China Stock Analysis

China Stock Analysis is an AI skill that provides tools and workflows for analyzing Chinese equity markets including A-shares, H-shares, and ADRs through financial data retrieval, technical indicators, fundamental screening, and regulatory context. It covers market data APIs, financial statement parsing, sector analysis, and reporting workflows that enable informed research on Chinese listed companies.

What Is This?

Overview

China Stock Analysis provides structured approaches to researching and evaluating Chinese public companies. It handles retrieving real-time and historical price data from Chinese exchanges including Shanghai and Shenzhen, parsing financial statements filed under Chinese accounting standards and IFRS, calculating technical indicators such as moving averages and RSI for trend analysis, screening stocks by fundamental metrics like P/E ratio and revenue growth, monitoring regulatory announcements from the CSRC and exchange filings, and generating research reports with charts and comparative analysis.

Who Should Use This

This skill serves quantitative analysts building China-focused trading strategies, portfolio managers evaluating Chinese equity exposure, financial engineers integrating Chinese market data into analytics platforms, and researchers studying Chinese capital market behavior.

Why Use It?

Problems It Solves

Accessing Chinese market data requires navigating APIs with different conventions than Western data providers. Financial statements from Chinese companies follow local accounting standards that differ from US GAAP. Regulatory announcements from Chinese authorities are published across multiple platforms in varied formats. Without automation, monitoring hundreds of A-share stocks for screening signals demands significant manual effort.

Core Highlights

Multi-exchange coverage retrieves data from Shanghai, Shenzhen, and Hong Kong markets through unified interfaces. Financial normalization converts Chinese accounting entries into comparable metrics for cross-market analysis. Regulatory monitoring tracks CSRC announcements and exchange disclosures for material events. Screening automation filters large stock universes by customizable fundamental and technical criteria.

How to Use It?

Basic Usage

import akshare as ak
import pandas as pd

df = ak.stock_zh_a_hist(
    symbol="600519", period="daily",
    start_date="20240101", end_date="20241231",
    adjust="qfq"
)

df["ma20"] = df["close"].rolling(window=20).mean()
df["ma60"] = df["close"].rolling(window=60).mean()

df["signal"] = (
    (df["ma20"] > df["ma60"]) &
    (df["ma20"].shift(1) <= df["ma60"].shift(1))
)

crossovers = df[df["signal"]]
print(f"Golden crosses found: {len(crossovers)}")
print(crossovers[["date", "close", "ma20", "ma60"]])

Real-World Examples

import akshare as ak
import pandas as pd

class ChinaStockScreener:
    def __init__(self):
        self.stocks = ak.stock_zh_a_spot_em()

    def screen_by_fundamentals(self, max_pe=20, min_roe=15):
        filtered = self.stocks[
            (self.stocks["pe_ratio"] > 0) &
            (self.stocks["pe_ratio"] <= max_pe)
        ].copy()
        results = []
        for _, row in filtered.head(50).iterrows():
            code = row["code"]
            try:
                fin = ak.stock_financial_analysis_indicator_em(
                    symbol=code
                )
                if not fin.empty:
                    latest_roe = float(
                        fin.iloc[0].get("roe", 0)
                    )
                    if latest_roe >= min_roe:
                        results.append({
                            "code": code,
                            "name": row["name"],
                            "pe": row["pe_ratio"],
                            "roe": latest_roe
                        })
            except Exception:
                continue
        return pd.DataFrame(results)

    def get_sector_performance(self, sector_name):
        sectors = ak.stock_board_industry_name_em()
        match = sectors[
            sectors["name"].str.contains(sector_name)
        ]
        if match.empty:
            return None
        return ak.stock_board_industry_hist_em(
            symbol=match.iloc[0]["name"],
            period="daily",
            start_date="20240101",
            end_date="20241231"
        )

screener = ChinaStockScreener()
value_stocks = screener.screen_by_fundamentals(
    max_pe=15, min_roe=20
)
print(value_stocks.to_string(index=False))

Advanced Tips

Cache API responses locally to avoid hitting rate limits during iterative analysis. Normalize financial data to a common currency when comparing A-shares with H-share or ADR equivalents. Schedule data retrieval outside Chinese market hours to access settled end-of-day figures.

When to Use It?

Use Cases

Use China Stock Analysis when building quantitative screening models for A-share selection, when monitoring sector rotation patterns across Chinese industry groups, when comparing dual-listed companies across Shanghai and Hong Kong, or when generating research reports on Chinese market trends.

Related Topics

AKShare data library usage, Tushare API integration, Chinese financial statement standards, technical analysis indicator calculation, and cross-border equity comparison methods complement Chinese stock analysis.

Important Notes

Requirements

Python environment with AKShare or Tushare installed for data retrieval. Network access to Chinese financial data APIs. Understanding of Chinese market conventions including trading hours and settlement rules.

Usage Recommendations

Do: validate data completeness after retrieval since Chinese market APIs occasionally return partial results during high-traffic periods. Use adjusted prices for historical analysis to account for stock splits and dividends. Cross-reference financial data from multiple sources for accuracy.

Don't: assume Chinese accounting metrics map directly to US GAAP equivalents without normalization. Treat screening results as investment recommendations without further qualitative analysis. Ignore trading halt and suspension data that can distort technical indicator calculations.

Limitations

Chinese market data APIs may have access restrictions or require registration for full coverage. Real-time data feeds from Chinese exchanges often carry additional licensing costs. Financial statement formats vary between companies, requiring flexible parsing logic.