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Why Quantitative Investing?

Before diving into technical analysis and investment strategies, we need to answer a fundamental question: Why do ordinary people need quantitative investing? The answer is simple: because humans are naturally bad at investing.

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Three Fatal Flaws of Human Investing

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1. Emotional Decision-Making: The Greed-Fear Cycle

Real scenario: When prices rise, you think “they’ll keep going up” and buy at the top; when prices fall, you fear “they’ll keep falling” and panic sell at the bottom.
Why does this happen?
  • Our brain’s reward system creates euphoria when making money, wanting more
  • Loss aversion makes us overreact to losses irrationally
  • Herd mentality makes us follow crowds rather than think independently
Real data: Studies show 90% of retail investors “buy high, sell low” - the exact opposite of successful investing.

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2. Information Processing Limitations: Hardware Constraints

Human brain limitations:
  • Can only focus on analyzing a few stocks at once
  • Cannot simultaneously process price, volume, financial data, and news sentiment
  • Easily influenced by recent events (availability bias)
  • Memory distorts, making historical recall inaccurate
Institutional advantages:
  • Professional analyst teams
  • Advanced data analysis tools
  • 24/7 market monitoring
  • Massive historical data for decisions
Reality for retail investors: You compete against professional teams with part-time effort and limited tools.

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3. Time and Energy Constraints: Life Beyond Investing

Real problems:
  • Work during market hours, can’t monitor real-time
  • Only evenings and weekends for research when markets are closed
  • Insufficient time to deeply research every stock
  • Can’t react quickly to breaking news or earnings releases
Time cost: Spending 2 hours daily on stock research equals 730 hours yearly - equivalent to a part-time job, but returns aren’t guaranteed to beat professionals.

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How Quantitative Investing Solves These Problems

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1. Emotional Neutrality: Algorithms Don’t Panic

Algorithm advantages:
  • No greed or fear, strictly follows preset rules
  • Won’t change strategy due to temporary market fluctuations
  • Can buy during maximum panic, sell during maximum greed
  • Historical data shows: mechanical execution typically outperforms emotional decisions
Real example: During March 2020 COVID crash, most people panic-sold, but quantitative systems identified technical bottoms and started accumulating, capturing the subsequent recovery.

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2. Data Advantage: Superhuman Information Processing

Algorithms can simultaneously:
  • Monitor thousands of stocks’ real-time price movements
  • Analyze each stock’s technical indicator status
  • Process macroeconomic data and market sentiment indicators
  • Identify price patterns invisible to human eyes
  • Make buy/sell decisions in milliseconds
Openstrat’s capabilities:
  • Simultaneously monitor 2000 popular US stocks
  • Real-time analysis across multiple timeframes
  • Identify technical bottom/top probabilities
  • 24/7 cryptocurrency market monitoring

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3. 24/7 Monitoring: The Tireless Market Sentinel

Algorithm’s vigilance:
  • Real-time US market monitoring (9:30 AM - 4:00 PM ET)
  • 24-hour cryptocurrency monitoring
  • Global coverage across Asian, European, and American markets
  • Immediate analysis and strategy adjustment during breaking events
Meaning for ordinary investors: You can sleep peacefully and focus on work while algorithms alert you to optimal investment opportunities.

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Quantitative Investing ≠ Full Automation

Important reminder: Quantitative investing doesn’t mean blindly following machines, but using scientific methods to assist your investment decisions.

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Correct Quantitative Investing Philosophy

  1. Algorithms handle data processing and pattern recognition
  2. Humans make final decisions and risk control
  3. Combine multiple signal sources for higher accuracy
  4. Continuous learning and strategy optimization

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Human-Machine Collaboration Advantages

  • Algorithms provide objective data analysis
  • Humans provide common sense and risk awareness
  • Algorithms process technical indicators
  • Humans consider fundamentals and macro environment

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Key Takeaways

  • Humans have natural limitations in investing: emotional bias, limited information processing, time constraints
  • Quantitative investing uses algorithms to overcome these limitations, providing more objective, comprehensive, timely market analysis
  • Quantitative investing isn’t full automation, but scientific human-machine collaboration
  • Proper use of quantitative tools can significantly improve investment decision quality
Next chapter preview: Learn to read market “language” - what do price charts tell us?