Background: Algorithmic Trading Regulations (2024-2025)

In 2024-2025, global algorithmic trading regulation entered a new phase. China established the world's strictest regulatory framework, while the US strengthened AI application compliance reviews. Understanding the regulatory environment is a prerequisite for quant system productionization.

I. China Algorithmic Trading Regulations

1.1 Regulatory Milestones

1.2 High-Frequency Trading Identification Criteria

Meeting any of the following conditions qualifies as high-frequency trading:

Reference Comparison: This standard is far stricter than US markets, reflecting regulatory focus on high-frequency trading risks.

1.3 Four Types of Abnormal Trading Behavior

Penalties:

• Trading restrictions
• Forced suspension
• Differentiated fees
• Severe cases: temporary market halt and report to CSRC

1.4 Compliance Reporting Requirements

All algorithmic trading investors must report:

• Account basic information
• Capital information
• Trading information
• Software information

Additional reporting for high-frequency trading:

• Server physical location
• System test report
• Fault emergency plan

Core Principle: "Report first, trade second"

1.5 Private Fund Special Requirements

Private fund managers must:

1. Develop dedicated business management and compliance risk control systems
2. Improve algorithmic trading order review and monitoring systems
3. Establish risk prevention and control mechanisms

1.6 Enforcement Case: Lingjun Investment

Date: February 20, 2025

Background:

• Shenzhen and Shanghai exchanges issued regulatory measure decisions
• This was the first penalty under the new algorithmic trading rules

Violation:

• Issues with buy/sell trading within first minute of open on February 19, 2024
• Although overall net buy was 187 million yuan for the day
• Abnormal trading at open triggered regulatory red line

Warning Significance: Even if overall direction is correct, instantaneous trading behavior must be compliant

1.7 2024 Enforcement Intensity

II. US AI Quantitative Trading Regulations

2.1 Regulatory Architecture

Core Rules:

• FINRA Rule 3110 (Supervision Rule)
• FINRA Rule 3120 (Supplemental Supervision Responsibilities)

2.2 AI Application Compliance Requirements

June 27, 2024, FINRA released Regulatory Notice 24-09:

2.3 AI-Washing Enforcement (2024 Focus)

Definition: False claims about AI capabilities

SEC Enforcement: Filed lawsuits against two investment advisory firms

• Charged with violating Marketing Rule
• False claims about AI technology application in investment decisions

Compliance Requirements:

• Truthfully disclose actual AI technology application
• Cannot exaggerate or mislead investors
• Strict anti-fraud review

2.4 Major Penalty Case: Two Sigma

Date: January 16, 2025

Penalty Amount: $90 million (industry record)

Violation Reasons:

• Failed to address algorithm vulnerabilities
• Other violations
• Supervision failures

Warning: Even top quant institutions face severe penalties for inadequate algorithm risk control

2.5 SEC Fiscal Year 2024 Enforcement Data

Trend: Enforcement focus shifting from penalty amounts to case volume and deterrent effect

III. EU MiFID II Framework

3.1 Regulatory Evolution

3.2 MiFID RTS 6 Requirements (Algorithmic Trading Regulatory Technical Standards)

3.3 FCA Review Report (August 2024)

UK Financial Conduct Authority released multi-firm review report on algorithmic trading controls:

Key Requirements:

• Comply with MiFID RTS 6 requirements
• Strengthen algorithmic trading risk management and monitoring
• Improve system resilience and emergency response capability

IV. Regulatory Impact on Strategies

4.1 Impact Level Analysis

4.2 Leading Institution Response

Actual Situation:

• Alpha strategy turnover of large quant institutions is generally not high
• Most leading strategies have turnover below implementation detail limits
• Can basically meet new regulations

4.3 Industry Frequency Reduction Trend

Driving Factors:

1. Regulatory constraints (hard limits)
2. Capacity considerations (high-frequency cannot support billions in AUM)

Results:

• Medium-low frequency strategies gain importance
• Excess returns will inevitably decline
• Requires managers to continuously innovate in strategy depth and breadth

V. Compliance System Design Recommendations

5.1 Trading Frequency Monitoring

# Example: Trading frequency monitor
class TradingFrequencyMonitor:
    """
    Monitor trading frequency to ensure not triggering
    high-frequency trading identification criteria
    """

    # China regulatory thresholds
    CHINA_SECOND_LIMIT = 300   # Per-second order+cancel limit
    CHINA_DAILY_LIMIT = 20000  # Daily order+cancel limit

    def __init__(self):
        self.second_counter = 0
        self.daily_counter = 0
        self.last_second = None

    def check_order(self, timestamp: datetime) -> dict:
        """
        Check if approaching regulatory threshold
        """
        # Update counter logic...

        return {
            'second_usage': self.second_counter / self.CHINA_SECOND_LIMIT,
            'daily_usage': self.daily_counter / self.CHINA_DAILY_LIMIT,
            'warning': self._should_warn(),
            'block': self._should_block()
        }

    def _should_warn(self) -> bool:
        """Warn at 80% threshold"""
        return (self.second_counter > self.CHINA_SECOND_LIMIT * 0.8 or
                self.daily_counter > self.CHINA_DAILY_LIMIT * 0.8)

    def _should_block(self) -> bool:
        """Block at 95% threshold"""
        return (self.second_counter > self.CHINA_SECOND_LIMIT * 0.95 or
                self.daily_counter > self.CHINA_DAILY_LIMIT * 0.95)

5.2 Abnormal Trading Detection

# Example: Abnormal trading behavior detection
class AbnormalTradingDetector:
    """
    Detect four types of abnormal trading behavior
    """

    def detect_spoofing(self, orders: list) -> bool:
        """
        Detect spoofing (frequent instant cancellations)
        """
        # Calculate cancel rate within short time window
        cancel_rate = self._calculate_cancel_rate(orders, window_seconds=1)
        return cancel_rate > 0.9  # 90%+ cancel rate considered abnormal

    def detect_layering(self, orderbook_changes: list) -> bool:
        """
        Detect layering (frequent ramping/dumping)
        """
        # Analyze order book change patterns
        pass

    def detect_burst_volume(self, trades: list,
                           window_seconds: int = 60) -> bool:
        """
        Detect short-term large volume
        """
        # Calculate volume anomaly within time window
        pass

5.3 Compliance Report Generation

Suggested daily compliance report content:

VI. Regulatory Trend Outlook

6.1 China Market

Positive Impacts:

• "Spoofing" and other improper behaviors suppressed
• "Fake quant" and market-disrupting behaviors cleaned up
• Enhanced market vitality and resilience

Long-term Outlook:

• Standardized regulatory environment will eliminate inferior institutions
• Raise overall industry standards
• Protect investor interests

6.2 Global Trends

VII. Further Reading

Official Documents

• CSRC: "Securities Market Algorithmic Trading Management Regulations (Trial)"
• Shanghai/Shenzhen/Beijing Exchanges: "Algorithmic Trading Management Implementation Details"
• FINRA: Regulatory Notice 24-09
• SEC: 2024 Examination Priorities
• ESMA: MiFID II/MiFIR Technical Standards

Industry Reports

• FINRA 2025 Annual Regulatory Report
• SEC 2024 Fiscal Year Enforcement Results

Core Insight: Regulation is the "second layer of risk control" for quantitative trading. Compliance is not a burden but protection - protecting market fairness and ensuring your strategy can run long-term.