Forecast for 2026: Outlook on Quantitative Trading and How to Allocate Alternative Assets

Author: Max.s

After experiencing intense volatility in 2024 and profound reshuffling in 2025, the quantitative finance industry is standing at a new crossroads. At the 2025/2026 China Quantitative Investment Cross-Year Summit last week, Dr. He Kang, Chief Strategist and Head of Financial Engineering at Huatai Securities Research Institute, delivered an in-depth speech titled “2025 Trends and 2026 Outlook for the Quantitative Industry.” This is not only a strategic report targeting the A-share market but also a battlefield manual on how Alpha is seeking new survival spaces in an increasingly crowded market.

For practitioners at the intersection of Web3 and traditional finance, this report sends a clear signal: Traditional Alpha is waning, and a new paradigm — whether it’s based on large models’ “Order as Token” or alternative assets represented by cryptocurrencies — is becoming a battleground for institutional investors.

Below is an in-depth review and industry outlook based on Dr. He Kang’s speech.

2025 is a year of “high prosperity” and “high volatility” coexisting for the quantitative industry. A notable data change is: the scale of private securities funds remains high, but the growth of public quantitative funds is even more rapid. By Q3 2025, the scale of public fund products with active management exceeded 200 billion yuan, with active quantitative strategies reaching 120 billion yuan.

Behind this lies an interesting structural change: the top player has changed.

The once-dominant players have been disrupted, with institutions like Bodao and Guojin emerging with highly flexible strategies. According to Dr. He’s research, these top-performing public quantitative funds are essentially “private funds disguised as public funds.” They have extremely high turnover rates, rapid strategy iteration, and even use T+0 intraday reversals at levels comparable to top private funds.

This phenomenon reveals the survival rule of 2025: as the difficulty of earning excess returns increases exponentially, only extreme flexibility can break through the red ocean. For investors, the old logic of “picking big brands and lying flat” is no longer applicable; they must use more refined attribution analysis to identify managers with genuine “agile development” capabilities.

Over the past five years, the mainstream narrative in the quantitative industry has been “full-position stock picking,” using Alpha from stock selection to cover market fluctuations. However, after the market education of 2025, “timing” has returned to the center stage. Dr. He classifies market managers into five categories: ABCDE, with the most noteworthy being E-type managers — — logic-based timing strategies. Unlike black-box predictions, these strategies build explicit “If A then B” logical chains.

The Rise of Sub-domain Modeling.

As market efficiency improves, it becomes increasingly difficult to discover universal factors across the entire market. Top managers are adopting a “divide and conquer” approach: segment the entire market into growth, cyclical, small-cap, micro-cap, and other “domains,” and train models within each domain separately. It’s like in Web3, where you can’t use the same logic to trade Bitcoin and on-chain Meme coins — — their pricing logic, liquidity features, and participant structures are entirely different. Through sub-domain modeling, quantitative strategies can extract higher excess returns in local markets.

If sub-domain modeling is tactical optimization, then the introduction of large language models (LLMs) is a strategic dimension reduction attack. Dr. He mentioned three application levels of large models in quantitative finance, with the most impressive being the third: viewing financial transactions as a language, i.e., “Order as Token.”

In traditional NLP (Natural Language Processing), GPT predicts the next word (Token); in financial large models, the input is a sequence of past prices, volumes, order flow, and the model predicts the next “price Token.” This is not just a technical migration but a revolution in thinking.

Traditional quantitative models are often based on statistical linear or nonlinear regressions, but Transformer architectures allow models to capture dependencies over very long cycles and complex nonlinear patterns. Imagine that future trading is not based on a few factors’ linear weighting but generated by a pre-trained financial large model, “producing” future price paths like text generation. This is akin to the intent-centric AI agent trading logic in the current Crypto space — — AI is no longer just an auxiliary tool but a direct execution entity.

The Blue Ocean of Alternative Data: Institutionalization of Cryptocurrency Markets

When excess returns in the A-share market are “squeezed” to the extreme, smart capital begins to shift its focus to lower-correlation alternative markets via total return swaps (TRS) or offshore entities.

Compared to the T+1 system and daily price limits in A-shares, crypto markets feature 24/7 trading, T+0 settlement, high volatility, and fragmented liquidity. For quantitative institutions with high-frequency trading capabilities and risk control models, this is reminiscent of the A-share market before 2015 — — abundant Alpha opportunities, and the competitive landscape is still evolving.

A particular strategy worth mentioning is Funding Rate Arbitrage. In perpetual contracts, longs and shorts pay funding fees to maintain price anchoring. During bull markets, longs often pay high rates to shorts. This creates a quasi-fixed income “market-neutral” strategy: buy spot, short the same value of perpetual contracts, hedge against price risk, and earn steady funding fees. The 1Token funding rate arbitrage index has become an industry benchmark.

Industry data shows that such strategies can generate annualized returns far exceeding traditional fixed income products during certain market cycles, with very low correlation to traditional assets (stocks, bonds). As a professional digital asset service provider, 1Token’s constructed index not only reflects the overall arbitrage space but also embodies the evolution of Crypto quant from “handcrafted workshops” to “institutionalized and index-based.”

For traditional finance practitioners, the significance of tracking indices like 1Token lies in providing a window to observe Web3 liquidity premiums. When funding rates stay high for a long time, it indicates extreme market sentiment and serves as a warning for spot selling pressure; conversely, it may signal a good opportunity to bottom fish.

Looking ahead to 2026, Dr. He Kang’s keywords are “dynamic” and “antifragile.”

From static allocation to dynamic game-playing. In the past, FOFs (funds of funds) or macro asset allocation often set static weights (e.g., 60/40). But in the future, dynamic adjustment mechanisms are necessary. For example, when a certain strategy (like micro-cap stock index increase) becomes overcrowded, the risk of herding and “踩踏” (stampede) increases, requiring proactive reduction of weight, even if its past performance remains strong.

Products with “cushioning” features, having experienced drawdowns, have heightened investors’ aversion to downside risk. Derivatives with “airbag” or “snowball” structures, and protected products via options, will become mainstream in 2026. This is similar to the logic of DeFi structured products — — sacrificing some upside potential for higher certainty and principal protection.

Seeking low-correlation assets. Whether it’s finding independent Alpha within A-shares or allocating to Hong Kong stocks, US stocks, or even Crypto assets, the core goal is to reduce overall portfolio correlation. Dr. He Kang emphasizes that although pure Alpha in Hong Kong stocks is difficult (due to poor liquidity and expensive shorting tools), it still holds value as part of diversification. Meanwhile, the unique driving logic of the Crypto market will become an important piece in hedging traditional financial risks.

Dr. He Kang’s speech essentially reveals the essence of financial engineering: the process of seeking certainty amid uncertainty.

In 2025, the traditional low-hanging fruit in the quantitative industry has been picked clean. Practitioners face only two paths: either to delve deeper into technology, leveraging large models to discover more complex nonlinear patterns; or to go offshore with assets, attacking a blue ocean like Crypto with dimensionality reduction.

For Web3 natives, this is also a warning: as top institutions like Huatai Securities begin to deeply research and focus on this field, the entry of mainstream players is only a matter of time. When traditional quantitative “dragon slaying” techniques are applied to decentralized trading markets, new dividends and brutal competition will arrive simultaneously.

In 2026, only the evolutionaries will survive, whether in TradFi or Crypto.