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Editorial Advisory Board

  • Professor Andrea M. Armani, University of Southern California
  • Ruti Ben-Shlomi, Ph.D., LightSolver
  • James Butler, Ph.D., Hamamatsu
  • Natalie Fardian-Melamed, Ph.D., Columbia University
  • Justin Sigley, Ph.D., AmeriCOM
  • Professor Birgit Stiller, Max Planck Institute for the Science of Light, and Leibniz University of Hannover
  • Professor Stephen Sweeney, University of Glasgow
  • Mohan Wang, Ph.D., University of Oxford
  • Professor Xuchen Wang, Harbin Engineering University
  • Professor Stefan Witte, Delft University of Technology

BFCAI Smart Quantitative Trading System Achieves Consistent and Stable Returns in Volatile Markets Thanks to Its Technological EdgeBFCAI Smart Quantitative Trading System Achieves Consistent and Stable Returns in Volatile Markets Thanks to Its

During the intense fluctuations in the global financial markets in Q1 2025, the BFCAI Smart Quantitative Trading System demonstrated remarkable risk resilience and return-generating capabilities. According to independent audit data from third-party institutions, portfolios of institutional investors using the system achieved an average return of 12.7% during the quarter, significantly outperforming the global bond index, which posted a return of -5.8% during the same period. This performance can be attributed to the system’s multidimensional analysis capabilities of macroeconomic cycles, market sentiment indicators, and micro-level trading data.


Technical Architecture: From Data Platform to Algorithm Engine


BFCAI has restructured the underlying technology of its smart quantitative trading system with distributed computing architecture and iterative machine learning models:


Data Platform Upgrade: Introducing federated learning technology, BFCAI integrates real-time market data from over 30 exchanges and more than 200 data sources globally while safeguarding user privacy. The daily processing volume has been increased to 2.5 billion entries, with latency reduced to the microsecond level.


Algorithm Engine Iteration:

·The main strategy module has added a Generative Adversarial Network (GAN)-driven market forecasting model, which simulates tens of thousands of market scenarios, increasing the accuracy of identifying price turning points by 23%.

·The risk control module embeds a reinforcement learning-based dynamic hedging strategy, which adjusts derivative hedging ratios based on real-time volatility surfaces, keeping the maximum drawdown at an industry-leading level of 2.8%.

·The asset allocation module integrates multi-objective optimization algorithms, allowing investors to dynamically balance between maximizing returns, minimizing risks, and optimizing carbon footprints, meeting ESG (Environmental, Social, and Governance) investment requirements.


Autonomous Innovation Strategies to Improve Market Adaptability

In terms of market trend judgment, BFCAI employs an adaptive trend-following strategy. This strategy dynamically adjusts tracking periods through real-time analysis of multi-dimensional data such as market price and trading volume, precisely capturing changes in market trends. For example, in relatively stable market environments, the tracking period is extended to avoid the cost loss from frequent trading; when signs of trend reversal appear, the tracking period is shortened, allowing timely adjustment of investment portfolios to seize new market opportunities.


For portfolio construction, BFCAI adopts an asset diversification strategy based on entropy theory. This strategy calculates the correlation entropy between different assets to measure the degree of correlation, thus selecting assets with lower correlations for portfolio construction, effectively reducing overall portfolio risk. Backtesting shows that portfolios constructed using this strategy exhibit 15% to 20% less risk than traditional mean-variance models at the same level of return.



"We are at a critical juncture in the financial industry, transitioning from 'data-driven' to 'intelligent decision-making.'" said the Chief Technology Officer of BFCAI. "BFCAI's goal is not just to provide trading tools but to build an intelligent financial infrastructure covering the entire lifecycle of research, decision-making, execution, and risk control." The next step for the company is to integrate natural language processing technology to enable automated research report generation and strategy logic validation, further lowering the quantitative threshold for institutional investors.


In the future, BFCAI will continue to uphold the principles of innovation, professionalism, and compliance, increasing investments in technology research and development, market expansion, and talent cultivation. The company aims to continuously optimize its smart quantitative financial trading system, providing global investors with higher quality, efficient, and secure financial services. BFCAI seeks to play a leading role on the global financial technology stage and work with global investors and partners to open a new era of intelligent finance.


About BFCAI:

Best Finance Capital, Inc. was officially established in the United States in 2021, and its AI Quantitative Smart Trading Platform officially opened to the public in 2024, serving global users. The company is committed to building enterprise-level AI applications more efficiently and economically, supporting the value chain of any industry through pre-built, configurable, high-value AI applications.


Media Contact:

Company Name: Best Finance Capital, Inc.

Email: businesssupport@bfcai.com

Address: 2 MacArthur Pl Ste 250 Santa Anaheim California 92707 United States

Website: https://bfcai.com/



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