What Is a Managed Futures Screener CTA Database Iasg?

Introduction: Understanding Managed Futures Screeners and the IASG Database

Managed futures represent one of the oldest and most established alternative investment strategies available to institutional and sophisticated investors. Unlike traditional long-only equity or bond portfolios, managed futures programs are traded by Commodity Trading Advisors (CTAs)—professional money managers who take long and short positions across futures, options, and forward contracts spanning commodities, currencies, interest rates, equity indices, and more. Today, the managed futures industry oversees well over $300 billion in assets globally, reflecting its role as a core diversification tool within institutional and family office portfolios, particularly as an uncorrelated complement to traditional equity and fixed income holdings, as well as strategies employed by a what-is-a-hedge-fund.

Evaluating the hundreds of CTA programs available requires robust data. This is where IASG—the International Advisors Securities Group—comes in. IASG operates one of the most widely used managed futures screener and CTA database platforms in the industry, tracking thousands of CTA programs with detailed historical performance data, risk metrics, and manager profiles. Investors and allocators rely on screening tools like IASG to efficiently narrow a large universe of trading advisors down to a manageable shortlist based on quantitative criteria. This article explains what IASG is, how its screener functions, the metrics it surfaces, and how allocators incorporate it into broader due diligence workflows.

What Is a CTA (Commodity Trading Advisor)?

A Commodity Trading Advisor, or CTA, is an individual or firm that provides advice regarding the buying or selling of futures contracts, options on futures, retail off-exchange forex, or swaps, and that does so for compensation as part of a regular business. The term originates from the Commodity Exchange Act and the rules promulgated by the Commodity Futures Trading Commission (CFTC), which together establish the legal framework governing how these advisors solicit clients, manage accounts, and disclose risk. While the name suggests a narrow focus on physical commodities, modern CTAs trade across a vastly broader opportunity set that includes financial futures, making the designation somewhat of a historical artifact rather than a literal description of strategy.

CTAs vs. Traditional Asset Managers and Hedge Fund Managers

CTAs differ meaningfully from traditional long-only asset managers and from many hedge fund structures. Where a traditional asset manager typically buys securities with an expectation of long-term appreciation, a CTA actively trades derivative instruments, frequently holding both long and short positions simultaneously across dozens of global markets. Unlike many hedge funds that pool investor capital into a commingled fund structure, CTAs most commonly trade through individually managed accounts held directly at a futures commission merchant, giving the underlying investor direct ownership and daily visibility into positions. For a broader comparison of pooled investment vehicles, see this overview of types-of-hedge-funds and this deeper look at hedge-fund-strategies-explained.

Markets and Instruments Traded

CTAs operate across an expansive range of asset classes, including agricultural and energy commodities, metals, currencies, global interest rate products, and equity index futures. Many programs also incorporate options on futures and, in some cases, forward contracts in foreign exchange markets. This breadth allows CTAs to construct globally diversified portfolios that can express views—or systematically respond to price action—across dozens of uncorrelated markets simultaneously.

Registration Requirements with the NFA and CFTC

CTAs must register with the National Futures Association (NFA) unless they qualify for a specific exemption, such as advising fewer than 15 clients within the preceding 12 months without public solicitation. Registered CTAs are subject to NFA oversight, including disclosure document filing requirements, periodic audits, and ongoing reporting obligations to the CFTC. This regulatory structure provides a baseline layer of transparency and accountability that allocators can verify independently through NFA's public records.

Common Trading Styles

The managed futures universe encompasses several distinct trading philosophies. Trend-following CTAs represent the largest subset of the managed futures space, using systematic models to identify and ride sustained price momentum across markets. Discretionary CTAs rely on manager judgment and qualitative analysis, while systematic macro programs blend quantitative models with fundamental economic inputs to position across interest rates, currencies, and equity indices based on broader macroeconomic themes.

What Is the IASG CTA Database?

The International Advisors Securities Group, commonly known as IASG, has operated for decades as one of the most widely referenced performance tracking platforms in the managed futures industry. Originally built to serve introducing brokers and futures professionals seeking a centralized way to evaluate trading advisors, IASG has evolved into a publicly accessible database that allows investors, allocators, and researchers to screen and compare CTA programs side by side. Its core purpose remains consistent: to aggregate standardized performance data from trading advisors so that users can conduct preliminary analysis without contacting each CTA individually.

Data Housed Within the Platform

The IASG database includes performance profiles for hundreds of active CTA trading programs, spanning strategies from long-term trend following to short-term systematic and discretionary macro approaches. Each profile typically contains monthly rate-of-return tables going back to the program's inception, along with calculated statistics such as compound annual growth rate, maximum drawdown, and annualized volatility. Profiles also list assets under management for the specific program, minimum investment thresholds required to open an account, fee structures, and basic biographical information about the advisory firm and its principals. This combination of quantitative and qualitative data gives users a reasonably complete snapshot of a program's historical behavior and operational parameters.

Self-Reporting and Data Updates

CTAs voluntarily submit their performance figures to IASG, typically on a monthly basis shortly after each reporting period closes. Data is typically updated monthly as CTAs report returns, meaning the database reflects a relatively current picture of how programs are performing, though there is inherently a short lag between the close of a trading month and the public posting of results. Because reporting is self-administered rather than independently audited by IASG itself, the accuracy and timeliness of each profile depends on the diligence of the advisor submitting the data—a limitation addressed in greater detail later in this article.

Who Relies on the IASG Database

The platform serves a diverse user base. Introducing brokers and futures commission merchants use it to identify programs suitable for client accounts. Institutional allocators and consultants use it as a first-pass screening tool before initiating formal due diligence. Due diligence teams at what-is-a-fund-of-funds structures and multi-strategy platforms reference it when building diversified CTA allocations, and individual accredited investors use it independently to research programs before contacting advisors directly.

How IASG Differs from Other Databases

IASG is frequently discussed alongside competing platforms such as BarclayHedge and Autumn Gold, each of which maintains its own CTA coverage, update cadence, and subscription model. While there is meaningful overlap in the programs listed across these databases, differences in reporting requirements, historical depth, and user interface mean that experienced allocators often cross-reference multiple sources rather than relying on a single platform exclusively.

How a Managed Futures Screener Works

At its core, a managed futures screener functions much like any financial screening tool: it allows users to input specific criteria, filter a large universe of CTA programs down to a manageable shortlist, and then compare those programs side by side. Understanding the mechanics of this process helps investors use platforms like IASG more effectively and interpret the results with appropriate context.

Inputting Filter Criteria

The screening process typically begins with the user selecting quantitative thresholds that matter most to their portfolio objectives. Common screener filters include CAGR, max drawdown, Sortino ratio, and correlation to S&P 500, alongside other standard inputs such as Sharpe ratio, assets under management, and minimum investment size. For example, an allocator seeking a conservative diversifier might filter for CTAs with a Sortino ratio above 1.0, a correlation to the S&P 500 below 0.2, and AUM exceeding $50 million to ensure sufficient operational scale. A more risk-tolerant investor might instead prioritize raw return potential, filtering for higher CAGR figures while accepting greater volatility.

Sorting and Ranking Mechanisms

Once filters are applied, the screener generates a results list that can typically be sorted or ranked by any available metric—annualized return, worst monthly drawdown, length of track record, or risk-adjusted performance ratios. This ranking functionality allows users to quickly identify which programs lead the pack on a given measure, though experienced allocators generally avoid ranking by a single metric alone, since a program with the highest raw return may carry disproportionate risk relative to peers with more balanced profiles.

Customizable Search Parameters

Beyond basic performance filters, most screeners allow for more granular customization. Users can narrow results by sector focus (e.g., agricultural commodities, financial futures, discretionary macro), geography of the advisory firm, or minimum track record length. A frequently used example is filtering CTAs with 5+ year track record and under 10% max drawdown—a combination that helps surface programs with both longevity and demonstrated downside discipline across at least one full market cycle. Additional parameters may include minimum investment thresholds, fee structures, and whether the program trades a single sector or a diversified multi-sector portfolio.

Exporting and Comparing Shortlists

After narrowing the universe to a workable shortlist—often 10 to 20 programs—most screeners allow users to export data into spreadsheet format or generate side-by-side comparison views. This step is critical for building internal due diligence documentation and for presenting candidate programs to investment committees. Comparative exports typically preserve monthly return streams, drawdown histories, and key ratios, enabling deeper statistical analysis outside the platform itself, such as correlation testing against an existing portfolio.

Limitations of Self-Reported Data

Because the underlying figures populating these screeners are self-reported by CTAs rather than independently audited, filter results should be treated as a starting point rather than a final verdict. Reporting inconsistencies, rounding differences, or delayed updates can subtly skew rankings. For this reason, allocators are encouraged to use screener output as a preliminary sorting mechanism—paired with the due diligence practices discussed later in this article, including cross-referencing data with regulatory filings and direct advisor interviews—rather than as a standalone basis for allocation decisions. For additional context on how due diligence teams evaluate pooled alternative structures, see what-is-a-fund-of-funds.

Key Metrics Found in a CTA Database

Once a shortlist of candidate programs has been assembled, the real analytical work begins. CTA databases present a standardized set of performance and risk metrics designed to allow allocators to compare dissimilar trading programs on common ground. Understanding what each metric actually measures—and its limitations—is essential before drawing conclusions about manager quality or portfolio fit.

Return Metrics: CAGR and Monthly Performance

The compound annual growth rate (CAGR) is the headline figure most investors look to first, summarizing a program's annualized return over its full track record. However, CAGR alone can be misleading if viewed in isolation—it says nothing about the path taken to achieve that return. For this reason, databases also display granular monthly return tables, allowing allocators to identify seasonality, losing streaks, and the distribution of winning versus losing months. Comparing monthly dispersion alongside CAGR gives a far more complete picture of how a strategy actually behaves across varying market regimes, a concept explored further in hedge-fund-strategies-explained.

Risk-Adjusted Metrics

Raw returns mean little without context on the risk taken to generate them. Standard deviation measures the volatility of monthly returns, while maximum drawdown captures the largest peak-to-trough decline a program has experienced—arguably the single most scrutinized statistic in managed futures due diligence. Beyond these, most databases calculate the Sharpe ratio (return per unit of total volatility), the Sortino ratio (return per unit of downside volatility only), and the Calmar ratio (return relative to maximum drawdown). Each ratio emphasizes a different dimension of risk, and allocators typically review all three together rather than relying on a single figure.

Correlation Statistics

A core appeal of managed futures is diversification, so databases typically publish correlation coefficients versus major benchmarks such as the S&P 500 and the Barclays Aggregate Bond Index. Programs with low or negative correlation to equities are often prioritized by allocators seeking crisis-period ballast, though correlation profiles can shift meaningfully depending on the market environment and the strategy's directional bias.

AUM, Minimums, and Track Record Length

Assets under management (AUM) signal both a program's capacity constraints and its credibility with existing investors, while minimum investment thresholds determine accessibility—typically ranging from $25,000 to $1 million or more depending on the advisor and share class. Track record length and manager tenure round out the picture, with longer histories providing more confidence that a strategy has survived multiple market cycles rather than a single favorable stretch.

As illustrated above, higher Sharpe ratios do not always correspond to larger AUM or longer track records—underscoring why allocators must weigh multiple metrics simultaneously rather than ranking programs on any single data point.

Benefits of Using a Managed Futures Screener Like IASG

For allocators tasked with surveying a universe of hundreds of active CTA programs, a screener like IASG offers immediate practical value by collapsing what would otherwise be weeks of manual outreach and spreadsheet-building into a matter of hours. Rather than contacting individual trading advisors one by one to request performance tapes and marketing decks, an allocator can filter the entire database by strategy type, drawdown tolerance, track record length, and AUM in a single session—generating a shortlist that would have taken considerably longer to assemble through cold outreach or broker introductions alone. This efficiency is particularly valuable for smaller family offices and independent advisors who lack dedicated research teams but still need defensible, data-driven manager selection processes.

Beyond speed, screeners provide meaningful transparency into how CTA programs have performed across varied and often stressful market cycles. Because IASG maintains monthly return histories stretching back years or even decades for some programs, investors can examine how a given strategy behaved not just in benign markets but during periods of acute equity stress. This matters because managed futures have historically demonstrated low to negative correlation to equities during major downturns—most notably in 2008, when many trend-following CTAs posted strongly positive returns as equity markets collapsed, and again in 2022, when systematic trend followers captured gains from sustained moves in interest rates, currencies, and commodities while traditional 60/40 portfolios suffered simultaneous losses in stocks and bonds.

Screeners also facilitate diversification by making it easy to compare and blend multiple CTA strategies—trend-following, discretionary macro, short-term systematic, and sector specialists—within a single allocation, reducing reliance on any one manager's style or market view. This is a meaningful advantage for allocators building multi-strategy managed futures portfolios rather than concentrating capital in a single program.

Importantly, these tools are not reserved exclusively for large institutions. Many CTA programs listed on IASG accept managed accounts with minimums accessible to accredited individual investors, meaning the same due diligence infrastructure used by pension consultants and fund-of-funds managers is also available to smaller allocators and those exploring how to become a hedge fund manager or build a track record within the alternatives space. how-to-become-a-hedge-fund-manager

Finally, because CTAs report performance using standardized formats and consistent metrics, screeners enable genuine apples-to-apples comparisons across managers—something far harder to achieve when evaluating offering memoranda independently, each formatted differently and emphasizing different time periods or benchmarks favorable to the manager in question.

Limitations and Risks of CTA Databases

While screening platforms like IASG provide invaluable transparency into the managed futures space, allocators must understand the inherent limitations of self-reported performance databases before relying on them as a sole basis for investment decisions. These tools are powerful starting points for due diligence—not substitutes for it.

Self-Reporting and Survivorship Bias

CTA databases depend on trading advisors voluntarily submitting their own performance data, creating an inherent self-reporting bias. Programs with poor results have little incentive to report, and many simply stop updating their profiles or close entirely rather than publicize failure. This creates survivorship bias: the universe of CTAs visible in any given database skews toward successful, surviving managers, while failed or underperforming programs quietly disappear from view. Academic research examining managed futures databases has repeatedly found that survivorship bias can overstate reported industry returns by roughly 1% to 3% annually, meaning the aggregate performance statistics investors see may be meaningfully more favorable than what a representative, unbiased universe of CTAs would have actually delivered.

Backfill Bias

A related distortion is backfill bias, which occurs when a CTA joins a database and is permitted to contribute historical performance data accumulated before it began formally reporting. Because managers tend to join databases only after establishing a track record they consider strong enough to market, the backfilled history is disproportionately likely to reflect favorable results rather than a neutral sample of the manager's full performance history, including early losses or false starts.

Past Performance Is Not Predictive

Even accurate, unbiased historical data carries an unavoidable caveat: past performance is not indicative of future results. Trend-following strategies that thrived during the rate-hiking, high-volatility environment of 2022 may underperform in prolonged low-volatility or range-bound markets. Manager skill, risk controls, and strategy robustness matter more than backward-looking Sharpe ratios alone.

Coverage Gaps Across Platforms

No single database captures the entire managed futures universe. Many CTAs report exclusively to one platform, maintain private arrangements with select brokers, or avoid public databases altogether due to capacity constraints or marketing preferences. This means IASG, BarclayHedge, and similar platforms each present only a partial picture of the broader CTA landscape, reinforcing the need to consult multiple sources.

The Case for Independent Verification

Given these biases, allocators should treat screener data as a preliminary filter rather than a final verdict. Independent verification—cross-checking NFA registration status, reviewing audited financials, and examining the manager's hedge-fund-structure-legal-framework and disclosure documents—remains essential before allocating capital to any CTA program.

IASG vs. Other Managed Futures Databases

IASG is one of several platforms institutional allocators and retail-accredited investors use to research CTA programs, but it is far from the only option. BarclayHedge, Autumn Gold, and Nilsson Hedge each occupy slightly different niches in terms of coverage depth, cost structure, and the sophistication of their analytical tools, and allocators conducting thorough due diligence typically cross-reference more than one source rather than relying on a single database.

Coverage, Cost, and Specialty Compared

IASG has historically distinguished itself through free public access and a straightforward, broker-friendly interface geared toward introducing CTA programs to prospective managed account clients. BarclayHedge, by contrast, maintains one of the industry's longest-running datasets—its indices date back decades—and offers more granular institutional-grade analytics, though much of its deepest data sits behind subscription paywalls. Autumn Gold is frequently cited for its depth in niche and emerging CTA strategies, while Nilsson Hedge has built a reputation around aggregating data across multiple sources with strong charting and comparison tools for quantitative due diligence teams.

Update frequency across these platforms is largely similar, with most CTAs reporting monthly returns in arrears, though the depth of historical detail and the granularity of risk statistics—rolling drawdowns, correlation matrices, and peer group rankings—vary meaningfully between providers.

Which Platforms Institutional Allocators Prefer

Institutional allocators conducting formal due diligence often gravitate toward BarclayHedge or Nilsson Hedge for their deeper historical datasets and more robust analytical functionality, reserving IASG primarily as an initial screening and discovery tool given its free access and broker-oriented positioning. Many sophisticated investors also draw comparisons to practices used in evaluating types-of-hedge-funds, where multiple data vendors are similarly triangulated to reduce single-source bias. Ultimately, no single database is considered definitive, and prudent allocators treat each platform as one input among several in a broader, multi-source due diligence process.

How Allocators and Investors Use CTA Screeners in Due Diligence

For institutional allocators, family offices, and sophisticated individual investors, a managed futures screener like IASG rarely functions as the final word on manager selection. Instead, it serves as the entry point into a much more rigorous due diligence process—one that layers quantitative screening with operational, legal, and qualitative assessment before any capital is committed.

Building a Shortlist Before Deeper Due Diligence

The typical workflow begins with broad filtering across the database to narrow thousands of possible CTA programs down to a manageable shortlist of perhaps 10 to 20 candidates. Allocators often apply initial screens for minimum track record (commonly three to five years), acceptable drawdown thresholds, and strategy fit within an existing portfolio. Only after this initial pass do teams move into the labor-intensive work of operational and legal review, which can take weeks or months per manager. This staged approach allows research teams to allocate scarce due diligence resources efficiently rather than performing full reviews on every program in the database.

Cross-Referencing with Regulatory Filings

Screener data should never be treated as a substitute for primary source documents. Prudent allocators cross-reference performance figures and firm details against a CTA's Form ADV (if dually registered as an investment adviser), NFA disclosure documents, and offering memoranda or managed account agreements. A basic due diligence checklist typically includes:

• Verifying registration status and disciplinary history through the NFA's BASIC system
• Comparing self-reported returns in the screener against audited performance records or account statements
• Reviewing fee structures, redemption terms, and minimum investment levels in offering documents
• Confirming assets under management figures against independent custodial or prime broker data where available

This cross-checking process mirrors the diligence standards applied to pooled vehicles, as discussed in our overview of hedge-fund-structure-legal-framework, where legal document review is equally essential before allocation.

Working Alongside Consultants and Fund-of-Funds Managers

Many institutional investors do not rely solely on in-house teams; they engage investment consultants or allocate through a what-is-a-fund-of-funds structure that specializes in managed futures. These intermediaries often maintain proprietary databases that supplement public screeners like IASG with deeper operational insight gained through direct manager relationships.

Portfolio Construction and Red Flags

Beyond individual manager selection, screeners help allocators construct diversified CTA portfolios by blending trend-following strategies with counter-trend, discretionary, or relative-value approaches to smooth overall return streams. When reviewing reported performance, experienced allocators watch for red flags such as unusually smooth equity curves, inconsistent reporting gaps, sudden AUM spikes without corresponding track record depth, or returns that appear statistically improbable relative to stated strategy and volatility targets.

Managed Futures vs. Hedge Funds: How They Differ in Databases

Although managed futures and what-is-a-hedge-fund strategies are frequently discussed in the same alternative investment conversations, the underlying structures that generate the data populating their respective databases are fundamentally different. Understanding these distinctions helps explain why IASG and similar CTA-focused platforms exist separately from hedge fund databases rather than as a unified alternatives repository.

The most significant structural difference lies in how capital is held. CTAs typically trade through individually managed accounts, meaning each client's assets remain in a segregated account at a futures commission merchant, with the CTA granted trading authority via a limited power of attorney. Hedge funds, by contrast, operate as pooled investment vehicles—limited partnerships or offshore corporations—where investor capital is commingled into a single fund structure. This distinction matters enormously for transparency: managed account investors can often view daily position-level activity and account balances, while hedge fund investors typically receive only monthly or quarterly net asset value statements and limited look-through into underlying holdings. This daily transparency versus monthly/quarterly reporting gap is one reason managed futures data tends to be more granular and verifiable within platforms like IASG.

Regulatory Divergence

Regulatory oversight further separates the two categories. CTAs and commodity pool operators register with the NFA and fall under CFTC jurisdiction, with rules governing futures and derivatives trading, disclosure documents, and reporting obligations. Hedge fund managers, particularly those trading equities, credit, and other securities, generally register as investment advisers with the SEC, subject to the Investment Advisers Act. While some managers—particularly those running types-of-hedge-funds involving commodities or managed futures components—may register with both bodies, the primary regulatory home differs, shaping what data must be disclosed and how often.

Why Databases Stay Segmented

These structural and regulatory differences explain why platforms specialize rather than merge. IASG's architecture is built around managed account reporting conventions, VAMI calculations, and NFA registration data that don't map cleanly onto hedge fund share class structures, side pockets, or lock-up provisions common in commingled funds. Allocators researching both strategy types often must consult separate specialized databases, reinforcing the value of cross-platform research when building a fully diversified alternatives allocation.

How AlphaMaven Complements Managed Futures Research

While IASG and its peer databases excel at providing granular, standardized CTA performance data, serious allocators rarely stop their research at a single platform. AlphaMaven adds a complementary layer by offering a centralized gateway into the broader alternative investment universe, housing 794+ fund listings and a database of 117,108+ companies spanning hedge funds, private equity, venture capital, real assets, and managed futures-adjacent strategies. For investors who have already screened CTA programs on IASG, AlphaMaven provides the contextual landscape needed to understand how those strategies fit within a broader portfolio of alternative managers.

This matters because managed futures rarely exist in isolation within an institutional or high-net-worth portfolio. Allocators typically evaluate CTAs alongside long/short equity funds, credit strategies, and multi-manager vehicles to build a diversified book of uncorrelated return streams. AlphaMaven's extensive company database allows researchers to cross-reference a CTA's parent organization, affiliated investment vehicles, or related fund structures—information that pure performance screeners like IASG are not designed to surface. An allocator might, for instance, discover that a trend-following CTA profiled on IASG is also affiliated with a hedge fund platform or multi-strategy complex listed on AlphaMaven, enriching the due diligence picture considerably.

AlphaMaven also proves valuable for investors researching fund-of-funds structures that blend managed futures exposure with other alternative strategies. Many institutional-quality multi-strategy vehicles allocate a sleeve of capital to CTAs precisely because of their historically low correlation to equities, and AlphaMaven's listings help investors identify which fund-of-funds sponsors are actively incorporating managed futures into their broader mandates.

Ultimately, the value proposition is consolidation. Rather than toggling between disconnected niche databases, allocators can use AlphaMaven as a central research hub to contextualize CTA screener data within the wider alternatives ecosystem—supporting more informed manager selection, peer comparison, and portfolio construction decisions across asset classes and strategy types.

Conclusion: Key Takeaways on CTA Screeners and the IASG Database

The IASG database remains one of the industry's most accessible tools for identifying, filtering, and comparing commodity trading advisors across trend-following, discretionary, and systematic macro strategies. With performance profiles spanning hundreds of active programs and monthly-updated metrics like CAGR, Sharpe ratio, and maximum drawdown, screeners like IASG give allocators a practical starting point for narrowing a universe of CTAs into a manageable shortlist worthy of deeper scrutiny.

That said, no screener is a substitute for rigorous due diligence. Self-reporting bias, survivorship bias, and backfill distortions mean that headline statistics should always be cross-checked against NFA filings, disclosure documents, and independent verification before capital is committed. Past performance, however favorable, offers no guarantee of future results—particularly for strategies that depend on trend persistence or volatility regimes that shift over time.

Sophisticated allocators rarely rely on a single data source. Pairing IASG's performance data with broader research platforms like AlphaMaven, along with insights from hedge-fund-strategies-explained comparisons, produces a more complete, triangulated view of manager quality and strategy fit. Used correctly, these tools help investors construct portfolios where managed futures serve their intended purpose: delivering liquid, transparent, and historically uncorrelated returns that strengthen diversification across market cycles.