Kenny Chan for AWS Community Builders

Posted on Mar 27

When AI Starts to Understand Market Narratives: How the AWS Financial Services Symposium Is Rethinking Event‑Driven Trading

#ai #aws #trader #machinelearning

当AI开始读懂市场叙事：AWS Financial Services Symposium重构事件驱动交易的底层逻辑

Thanks to Jacky Wu for the invitation to the AWS Financial Services Symposium. David Sung and Michelle Hong shared industry insights on AI in the AWS Financial Services. Danny Chan supports the AWS FSI Acceleration Professional Services Group.

Key Takeaways

① Turning unstructured information into signals.

Unstructured data is converted into embeddings and analyzed using vector similarity matching. This enables semantic-level AI tagging that systematically links news, macro events, and cross-asset price movements into a signal layer. As a result, human traders can focus on the highest-value work in trading alpha: (1) building hypotheses, (2) designing trade structures, and (3) managing execution-level strategy.

将非结构化信息通过 embedding 向量化，利用 vector similarity matching 进行语义级 AI Tagging，把新闻、宏观事件与跨资产价格行为系统性地映射为信号层。由此，人类交易员得以专注於 alpha ①假设构建、②交易架构设计与③执行层博弈等最高价值的交易实务。

② Fixing long-standing problems in discretionary macro trading.

Macro discretionary trading has long faced structural issues: (1) strong path dependence, (2) limited data samples, and (3) difficulty in standardized backtesting. From a regime perspective, the macro environment of 2026 looks closer to a 1999-style regime, shaped by three overlapping themes: (1) high-valuation technology narratives, (2) rising geopolitical uncertainty, and (3) repeated testing of real-economy fundamentals. In this framework, AI tagging continuously labels (1) narrative spread, (2) risk-factor exposure, and (3) asset co-movement, providing a more stable signal layer for macro hedging alpha.

宏观主观交易长期面临①路径依赖强、②样本稀疏、③难以标准化回测的结构性问题。从交易范式看，2026 年的宏观环境更接近 1999-style regime：①高估值科技叙事、②地缘政治不确定性抬升、③真实经济基本面的反复验证三条主线相互缠绕。在这一框架下，AI Tagging 通过对①叙事扩散、②风险因子暴露、③资产联动的持续标注，为 macro hedging alpha 提供了更稳定的信号层。

③ Validating execution at the trading desk level.

At the execution layer, a futures paper-trading pipeline is built in a testnet environment to test (1) matching logic, (2) slippage assumptions, and (3) risk-control parameters. This environment is chosen because professional market-making (MM) teams are already testing strategies on the same testnet, allowing liquidity, order-book behavior, and counterparty actions to be observed more realistically.

在交易桌执行层面，基於 testnet 环境搭建 futures paper trading 流水线，用於验证①撮合逻辑、②滑点假设、③风控参数。之所以选择该环境，是因为已有专业做市商（MM）团队在同一测试网络进行策略试盘，能更真实地暴露流动性、盘口与对手方行为。

Stories of how macro traders earn in the market?

AWS FSI Partner Sponsors (Golden Tier)

① Sensors Data (神策数据)

Banks are divided into different types: retail banking, investment banking, virtual banking, and commercial banking. Because of this, a full 360‑degree customer journey looks different for each type. For example, when activating deposits, AI must understand user experience and financial product promotion in different ways. By using each bank’s own business data, AI helps match products to real market needs.

银行细分成「零售银行，投资银行，虚拟银行，商业银行」。因此，360客户日程各有异同，比如盘活存款时，AI理解用户体验和推销理财产品的过程都不一样，通过公司的业务数据来契合市场需求。

② IntSig (合合信息)

IntSig’s strength is long‑context AI reasoning. In finance, balance sheets span long time periods and often require reading rolling annual reports to understand the business value behind each number. With long‑context AI, IntSig allows AI to directly understand financial accounting logic, turning complex financial data into real business actions.

超长上下文推理是合合的AI优势。金融行业的资产负债表是超长距度，需结合「滚动年报」才能解读每一个数据背後的商业价值。而合合信息利用AI超长上下文，让AI直接拥有「金融会计」的数字感知，交付真正读懂金融数据的商业行动。

③ InsureMO (易保云)

InsureMO has built a full set of insurance‑focused, “lobster‑style” APIs that are highly practical. These APIs give AI strong built‑in capabilities, including customer KYC, credit checks, background audits, and message delivery across front, middle, and back offices. This helps insurance AI fit market needs more directly.

沉淀了保险行业的龙虾型API最适用服务，等於直接提供AI最全面的power skill，包括由前中後台的客户KYC，信贷核查，背调审计，发送短信结果等，将保险龙虾AI契合市场需求。

④ DBAPPSecurity (安恒信息)

Its security engine is built on 20 years of cybersecurity experience, using rule libraries developed through real red‑team penetration testing. This kind of human security judgment cannot be replaced by AI. By bringing human security thinking into business operations, AI can help monitor and control security risks at the business level. This represents a new future model for enterprise security.

安全引擎是来自二十年资安经验，由红队渗透而建立的安全规则库，这是AI无法取代的人类思捷。同时，把「人类安全思捷」引入到「商业行动」，AI在业务层把关商业市场的安全风险，这是未来的安全新范式。

⑤ Deloitte Consulting (德勤咨询)

Deloitte’s AI advantage lies in topology relationship graphs. By combining large client data sets with mathematical algorithms, AI can identify patterns and connections more reliably. This creates new insights for the consulting industry and delivers real business value to top 100 enterprises.

拓扑关系图是德勤的AI优势，结合了庞大的客户库数据，通过数学算法让AI稳定识别信息，这是咨询行业的新洞察，爲百强企业提供商业行动价值。

My name is Kenny Chan. I work as a macro trader and structured broker, and I also use QuantAI tools as part of the AWS Community Builder.

I focus on global markets, manage profits, and aim to build a strong career in trading. My main skills include macro trading, structured brokerage, AI-based solutions, and risk management.

We are an innovative AI-based financial services company focused on technology-driven trading. We also explore areas of tech innovation, such as AI venture capital and private equity, macro and crypto quantitative trading, and CTF financial security compliance.

我们是一家创新型AI金融服务企业，专注於技术驱动交易。更涉猎科技创新领域，如AI创投私募，宏观和Crypto量化交易，CTF金融安全合规等。

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