On July 25, 2026, the fintech industry witnessed a major announcement—QuantWise Tech officially launched its new-generation AI quantitative trading platform, "QuantWise·Tianshu," in Beijing. The platform is hailed as "the first intelligent trading system to achieve full-process automated decision-making," marking a new stage in AI applications in financial investment. According to QuantWise Tech CEO Zhang Rui at the launch event, the platform achieved an average annualized excess return of 18.7% in backtesting, with maximum drawdown controlled within 4.2%, significantly outperforming traditional quantitative strategies.
Technical Highlights: Integrated Innovation of Deep Learning and Reinforcement Learning
The core technical architecture of "QuantWise·Tianshu" is based on a Transformer time-series model and the Proximal Policy Optimization (PPO) algorithm. It can simultaneously process market data, sentiment data, macroeconomic indicators, and alternative data (such as satellite imagery and supply chain data), constructing a feature space of over 5,000 dimensions. Unlike traditional quantitative models that rely on manual feature engineering, this platform employs a self-supervised learning mechanism to automatically identify non-linear correlation patterns in the market. Additionally, the built-in reinforcement learning module dynamically adjusts strategy parameters based on real-time market feedback, enabling adaptive risk control.
Dr. Li Min, Chief Scientist at QuantWise Tech, explained: "In the past, quantitative trading required a massive amount of manual coding and rule construction. The Tianshu platform, through end-to-end learning, goes directly from data to decision, significantly reducing human bias and overfitting risks. We have also embedded a causal inference module in the model to avoid strategy failure caused by misjudgment of correlations."
Impact on the Industry: From Auxiliary Tool to Decision Maker
The platform's launch sparked widespread discussion in the industry. Senior quantitative trader and private equity fund manager Chen Tao said: "If the Tianshu's actual performance matches the claims, it will reshape the competitive landscape of the quantitative industry. Small and medium-sized institutions may no longer need to build large quantitative teams; they can simply access the platform to obtain top-tier strategies." However, some analysts expressed concerns about the "black box" nature of AI trading. Fintech researcher Wang Lin pointed out: "As more and more trades are dominated by AI, the market may face systemic homogeneity risks. If models misjudge simultaneously, it could trigger a flash crash."
In response, QuantWise Tech emphasized that its platform has complete compliance and monitoring mechanisms. All strategy decisions generate interpretable reports through the "AI Audit Module" and are equipped with circuit breaker thresholds. Currently, the Tianshu platform has entered pilot cooperation with several securities firms, including CITIC Securities and Huatai Securities, and is expected to open to qualified investors in the fourth quarter.
Future Trends in Fintech and Data Intelligence
QuantWise Tech's launch is a microcosm of the deep integration of fintech and data intelligence. Looking at the first half of 2026, the financial data tools track continued to heat up: multiple banks launched smart investment advisory services based on large models; the central bank digital currency (DC/EP) pilot expanded to 50 cities, with smart contract functionality introduced; big data risk control platforms widely adopted federated learning technology to resolve the conflict between data silos and privacy protection.
According to the latest report from International Data Corporation (IDC), global fintech spending is expected to exceed $250 billion in 2026, with AI and big data accounting for over 40%. In China, with the advancement of the "Digital China" strategy, the digital transformation of the financial industry has entered deep waters. Data intelligence companies are no longer just providing tools; they are deeply involved in the business restructuring of financial institutions. QuantWise Tech's practice demonstrates that from quantitative trading to credit approval, from anti-fraud to compliance management, data intelligence is becoming the core engine driving financial innovation.
Challenges and Outlook
Despite the bright prospects, the development of fintech still faces multiple challenges. First is data security and privacy compliance. The "Financial Data Security Management Regulations," effective in 2026, impose higher requirements on data usage, forcing companies to find a balance between model performance and compliance. Second is algorithmic ethics: who should be held responsible for losses caused by AI autonomous decision-making? The industry has yet to reach a consensus. Finally, there is a talent gap—composite talents who understand both finance and AI are in short supply.
QuantWise Tech CEO Zhang Rui stated at the conclusion of the launch: "Technological innovation is important, but the essence of fintech is to serve the real economy. We hope that through the Tianshu platform, more investors can enjoy the benefits of intelligentization while adhering to risk control bottom lines." As AI quantitative trading gradually moves to the forefront, a more efficient, transparent, but also more complex financial era is arriving.
