# Quantitative Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Quantitative", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

When LPs Teach Me Investment with Doubao: A Self-Narrative of a Private Equity GP Switching Careers

When LPs Use Doubao to Teach Investing: A Transition Story of a Private Equity GP AI is making life increasingly difficult for small private equity fund managers, as a former GP of an offshore dollar fund reveals. The fund, managing tens of millions in US stocks, outperformed the Nasdaq but struggled with fundraising. Its traditional Cayman SPC/BVI structure failed to attract major Asian LPs, who now prefer Hong Kong LPF or Singapore VCC frameworks. The rise of AI-powered quantitative strategies has further squeezed the space for funds like his, which relied on subjective, discretionary investing. AI tools have leveled the information playing field, empowering LPs—often high-net-worth individuals, entrepreneurs, or family offices—to analyze investments themselves using chatbots like Doubao. This has eroded trust in GPs' expertise, leading to more frequent challenges over investment decisions and even withdrawals, especially during market rallies when retail investors sometimes outperform funds. Friction arises not necessarily from AI's capabilities but from how LPs use it. Many rely on conversational AI for validation rather than rigorous analysis, sometimes receiving misleading or hallucinated advice. While AI democratizes research, effective investing still requires discerning real insight from plausible-sounding output. Ultimately, AI is unlikely to fully replace GPs. Asset management remains a trust-based service. However, the industry must adapt. The future may see "human私募" (private equity) learning from AI and focusing more on providing value beyond pure analysis—perhaps by mastering the emotional intelligence and trust-building that machines cannot replicate.

Odaily星球日报06/09 02:39

When LPs Teach Me Investment with Doubao: A Self-Narrative of a Private Equity GP Switching Careers

Odaily星球日报06/09 02:39

Secures Over $60 Million in Funding from Dragonfly, Sequoia, and Others. An In-Depth Look at the On-Chain Derivatives Protocol Variational | CryptoSeed

Variational, a decentralized derivatives trading platform, has raised over $61.8 million in total funding, including a recent $50 million Series A led by Dragonfly Capital. Investors include Sequoia Capital, Coinbase Ventures, Bain Capital Crypto, and Hack VC. The platform, with over $810 million in Open Interest (ranking 4th among on-chain derivatives protocols), is founded by Lucas Schuermann and Edward Yu. Both are Columbia University graduates and former partners at quant fund Qu Capital. They later held senior roles at Genesis Trading, handling billions in trading volume. The broader team includes talent from Google, Meta, and top trading firms like Jane Street. Operating on Arbitrum, Variational distinguishes itself by aggregating external liquidity from CEXs, DEXs, and traditional market makers using an RFQ (Request-for-Quote) model, positioning itself more as a broker than an exchange like Hyperliquid. Its dual product line features Omni for retail users (offering up to 50x leverage on ~450+ crypto and TradFi pairs) and Pro for institutional OTC trading. Currently in a pre-TGE phase, Variational has launched an "Omni Points" system, with 50% of its upcoming $VAR token supply allocated for community incentives through trading-based rewards and referrals until Q3 2026.

链捕手05/22 06:25

Secures Over $60 Million in Funding from Dragonfly, Sequoia, and Others. An In-Depth Look at the On-Chain Derivatives Protocol Variational | CryptoSeed

链捕手05/22 06:25

BIT Research: Why Could Gold Rush to $5,000 Faster After the Sudden Plunge?

The article "BIT Research: After Gold's Sharp Decline, Why Could It Reach $5,000 Faster?" presents a bullish outlook for gold despite recent price drops. It argues that the core bullish structure remains intact, with the recent sell-off seen as a correction rather than a trend reversal. The primary drivers are expected to be a re-pricing of the US dollar and the interest rate path. The market currently prices in a potential 2026 rate hike, yet simultaneously expects a more dovish stance from incoming Fed Chair Kevin Warsh, creating a contradiction. A correction of these expectations could weaken the dollar and lower real rates, fueling gold's ascent. Additionally, the expanding US national debt provides long-term structural support. Technically, gold is forming a consolidation pattern with higher lows, suggesting underlying strength. Quantitative and trend models have turned bullish, with historical data from similar signals pointing to an average ~12.8% gain over two months, projecting a target near $5,306. Key upcoming catalysts include the June FOMC meeting, the July BRICS summit, and the September US fiscal cliff. In summary, the convergence of a potential dollar downturn, interest rate re-pricing, supportive technicals, and upcoming macro events could propel gold into a new phase of accelerated growth, with $5,300 as a near-term target.

marsbit05/08 07:39

BIT Research: Why Could Gold Rush to $5,000 Faster After the Sudden Plunge?

marsbit05/08 07:39

Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

Quantitative AI trading platform AlphaNet has raised $10 million in a seed funding round led by Joffre Capital, with participation from Huashan Capital and an Asia-based quantitative trading consortium. The platform, which is preparing for a public launch, aims to level the playing field for retail investors by providing end-to-end automated trading access to institutional-grade quantitative strategies. Its offerings include a strategy marketplace, one-click deployment, and proprietary execution algorithms. Incubated by prop trading firm Tensor Investment, AlphaNet currently features over 30 strategies and plans to open its platform to other quant teams by the end of Q2. The funding brings operational expertise from Joffre Capital, scaling resources from Huashan, and additional strategies from the consortium.

TheNewsCrypto04/16 06:05

Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

TheNewsCrypto04/16 06:05

Analyst Shares Dogecoin Quantitative Roadmap To New All-Time Highs, Here’s What It Says

Cryptocurrency analyst Cryptollica has shared a quantitative roadmap suggesting Dogecoin (DOGE) could reach new all-time highs. The analyst, DOGE is transitioning from a meme-driven asset to one gaining institutional interest. The roadmap identifies $0.08 as a critical "absolute bedrock" support level, where institutional buyers have historically absorbed selling pressure. Currently trading near this level, DOGE is quantitatively refusing to break lower. If the bear market extends, DOGE may bottom around $0.08 before a bullish reversal propels it above $0.50 by late 2026 or early 2027. The analyst highlights a "Terminal Apex" where downward momentum has exhausted, and pricing asymmetry is at a peak. Algorithms are accumulating supply while retail investors panic-sell. Two strategies are outlined: accumulating near current levels just above $0.08, or waiting for a confirmed breakout candle—though that may mean buying at a higher price. At the time of writing, DOGE is trading around $0.095, down over 5% in 24 hours.

bitcoinist03/19 23:31

Analyst Shares Dogecoin Quantitative Roadmap To New All-Time Highs, Here’s What It Says

bitcoinist03/19 23:31

Private Equity's "Detroit: Become Human" Moment: When AI Takes Over Alpha, What's Left for Human Fund Managers?

A recent report by Anthropic highlights that 94% of financial roles are susceptible to AI replacement, though only 28% have been automated so far. The private fund industry is rapidly adopting AI to enhance efficiency and reduce costs, especially in research, operations, and sales. AI researchers, such as tools like OpenClaw, offer advantages over human analysts: they work 24/7, require no salary or benefits, and avoid human limitations like bias or job-hopping. Quantitative funds, with strong computational infrastructure, are already deploying multi-agent systems that minimize human involvement. In contrast, subjective fund managers face pressure to adopt AI to stay competitive, as散户 investors also use AI to narrow information gaps. However, AI has limitations: it cannot fully grasp market sentiment, human irrationality, or emotional nuances in investing. If AI eventually replaces most fund managers and researchers, markets may become efficient, eliminating alpha opportunities. The future role of humans in asset management may lie in intuition, passion for investing, and handling uncertainty—areas where AI falls short. The industry must determine how humans and AI can collaborate effectively, rather than merely reacting to technological change.

marsbit03/18 02:54

Private Equity's "Detroit: Become Human" Moment: When AI Takes Over Alpha, What's Left for Human Fund Managers?

marsbit03/18 02:54

Polymarket Arbitrage Bible: The Real Gap Lies in Mathematical Infrastructure

This article explores the mathematical infrastructure behind profitable arbitrage strategies on Polymarket, emphasizing that the key advantage lies not in speed alone but in advanced computational techniques. While manual traders might identify simple arbitrage opportunities (e.g., buying both YES and NO shares when their sum is less than $1), quantitative systems use integer programming to analyze complex logical dependencies across thousands of markets, avoiding brute-force enumeration of exponentially many outcomes. The core method involves: - Modeling price constraints via marginal polytopes. - Using Bregman projections (KL divergence) to compute optimal arbitrage trades, respecting the information-sensitive nature of probability-based pricing in LMSR (Logarithmic Market Scoring Rule) mechanisms. - Applying the Frank-Wolfe algorithm with integer programming solvers like Gurobi to efficiently approximate solutions without full enumeration. Execution challenges include non-atomic order execution in CLOBs, liquidity constraints, and VWAP-based slippage estimation. Real-world data shows $39.7M in extracted profits over one year, with the top arbitrageur earning over $2M. The gap between casual and professional trading is attributed to sophisticated math infrastructure, not luck.

marsbit03/11 05:09

Polymarket Arbitrage Bible: The Real Gap Lies in Mathematical Infrastructure

marsbit03/11 05:09

Gate Launches TradFi API and Multi-Leverage Mechanism to Build an Integrated Smart Trading Infrastructure

Gate has officially launched its TradFi trading API and upgraded its TradFi product leverage mechanism, enhancing its multi-asset trading ecosystem. The newly introduced API supports automated trading across metals, forex, indices, commodities, and other major global asset classes. It enables users to deploy strategies, manage orders, and monitor assets programmatically, providing an efficient execution environment for quantitative teams, institutional traders, and professional investors. The API offers functionalities such as programmatic order submission and management, real-time market data, order book depth, and access to account and position information, improving operational and risk management efficiency. Additionally, Gate introduced an adjustable multi-tier leverage system, offering up to 500x leverage with multiple options to support diverse trading strategies and improve capital flexibility. The platform maintains a unified account structure, allowing users to trade both digital and traditional financial assets under a single account using USDT as the unified margin asset. This integration enhances cross-market capital efficiency and risk management. The combination of API-driven trading and multi-leverage mechanisms strengthens Gate’s position as a comprehensive trading platform, catering to growing demand for cross-asset strategies amid global market volatility. Gate, founded in 2013 by Dr. Han, is a leading global cryptocurrency exchange serving over 50 million users with more than 4,400 supported crypto assets.

marsbit03/03 10:00

Gate Launches TradFi API and Multi-Leverage Mechanism to Build an Integrated Smart Trading Infrastructure

marsbit03/03 10:00

High-Frequency Trading, $100K Annual Income: The Most 'Boring' Profit Myth on Polymarket

A user known as planktonXD (0x4ffe49ba2a4cae123536a8af4fda48faeb609f71) has generated over $106,000 in profit on Polymarket within a year by executing more than 61,000 predictions—averaging around 170 trades per day. This high-frequency, automated strategy focuses on exploiting small, certain opportunities rather than betting on high-risk, high-reward outcomes. The approach is characterized by market-making and micro-arbitrage: placing orders on both sides of the order book to capture spreads or profiting from mispriced options in low-liquidity markets. The largest single win was only $2,527, illustrating a disciplined, risk-managed method that avoids large drawdowns. The bot operates across diverse categories—sports, weather, crypto prices, politics—constantly scanning for pricing inefficiencies. Notable examples include buying heavily undervalued options in niche markets, such as esports matches or extreme crypto price movements, where probability is mispriced due to emotional trading or thin order books. For instance, a $16 bet on SOL falling to $130 (priced at 0.7¢, implying <1% chance) returned $1,574 during a volatile period. Key takeaways: The strategy highlights the power of compounding small gains, the necessity of automation and API tools, and the superiority of high-probability opportunities over high-risk bets. In prediction markets, the most advanced approach isn’t forecasting—it’s managing probability and liquidity.

marsbit02/11 13:06

High-Frequency Trading, $100K Annual Income: The Most 'Boring' Profit Myth on Polymarket

marsbit02/11 13:06

Wall Street's Top Quantitative Firm Jump Trading Enters the Prediction Market, Is the Era of Retail Investors Over?

Wall Street quantitative trading giant Jump Trading is entering the prediction market sector through strategic partnerships with leading platforms Kalshi and Polymarket. In exchange for providing liquidity, Jump will receive equity stakes in both companies—a fixed share in Kalshi and a performance-based stake in Polymarket tied to its U.S. trading volume. Prediction markets have faced persistent liquidity challenges, with platforms often experiencing shallow order books and wide bid-ask spreads outside of major events. While Kalshi previously engaged SIG as a market maker and Polymarket relied on decentralized incentives and algorithmic traders, both platforms have struggled to maintain stable, deep liquidity consistently. The equity-for-liquidity model aligns incentives: platforms gain access to Jump’s sophisticated, low-latency market-making capabilities, while Jump positions itself to benefit from the sector’s growth—Kalshi and Polymarket are valued at approximately $11B and $9B, respectively. Market making in prediction markets offers potential profits from spreads, incentives, and arbitrage, but it also carries significant risks, including event-driven volatility, limited hedging options, and regulatory uncertainty. While Jump’s advanced infrastructure and cross-asset experience may allow it to capture alpha and leverage equity upside, smaller players face high barriers to entry. The move signals a maturation of the prediction market space, with institutional participation likely to improve liquidity but also centralize influence among top-tier firms.

marsbit02/10 14:39

Wall Street's Top Quantitative Firm Jump Trading Enters the Prediction Market, Is the Era of Retail Investors Over?

marsbit02/10 14:39

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