The Covert War in Crypto Quantitative Trading: Victory Is Shifting from Strategies to Infrastructure
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The Covert War in Crypto Quantitative Trading: Victory Is Shifting from Strategies to Infrastructure
For infrastructure service providers like QSG, the opportunity lies not merely in selling a set of tools to quant teams, but in participating in the rewriting of the underlying standards for crypto trading.
Navigating Web3 tides with focused insightsAuthor: TechFlow
When discussing the big winners in cryptocurrency, people usually think of exchanges, market makers, or “diamond-handed” retail traders who struck it rich overnight during bull markets.
Retail investors ride the rollercoaster between bull and bear markets—some buy at the peak, others liquidate at the bottom. Market volatility becomes their nightmare.
Yet amid this speculative, frenzied landscape, one group consistently generates stable profits: quantitative trading teams.
These mysterious winners rarely appear in public view. They don’t flaunt returns on social media, avoid KOL call-outs, and seldom grant interviews to the press. They operate like a “shadow force” behind the markets—quietly harvesting profits from every crypto price swing.
So what exactly enables them to profit steadily?
In this highly uncertain market, how do they achieve both offensive agility and defensive resilience—transforming trading into a science?
The Quant Landscape: From Arbitrage “Brick-Moving” to an Arms Race
The history of crypto quant trading condenses half a century of evolution from traditional finance.
The Arbitrage “Brick-Moving” Era (2017–2018): Rules were simple and brutal—price discrepancies for the same asset across exchanges often reached 5–10%. A programmer running scripts across multiple exchanges on a few laptops could generate several-fold annualized returns. BitMEX founder Arthur Hayes and FTX founder SBF both earned their first fortunes through arbitrage. In 2018, SBF noticed a 10% Bitcoin premium in Japan, assembled a small team, and executed cross-exchange arbitrage—earning roughly $20 million within weeks, giving birth to Alameda Research.
It was an era of wild, unregulated growth. “Back then, things were truly chaotic: primary token sales were failing left and right; secondary markets were crashing hard. Many token-focused funds pivoted into quant trading,” recalls Leo, a former crypto VC professional. “Early crypto quants fell into three main camps: Wall Street veterans, former A-share traders, and pure crypto-native ‘self-taught’ operators.” In this frontier period, practitioners experimented through bear markets—some used hundreds of bitcoins as test capital; others cursed under their breath at exchange APIs that crashed constantly.
The Professionalization Era (2020–2023): The DeFi Summer ignited the fuse. A wave of teams with backgrounds in traditional finance and internet technology flooded in, driving genuine elite professionalization in crypto quant trading. A more critical shift occurred on the funding side: family offices and institutional asset managers replaced miners as the new financial backers of quant teams.
“Top-tier quant teams today are essentially ‘sponsored’—they serve major family offices or asset managers. With no shortage of capital, they deliberately maintain low profiles,” says Grace, a business development lead at a crypto-focused asset manager, describing the ecosystem.
Stephanie, Partner at multi-strategy quant fund Target Capital, notes that their clients are primarily well-known family offices based in Singapore and Hong Kong. For these family offices, crypto quant’s appeal is straightforward: they’re not afraid of missing explosive rallies—but absolutely cannot tolerate sharp drawdowns. Annualized returns of 15–25% with stability hold far greater appeal than the remote chance of a 100x return on a token that could go to zero. Crypto quant thus became many traditional family offices’ first step into the crypto world.
The Institutionalization Era (2024–Present): BTC spot ETF approvals, gradual global regulatory frameworks, and large-scale entry by traditional financial institutions are transforming crypto markets—from a “retail poker table” into an “institutional battlefield.”
But this transition brings a challenge all practitioners feel acutely: strategies are becoming increasingly crowded—and increasingly identical.
Leo puts it bluntly: “Over 80% of secondary-market quant teams now run highly neutral arbitrage strategies. Strategy homogenization is severe.”
Oliver Chen, CFO & COO of QSG, observes the same trend from the perspective of an infrastructure provider: “A clear example is funding rate arbitrage. Large funds, family offices, and LPs generally prefer low-drawdown, smooth-return strategies—so this strategy has become extremely crowded over the past few years. The problem? When everyone’s signals and trading logic converge, what ultimately determines performance differentials is infrastructure.”
Even more awkwardly, crypto’s elevated risk-free yields are delivering a “dimensional strike” against traditional quant strategies. During bull markets, Pendle’s risk-free yield can exceed 30%—meaning your painstakingly modeled alpha may underperform simply holding assets on-chain.
When strategies reach the point of diminishing marginal returns, competition shifts dimensions.
From Strategy Wars to Infrastructure Wars: Why Now Is the Infrastructure Window?
This inflection point already happened in traditional finance.
In 2010, Spread Networks spent $300 million tunneling fiber-optic cable through the Appalachian Mountains—just to shave 3 milliseconds off latency between Chicago and New York. Jump Trading went further, erecting microwave towers atop the Chicago Mercantile Exchange building to transmit orders wirelessly at near-light speed—bypassing fiber entirely. Wall Street’s high-frequency trading arms race lasted nearly two decades, culminating in a consensus: when everyone’s strategies are sufficiently sophisticated, victory goes to whoever has faster, more reliable, and exchange-proximate infrastructure.
Crypto markets are rapidly replaying this path—and 2024–2026 happens to be the acceleration window for this arms race, propelled by three converging forces.
First, post-ETF, the market’s participant structure has shifted. BTC and ETH spot ETFs have enabled massive inflows of traditional capital, making trading structures more institutional. Institutional money doesn’t chase 100x tokens—it prioritizes steady returns, drawdown control, and execution quality. This directly raises the bar for infrastructure requirements.
Second, trade opportunity complexity is rising. Alpha is no longer hidden only in price spreads across centralized exchanges—it also lives in gaps between CEXs and DEXs, between perpetual and spot markets, and between on-chain yield and centralized market rates. Capturing these cross-market, cross-protocol opportunities demands far higher standards for market data acquisition, order routing, execution, and risk controls than ever before.
Third, AI accelerates strategy generation—making infrastructure comparatively scarcer. Previously, moving from idea to backtest to live deployment could take researchers and engineers weeks of iterative work. Today, AI compresses the front-end cycle of strategy research: data cleaning, factor hypothesis generation, code writing, and backtesting frameworks can all be assembled faster. But the faster strategies are generated, the faster they homogenize. What truly separates winners is no longer “who conceived a novel factor first,” but “who can inject signals into live markets fastest—and convert theoretical returns into real P&L amid latency constraints, slippage, and permission limits.”
More notably, AI is spawning an entirely new trading paradigm: AI Agents. Historically, the trading decision chain was linear: “researcher generates idea → engineer implements strategy → system executes trade.”
Now, AI Agents attempt to collapse those three steps into one: autonomously perceiving market conditions, generating trading decisions, and directly invoking execution channels to complete trades. As algorithmic trading adoption grows exponentially—and more trading behavior originates from AI Agents rather than humans—the demand for underlying infrastructure surges. AI Agents won’t call exchanges to negotiate VIP status, manually switch AWS nodes, or rely on human intuition to decide whether to cancel orders during extreme volatility. They require standardized, ultra-low-latency, highly reliable infrastructure interfaces—always available, always responsive.
QSG’s CSO Tommy Ho puts it more directly: “Strategy importance hasn’t declined—but strategies are becoming increasingly infrastructure-dependent. Many crypto-native traders deeply understand markets and possess exceptional intuition, yet they struggle to compete with large institutions possessing full-stack infrastructure teams on low-latency market data, order execution, and AWS environment optimization. Strategies aren’t unimportant—they’ve shifted from being the ‘sole core’ to being ‘one of several cores.’”
Deconstructing the Quant Tech Stack: How Many Layers Does One Trade Traverse?
How many technical hurdles must a quant team overcome between spotting an opportunity and pocketing profits?
Most assume quant trading is simply “write a strategy and run it.” Reality is far more complex. A complete quant trade—from signal generation to realized P&L—must traverse at least four technical layers. Each layer presents its own barriers and cost sinks; any weakness directly erodes the alpha generated by the strategy.
Layer One: Faster Market Data Acquisition
This is the starting point of the entire trading chain—and the most frequently overlooked环节.
Most quant teams pull market data via exchanges’ public WebSocket APIs. The issue? These channels are fundamentally “retail-grade.” Latency gaps between public feeds and internal market-maker channels can span multiples—even orders of magnitude. In calm markets, this gap isn’t fatal. But crypto markets never lack extreme moments.
During volatile events and message traffic surges, public feed latency can balloon from milliseconds to seconds. In high-leverage perpetual markets, that’s enough time for price to breach multiple quote levels—or even trigger cascading stop-losses or liquidations. Your model still processes delayed data while the real market has already moved on.
This is why, over the past two years, some crypto quant teams have begun outsourcing “market data pipelines” from internal engineering projects to specialized infrastructure providers. QSG is a representative player in this trend.
Its entry point isn’t “providing a faster API”—but productizing low-latency trading capabilities previously locked inside top-tier market makers, making them accessible to mid- and small-sized quant teams. Take its market data product, Sytus Feed: during extreme volatility, it compresses latency from seconds (on public networks) down to sub-100ms—and significantly reduces latency jitter.
QSG’s uniqueness lies in its non-traditional SaaS origin. Instead of approaching trading from a software-first lens, it reverse-engineers products from frontline quant and market-making experience. Core team members hail from Kronos Research, Jane Street, WorldQuant, and maintain Binance’s highest-tier VIP and market-maker statuses.
Oliver candidly admits other teams offer similar low-latency services—but QSG’s moat stems from the fusion of “trading understanding + engineering capability + execution experience.” “If we merely sold colocation data, baseline conditions wouldn’t differ much across providers. The real challenge is: once everyone has colo, how do you gain another 30–50% performance edge via receiver-side optimization, sender-side tuning, network path engineering, kernel-level adjustments, and deep exchange API expertise?”
Infrastructure challenges in crypto aren’t just technical problems—they’re trading problems and extreme-event problems. Across historical flash crashes and black swan events, systems expose vulnerabilities: delayed quotes, reconnection failures, matching engine congestion, failed cancellations. Without firsthand experience navigating these scenarios, it’s nearly impossible to build robust infrastructure purely through theoretical engineering.
Layer Two: More Reliable Order Execution
Seeing the right price means nothing if your order can’t be placed fast enough.
For most teams, the order execution bottleneck isn’t network latency—it’s internal engineering capacity. Building a deeply optimized low-latency execution stack requires engineers fluent in Linux kernel tuning, NIC driver optimization, and user-space networking stacks—skills scarce globally, and exceptionally rare in crypto.
A telling illustration of infrastructure’s importance: during a high-volatility event in December 2024, most trading firms saw order latency spike above 120ms, while teams using institutional-grade execution channels maintained ~40ms latency. In client benchmarks, latency improvements exceeded 90% across certain exchange environments.
Tommy tells us many clients stumble on the same trap across both market data and execution layers: “Teams often focus only on average latency, assuming their system is ‘fast enough.’ But during actual market turbulence, tail latency—not average latency—determines whether you receive quotes in time, submit orders promptly, and manage risk effectively. We say internally: P50 defines how fast you *look* in normal times; P99 determines whether you *survive* during peak market stress.”
Market data acquisition and order execution together form the end-to-end “see-to-eat” pipeline. Every extra millisecond of latency along this chain discounts the strategy’s realized returns. Cross-exchange arbitrage teams feel this most acutely: the physical distance between Tokyo and Hong Kong itself imposes latency. Some infrastructure providers offer inter-regional connectivity tools that cut round-trip latency by >30%; others automatically route to the cloud node optimal for a target exchange. These are “last-mile” optimizations—individually subtle, collectively decisive in homogenized strategy competition.
Layer Three: Higher Exchange Privileges
There’s an open secret in quant circles: the same strategy can yield twice the returns when run on a VIP3 account versus a VIP9 account.
The reason is direct. Higher VIP tiers mean lower fees (top-tier VIP maker fees can even be negative—exchanges pay you), looser API rate limits, and better lending rates. Crucially, market-maker status grants access to low-latency dedicated endpoints—orders travel far faster than via public APIs.
But attaining top-tier VIP status carries enormous barriers. For instance, Binance VIP9 requires monthly futures trading volume of $25 billion. If the strategy itself isn’t profitable, the friction costs incurred solely to sustain that volume could amount to tens of millions in annual losses. It’s the classic chicken-or-egg dilemma: you need top-tier VIP cost advantages to make the strategy profitable—but you need to first generate that volume to qualify.
QSG’s service at this layer avoids brokerage models. Amid explicit bans on brokered services by major exchanges, QSG forged a win-win path: leveraging high-frequency trading tech to help clients generate genuine incremental trading volume on their *own* accounts—reaching market-maker eligibility and VIP thresholds organically. Resulting fee discounts, low-latency endpoints, and institutional lending privileges are all bound to the client’s *own* account—not sub-accounts or proxy relationships. For exchanges, this delivers authentic liquidity growth; for clients, tangible cost savings—aligning incentives.
Layer Four: Lower-Slippage Large-Order Execution
A quant fund managing tens of millions of dollars often finds its biggest headache isn’t strategy—but execution.
When establishing or closing large positions, market liquidity may be insufficient. A single large market-order can trigger slippage costing several basis points. Executing dozens of such orders monthly accumulates slippage costs sufficient to erase strategy profits. Traditional finance has mature block-trading channels and dark pools; crypto has long lacked equivalents.
Some infrastructure providers are now importing institutional finance’s large-order execution, smart-routing, and price-matching mechanisms into crypto. QSG’s large-order execution service is one such case: using algorithmic trading and execution optimization to reduce large-order impact on public order books. Tommy notes that in select scenarios, they improved clients’ execution costs by ~3 basis points versus standard TWAP strategies. “Not all clients care about hundreds of microseconds,” he says. “But CTAs, long/short funds, and large-capacity rebalancing teams prioritize slippage on big orders. Over time, that translates into very real P&L improvement.”
After assembling all four layers, a key question emerges: Build In-House or Integrate Externally?
Early quant teams favored “full-stack” self-builds—developing everything from strategies to infrastructure internally. But this model’s cost is becoming untenable.
Tommy shares a telling client story: a lean, high-performing live quant team with strong strategy skills—but no bandwidth to rebuild VPCs, networks, market-data formats, order protocols, and exchange routing paths from scratch. “Their pain point wasn’t strategy design—it was cyclical opportunity windows. If a strategy is profitable *now*, but the team spends months building AWS environments, market-data systems, and order-execution engines, the market opportunity may vanish before infrastructure is ready.”
For many mid- and small-sized quant teams, the real question is no longer “Can we build it ourselves?” but “Is it *worth* building ourselves?”
If a team’s core advantage lies in strategy research, capital management, and risk control, dedicating a year to low-latency networking, exchange permissions, execution engines, and large-order systems may not be optimal. In-house infrastructure offers stronger control—but also higher fixed costs, longer time-to-market, and heavier maintenance burdens. For teams of 5–15 people, specialized division of labor may be more realistic than full-stack self-reliance.
Even more critically: the time math is lethal. Building a low-latency trading system from scratch conservatively takes 6–12 months. During that time, competitors using off-the-shelf infrastructure have already captured that same alpha. Alpha has a natural shelf life—the market’s inefficiency window narrows rapidly as participants multiply. Every day spent reinventing wheels degrades strategy precision.
Of course, externalizing infrastructure doesn’t mean abandoning engineering capability. Strategy logic, risk frameworks, capital management, and exception handling must remain firmly in-house. Third-party infrastructure solves *execution-layer efficiency*—not core investment capability.
QSG is now live on AWS Marketplace, operating under standard enterprise SaaS terms. For traditional financial institutions entering crypto, this means compliant procurement pathways, standardized billing, and zero exposure to tokens or native crypto complexities.
Crypto markets are rapidly entering an “era of specialization.” Just as traditional quant funds outsource execution to prime brokers and data to Bloomberg, crypto quant teams are beginning to outsource infrastructure to specialized third parties. Strategies remain proprietary core assets—but infrastructure need not be.
Conclusion
Oliver’s outlook is clear: “Crypto quant infrastructure will evolve from an ‘optional tool’ to a ‘standard requirement for professional trading teams.’ As AI penetrates deeper into strategy research, signal generation, and parameter optimization, strategy development barriers will fall—enabling more teams to generate similar trading ideas at lower cost. Strategies themselves will grow increasingly crowded; true differentiation will revert to underlying execution capability.”
He summarizes the future competitive landscape as a formula: AI Strategy + Data Quality + Execution System + Low-Latency Infrastructure + Risk Control Capability. Not single-dimensional competition—but holistic capability contests.
In Oliver’s roadmap, one evolution path for QSG is using AI Agents to integrate market data, order placement, node optimization, data feeds, large-order execution, and risk monitoring into a unified intelligent trading infrastructure. “Future AI Agents will act like co-pilots for trading infrastructure—helping teams monitor markets, diagnose system issues, and optimize execution paths,” he says. Tommy adds a vivid analogy: “Like the App Store explosion after smartphones went mainstream—when more traders adopt programmatic and AI-assisted trading, they won’t need to build infrastructure from scratch. They’ll need a plug-and-play trading infrastructure network they can invoke instantly.”
This path is already charted in traditional finance. Bloomberg Terminals, major prime brokers—these infrastructures ultimately defined Wall Street’s rules of engagement. The crypto quant space awaits its own “infrastructure moment.”
For infrastructure providers like QSG, the opportunity extends beyond selling tools to quant teams—it’s participating in rewriting crypto’s foundational standards: how market data is acquired, how orders are executed, how large trades are matched, and how exchange privileges are service-ified.
As crypto evolves from frontier chaos to institutional maturity, capabilities once buried inside top-tier market makers are being decomposed, packaged, and gradually transformed into public infrastructure accessible to broader teams.
When the next BTC extreme volatility hits, while most wait those crucial seconds for quotes to refresh—the real battle may already be over.
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