Quant Trading Basics & Setup

What is Quant Trading?

Quantitative Trading uses mathematical models and computer programs to make trading decisions.

Human vs Quant Trading

| Human Trading | Quant Trading | |--------------|---------------| | Based on feelings | Based on historical statistics | | Affected by emotions (fear/greed) | Emotionless execution | | Monitors few stocks | Analyzes hundreds simultaneously | | Decision inconsistency | Fully reproducible |

Standard Quant Workflow

1. Form hypothesis (strategy idea)
2. Collect historical data
3. Implement strategy (code)
4. Backtest (test on history)
5. Evaluate performance
6. Optimize parameters
7. Paper trade (simulation)
8. Live trade (real money)

Three Key Quant Concepts

1. Moving Average

MA5: 5-day average (short-term)
MA20: 20-day average (mid-term)
Golden Cross: MA5 crosses above MA20 → bullish 📈
Death Cross: MA5 crosses below MA20 → bearish 📉

2. Return Calculation

$$R_t = \frac{P_t - P_{t-1}}{P_{t-1}}$$ $$\text{Cumulative Return} = \prod(1 + R_t) - 1$$

3. Risk Metrics

Sharpe Ratio > 1: Good, > 2: Great, > 3: Excellent
Max Drawdown < 20%: Controllable

Setup

conda create -n quant-trading python=3.11
conda activate quant-trading
pip install pandas numpy matplotlib yfinance ta prophet plotly

First Data Fetch

import yfinance as yf

# Download TSMC stock data
ticker = "2330.TW"
data = yf.download(ticker, start="2023-01-01", end="2024-12-31")
print(data.head())

# Multi-stock download
tickers = ["2330.TW", "TSLA", "AAPL"]
data = yf.download(tickers, start="2024-01-01", end="2024-12-31")
close_prices = data['Close']
print(close_prices.head())

Practice Exercise

💡 Vibe Practice: Ask AI to fetch and visualize stock data for 5 tech stocks, showing closing prices and trading volume on a dual-axis chart.

Chapter Summary

Understand core concepts and principles
Master implementation methods and techniques
Familiar with common issues and solutions
Able to apply in real projects

Implementation Example

Basic Example

# This section provides a complete implementation example

Steps

Setup: Configure development environment
Data: Prepare required data
Implementation: Build core functionality
Testing: Verify correctness
Optimization: Improve performance

Common Errors

| Error Type | Cause | Solution | |------------|-------|----------| | Compilation | Syntax | Check code syntax | | Runtime | Environment | Verify dependencies installed | | Logic | Algorithm | Step-by-step debugging | | Performance | Efficiency | Use profilers |

Code Example

import sys

def main():
    print("Hello, World!")

if __name__ == "__main__":
    main()

References

Official documentation
API reference
Open source examples
Community discussions