QSG Unveils First AI-Native Trading Platform MetisAI: From a Single Trading Idea to Verifiable, Deployable Trading Strategies

Quantech Services Group (QSG), an institutional-grade quantitative trading infrastructure technology company, announced today that MetisAI, its AI-native investment strategy and trading execution platform developed by its team, is officially launching.

As QSG’s AI-native product aimed at bringing institutional-grade quantitative research and trading execution capabilities to a broader market, MetisAI centers on the core proposition “All in One, One for All. Intelligence Behind Every Step.”, consolidating strategy research, backtesting, deployment, trading execution, and continuous monitoring—originally scattered across different tools and specialized teams—onto a single platform.

MetisAI empowers investors who have investment ideas but may lack programming skills, quantitative teams, or trading infrastructure to more efficiently transform their concepts into testable, adjustable, and directly deployable trading strategies.

This public debut was co-hosted with Binance VIP, using closed-door discussions to first validate the real needs of professional investors, gradually bringing research and execution capabilities once primarily held by quantitative teams and institutions to a wider audience of investors.

Consolidating Fragmented Trading Tools into a Complete Workflow

In the past, truly putting a trading idea into the market typically required separately using multiple system tools such as strategy development, historical backtesting, trading APIs, order management, and asset monitoring. Beyond technical barriers, differences in data formats, risk parameters, and operational workflows between systems made strategy deployment complex and time-consuming.

MetisAI integrates this fragmented and complex path into a complete trading workflow:

• Natural Language Strategy Creation: Users simply describe their trading idea, investment objectives, and risk boundaries, and MetisAI converts the concept into a structured strategy;
• Historical Backtesting & Optimization: View key metrics such as returns, volatility, and maximum drawdown, and analyze and adjust with MetisAI assistance;
• Risk Condition Setup: Set maximum risk exposure, acceptable drawdowns, and other execution limits prior to deployment;
• Strategy Deployment & Execution: After user confirmation and authorization, connect the strategy to a live trading environment;
• Continuous Monitoring & Management: Track strategy status, positions, performance, and risks in real-time, with the ability to adjust or halt operations at any time.

The overall architecture is built upon a clear division of labor between humans and machines:

• Humans define objectives and risk boundaries;
• MetisAI transforms concepts into verifiable, deployable strategies;
• An institutional-grade trading system reliably brings strategies to market.

Beyond Strategy Generation: Bringing Strategies to Market for Real

The market already has many AI tools capable of providing market analysis, investment insights, or strategy recommendations. However, a significant gap remains between a seemingly reasonable text description and a fully functional trading strategy that can be backtested, deployed, and run continuously.

MetisAI aims to bridge exactly this distance from “idea” to “live trading.”

Unlike general AI tools that remain limited to dialogue or content generation, MetisAI integrates AI directly into the complete trading workflow. Users can discuss strategy logic with AI, interpret backtest results, adjust conditions, and continuously monitor execution status after deployment. Research, decision-making, and execution are no longer scattered across multiple systems but are continuously iterated within a unified environment.

Institutional-Grade Trading Capabilities Rooted in Real Markets

MetisAI is built by the QSG team, inheriting their practical experience in high-frequency trading, quantitative research, low-latency market connectivity, and trading execution systems.

Whether a strategy performs effectively in real markets depends not only on trading logic but also on market data, order management, fill quality, risk control, and system stability. MetisAI incorporates these capabilities—once primarily serving professional quantitative and institutional trading teams—into its AI-native trading workflow.

Recently, the QSG team leveraged MetisAI to assist with strategy research and decision-making, participating in the Liquidity Arena 2026 AI Quantitative Trading Competition Track A Phase 1, hosted by LTP, ultimately securing third place overall; among the top three teams, they ranked first in both return on investment and the Sharpe Ratio. This competition achievement demonstrates MetisAI's practical potential in strategy creation, validation, and continuous optimization.

“We built MetisAI not starting from a chatbot, but from years of practical experience in quantitative research and trading execution in real markets,” said QSG CSO Tommy Ho. “The market doesn’t lack trading ideas; the real challenge lies in transforming those ideas into strategies that can be verified, deployed, and continuously managed. MetisAI aims to bring this capability—which was once exclusive to professional teams—to more investors.”

For users, this means tasks that previously required collaborative efforts from quantitative research, software engineering, and trading execution teams can now be accomplished more efficiently on a single platform.

Assets Remain in Users’ Own Accounts

MetisAI operates on a non-custodial model. Users do not need to transfer assets to the platform, nor does MetisAI hold or safeguard user assets.

Once authorized by the user, strategies are executed through their connected personal trading accounts. Control over assets remains entirely with the user, reducing additional custodial counterparty risk exposure from the platform.

Going forward, MetisAI will also provide a cross-platform, cross-asset consolidated view, allowing users to track positions, strategy status, performance, and execution within a unified interface, thereby reducing the managerial burden caused by fragmented information.

Debuting at a Binance VIP Closed-Door Session, Building Trust from the Professional Market

For its official public debut, MetisAI chose to co-host a VIP closed-door session with Binance, the world’s largest cryptocurrency exchange, to engage directly with professional investors possessing actual market experience, thoroughly discussing needs related to strategy research, risk setting, trading execution, and asset management.

For a brand-new AI-assisted trading platform, this is more than a product launch; it marks an important milestone for MetisAI to enter professional trading scenarios and undergo real-world demand testing. Making its debut in a closed-door session co-hosted with Binance VIP, MetisAI was able to present its product philosophy and application scenarios to professional investors and industry insiders, demonstrating how AI combined with QSG’s accumulated institutional-grade trading capabilities can be implemented in strategy research, trading execution, and risk management, laying a stronger foundation of trust for the product’s market entry.

The event showcased how users can create trading strategies using natural language, review historical backtesting results, set risk conditions, and connect strategies to subsequent deployment and monitoring workflows. Through direct engagement and feedback from frontline users, MetisAI will continue to refine the product experience and validate real-world needs across various trading scenarios.

QSG Co-Founder Clark Hsu stated, “Choosing to meet the market for the first time during a closed-door event co-hosted with Binance VIP holds significant meaning for us. For an AI trading platform, trust cannot be built solely on concepts or slogans; it must come from real-market experience, verifiable technical capabilities, and practical validation by professional users. Professional investors care not just about whether AI can generate strategies, but more importantly about how strategies are validated, how orders are executed, how risks are managed, and how a comprehensive view of cross-platform assets can be continuously maintained. These are precisely the core challenges MetisAI aims to solve.”

Democratizing Quantitative Investing Capabilities

MetisAI’s mission is “Make quantitative investing accessible to everyone.”—enabling everyone to utilize quantitative investing capabilities.

MetisAI aims to reduce not only the barrier to writing code but the overall complexity spanning research, backtesting, deployment, and execution. Users do not need to piece together disparate technical tools themselves, nor build a full quantitative and engineering team upfront, allowing them to explore systematic trading in a more intuitive, transparent, and controlled manner.

“Intelligence Behind Every Step.” For MetisAI, intelligence goes beyond generating a trading viewpoint; it encompasses validating hypotheses, understanding risks, and ensuring every execution step can be adjusted and optimized.

From a single idea to a fully verifiable, deployable, and continuously running strategy.

This is the new trading experience MetisAI strives to deliver to every investor.

MetisAI Public Beta Expected to Launch on September 30, 2026

MetisAI is expected to officially open its public beta on September 30, 2026. The initial phase will operate on an invitation-only basis. Users interested in participating in the first-phase public beta can register at the MetisAI Official Website and submit a beta application during the registration process; beta access will be rolled out in batches via referral codes.

During the public beta, MetisAI will continuously gather frontline user feedback, optimize the product experience, and gradually roll out additional market connectivity, strategy tools, and asset integration features. Users are also encouraged to follow MetisAI’s official social media channels for beta updates and the latest announcements.

About MetisAI

MetisAI is an all-in-one AI-driven investment strategy and trading execution platform built by QSG, dedicated to bringing quantitative research and institutional-grade trading capabilities to a broader investor base.

Users can create trading strategies using natural language, conduct historical backtesting, set risk boundaries, and link strategies to deployment, trading execution, and continuous monitoring. Operating on a non-custodial model, MetisAI does not require users to transfer assets to the platform, allowing them to maintain control over assets, authorizations, and trading decisions while utilizing AI and quantitative tools.

Official Website: https://metisai.trade/en/
X: @metisai_trade
Telegram: t.me/metisAI_trade
Instagram: @metisai_

About QSG

QSG is a fintech company specializing in institutional-grade trading infrastructure, quantitative research, and trading execution technology, providing low-latency market data, trading connectivity, execution optimization, and strategy research services to professional investors, trading teams, and AI-native financial applications.

Through MetisAI, QSG further transforms its years of accumulated quantitative and trading infrastructure capabilities into a more intuitive, all-in-one product, enabling more investors to build, validate, and execute their own trading strategies using AI.

Official Website: https://quantech.services/

Risk Disclosure

MetisAI provides only technical tools related to strategy creation, backtesting, and trading execution. It guarantees no profitability and does not constitute any investment, financial, legal, or tax advice.

All strategy creation and trading operations are initiated and completed by the user. Information, analysis, and conversational content provided by AI are for reference only; the decision to adopt, deploy, and execute rests solely with the user, who assumes full responsibility.

Historical backtesting results do not guarantee future performance. Financial product prices may fluctuate significantly, and users should carefully assess relevant risks based on their individual financial situations and risk tolerance.