The Cross-Border Data Problem
Most research infrastructure is built for a single region. Traditional global terminals have limited China-data depth; domestic services like Wind or Choice are deep on China but shallow globally. Every cross-border researcher knows this fragmentation — it is the reason they keep three terminals open and spend half their day moving data between them.
KSINQ’s data layer was built to solve this specific problem: how do you create a single analytical environment where Chinese and global market data are equally accessible, equally queryable, and equally integrated into the reasoning process?
The Architecture: Two Pillars
AKShare — The China Pillar. AKShare is an open-source financial data library that covers A-shares, Hong Kong equities, mainland futures, fund data (including QDII/LOF NAV and premium data), and Chinese macro-economic indicators from the National Bureau of Statistics, the central bank, and SAFE. We chose AKShare over commercial alternatives (Wind, Choice) for three reasons specific to our AI-native workflow.
First, API-first design. AKShare was built as a Python library, not a GUI terminal. This means it integrates naturally into programmatic workflows — a critical requirement when the “user” is an AI model making real-time data requests through MCP, not a human clicking through a desktop application. Commercial terminals have APIs, but they were designed as afterthoughts to terminal products. AKShare’s API is the product.
Second, open-source transparency. When our model queries AKShare for a company’s financial data, we can inspect the exact data source, the parsing logic, and the transformation pipeline. With proprietary terminals, the data is a black box — you get a number, but you cannot verify the chain from raw filing to displayed value. For a research process built on falsifiability, this transparency is not optional.
Third, cost structure. AI-native research workflows make orders of magnitude more data requests than human analysts. A workflow that queries 50 companies across 10 financial metrics for a sector screen generates 500 API calls in seconds. Commercial terminal licensing is priced for human usage patterns, not AI-scale throughput. AKShare’s open-source model eliminates this constraint.
Global Financial Data — The Global Pillar. The global pillar is accessed through a licensed cross-border financial data provider covering US and international equities, fixed income, derivatives, macro-economic indicators, credit ratings, and ESG metrics. Through MCP integration, Claude can query this feed directly — pulling US peer financials, global sector benchmarks, and macro indicators in the same analytical pass that queries AKShare for Chinese data.
The combination is greater than the sum of its parts. A single research conversation can start with AKShare data showing that a Chinese chemical company’s gross margins have expanded for three consecutive quarters, then pivot to global data showing that US peers’ margins are compressing over the same period, then ask: “What explains this divergence, and is it sustainable?” The model reasons across both datasets without the researcher having to switch tools, export files, or manually align data formats.
Solving the Hard Problems
Data normalization. Chinese companies report under Chinese Accounting Standards (CAS), which differ from US GAAP and IFRS in treatment of revenue recognition, lease accounting, and government subsidies. Our data layer includes a normalization module that adjusts for these differences when performing cross-border comparisons. This is not a trivial problem — the adjustments are context-dependent and sometimes require judgment calls that the model must flag for human review.
Temporal alignment. Chinese listed companies report semi-annually (with Q1/Q3 interim updates), while US companies report quarterly. Fiscal year-end dates differ. Our data layer handles temporal alignment by standardizing to trailing-twelve-month (TTM) metrics for comparability, and explicitly flagging when temporal misalignment exceeds one quarter.
QDII/LOF premium data. This is KSINQ’s most differentiated data capability. AKShare provides real-time and historical NAV data for QDII and LOF funds, which we combine with market price data to calculate premium/discount rates. This feeds directly into our QDII Premium Monitor tool and our cross-border research analysis.
The Integration Layer: Readwise and Dify
Raw market data is necessary but insufficient. Research also requires qualitative information — sell-side opinion, news flow, regulatory announcements, academic research. Readwise serves as our research content ingestion layer, automatically collecting and organizing this qualitative material. Through MCP, Claude can query our Readwise library to answer questions like: “What is the sell-side consensus on copper demand growth in 2026?” or “Have any analysts flagged the same margin divergence I’m seeing in the data?”
Dify orchestrates the entire workflow — connecting AKShare, the global data layer, and Readwise through automated pipelines that run daily. Our morning briefing, for example, is generated by a Dify workflow that queries overnight market moves from AKShare and the global data layer, checks for relevant news in Readwise, and produces a structured signal report that arrives before the researcher sits down.