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 typically think of exchanges, market makers, or “diamond-handed” retail traders who struck it rich overnight during bull markets.
Retail investors experience wild swings between bull and bear cycles—some buy at the peak; others cut losses 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, don’t join KOLs’ pump-and-dump calls, and seldom grant interviews to the press. They operate like a “shadow force” behind the markets—quietly harvesting profits from every crypto price fluctuation.
So what exactly enables them to profit steadily?
In this highly uncertain market, how do they achieve both offensive flexibility and defensive resilience—turning trading into a science?
The Quant World: From Arbitrage Bricklaying to an Arms Race
The history of crypto quant trading compresses half a century of evolution in traditional finance.
The Arbitrage Bricklaying Era (2017–2018): Rules were simple and crude—price spreads for the same asset across different exchanges often reached 5–10%. A single programmer running scripts across multiple exchanges on a few laptops could generate several-fold annual returns. Arthur Hayes, founder of BitMEX, and Sam Bankman-Fried (SBF), founder of FTX, both earned their first fortunes through arbitrage. In 2018, SBF noticed a 10% Bitcoin premium in Japan, gathered a few friends, and began arbitraging—earning roughly $20 million in just a few weeks, leading to the founding of Alameda Research.
It was a time of wild, unregulated growth. “Back then, things were truly brutal—massive first-round token failures, secondaries crashing hard, and many token-focused funds pivoting to quant,” recalls Leo, a former crypto VC professional. “Early crypto quant teams came from three main backgrounds: Wall Street returnees, former A-share traders, and pure crypto ‘self-taught’ practitioners.” In this frontier era, teams experimented through bear markets—some honed skills using hundreds of BTC; others cursed at exchange APIs that crashed constantly.
The Professionalization Era (2020–2023): DeFi Summer ignited the fuse. Large numbers of teams with traditional finance and internet-sector backgrounds entered the space, accelerating true elite professionalization in crypto quant. A more critical shift occurred on the funding side: family offices and institutional asset managers replaced miners as the new capital providers for quant teams.
“Top-tier quant teams today are essentially ‘sponsored’—serving major family offices or asset managers. With no shortage of capital, they now deliberately stay low-profile,” says Grace, a BD lead at a crypto-focused asset manager, describing the ecosystem.
Stephanie, Partner at multi-strategy quant fund Target Capital, notes their clients are primarily prominent family offices based in Singapore and Hong Kong. For such 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 far outweigh the allure of 100x tokens that may go to zero. Crypto quant thus became many traditional family offices’ first gateway into the crypto world.
The Institutionalization Era (2024–Present): The approval of BTC spot ETFs, gradual global regulatory frameworks, and massive entry by traditional financial institutions are transforming crypto’s “retail poker table” into an “institutional battlefield.”
But a challenge all practitioners feel has emerged: strategies are becoming increasingly crowded—and increasingly identical.
Leo puts it bluntly: “Over 80% of secondary-market teams run highly neutral arbitrage strategies. Strategy homogeneity is severe.”
Oliver Chen, CFO & COO of QSG, observes the same trend from the infrastructure provider perspective: “A clear example is funding-rate arbitrage. Large funds, family offices, and LPs typically prefer low-drawdown, smooth-return strategies—so these have become extremely crowded over the past few years. The problem? As everyone’s signals and trading logic converge, infrastructure—rather than strategy—is what ultimately determines performance differentiation.”
Even more awkwardly, sky-high risk-free yields in crypto markets deliver a “dimensional strike” against traditional quant strategies. During bull runs, Pendle’s risk-free yields hit 30%—meaning your painstakingly modeled alpha might underperform simply holding assets on-chain.
When strategies reach diminishing returns, competition shifts dimensions.
From Strategy Wars to Infrastructure Wars: Why Now Is the Infrastructure Window?
This pivot already happened in traditional finance.
In 2010, Spread Networks spent $300 million laying fiber-optic cable across the Appalachian Mountains—just to shave off 3 milliseconds between Chicago and New York. Jump Trading went further, erecting microwave towers atop the Chicago Board of Trade 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 consensus: when everyone’s strategies are sufficiently intelligent, victory goes to whoever has faster, more stable, and exchange-proximate infrastructure.
Crypto markets are rapidly replaying this path—and 2024–2026 marks the acceleration window for this arms race, driven by three concurrent forces.
First, post-ETF, market participant structure has changed. BTC and ETH spot ETFs have enabled large-scale inflows of traditional capital, making trading structures more institutional. Institutional money doesn’t chase 100x tokens—it prioritizes stable returns, drawdown control, and execution quality. This directly raises infrastructure requirements.
Second, trading opportunity complexity is rising. Alpha no longer hides only in price spreads across centralized exchanges—it resides also in CEX–DEX gaps, perpetual–spot discrepancies, and yield differentials between on-chain protocols and centralized markets. Capturing these cross-market, cross-protocol opportunities demands far higher standards for market data acquisition, routing, execution, and risk management than before.
Third, AI accelerates strategy generation—making infrastructure comparatively scarcer. Historically, turning a strategy idea into live deployment required researchers and engineers iterating for weeks. Today, AI compresses front-end research cycles: data cleaning, factor hypothesis testing, code generation, and backtesting frameworks can all be built faster. But faster strategy generation means faster homogenization. Real differentiation no longer hinges solely on “who conceived a new factor first”—but on who can most rapidly connect signals to live markets and convert theoretical returns into real P&L—under latency constraints, slippage pressure, and permission limits.
More notably, AI is spawning an entirely new trading paradigm: AI Agents. Previously, trading decisions followed a linear chain—“researcher conceives idea → engineer implements strategy → system executes trade.”
Now, AI Agents attempt to collapse all three steps into one: autonomously perceiving market states, generating trade decisions, and directly invoking execution channels. As algorithmic trading adoption grows exponentially—and more trades originate from AI Agents rather than humans—the demand for underlying infrastructure surges sharply. AI Agents won’t call exchanges to negotiate VIP tiers, manually switch AWS nodes, or rely on human judgment to cancel orders during extreme moves. They require standardized, ultra-low-latency, highly reliable infrastructure interfaces—always available, always responsive.
Tommy Ho, CSO of QSG, puts it more directly: “Strategy importance hasn’t declined—but strategies increasingly depend on infrastructure. Many crypto-native traders deeply understand markets and possess acute intuition, yet they struggle to compete with large institutions possessing full-stack infra teams on low-latency market data, order execution, and AWS environment optimization. Strategy isn’t less important—it’s shifted from being the ‘sole core’ to being ‘one of several cores.’”
Deconstructing the Quant Team 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 “writing a strategy and running it.” Reality is far more complex. A complete quant trade—from signal generation to realized profit—must traverse at least four technical layers. Each layer carries its own barriers and cost black holes; any weakness directly erodes strategy-generated alpha.
Layer One: Faster Market Data Acquisition
This is the starting point of the entire trading chain—and the most easily overlooked.
Most quant teams fetch market data via exchanges’ public WebSocket APIs. The issue? This channel is inherently “retail-grade.” Latency gaps between public feeds and internal market-maker channels can span multiples. In calm markets, this gap isn’t fatal—but crypto markets never lack extremes.
During volatile events and surging news traffic, public feed latency can balloon from milliseconds to seconds. In high-leverage perpetual markets, this is enough for prices to traverse multiple order-book levels—or even trigger cascading liquidations. Models still process delayed data while real markets have already moved on.
Hence, over the past two years, some crypto quant teams have begun outsourcing “market-data pipelines” from internal engineering tasks to specialized infrastructure providers. QSG is a representative of this trend.
Its approach isn’t merely “providing a faster API”—but productizing low-latency trading capabilities previously confined within 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 (public net) down to sub-100ms—with significantly reduced jitter.
QSG’s uniqueness lies in its non-SaaS origin: it reverse-engineers products from frontline quant and market-making experience—not from generic software-first thinking. Core team members hail from Kronos Research, Jane Street, and WorldQuant—and maintain Binance’s highest-tier VIP and market-maker status.
Oliver candidly admits other teams offer similar low-latency services—but QSG’s moat stems from combining “trading insight + engineering capability + execution experience.” “Selling colocation data alone doesn’t create meaningful differentiation—everyone starts from similar baseline conditions. The real challenge lies in squeezing another 30–50% performance advantage *on top* of colocation—by optimizing the receiving end, sending end, network paths, OS kernel tuning, and deep exchange API understanding.”
Crypto infrastructure challenges aren’t just technical—they’re also trading and extreme-event challenges. Historical extreme events repeatedly expose system weaknesses: delayed market data, reconnection failures, matching engine congestion, failed order cancellations. Without firsthand experience navigating these, 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 submission speed can’t keep up.
Execution bottlenecks for most teams lie not in network latency—but in their own engineering capacity. Building a deeply optimized low-latency execution pipeline requires engineers skilled in Linux kernel tuning, NIC driver optimization, and user-space networking stacks—talent scarce globally, and exceptionally rare in crypto.
A telling infrastructure-relevance scenario: 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 consistency. Client benchmarks showed >90% latency improvement in certain exchange environments.
Tommy explains how many clients stumble on the same pitfall across data and execution layers: “Many teams initially focus only on average latency—deeming their systems ‘fast enough.’ But during actual market turbulence, tail latency—not average latency—determines whether you receive timely market data, submit orders promptly, and manage risk effectively. We say internally: P50 tells you how fast you *look* in normal times—but P99 decides whether you *survive* during peak market intensity.”
Market data acquisition and order execution together form the full “see-to-eat” chain. Every extra millisecond of latency along this chain discounts actual strategy returns. Cross-exchange arbitrage teams feel this most acutely: physical distance between Tokyo and Hong Kong *is* latency. Some infrastructure tools reduce round-trip latency across regions by >30%; others auto-select optimal cloud nodes for target exchanges. These are “last-mile” optimizations—individually unremarkable, but collectively decisive in winning homogeneous-strategy races.
Layer Three: Higher Exchange Privileges
Quant industry’s open secret: running the *same* strategy on a VIP3 vs. VIP9 account can yield double the returns.
Reasons are direct: higher VIP tiers mean lower fees (top-tier VIP maker fees can even go negative—exchanges pay you), looser API rate limits, and better lending rates. Crucially, market-maker status grants access to low-latency dedicated endpoints—orders routed via these outpace public APIs by wide margins.
But attaining top-tier VIP status carries steep barriers. Binance VIP9, for instance, requires monthly futures volume exceeding $25 billion. If your strategy isn’t profitable, friction costs alone incurred just to sustain that volume could exceed $10M annually—a classic chicken-or-egg dilemma: you need VIP cost advantages to make your strategy profitable—but you need proven volume to qualify for VIP status.
QSG’s service model here avoids brokerage. Amid explicit exchange prohibitions on brokerage operations, QSG forged a win-win path with exchanges: using high-frequency trading tech to help clients generate *real incremental volume* on their *own accounts*, thereby meeting market-maker and VIP thresholds. Resulting fee discounts, low-latency endpoints, and institutional-grade borrowing rights are all bound to the client’s *own account*—not via sub-accounts or proxy relationships. For exchanges, this means genuine liquidity growth; for clients, tangible cost savings—aligned incentives.
Layer Four: Lower-Slippage Large-Order Execution
A quant fund managing tens of millions in AUM often finds execution—not strategy—to be its biggest headache.
When establishing or closing large positions, market liquidity may fall short. A single large market-order can incur slippage costing several basis points. Executing dozens of such orders monthly accumulates slippage costs sufficient to erase strategy profits. Traditional finance offers 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 exemplifies this: using algo-trading and execution optimization to minimize market impact on public order books. Tommy notes that in certain scenarios, they’ve improved execution costs by ~3 bps versus standard TWAP strategies. “Not all clients care about hundreds of microseconds,” he says. “But CTAs, long/short funds, and large-position rebalancing teams prioritize slippage on big orders. Cumulatively, that’s 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” models—building everything from strategy to infrastructure internally. But this model’s cost is becoming unsustainable.
Tommy shares a striking client case: a lean, live-running quant team with strong strategy capabilities—but no bandwidth to rebuild VPCs, networks, market-data formatting, order protocols, and exchange routing from scratch. “Their pain point isn’t strategy development—it’s strategy *timeliness*. If a strategy is profitable *now*, but the team spends months setting up AWS environments, market-data systems, and order-execution engines, the market opportunity may vanish by the time infrastructure is ready.”
For many mid- and small-sized quant teams, the question is no longer “*Can* we build it ourselves?” but “*Should* we build it ourselves?”
If a team’s core strengths lie in strategy research, capital management, and risk control, spending a year on low-latency networking, exchange privileges, execution engines, and large-order systems may not be optimal. Building infrastructure in-house offers greater control—but also entails higher fixed costs, longer time-to-market, and heavier maintenance burdens. For teams of 5–15 people, specialization may prove more realistic than full-stack self-reliance.
More critically, there’s the time ledger. Conservatively, building a low-latency trading system from scratch takes 6–12 months. During that period, competitors using off-the-shelf infrastructure may have already captured the 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-control 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 listed 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 “professional specialization.” Just as traditional quant funds delegate execution to prime brokers and data to Bloomberg, crypto quant teams are beginning to outsource infrastructure to specialists. Strategy remains their core asset—infrastructure need not be.
Conclusion
Oliver’s outlook is unequivocal: “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-entry barriers will fall—enabling more teams to generate similar ideas at lower cost. Strategies themselves will grow increasingly crowded; true differentiation will revert to foundational execution capability.”
He summarizes future competition as a formula: AI-driven strategy + data quality + execution system + low-latency infrastructure + risk-control capability. It’s no longer a single-dimensional contest—but a holistic capability race.
In Oliver’s roadmap, one evolution path for QSG involves using AI Agents to integrate market data, order submission, node optimization, data ingestion, large-order execution, and risk monitoring into a unified intelligent trading infrastructure. “Future AI Agents will act as co-pilots for trading infrastructure—helping teams monitor markets, diagnose system issues, and optimize execution paths,” he says. Tommy adds a vivid analogy: “Just as smartphones triggered an App Store explosion, as more traders adopt programmatic and AI-assisted trading, they’ll need not custom-built infrastructure—but a plug-and-play trading infrastructure network.”
This path is already trodden 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 about co-authoring the *redefinition of crypto trading’s foundational standards*: how market data is acquired, how orders are executed, how large trades are matched, and how exchange privileges are productized.
As crypto matures from frontier chaos to institutional rigor, capabilities once locked inside top-tier market makers are now being disassembled, packaged, and gradually transformed into public infrastructure accessible to broader teams.
When the next BTC extreme volatility hits, while most wait seconds for their charts to refresh—the real battle may already be over.
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