Background: US vs China Quantitative Market Differences

A-shares (China) and US stocks have significant differences in trading rules, market structure, and data availability. Understanding these differences is a prerequisite for developing cross-market strategies.

1. Trading Rules Comparison

2. T+1 Strategy Impact

2.1 China A-Shares T+1

Rule: Stocks bought today can only be sold the next day

Impact:

• Cannot do intraday swings
• Cannot quickly stop loss
• Cannot avoid overnight risk

Strategy Response:

# A-share strategies must consider overnight positions
def should_buy(signal, overnight_risk):
    if overnight_risk > threshold:
        return False  # Risk too high, don't open position
    return signal > 0

2.2 US Stock Day Trading

Rules:

• Pattern Day Trader (PDT) rule
• Account < $25,000: Maximum 3 day trades in 5 trading days
• Account ≥ $25,000: No restrictions
• Note: FINRA has approved changes to PDT rules, planning to replace fixed amount limits with more flexible risk margins (pending SEC final approval)

Advantages:

• Can do intraday strategies
• Quick stop losses
• Utilize intraday volatility

3. Price Limit System

3.1 A-Share Price Limits

3.2 Hong Kong Volatility Control Mechanism (VCM)

Rules:

• Designed to mitigate extreme intraday volatility.
• HSI/HSCEI constituents: ±10% (5-minute price deviation).
• After triggering, enters 5-minute cooling period with fixed price range trading.

Impact:

• Consecutive limit up/down makes execution impossible
• Cannot buy at limit up (liquidity disappears)
• Cannot sell at limit down (passive stop loss fails)

Strategy Considerations:

# Price limit detection
def is_limit_up(price, prev_close, limit=0.10):
    return price >= prev_close * (1 + limit - 0.001)

def is_limit_down(price, prev_close, limit=0.10):
    return price <= prev_close * (1 - limit + 0.001)

3.3 US Stock Circuit Breakers

Index Circuit Breakers:

• Level 1 (7%): 15-minute halt
• Level 2 (13%): 15-minute halt
• Level 3 (20%): Trading halted for the day

Individual Stock Circuit Breakers (LULD):

• 5-minute halt when price deviation is too large

4. Short Selling Mechanisms

4.1 A-Share Securities Lending

Restrictions:

• Must open margin trading account
• Limited securities available, hard to borrow popular stocks
• High borrowing costs (8-10% annualized)
• Some stocks prohibited from lending

Practical Impact:

• Short strategies difficult to implement
• Market neutral strategies have high costs
• Limited hedging tools

4.2 US Stock Short Selling

Process:

1. Borrow shares
2. Sell them
3. Buy back to return

Costs:

• Borrow fee (0.3% - 50%+ annualized)
• Dividend compensation

Easy-to-Borrow vs Hard-to-Borrow:

• Large caps easy to borrow
• Small caps, popular short targets hard to borrow and expensive

5. Data Availability

5.1 A-Share Data

Free Data Sources:

• Tushare Pro: https://tushare.pro (points system, active community can get free access)
• AKShare: https://akshare.xyz (open source free, rich interfaces)
• BaoStock: http://baostock.com

5.2 US Stock Data

6. Market Participant Structure

6.1 A-Shares

Impact:

• High volatility
• Strong momentum effects
• Emotion-driven price deviations

6.2 US Stocks

Impact:

• Relatively rational
• Factors more persistently effective
• High passive investment share

7. Strategy Differences

7.1 Effective A-Share Strategies

7.2 Effective US Stock Strategies

8. Technical Implementation Differences

8.1 Backtesting Considerations

A-Shares:

# Factors A-share backtesting must consider
class ChinaBacktester:
    def __init__(self):
        self.t_plus_1 = True  # T+1 restriction
        self.limit_up_down = 0.10  # Price limits
        self.min_lot = 100  # Minimum trading unit

    def can_sell(self, position, trade_date):
        # Check if T+1 is satisfied
        return position.buy_date < trade_date

    def check_tradeable(self, price, prev_close):
        # Check for price limits
        if self.is_limit_up(price, prev_close):
            return False  # Cannot buy at limit up
        if self.is_limit_down(price, prev_close):
            return False  # Cannot sell at limit down
        return True

US Stocks:

# US stock backtesting is simpler
class USBacktester:
    def __init__(self):
        self.t_plus_0 = True  # Can day trade
        self.fractional_shares = True  # Fractional shares allowed

8.2 Live Trading Interfaces

A-Shares:

• Broker proprietary APIs (requires application)
• PTrade, QMT and other programmatic interfaces
• Third-party interfaces (gray area)

US Stocks:

• Interactive Brokers API
• Alpaca API
• TD Ameritrade API

9. Regulatory Differences

9.1 A-Share Regulation

• CSRC, Shanghai and Shenzhen exchanges
• Programmatic trading requires registration
• Strict abnormal trading monitoring
• Severe insider trading penalties

9.2 US Stock Regulation

• SEC, FINRA
• HFT requires registration
• Reg NMS governs execution
• Pattern Day Trader rules

10. Practical Recommendations

10.1 A-Share Strategy Development

1. Consider T+1: Strategy period must be at least overnight
2. Handle price limits: Exclude limit up/down days in backtesting
3. Note turnover: A-share turnover is high, transaction costs accumulate quickly
4. Policy risk awareness: Policy significantly impacts A-shares

10.2 US Stock Strategy Development

1. Note PDT rules: Small accounts have day trading restrictions
2. Consider pre/post market: Major news often released pre/post market
3. Borrow costs: Short strategies must consider Hard-to-Borrow costs
4. Liquidity tiering: Large and small cap liquidity differs significantly

10.3 Cross-Market Strategies

# Market rules configuration
def get_market_rules(market):
    if market == 'CN':
        return {
            't_plus': 1,
            'limit': 0.10,
            'min_lot': 100,
            'short_available': False
        }
    elif market == 'HK':
        return {
            't_plus': 2,
            'limit': 'VCM',
            'min_lot': 'Varies',
            'short_available': True
        }
    elif market == 'US':
        return {
            't_plus': 0,
            'limit': None,
            'min_lot': 1,
            'short_available': True
        }

Core principle: Don't directly apply US stock strategies to A-shares, and vice versa. Each market has unique rules and participant structures; strategies must adapt to local market characteristics.