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05 / OUR IN-HOUSE QUANTITATIVE SYSTEM

Tomorrow’s Star
Quantitative System

Tomorrow’s Star Quantitative System TSQS

The Future of Investing
Is Data-Driven.

Data, algorithms and artificial intelligence are reshaping financial markets. As markets become more complex, approaches rooted in experience and professional judgment are increasingly being combined with systematic analysis and intelligent decision-support technologies.

Against this backdrop, we are developing the Tomorrow’s Star Quantitative System (TSQS) in-house. It explores the integration of financial technology and modern investment thinking, with the aim of providing scientific, systematic technical support for investment research, strategy analysis, trading decisions and risk management.

TSQSIN-HOUSE
QUANTITATIVE SYSTEM

Tomorrow’s Star
Quantitative System

01DATA
02MODELS
03RISK
RESEARCH · VALIDATION · EVOLUTION

Tomorrow’s investment capabilities depend on more than access to information. They also depend on how effectively we process it, recognize market patterns, manage risk and continue to refine decisions. This belief is the foundation of TSQS.

01 / THE EVOLUTION OF INVESTING

From experience-led
to data-informed.

Why is quantitative investing becoming an important direction for the industry?

Investment decisions have traditionally drawn heavily on market experience, financial knowledge and personal judgment. Markets now combine vast amounts of data, rapid information flows and complex trading mechanisms. Price movements, trading volumes, liquidity, sentiment and macroeconomic factors interact continuously; manual observation alone cannot consistently capture every potential influence.

Quantitative investing offers a systematic response. Mathematical models, statistical analysis, algorithms and data-processing techniques can examine market information within a defined research framework, identify statistically meaningful characteristics and support investment decisions. As artificial intelligence, machine learning and financial data infrastructure develop, quantitative methods may play a broader role in modern asset management.

Quantitative investing is not simply about letting computers trade for people. It is about grounding decisions in clearly defined data, models and risk controls.

02 / SIX CORE STRENGTHS

A systematic approach.
Six connected dimensions.

Our core strengths are organized around six research and development priorities, connecting market understanding, strategy discipline and risk awareness.

01DATA & MARKET INTELLIGENCE

Multi-Dimensional
Market Intelligence

Markets are shaped by many factors; a single indicator rarely provides a complete picture. TSQS treats multi-dimensional market analysis as an important area of development, bringing together price movements, volume, volatility, liquidity and other relevant financial data within a systematic analytical framework.

The aim is to extract meaningful market characteristics from complex information and provide a stronger data foundation for strategy research and investment decisions.

02RULES & RESEARCH DISCIPLINE

Systematic
Investment Decisions

Fast-moving markets can expose investors to emotional reactions, short-term noise and cognitive biases. Quantitative methods translate research into explicit, actionable and testable rules through predefined strategy logic, screening criteria and risk constraints.

TSQS aims to bring data analysis and rule-based methods into the decision process, helping reduce the influence of irrational emotions on trading.

03TESTING & STRATEGY EVALUATION

Intelligent
Strategy Research

Different market conditions create different opportunities and risks for investment strategies. TSQS is developing a multi-strategy research framework, exploring statistical analysis, price behavior, market structure and multi-dimensional strategy evaluation.

Historical testing, model validation and iterative research help examine when a strategy may be applicable and build a foundation for diversified quantitative research.

04INFORMATION & RESEARCH EFFICIENCY

Efficient
Data Processing

Financial information changes faster than traditional manual research can process. Programmatic data collection, organization, calculation and analysis can improve the efficiency of standardized research tasks and support ongoing monitoring across many instruments and market variables.

TSQS aims to make financial data processing and strategy analysis more efficient, helping research respond more promptly to changing market conditions.

05RISK AWARENESS & CONSTRAINTS

Risk Management
Framework

No investment strategy can eliminate market risk. A mature quantitative approach considers risk identification, assessment and constraints alongside potential returns.

Risk management is a core development direction for TSQS. We are exploring a systematic framework covering market volatility, strategy drawdowns, trading liquidity, portfolio exposure and the risk of strategy failure.

06VALIDATION & CONTINUOUS EVOLUTION

Continuous Research
& Model Optimization

Markets do not stand still. Market structure, liquidity, participant behavior and macroeconomic conditions all change. Models therefore need ongoing data testing and evaluation of their validity and scope of application.

TSQS follows a research, validation and refinement process, using continued technical iteration to improve its analytical framework and explore more resilient research methods across market environments.

Good investment technology should help investors understand risk as well as identify opportunity.

03 / FIVE TRENDS SHAPING THE FUTURE

The next chapter
of investing.

01Data-informed investment decisions

Investment is moving from an emphasis on personal experience toward an approach that combines multi-dimensional data, quantitative analysis and professional judgment.

Data quality, analytical capability and research methodology will be important components of an institution’s competitive capabilities.

02Deeper integration of AI and quantitative models

Artificial intelligence and machine learning offer additional tools for quantitative research. They may support feature recognition in complex data, market-state analysis, strategy research and portfolio risk assessment.

Competitive investment frameworks will increasingly depend on collaboration between financial expertise, mathematical models and intelligent computing.

03Multiple strategies and broader risk management

Markets are cyclical, complex and uncertain. A single strategy may struggle to adapt consistently. Studying strategies’ risk and return characteristics, correlations and conditions of applicability can provide a broader set of portfolio-management tools.

Risk management is also expanding beyond individual trade stop-losses to address strategy, portfolio, liquidity and overall exposure.

04Greater access to investment technology

Advanced quantitative tools were once concentrated in institutions with specialist research teams and technical infrastructure. Improvements in computing, financial data services and software are creating more opportunities to learn about systematic investment methods.

Investment technology may make professional financial analysis tools more accessible to a wider range of investors.

05Human expertise and intelligent systems working together

The development of quantitative investing does not mean that investment professionals will be entirely replaced. Systems excel at processing structured data, executing defined rules and performing large-scale calculations. People play essential roles in interpreting economic conditions, assessing unusual events, governing strategies and overseeing risk.

A more mature investment approach combines professional research, intelligent algorithms and disciplined risk management.

04 / OUR TECHNOLOGY VISION

Connecting professional research
with everyday investors.

Our ambition for TSQS extends beyond a single trading tool. We aim to develop a long-term financial technology product framework spanning data analysis, quantitative strategies, investment research and risk management.

We take a long-term view, grounded in technical innovation, professional research and risk awareness, as we explore the practical value of advanced financial technology.

As development and validation progress, our investor education work will help people understand the principles, applications and limitations of quantitative investing. Our goal is to make quantitative thinking and financial technology more accessible, supporting a rational, scientific and systematic understanding of investing.

Tomorrow’s Star Quantitative System

Discover Patterns Through Data.
Explore the Future Through Technology.

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