Skip to content

Prerequisites: A-Share Market Fundamentals

Overview

Item Description
Suitable for Readers unfamiliar with A-share code conventions, T+1 settlement, limit up/down, fees/taxes, and major indices
Estimated time ~45 minutes
Value Helps you correctly understand backtest assumptions and execution constraints in eqlib

This article is for readers who have not traded A-shares or need to learn the rules of the Chinese stock market. Understanding these rules will help you write more realistic strategies and correctly interpret the metrics in backtest reports.

Table of Contents

  1. What Are A-Shares
  2. Stock Code Format & Exchanges
  3. Trading Hours
  4. T+1 Settlement
  5. Limit Up/Down System
  6. ST Stocks & Delisting Risk
  7. Trading Costs: Fees & Taxes
  8. Major Market Indices
  9. Fundamental Data: PE / PB / ROE etc.
  10. Capital Flow
  11. North-bound Capital
  12. Margin Trading
  13. Limit Up/Down Statistics
  14. Restricted Share Unlock
  15. How These Rules Are Reflected in eqlib
  16. Next Steps

1. What Are A-Shares

A-shares are stocks listed on the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE) in mainland China, denominated in Renminbi (RMB/CNY).

Type Description
A-shares Listed in mainland China, RMB-denominated — the focus of this article
H-shares Shares of the same companies listed in Hong Kong, HKD-denominated
B-shares Legacy shares denominated in foreign currency, thinly traded
US ADRs Depositary receipts of Chinese companies listed in the United States

EasyQuant only supports A-shares — it does not support Hong Kong stocks, US stocks, futures, options, or cryptocurrencies.

2. Stock Code Format & Exchanges

2.1 Code Conventions

A-share codes are 6-digit numbers; the prefix determines the exchange and board:

Prefix Exchange Board Examples
600xxx 601xxx 603xxx 605xxx Shanghai (SSE) SSE Main Board 601390 (ICBC) 600519 (Kweichow Moutai)
000xxx 001xxx Shenzhen (SZSE) SZSE Main Board 000858 (Wuliangye)
002xxx 003xxx Shenzhen SME Board (merged into Main Board) 002415 (Hikvision)
300xxx 301xxx Shenzhen ChiNext (GEM) 300750 (CATL)
688xxx 689xxx Shanghai STAR Market 688981 (SMIC)
8xxxxx NEEQ Beijing Stock Exchange —

2.2 Index Code Format (with Exchange Suffix)

In eqlib, index codes require an exchange suffix:

Index Code Notes
CSI 300 000300.XSHG Most commonly used benchmark
SSE Composite 000001.XSHG Reflects the overall Shanghai market
SZSE Component 399001.XSHE Reflects the overall Shenzhen market
CSI 500 000905.XSHG Mid-cap stocks
ChiNext Index 399006.XSHE Overall ChiNext market
STAR 50 000688.XSHG Top STAR Market stocks
SSE 50 000016.XSHG Shanghai blue chips

Rules (project convention): - 000xxx.XSHG = Shanghai index (e.g., CSI 300) - 399xxx.XSHE = Shenzhen index (e.g., SZSE Component, ChiNext Index) - 000xxx.XSHE or bare 000xxx = Shenzhen stock (e.g., Wuliangye 000858)

# Set benchmark
set_benchmark('000300.XSHG')   # CSI 300

# Get index constituent stocks
from eqlib import get_index_stocks
hs300_stocks = get_index_stocks('000300.XSHG')

3. Trading Hours

A-share trading is divided into two sessions (Beijing Time, UTC+8):

Session Time Description
Call auction 9:15–9:25 Pre-market orders to determine the opening price
Morning session 9:30–11:30 Continuous trading
Lunch break 11:30–13:00 No trading
Afternoon session 13:00–15:00 Continuous trading

Impact on eqlib: - Daily backtests use the 9:30 open price as the fill price (on the next trading day) - Minute-level bars run from 09:30 to 15:00, excluding the lunch break - Public holidays (Chinese New Year, National Day, etc.) have no trading days

4. T+1 Settlement

T+1 is one of the most important A-share rules:

Stocks bought today cannot be sold until the next trading day.

By comparison, the US market settled on T+3 until 2024 when it moved to T+1 (with restrictions on day trading); Hong Kong settles on T+2.

Impact on Strategies

# In backtests: eqlib handles T+1 constraints automatically
# Buy today -> closeable_amount = 0 today -> closeable_amount = shares bought tomorrow

def market_open(context):
    position = context.portfolio.positions.get('601390')
    if position:
        # closeable_amount = shares available for immediate sale (after T+1 restriction)
        can_sell = position.closeable_amount
        # Shares just bought are not included in can_sell

Strategy implications: You cannot do same-day "sell high, buy low" (intraday T+0 trading), and you must hold until the next day before selling. Day-trading / scalping strategies are not feasible.

5. Limit Up/Down System

A-share daily price movements are capped:

Board Limit
SSE/SZSE Main Board (incl. SME Board) ±10%
ChiNext (GEM) ±20%
STAR Market ±20%
ST / *ST stocks ±5%
Beijing Stock Exchange ±30%

Impact on Strategies

# A stock that hit the limit-up price cannot be bought (no sellers)
# A stock that hit the limit-down price cannot be sold (no buyers)
# eqlib backtests ignore liquidity constraints; live trading must account for them

# Best practice: filter out stocks that have hit limit-up today
hist = attribute_history(security, 2, '1d', ['close'])
prev_close = hist['close'].iloc[-2]
today_close = hist['close'].iloc[-1]
limit_up = today_close >= prev_close * 1.099  # near limit-up

if limit_up:
    continue  # skip, do not buy limit-up stocks

6. ST Stocks & Delisting Risk

What ST Means

Label Meaning
ST Special Treatment: the company has reported losses for two consecutive years or has negative net assets
*ST Delisting Risk Warning: three consecutive years of losses, faces delisting
S*ST Currently undergoing regulatory restructuring

ST stocks have the following characteristics: - Limit up/down band compressed from ±10% to ±5% - Poor liquidity, prone to sharp declines - Opaque information, extremely high risk

Filtering ST Stocks in Strategies

from eqlib import filter_st_stocks

candidates = ['601390', '600519', '000858', '000001']

# Filter out ST / *ST stocks
safe_stocks = filter_st_stocks(candidates)

7. Trading Costs: Fees & Taxes

7.1 Standard Fee Schedule (2024)

Cost Buy Sell Description
Commission (Brokerage) 0.02%–0.03% 0.02%–0.03% Paid to the broker, minimum 5 CNY
Stamp duty 0 0.05% Paid to the tax authority (halved from 0.1% in August 2023)
Transfer fee (SSE only) 0.00002% 0.00002% Negligible

7.2 Actual Cost of a Round-Trip Trade

Example: buying and selling 100,000 CNY worth of stock (commission 0.025%, minimum 5 CNY): - Buy: 100,000 × 0.025% = 25 CNY - Sell: 100,000 × 0.025% + 100,000 × 0.05% = 25 + 50 = 75 CNY - Total round-trip cost: 100 CNY (0.10%)

Frequent trading seriously erodes returns!

7.3 Setting Trading Costs in eqlib

def initialize(context):
    set_order_cost(OrderCost(
        open_tax=0,               # buy stamp duty (currently 0)
        close_tax=0.0005,         # sell stamp duty 0.05% (current as of 2024)
        open_commission=0.00025,   # buy commission 0.025%
        close_commission=0.00025,  # sell commission 0.025%
        min_commission=5,         # minimum commission 5 CNY
    ))

If you do not set trading costs, eqlib defaults to zero fees — this will inflate your backtest results! Always set realistic fees.

8. Major Market Indices

Index Code (eqlib format) Constituents Common Use Case
CSI 300 000300.XSHG 300 large-cap blue chips Most commonly used benchmark
CSI 500 000905.XSHG 500 mid-cap stocks Mid-cap strategy benchmark
SSE Composite 000001.XSHG All SSE-listed stocks Overall market indicator
ChiNext Index 399006.XSHE Top 100 ChiNext stocks Growth strategy benchmark
STAR 50 000688.XSHG Top 50 STAR Market stocks Tech strategy benchmark

Which Benchmark to Choose?

  • Main-board blue-chip strategies: CSI 300 (000300.XSHG)
  • Mid/small-cap strategies: CSI 500 (000905.XSHG)
  • Growth / innovation strategies: ChiNext Index (399006.XSHE)
  • Whole-market strategies: SSE Composite (000001.XSHG)

9. Fundamental Data: PE / PB / ROE etc.

Commonly used fundamental factors in quantitative strategies:

Metric Full Name Meaning Lower is...
PE Price-to-Earnings P/E ratio = price / earnings per share More "cheap" (value factor)
PB Price-to-Book P/B ratio = price / book value per share More "cheap"
PS Price-to-Sales P/S ratio = market cap / annual revenue —
ROE Return on Equity Net income / equity Higher is better (quality factor)
ROA Return on Assets Net income / total assets Higher is better
EPS Earnings Per Share Earnings per share Higher is better
Gross Margin Gross Margin Gross profit / revenue Higher is better (competitive moat) 
from eqlib import fetch_factor_data

# Fetch multi-dimensional fundamental data for candidate stocks
candidates = ['601390', '600519', '000858']
df = fetch_factor_data(candidates, fields=['pe', 'pb', 'roe'])
#        pe      pb    roe
# 601390  5.4    0.62   11.2
# 600519  28.1   10.3   35.6
# 000858  19.7    6.1   24.8

# Sort by PE ascending (value stock selection)
df_sorted = df.sort_values('pe', ascending=True)

10. Capital Flow

Capital flow reflects whether institutional (large-order) investors are net buyers or net sellers on a given day:

Metric Meaning
Net main inflow Net buy amount of large orders (orders > 500,000 CNY)
Super-large orders Single order > 1,000,000 CNY
Large orders Single order 500,000–1,000,000 CNY
Medium orders Single order 100,000–500,000 CNY
Small orders Single order < 100,000 CNY

Consecutive days of net main inflow → Institutions are accumulating, often a precursor to a price rise.

from eqlib import get_money_flow

money_flow = get_money_flow('601390', days=5)
# Returns a DataFrame with fields including net_main (net main inflow)

11. North-bound Capital

North-bound capital refers to funds flowing from Hong Kong into the A-share market via the Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect channels, often called "smart money."

11.1 What Is North-bound Capital

Type Description
Shanghai Connect Hong Kong investors buy SSE stocks through the Hong Kong exchange
Shenzhen Connect Hong Kong investors buy SZSE stocks through the Hong Kong exchange
North-bound capital Shanghai Connect + Shenzhen Connect combined (foreign capital flowing into A-shares)
South-bound capital Mainland investors buying Hong Kong stocks (the reverse direction)

Characteristics of North-bound capital: - Dominated by institutional investors, more rational decision-making - Longer holding periods, preference for blue-chip stocks - Often regarded as a market bellwether

11.2 Fetching North-bound Capital Data in eqlib

from eqlib import get_north_money_flow

# Get North-bound capital flow for the past 3 months
north = get_north_money_flow(start_date="2024-01-01", end_date="2024-03-31")

# Returned fields:
# - date: trading date
# - net_buy: net buy amount (100M CNY)
# - total_buy: total buy amount (100M CNY)
# - total_sell: total sell amount (100M CNY)

# Calculate 5-day net buy
recent_5d = north["net_buy"].tail(5).sum()
if recent_5d > 50:
    print("Strong North-bound capital inflow, positive market sentiment")
elif recent_5d < -50:
    print("Strong North-bound capital outflow, watch for risk")

11.3 Strategy Application

Consecutive North-bound capital inflows/outflows can serve as a market timing signal:

# Check North-bound capital trend in your strategy
north = get_north_money_flow()
if not north.empty:
    recent_3d = north["net_buy"].tail(3).sum()
    if recent_3d < -50:  # net outflow exceeding 5B CNY over 3 days
        g.market_sentiment = "bearish"  # position reduction signal

12. Margin Trading

Margin trading is a leverage mechanism where investors borrow funds or shares from their broker.

12.1 Basic Concepts

Type Description
Margin buy (financing) Borrow money to buy stocks (bullish, adding leverage)
Short sell (securities lending) Borrow shares to sell (bearish, shorting)
Margin balance Total outstanding margin-buy amount
Short balance Total outstanding short-sell amount

Rising margin balance → Growing bullish sentiment Falling margin balance → Waning bullish sentiment

12.2 Fetching Margin Trading Data in eqlib

from eqlib import get_margin_data

# Get margin trading data for the past 3 months
margin = get_margin_data(start_date="2024-01-01", end_date="2024-03-31")

# Returned fields:
# - date: trading date
# - margin_balance: outstanding margin-buy balance (100M CNY)
# - margin_buy: new margin-buy amount (100M CNY)
# - margin_repay: margin-buy repayment amount (100M CNY)
# - short_balance: outstanding short-sell balance (100M CNY)

# Calculate margin balance change rate
margin["balance_change_pct"] = margin["margin_balance"].diff(5) / margin["margin_balance"].shift(5) * 100

12.3 Caveats

  • margin_repay has NaN in the first row (no prior-day balance to compute from)
  • Rapidly growing margin balance may signal excessive leverage — be alert to pullback risk

13. Limit Up/Down Statistics

Limit up/down statistics reflect overall market sentiment and systemic risk.

13.1 Market Breadth Indicators

Indicator Description
Limit-up count Number of stocks hitting the daily upper price limit
Limit-down count Number of stocks hitting the daily lower price limit
Limit-up/down ratio Limit-up count / limit-down count

Many limit-up stocks → Positive market sentiment Many limit-down stocks → Pessimistic market sentiment Limit-down count > 100 → Potential systemic risk

13.2 Fetching Limit Up/Down Statistics in eqlib

from eqlib import get_limit_up_down_stats

# Get limit up/down statistics for the past 30 days
limit_stats = get_limit_up_down_stats()

# Returned fields:
# - date: trading date
# - limit_up_count: number of limit-up stocks
# - limit_down_count: number of limit-down stocks

# Systemic risk alert
latest_down = limit_stats["limit_down_count"].iloc[-1]
if latest_down > 100:
    print("Systemic risk alert: limit-down count exceeds 100")

13.3 Caveats

  • The API only supports the most recent 30 trading days
  • Limit up/down data may change during trading hours

14. Restricted Share Unlock

Restricted share unlock refers to previously non-tradeable shares becoming eligible for public trading, which may create selling pressure.

14.1 Types of Unlock

Type Description
IPO founder lock-up shares Shares that IPO shareholders committed to holding for a specified period
Private placement lock-up shares Shares that private placement participants committed to holding
Equity incentive lock-up shares Shares locked up under employee incentive plans

After unlocking, original holders can freely sell. If the unlock volume is large and holders are eager to reduce their positions, the stock price may come under pressure.

14.2 Fetching Unlock Data in eqlib

from eqlib import get_restriction_release

# Get the unlock schedule for the next 30 days
releases = get_restriction_release(days=30)

# Returned fields:
# - code: stock code
# - name: stock name
# - release_date: unlock date
# - release_amount: number of shares unlocked (10,000 shares)
# - release_value: market value of unlocked shares (100M CNY)
# - release_pct: percentage of pre-unlock tradeable market cap

# Sort by unlock market value, focus on large unlocks
large_releases = releases[releases["release_value"] > 50]
print(f"Large unlocks (>5B CNY) in the next 30 days: {len(large_releases)} stocks")

14.3 Strategy Application

Exclude stocks with imminent large unlocks during stock selection:

# Exclude stocks with unlock market value exceeding 10B CNY in the next 30 days
releases = get_restriction_release(days=30)
large_release_codes = releases[releases["release_value"] > 100]["code"].tolist()

candidates = ["601390", "600519", "000858"]
safe_candidates = [c for c in candidates if c not in large_release_codes]

15. How These Rules Are Reflected in eqlib

Rule How eqlib Handles It
T+1 settlement position.closeable_amount is 0 on the day of purchase
Limit up/down Backtests do not auto-filter (manual logic required); affects order fills in live trading
Stamp duty Set via OrderCost(close_tax=0.0005)
ST filtering filter_st_stocks(securities)
Index constituents get_index_stocks('000300.XSHG')
Benchmark comparison set_benchmark('000300.XSHG')
Fundamental data fetch_factor_data(), get_fundamentals()
North-bound capital get_north_money_flow()
Margin trading get_margin_data()
Limit up/down statistics get_limit_up_down_stats()
Restricted share unlock get_restriction_release()

16. Next Steps