High-frequency trading (HFT)
We leverage advanced algorithms and high-speed computing power to perform high-frequency trading strategies that automatically execute large numbers of orders at the millisecond or even microsecond level. Our system optimizes trading performance by providing customers with rapid market entry and exit points by accurately capturing price differences and market inefficiencies.
Algorithm execution strategy
RaiseGrid Developed execution algorithms (e. g., VWAP, TWAP) to help institutional investors maximize trading efficiency and reduce market shocks. By finely adjusting the timing and quantity allocation of transactions, our algorithm ensures that customers execute large-scale orders at the optimal average price.
Investment strategy optimization
Multi-factor model
We use a multi-factor investment strategy to analyze market behavior by combining historical and real-time data. Factors such as momentum, value, quality, and volatility are used to build models that predict asset prices and guide investment decisions to achieve excess returns.
Portfolio optimization and asset allocation
Using quantitative models and machine learning techniques, we help customers optimize their portfolio allocation. By calculating the correlation and expected risk-return ratios between different assets, our strategy helps clients balance risk and return in a variety of market environments.
Risk modeling and quantitative assessment
Our quantitative team has developed advanced risk management models, such as historical simulation and econometrics approaches, to quantify and manage market risk, credit risk, and liquidity risk. By monitoring and assessing potential risks in real time, our system is able to provide customers with timely risk adjustment recommendations.
Stress testing and scenario analysis
Stress testing and scenario analysis using quantitative methods to help customers understand the possible impact of extreme market events on their portfolio. Our model evaluates asset performance under a range of predetermined adverse conditions, ensuring that clients can respond to sudden economic and financial crises.
Hedge strategies and market-neutral strategies
RaiseGrid Developed and implemented advanced statistical arbitrage strategies that are based on complex mathematical models and statistical methods, such as co-integration and hedging ratio analysis. Our system is able to identify and exploit the price deviations between different assets to perform hedging operations to achieve market neutrality, thus reducing the impact of market fluctuations on investment returns. This strategy is particularly suitable for a diversified portfolio to help customers reduce risk without sacrificing potential gains.
Dynamic hedging and risk adjustment
Our quantitative platform tracks asset prices and market movements in real time, automatically adjusting hedging positions to maintain optimal exposure levels. This includes dynamic hedging using options and futures to ensure that the portfolio remains stable in the face of market uncertainty and volatility.
Machine-learning-driven credit evaluation
RaiseGrid Use advanced machine learning techniques to develop quantitative credit scoring models, which can analyze and process a large amount of consumer financial data and predict the credit risk of borrowers. By integrating consumer transaction history, solvency, and market data, our model provides accurate credit scoring services for financial institutions, enabling scientific and automated credit decisions.
Enterprise credit and default probability model
In the field of corporate finance, our quantitative model predicts the default probability and credit rating by analyzing its financial statements, market behavior and macroeconomic factors. This information helps banks and investors conduct more accurate risk assessment and credit pricing, while optimizing capital allocation and risk management strategies.