percent), the Swiss Franc (-15 percent) and Bitcoin (-11 percent) following next with lower trend scores.

3-Year Strength Trends:
US Dollar Index (33.7 percent) vs US Dollar Index previous week (33.8 percent)
EuroFX (9.2 percent) vs EuroFX previous week (26.2 percent)
British Pound Sterling (23.3 percent) vs British Pound Sterling previous week (26.6 percent)
Japanese Yen (-22.4 percent) vs Japanese Yen previous week (-4.9 percent)
Swiss Franc (-15.4 percent) vs Swiss Franc previous week (-9.9 percent)
Canadian Dollar (53.2 percent) vs Canadian Dollar previous week (54.2 percent)
Australian Dollar (45.8 percent) vs Australian Dollar previous week (46.3 percent)
New Zealand Dollar (4.9 percent) vs New Zealand Dollar previous week (9.9 percent)
Mexican Peso (2.5 percent) vs Mexican Peso previous week (7.5 percent)
Brazilian Real (-31.3 percent) vs Brazilian Real previous week (-28.5 percent)
Bitcoin (-10.6 percent) vs Bitcoin previous week (-13.8 percent)

Individual COT Forex Markets:

US Dollar Index Futures:

The US Dollar Index large speculator standing this week recorded a net position of -3,730 contracts in the data reported through Tuesday. This was a weekly advance of 101 contracts from the previous week which had a total of -3,831 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 34.1 percent. The commercials are Bullish with a score of 66.8 percent and the small traders (not shown in chart) are Bearish with a score of 33.3 percent.

Price Trend-Following Model: Weak Uptrend

Our weekly trend-following model classifies the current market price position as: Weak Uptrend.

US DOLLAR INDEX Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	60.0	24.0	9.9
– Percent of Open Interest Shorts:	72.5	11.4	9.9
– Net Position:	-3,730	3,739	-9
– Gross Longs:	17,929	7,158	2,952
– Gross Shorts:	21,659	3,419	2,961
– Long to Short Ratio:	0.8 to 1	2.1 to 1	1.0 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	34.1	66.8	33.3
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	33.7	-31.8	-12.3

Euro Currency Futures:

The Euro Currency large speculator standing this week recorded a net position of 132,656 contracts in the data reported through Tuesday. This was a weekly lowering of -30,156 contracts from the previous week which had a total of 162,812 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 79.3 percent. The commercials are Bearish-Extreme with a score of 20.0 percent and the small traders (not shown in chart) are Bullish with a score of 73.9 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

EURO Currency Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	32.1	55.2	10.4
– Percent of Open Interest Shorts:	17.1	75.5	5.0
– Net Position:	132,656	-179,767	47,111
– Gross Longs:	283,592	487,595	91,580
– Gross Shorts:	150,936	667,362	44,469
– Long to Short Ratio:	1.9 to 1	0.7 to 1	2.1 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	79.3	20.0	73.9
– Strength Index Reading (3 Year Range):	Bullish	Bearish-Extreme	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	9.2	-8.2	-0.4

British Pound Sterling Futures:

The British Pound Sterling large speculator standing this week recorded a net position of -25,270 contracts in the data reported through Tuesday. This was a weekly lift of 5,268 contracts from the previous week which had a total of -30,538 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 28.9 percent. The commercials are Bullish with a score of 70.2 percent and the small traders (not shown in chart) are Bullish with a score of 52.6 percent.

Price Trend-Following Model: Weak Downtrend

Our weekly trend-following model classifies the current market price position as: Weak Downtrend.

BRITISH POUND Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	38.0	46.8	13.9
– Percent of Open Interest Shorts:	50.2	34.5	14.0
– Net Position:	-25,270	25,504	-234
– Gross Longs:	79,003	97,243	28,832
– Gross Shorts:	104,273	71,739	29,066
– Long to Short Ratio:	0.8 to 1	1.4 to 1	1.0 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	28.9	70.2	52.6
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	23.3	-24.9	22.9

Japanese Yen Futures:

The Japanese Yen large speculator standing this week recorded a net position of -45,164 contracts in the data reported through Tuesday. This was a weekly decline of -53,979 contracts from the previous week which had a total of 8,815 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 38.3 percent. The commercials are Bullish with a score of 62.5 percent and the small traders (not shown in chart) are Bearish with a score of 35.2 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

JAPANESE YEN Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	38.0	38.8	15.1
– Percent of Open Interest Shorts:	53.3	23.3	15.3
– Net Position:	-45,164	45,819	-655
– Gross Longs:	111,743	114,303	44,360
– Gross Shorts:	156,907	68,484	45,015
– Long to Short Ratio:	0.7 to 1	1.7 to 1	1.0 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	38.3	62.5	35.2
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-22.4	21.2	-6.1

Swiss Franc Futures:

The Swiss Franc large speculator standing this week recorded a net position of -43,392 contracts in the data reported through Tuesday. This was a weekly decrease of -3,126 contracts from the previous week which had a total of -40,266 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 13.0 percent. The commercials are Bullish with a score of 76.4 percent and the small traders (not shown in chart) are Bullish with a score of 62.8 percent.

Price Trend-Following Model: Weak Uptrend

Our weekly trend-following model classifies the current market price position as: Weak Uptrend.

SWISS FRANC Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	13.9	71.5	14.5
– Percent of Open Interest Shorts:	59.0	22.5	18.4
– Net Position:	-43,392	47,163	-3,771
– Gross Longs:	13,395	68,778	13,977
– Gross Shorts:	56,787	21,615	17,748
– Long to Short Ratio:	0.2 to 1	3.2 to 1	0.8 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	13.0	76.4	62.8
– Strength Index Reading (3 Year Range):	Bearish-Extreme	Bullish	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-15.4	16.8	-11.5

Canadian Dollar Futures:

The Canadian Dollar large speculator standing this week recorded a net position of -42,250 contracts in the data reported through Tuesday. This was a weekly decline of -1,665 contracts from the previous week which had a total of -40,585 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 76.0 percent. The commercials are Bearish with a score of 30.4 percent and the small traders (not shown in chart) are Bearish with a score of 35.4 percent.

Price Trend-Following Model: Weak Uptrend

Our weekly trend-following model classifies the current market price position as: Weak Uptrend.

CANADIAN DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	28.6	55.7	12.8
– Percent of Open Interest Shorts:	47.9	34.8	14.4
– Net Position:	-42,250	45,815	-3,565
– Gross Longs:	62,705	122,096	28,109
– Gross Shorts:	104,955	76,281	31,674
– Long to Short Ratio:	0.6 to 1	1.6 to 1	0.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	76.0	30.4	35.4
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	53.2	-49.9	15.5

Australian Dollar Futures:

The Australian Dollar large speculator standing this week recorded a net position of -18,846 contracts in the data reported through Tuesday. This was a weekly advance of 114 contracts from the previous week which had a total of -18,960 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 62.9 percent. The commercials are Bearish with a score of 27.8 percent and the small traders (not shown in chart) are Bullish-Extreme with a score of 100.0 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

AUSTRALIAN DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	36.6	45.0	17.3
– Percent of Open Interest Shorts:	44.8	46.6	7.6
– Net Position:	-18,846	-3,610	22,456
– Gross Longs:	83,955	103,295	39,790
– Gross Shorts:	102,801	106,905	17,334
– Long to Short Ratio:	0.8 to 1	1.0 to 1	2.3 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	62.9	27.8	100.0
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bullish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	45.8	-44.0	22.7

New Zealand Dollar Futures:

The New Zealand Dollar large speculator standing this week recorded a net position of -48,851 contracts in the data reported through Tuesday. This was a weekly reduction of -5,488 contracts from the previous week which had a total of -43,363 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 9.1 percent. The commercials are Bullish-Extreme with a score of 89.6 percent and the small traders (not shown in chart) are Bearish with a score of 45.7 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

NEW ZEALAND DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	11.3	81.8	4.7
– Percent of Open Interest Shorts:	68.6	23.8	5.3
– Net Position:	-48,851	49,362	-511
– Gross Longs:	9,613	69,662	4,002
– Gross Shorts:	58,464	20,300	4,513
– Long to Short Ratio:	0.2 to 1	3.4 to 1	0.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	9.1	89.6	45.7
– Strength Index Reading (3 Year Range):	Bearish-Extreme	Bullish-Extreme	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	4.9	-7.4	29.9

Mexican Peso Futures:

The Mexican Peso large speculator standing this week recorded a net position of 103,558 contracts in the data reported through Tuesday. This was a weekly lowering of -5,743 contracts from the previous week which had a total of 109,301 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish-Extreme with a score of 80.8 percent. The commercials are Bearish-Extreme with a score of 19.4 percent and the small traders (not shown in chart) are Bearish with a score of 48.4 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

MEXICAN PESO Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	63.7	33.1	3.0
– Percent of Open Interest Shorts:	20.8	77.9	1.1
– Net Position:	103,558	-108,180	4,622
– Gross Longs:	153,670	79,940	7,287
– Gross Shorts:	50,112	188,120	2,665
– Long to Short Ratio:	3.1 to 1	0.4 to 1	2.7 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	80.8	19.4	48.4
– Strength Index Reading (3 Year Range):	Bullish-Extreme	Bearish-Extreme	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	2.5	-2.6	1.2

Brazilian Real Futures:

The Brazilian Real large speculator standing this week recorded a net position of 17,874 contracts in the data reported through Tuesday. This was a weekly boost of 257 contracts from the previous week which had a total of 17,617 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 52.9 percent. The commercials are Bearish with a score of 46.3 percent and the small traders (not shown in chart) are Bearish with a score of 38.5 percent.

Price Trend-Following Model: Weak Downtrend

Our weekly trend-following model classifies the current market price position as: Weak Downtrend.

BRAZIL REAL Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	66.3	27.5	5.1
– Percent of Open Interest Shorts:	43.7	54.1	1.1
– Net Position:	17,874	-21,003	3,129
– Gross Longs:	52,400	21,753	4,017
– Gross Shorts:	34,526	42,756	888
– Long to Short Ratio:	1.5 to 1	0.5 to 1	4.5 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	52.9	46.3	38.5
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-31.3	30.7	-0.2

Bitcoin Futures:

The Bitcoin large speculator standing this week recorded a net position of 69 contracts in the data reported through Tuesday. This was a weekly increase of 803 contracts from the previous week which had a total of -734 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 54.2 percent. The commercials are Bullish with a score of 53.6 percent and the small traders (not shown in chart) are Bearish with a score of 40.7 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

BITCOIN Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	80.2	5.3	5.0
– Percent of Open Interest Shorts:	79.9	5.8	4.8
– Net Position:	69	-134	65
– Gross Longs:	19,118	1,257	1,204
– Gross Shorts:	19,049	1,391	1,139
– Long to Short Ratio:	1.0 to 1	0.9 to 1	1.1 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	54.2	53.6	40.7
– Strength Index Reading (3 Year Range):	Bullish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-10.6	10.2	2.1

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